Data Fusion and IoT for Smart Ubiquitous Environments: A Survey
---
paper_title: UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies

paper_content:

The education industry around the globe is undergoing major transformations. Organizations, such as Coursera are advancing new business models for education. A number of major industries have dropped degrees from the job requirements. While the economics of higher education institutions are under threat in a continuing gloomy global economy, digital and lifelong learners are increasingly demanding new teaching and learning paradigms from educational institutions. There is an urgent need to transform teaching and learning landscape in order to drive global economic growth. The use of distance eTeaching and eLearning (DTL) is on the rise among digital natives alongside our evolution toward smart societies. However, the DTL systems today lack the necessary sophistication due to several challenges including data analysis and management, learner-system interactivity, system cognition, resource planning, agility, and scalability. This paper proposes a personalised Ubiquitous eTeaching & eLearning (UTiLearn) framework that leverages Internet of Things, big data, supercomputing, and deep learning to provide enhanced development, management, and delivery of teaching and learning in smart society settings. A proof of concept UTiLearn system has been developed based on the framework. A detailed design, implementation, and evaluation of the UTiLearn system, including its five components, are provided using 11 widely used datasets.
---
paper_title: The Internet of Things: A survey

paper_content:

This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.
---
paper_title: Multi-Sensor Data Fusion: An Introduction

paper_content:

This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science, as well as researchers and professional engineers. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended. Although conceptually simple, the study of multi-sensor data fusion presents challenges that are unique within the education of the electrical engineer or computer scientist. To become competent in the field the student must become familiar with tools taken from a wide range of diverse subjects including: neural networks, signal processing, statistical estimation, tracking algorithms, computer vision and control theory. All too often the student views multi-sensor data fusion as a miscellaneous assortment of different processes which bear no relationship to each other. In this book the processes are described using a common statistical framework. As a consequence, the underlying pattern of relationships that exists between the different methodologies is made evident.
---
paper_title: Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT)

paper_content:

Internet of Things (IoT) is set to revolutionize all aspects of our lives. The number of objects connected to IoT is expected to reach 50 billion by 2020, giving rise to an enormous amounts of valuable data. The data collected from the IoT devices will be used to understand and control complex environments around us, enabling better decision making, greater automation, higher efficiencies, productivity, accuracy, and wealth generation. Data mining and other artificial intelligence methods would play a critical role in creating smarter IoTs, albeit with many challenges. In this paper, we examine the applicability of eight well-known data mining algorithms for IoT data. These include, among others, the deep learning artificial neural networks (DLANNs), which build a feed forward multi-layer artificial neural network (ANN) for modelling high-level data abstractions. Our preliminary results on three real IoT datasets show that C4.5 and C5.0 have better accuracy, are memory efficient and have relatively higher processing speeds. ANNs and DLANNs can provide highly accurate results but are computationally expensive.
---
paper_title: A Review of Data Fusion Techniques

paper_content:

The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed. These methods and algorithms are presented using three different categories: (i) data association, (ii) state estimation, and (iii) decision fusion.
---
paper_title: Data fusion in intelligent transportation systems: Progress and challenges - A survey

paper_content:

In intelligent transportation systems (ITS), transportation infrastructure is complimented with information and communication technologies with the objectives of attaining improved passenger safety, reduced transportation time and fuel consumption and vehicle wear and tear. With the advent of modern communication and computational devices and inexpensive sensors it is possible to collect and process data from a number of sources. Data fusion (DF) is collection of techniques by which information from multiple sources are combined in order to reach a better inference. DF is an inevitable tool for ITS. This paper provides a survey of how DF is used in different areas of ITS.
---
paper_title: The Internet of Things: A survey

paper_content:

This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.
---
paper_title: Multisensor data fusion: A review of the state-of-the-art

paper_content:

There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.
---
paper_title: The internet of things: a survey

paper_content:

In recent year, the Internet of Things (IoT) has drawn significant research attention. IoT is considered as a part of the Internet of the future and will comprise billions of intelligent communicating `things'. The future of the Internet will consist of heterogeneously connected devices that will further extend the borders of the world with physical entities and virtual components. The Internet of Things (IoT) will empower the connected things with new capabilities. In this survey, the definitions, architecture, fundamental technologies, and applications of IoT are systematically reviewed. Firstly, various definitions of IoT are introduced; secondly, emerging techniques for the implementation of IoT are discussed; thirdly, some open issues related to the IoT applications are explored; finally, the major challenges which need addressing by the research community and corresponding potential solutions are investigated.
---
paper_title: From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

paper_content:

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user's daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).
---
paper_title: Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

paper_content:

This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.
---
paper_title: Advances in Multi-Sensor Data Fusion: Algorithms and Applications

paper_content:

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of “algorithm fusion” methods; (3) Establishment of an automatic quality assessment scheme.
---
paper_title: Function Placement of Data Fusion for Active Networks Paradigm in Wireless Sensor Networks

paper_content:

Active Networks paradigm integrated with distributed data fusion has the potential to significantly reduce energy dissipation in WSNs, where energy conservation is the most challenging issue. This work aims to minimize energy cost when distributed data fusion is deployed for Active Networks computing paradigm. An approximate solution with much less computational complexity than optimal solution is proposed for large networks called P2lace, which includes two phases, task graph partition and task graph placement. An extensive experimental evaluation compares approximate solution with optimal solution. The results show that our approximate solution is scalable to different task graph characteristics and network size and only causes little more transmission cost than optimal solution.
---
paper_title: Energy Efficient Distributed Data Fusion In Multihop Wireless Sensor Networks

paper_content:

This thesis addresses a transmission energy problem for wireless sensor networks. There are two types of wireless sensor networks. One is single-hop sensor network where data from each sensor is directly transmitted to a fusion center, and the other is multihop sensor network where data is relayed through adjacent sensors. In the absence of a moving agent for data collection, multihop sensor network is typically much more energy efficient than single-hop sensor network since the former avoids long distance data transmission. Progressive data fusion is a distributed fusion method that fuses data as they hop through sensors, which is effective to further reduce the energy cost. With the knowledge of a routing tree and all channel state information, the transmission energy allocated for each sensor can be pre-determined to even further reduce the energy cost while satisfying a pre-determined performance. In this thesis, we develop several energy planning algorithms for the above purpose. Specifically we designed two energy planning algorithms for progressive estimation with digital transmissions between sensors and one energy planning algorithm for progressive estimation with analog transmission. We also show that digital transmission is more efficient in transmission energy than analog transmission if the available transmission time-bandwidth product for each link and each observation sample is not too limited.We also study energy cost for consensus estimation which is a distributed fusion method for peer-to-peer multihop sensor networks. The impact of fusion weights and energy allocation for each sensor is also investigated. We demonstrate that to achieve an approximately same performance, the total energy cost for consensus estimation can be much higher than that for progressive estimation, but the peak energy for the former is less thanthat for the latter.
---
paper_title: Energy Efficient Computation of Data Fusion in Wireless Sensor Networks Using Cuckoo Based Particle Approach (CBPA)

paper_content:

Energy efficient communication is a plenary issue in Wireless Sensor Networks (WSNs). Contemporary energy efficient optimization schemes are focused on reducing power consumption in various aspects of hardware design, data processing, network protocols and operating system. In this paper, optimization of network is formulated by Cuckoo Based Particle Approach (CBPA). Nodes are deployed randomly and organized as static clusters by Cuckoo Search (CS). After the cluster heads are selected, the information is collected, aggregated and forwarded to the base station using generalized particle approach algorithm. The Generalized Particle Model Algorithm (GPMA) transforms the network energy consumption problem into dynamics and kinematics of numerous particles in a force-field. The proposed approach can significantly lengthen the network lifetime when compared to traditional methods.
---
paper_title: Analytical and Computational Evaluation of Scalable Distributed Fusion Algorithms

paper_content:

The theoretical fundamentals of distributed information fusion have been developed over the past two decades and are now fairly well established. However, practical applications of these theoretical results to dynamic sensor networks have remained a challenge. There has been a great deal of work in developing distributed fusion algorithms applicable to a network centric architecture. In general, in a distributed system such as ad hoc sensor networks, the communication architecture is not fixed. In those cases, the distributed fusion approaches based on pedigree information may not scale because of limited communication bandwidth. In this paper, we focus on scalable fusion algorithms and conduct analytical performance evaluation to compare their performance. The goal is to understand the performance of these algorithms under different operating conditions. Specifically, we evaluate the performance of channel filter fusion, naive fusion, Chernoff fusion, Shannon fusion, and Bhattacharyya fusion algorithms. We also compare their performance to "optimal" centralized fusion under a specific communication pattern. The results show that the channel filter fusion, representing a first order approximation to the information graph fusion, is the only "consistent" fusion algorithm.
---
paper_title: Handbook of Multisensor Data Fusion

paper_content:

Multisensor Data Fusion D. L. Hall and J. Llinas Data Fusion Perspectives and Its Role in Information Processing O. Kessler and F. White Revisions to the JDL Data Fusion Model A. N. Steinberg and C. L. Bowman Introduction to the Algorithms of Data Association in Multiple-Target Tracking J. K. Uhlmann The Principles and Practice of Image and Spatial Data Fusion E. Waltz and T. Waltz Data Registration R. R. Brooks and L. Grewe Data Fusion Automation: A Top-Down Perspective R. Antony Overview of Distributed Decision Fusion M. E. Liggins Introduction to Particle Filtering: The Next Stage in Tracking M. E. Liggins and K-C. Chang Target Tracking Using Probabilistic Data Association-Based Techniques with Applications to Sonar, Radar, and EO Sensors T. Kirubarajan and Y. Bar-Shalom An Introduction to the Combinatorics of Optimal and Approximate Data Association J. K. Uhlmann A Bayesian Approach to Multiple-Target Tracking L. D. Stone Data Association Using Multiple-Frame Assignments A. B. Poore, S. Lu, and B. J. Suchomel General Decentralized Data Fusion with Covariance Intersection S. Julier and J. K. Uhlmann Data Fusion in Nonlinear Systems S. Julier and J. K. Uhlmann Random Set Theory for Multisource-Multitarget Information Fusion R. Mahler Distributed Fusion Architectures, Algorithms, and Performance within a Network-Centric Architecture M. E. Liggins and K-C. Chang Foundations of Situation and Threat Assessment A. N. Steinberg An Introduction to Level 5 Fusion: The Role of the User E. Blasch Perspectives on the Human Side of Data Fusion: Prospects for Improved Effectiveness using Advanced Human-Computer Interfaces D. L. Hall, C. M. Hall, and S. A. H. McMullen Requirements Derivation for Data Fusion Systems E. Waltz and D. L. Hall A Systems Engineering Approach for Implementing Data Fusion Systems C. L. Bowman and A. N. Steinberg Studies and Analyses within Project Correlation: An In-Depth Assessment of Correlation Problems and Solution Techniques J. Llinas, Capt. L. McConnell, C. L. Bowman, D. L. Hall, and P. Applegate Data Management Support to Tactical Data Fusion R. Antony Assessing the Performance of Multisensor Fusion Processes J. Llinas A Survey of COTS Software for Multisensor Data Fusion S. A. Hall McMullen, R. R. Sherry, and S. Miglani A Survey of Multisensor Data Fusion Systems M. L. Nichols Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems C. S. Byington and A. K. Garga Adapting Data Fusion to Chemical and Biological Sensors D. C. Swanson Fusion of Ground and Satellite Data via the Army Battle Command System S. Aungst, M. Campbell, J. Kuhns, D. Beyerle, T. Bacastow, and J. Knox Developing Information Fusion Methods for Combat Identification T. M. Schuck, J. Bockett Hunter, and D. D. Wilson
---
paper_title: Advances in Multi-Sensor Data Fusion: Algorithms and Applications

paper_content:

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of “algorithm fusion” methods; (3) Establishment of an automatic quality assessment scheme.
---
paper_title: On Context Awareness for Multisensor Data Fusion in IoT

paper_content:

With the advances in sensor technology, data mining techniques and the internet, information and communication technology further motivates the development of smart systems such as intelligent transportation systems, smart utilities and smart grid. With the availability of low cost sensors, there is a growing focus on multi-sensor data fusion (MSDF). Internet of Things (IoT) is currently connecting more than 9 billion devices. IoT includes the connectivity of smart things which focuses more on the interactions and interoperations between things and people. Key problem in IoT middleware is to develop efficient decision level intelligent mechanisms. Therefore, we focus on IoT middleware using context-aware mechanism. To get automated inferences of the surrounding environment, context -aware concept is adopted by computing world in combination with data fusion. We conduct a comprehensive review on context awareness for MSDF in IoT and discuss the future directions in the area of context-aware computing.
---
paper_title: On Multihop Distances in Wireless Sensor Networks with Random Node Locations

paper_content:

Location and intersensor distance estimations are important functions for the operation of wireless sensor networks, especially when protocols can benefit from the distance information prior to network deployment. The maximum multihop distance that can be covered in a given number of hops in a sensor network is one such parameter related with coverage area, delay, and minimal multihop transmission energy consumption estimations. In randomly deployed sensor networks, intersensor distances are random variables. Hence, their evaluations require probabilistic methods, and distance models should involve investigation of distance distribution functions. Current literature on analytical modeling of the maximum distance distribution is limited to 1D networks using the Gaussian pdf. However, determination of the maximum multihop distance distribution in 2D networks is a quite complex problem. Furthermore, distance distributions in 2D networks are not accurately modeled by the Gaussian pdf. Hence, we propose a greedy method of distance maximization and evaluate the distribution of the obtained multihop distance through analytical approximations and simulations.
---
paper_title: Multi-Sensor Data Fusion: An Introduction

paper_content:

This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science, as well as researchers and professional engineers. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended. Although conceptually simple, the study of multi-sensor data fusion presents challenges that are unique within the education of the electrical engineer or computer scientist. To become competent in the field the student must become familiar with tools taken from a wide range of diverse subjects including: neural networks, signal processing, statistical estimation, tracking algorithms, computer vision and control theory. All too often the student views multi-sensor data fusion as a miscellaneous assortment of different processes which bear no relationship to each other. In this book the processes are described using a common statistical framework. As a consequence, the underlying pattern of relationships that exists between the different methodologies is made evident.
---
paper_title: Analyzing the Combination of Conflicting Belief Functions

paper_content:

We consider uncertain data which uncertainty is represented by belief functions and that must be combined. The result of the combination of the belief functions can be partially conflictual. Initially Shafer proposed Dempster's rule of combination where the conflict is reallocated proportionally among the other masses. Then Zadeh presented an example where Dempster's rule of combination produces unsatisfactory results. Several solutions were proposed: the TBM solution where masses are not renormalized and conflict is stored in the mass given to the empty set, Yager's solution where the conflict is transferred to the universe and Dubois and Prade's solution where the masses resulting from pairs of conflictual focal elements are transferred to the union of these subsets. Many other suggestions have then been made, creating a 'jungle' of combination rules. We discuss the nature of the combinations (conjunctive versus disjunctive, revision versus updating, static versus dynamic data fusion), argue about the need for a normalization, examine the possible origins of the conflicts, determine if a combination is justified and analyze many of the proposed solutions.
---
paper_title: A generalized approach for inconsistency detection in data fusion from multiple sensors

paper_content:

This paper presents a sensor fusion strategy based on Bayesian method that can identify the inconsistency in sensor data so that spurious data can be eliminated from the sensor fusion process. The proposed method adds a term to the commonly used Bayesian technique that represents the probabilistic estimate corresponding to the event that the data is not spurious conditioned upon the data and the true state. This term has the effect of increasing the variance of the posterior distribution when data from one of the sensors is inconsistent with respect to the other. The proposed strategy was verified with the help of extensive simulations. The simulations showed that the proposed method was able to identify inconsistency in sensor data and also confirmed that the identification of inconsistency led to a better estimate of desired state variable.
---
paper_title: Implementation and evaluation of a constraint-based multimodal fusion system for speech and 3D pointing gestures

paper_content:

This paper presents an architecture for fusion of multimodal input streams for natural interaction with a humanoid robot as well as results from a user study with our system. The presented fusion architecture consists of an application independent parser of input events, and application specific rules. In the presented user study, people could interact with a robot in a kitchen scenario, using speech and gesture input. In the study, we could observe that our fusion approach is very tolerant against falsely detected pointing gestures. This is because we use speech as the main modality and pointing gestures mainly for disambiguation of objects. In the paper we also report about the temporal correlation of speech and gesture events as observed in the user study.
---
paper_title: Predictive Analytics by Using Bayesian Model Averaging for Large-Scale Internet of Things

paper_content:

Massive events can be produced today because of the rapid development of the Internet of Things (IoT). Complex event processing, which can be used to extract high-level patterns from raw data, has become an essential part of the IoT middleware. Prediction analytics is an important technology in supporting proactive complex event processing. In this paper, we propose the use of dynamic Bayesian model averaging to develop a high-accuracy prediction analytic method for large-scale IoT application. This method, which is based on a new multilayered adaptive dynamic Bayesian network model, uses Gaussian mixture models and expectation-maximization inference for basic Bayesian prediction. Bayesian model averaging is implemented by using Markov chain Monte Carlo approximation, and a novel dynamic Bayesian model averaging method is proposed based on event context clustering. Simulation experiments show that the proposed prediction analytic method has better accuracy compared to traditional methods. Moreover, the proposed method exhibits acceptable performance when implemented in large-scale IoT applications.
---
paper_title: Multi-Sensor Data Fusion with MATLAB®

paper_content:

Using MATLAB examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for aerospace applications, although the methods can also be applied to systems in other areas, such as biomedicine, military defense, and environmental engineering. After presenting several useful strategies and algorithms for DF and tracking performance, the book evaluates DF algorithms, software, and systems. It next covers fuzzy logic, fuzzy sets and their properties, fuzzy logic operators, fuzzy propositions/rule-based systems, an inference engine, and defuzzification methods. It develops a new MATLAB graphical user interface for evaluating fuzzy implication functions, before using fuzzy logic to estimate the unknown states of a dynamic system by processing sensor data. The book then employs principal component analysis, spatial frequency, and wavelet-based image fusion algorithms for the fusion of image data from sensors. It also presents procedures for combing tracks obtained from imaging sensor and ground-based radar. The final chapters discuss how DF is applied to mobile intelligent autonomous systems and intelligent monitoring systems. Fusing sensors data can lead to numerous benefits in a systems performance. Through real-world examples and the evaluation of algorithmic results, this detailed book provides an understanding of MSDF concepts and methods from a practical point of view. Select MATLAB programs are available for download on www.crcpress.com
---
paper_title: Tracker-Level Fusion for Robust Bayesian Visual Tracking

paper_content:

We propose a tracker-level fusion framework for robust visual tracking. The framework combines trackers addressing different tracking challenges to improve the overall performance. A novelty of the proposed framework is the inclusion of an online performance measure to identify the track quality level of each tracker so as to guide the fusion. The fusion is then based on appropriately mixing the prior state of the trackers. Moreover, the track-quality level is used to update the target appearance model. We demonstrate the framework with two Bayesian trackers on video sequences with various challenges and show its robustness compared with the independent use of the two individual trackers, and also compared with state-of-the-art trackers that use tracker-level fusion.
---
paper_title: Handbook of Multisensor Data Fusion

paper_content:

Multisensor Data Fusion D. L. Hall and J. Llinas Data Fusion Perspectives and Its Role in Information Processing O. Kessler and F. White Revisions to the JDL Data Fusion Model A. N. Steinberg and C. L. Bowman Introduction to the Algorithms of Data Association in Multiple-Target Tracking J. K. Uhlmann The Principles and Practice of Image and Spatial Data Fusion E. Waltz and T. Waltz Data Registration R. R. Brooks and L. Grewe Data Fusion Automation: A Top-Down Perspective R. Antony Overview of Distributed Decision Fusion M. E. Liggins Introduction to Particle Filtering: The Next Stage in Tracking M. E. Liggins and K-C. Chang Target Tracking Using Probabilistic Data Association-Based Techniques with Applications to Sonar, Radar, and EO Sensors T. Kirubarajan and Y. Bar-Shalom An Introduction to the Combinatorics of Optimal and Approximate Data Association J. K. Uhlmann A Bayesian Approach to Multiple-Target Tracking L. D. Stone Data Association Using Multiple-Frame Assignments A. B. Poore, S. Lu, and B. J. Suchomel General Decentralized Data Fusion with Covariance Intersection S. Julier and J. K. Uhlmann Data Fusion in Nonlinear Systems S. Julier and J. K. Uhlmann Random Set Theory for Multisource-Multitarget Information Fusion R. Mahler Distributed Fusion Architectures, Algorithms, and Performance within a Network-Centric Architecture M. E. Liggins and K-C. Chang Foundations of Situation and Threat Assessment A. N. Steinberg An Introduction to Level 5 Fusion: The Role of the User E. Blasch Perspectives on the Human Side of Data Fusion: Prospects for Improved Effectiveness using Advanced Human-Computer Interfaces D. L. Hall, C. M. Hall, and S. A. H. McMullen Requirements Derivation for Data Fusion Systems E. Waltz and D. L. Hall A Systems Engineering Approach for Implementing Data Fusion Systems C. L. Bowman and A. N. Steinberg Studies and Analyses within Project Correlation: An In-Depth Assessment of Correlation Problems and Solution Techniques J. Llinas, Capt. L. McConnell, C. L. Bowman, D. L. Hall, and P. Applegate Data Management Support to Tactical Data Fusion R. Antony Assessing the Performance of Multisensor Fusion Processes J. Llinas A Survey of COTS Software for Multisensor Data Fusion S. A. Hall McMullen, R. R. Sherry, and S. Miglani A Survey of Multisensor Data Fusion Systems M. L. Nichols Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems C. S. Byington and A. K. Garga Adapting Data Fusion to Chemical and Biological Sensors D. C. Swanson Fusion of Ground and Satellite Data via the Army Battle Command System S. Aungst, M. Campbell, J. Kuhns, D. Beyerle, T. Bacastow, and J. Knox Developing Information Fusion Methods for Combat Identification T. M. Schuck, J. Bockett Hunter, and D. D. Wilson
---
paper_title: Track Creation and Deletion Framework for Long-Term Online Multiface Tracking

paper_content:

To improve visual tracking, a large number of papers study more powerful features, or better cue fusion mechanisms, such as adaptation or contextual models. A complementary approach consists of improving the track management, that is, deciding when to add a target or stop its tracking, for example, in case of failure. This is an essential component for effective multiobject tracking applications, and is often not trivial. Deciding whether or not to stop a track is a compromise between avoiding erroneous early stopping while tracking is fine, and erroneous continuation of tracking when there is an actual failure. This decision process, very rarely addressed in the literature, is difficult due to object detector deficiencies or observation models that are insufficient to describe the full variability of tracked objects and deliver reliable likelihood (tracking) information. This paper addresses the track management issue and presents a real-time online multiface tracking algorithm that effectively deals with the above difficulties. The tracking itself is formulated in a multiobject state-space Bayesian filtering framework solved with Markov Chain Monte Carlo. Within this framework, an explicit probabilistic filtering step decides when to add or remove a target from the tracker, where decisions rely on multiple cues such as face detections, likelihood measures, long-term observations, and track state characteristics. The method has been applied to three challenging data sets of more than 9 h in total, and demonstrate a significant performance increase compared to more traditional approaches (Markov Chain Monte Carlo, reversible-jump Markov Chain Monte Carlo) only relying on head detection and likelihood for track management.
---
paper_title: Human detection and tracking in an assistive living service robot through multimodal data fusion

paper_content:

A new method is proposed for using a combination of measurements from a laser range finder and a depth camera in a data fusion process that benefits from each modality's strong side. The combination leads to a significantly improved performance of the human detection and tracking in comparison with what is achievable from the singular modalities. The useful information from both laser and depth camera is automatically extracted and combined in a Bayesian formulation that is estimated using a Markov Chain Monte Carlo (MCMC) sampling framework. The experiments show that this algorithm can track robustly multiple people in real world assistive robotics applications.
---
paper_title: Bias phenomenon and compensation for PDA/JPDA algorithms

paper_content:

Probabilistic Data Association (PDA) and Joint PDA (JPDA) algorithms are approaches for target tracking which have received considerable attention. It has been observed for some years that they both yield biased tracks in a multitarget environment. However, most work assumes no false alarms and the rejection phenomenon of the JPDA algorithm has not been reported. In this paper, the general procedure of multitarget tracking and the PDA/JPDA algorithms are first described. Their bias phenomenon is simulated and investigated. It is observed that(1)the JPDA algorithm has less bias than the PDA algorithm in a clean environment. Both of them yield coalescence (2)the JPDA algorithm has coalescence and rejection bias phenomenon while the PDA algorithm has only coalescence phenomenon in a clutter environment. Bias compensated algorithms are then presented using the polynomial regression method. Simulations are carried out to select the order of polynomial regression. Monte Carlo simulations also demonstrate the effectiveness of the compensated PDA/JPDA algorithms.
---
paper_title: Computational complexity analysis for multiple hypothesis tracking

paper_content:

This paper presents a detailed analysis of computational complexity of Multiple Hypothesis Tracking (MHT). The result proves that the computational complexity of MHT is dominated by the number of hypotheses. Effects of track merging and pruning are analyzed also. Certain common design parameters of MHT, such as thresholds, are also discussed in detail. The results of this paper provide a guidance for selecting parameters in an MHT tracker and predicting its performance. Among the design parameters discussed in this paper, track merging appears to be the most important way for controlling the computational complexity of MHT. Thresholds for track deletion are also critical. If not all measurements are allowed to initiate new tracks, the number of new tracks can also be used for tuning the computation requirement of MHT, but it is not as significant as thresholds.
---
paper_title: Switching adaptive filter design using Bayesian classification approach for multi-sensor data fusion

paper_content:

The aim of this paper is to present an adaptive filtering fusion approach for tracking the same maneuvering target in a multi-sensor environment. The hierarchical estimation fusion consists of several local nodes and a global node. A linear Kalman filter is employed by each local node to perform the tracking function and the resulting track file communicates to the global node. In the global node, an algorithm, which consists of dual-band Information Matrix Filter (IMF) and a two-category Bayesian classifier, is employed to generate an appropriately global estimate. By incorporating Bayesian decision rule into a classification scheme, a Bayesian classifier is developed which involves switching between high-level-band IMF and low-level-band IMF against the rapid variation of target dynamics. The proposed filter, so-called switching adaptive filter, has better estimation accuracy than each individual IMF. Computer simulation results are included to demonstrate the effectiveness of proposed algorithm.
---
paper_title: Adaptive Information Matrix Filtering fusion with nonlinear classifier

paper_content:

An adaptive filtering approach is present for fusing the tracks of multi-sensor surveillance systems. The approach is an algorithm of hierarchical estimation fusion which consists of several local nodes and a global node. A linear Kalman filter is employed by each local node to produce the track estimate of the same target. The outputs of all local nodes are transmitted to the global node. In this node, an adaptive filter, which consists of the dual-band Information Matrix Filter (IMF) and a nonlinear classifier, is utilized to combine the local estimates to generate a global estimate appropriately. The feature vectors are yielded by dual-band IMF. The classifier, which is designed by using the radial basis function network in Gaussian form, is used to divide the feature space into regions that correspond to decide the output of either high-level-band IMF or low-level-band IMF against the uncertainties of target dynamics. The proposed filter has better tracking performance than each individual IMF. Simulation results are included to demonstrate the effectiveness of proposed filter.
---
paper_title: A Method for Judicious Fusion of Inconsistent Multiple Sensor Data

paper_content:

One of the major problems in sensor fusion is that sensors frequently provide spurious observations which are difficult to predict and model. The spurious measurements from sensors must be identified and eliminated since their incorporation in the fusion pool might lead to inaccurate estimation. This paper presents a unified sensor fusion strategy based on a modified Bayesian approach that can automatically identify the inconsistency in sensor measurements so that the spurious measurements can be eliminated from the data fusion process. The proposed method adds a term to the commonly used Bayesian formulation. This term is an estimate of the probability that the data is not spurious, based upon the measured data and the unknown value of the true state. In fusing two measurements, it has the effect of increasing the variance of the posterior distribution when measurement from one of the sensors is inconsistent with respect to the other. The increase or decrease in variance can be estimated using the information theoretic measure "entropy." The proposed strategy was verified with the help of extensive computations performed on simulated data from three sensors. A comparison was made between two different fusion schemes: centralized fusion in which data obtained from all sensors were fused simultaneously, and a decentralized or sequential Bayesian scheme that proved useful for identifying and eliminating spurious data from the fusion process. The simulations verified that the proposed strategy was able to identify spurious sensor measurements and eliminate them from the fusion process, thus leading to a better overall estimate of the true state. The proposed strategy was also validated with the help of experiments performed using stereo vision cameras, one infrared proximity sensor, and one laser proximity sensor. The information from these three sensing sources was fused to obtain an occupancy profile of the robotic workspace
---
paper_title: Multi-target tracking using joint probabilistic data association

paper_content:

The Probabilistic Data Association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, some new theoretical results are presented on the Joint Probabilistic Data Association (JPDA) algorithm, in which joint posterior probabilities are computed for multiple targets in Poisson clutter. The algorithm is applied to a passive sonar tracking problem wlth multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e. low) probabilities of detection at each sample time. Simulation results are presented for two heavily interfering targets; these illustrate the dramatic improvements obtained by computing joint probabilities.
---
paper_title: Multisensor fusion and integration: approaches, applications, and future research directions

paper_content:

Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview of current sensor technologies and describes the paradigm of multisensor fusion and integration as well as fusion techniques at different fusion levels. Applications of multisensor fusion in robotics, biomedical system, equipment monitoring, remote sensing, and transportation system are also discussed. Finally, future research directions of multisensor fusion technology including microsensors, smart sensors, and adaptive fusion techniques are presented.
---
paper_title: Bayesian Data Fusion for Distributed Target Detection in Sensor Networks

paper_content:

In this correspondence, we study different approaches for Bayesian data fusion for distributed target detection in sensor networks. Due to communication and bandwidth constraints, we assume that each sensor can only transmit a local decision to the fusion center (FC), which is in charge to take the final decision about the presence of a target. The optimal Bayesian test statistic at the FC is derived in the case where both the number and locations of the sensors are known. On the other hand, if both the number and the locations of the sensors are unknown, the optimal Bayesian test statistic is computed based on the same observations that the Scan Statistic test utilizes. The performances of the different approaches are compared through simulation.
---
paper_title: An adaptive filter for multi-sensor track fusion

paper_content:

An adaptive filter is developed for use in multi-sensor surveillance systems for target tracking. The hierarchical architecture consists of local processors and global processor for distributed fusion. A linear Kalman filter is employed in each local processor to track the same target which is described in the reference Cartesian coordinate system with the radar measuring range, bearing and elevation angle in the spherical coordinate system. The global processor utilizes Information Matrix Filter (IMF) with two different process noise levels to combine local processor outputs. A decision logic approach incorporates with dual-band IMF to develop switching capability as adaptive manner to respond the target dynamics. The high-level band IMF is selected for maneuver period and the proper low-level band IMF is chosen for quiescent period. The resulting filter has better tracking performance than each individual IMF. Simulation results are included to demonstrate the effectiveness of proposed algorithm.
---
paper_title: Airborne learning algorithms: a case of a ground target multiple hypothesis tracker

paper_content:

In current practice there is a wide disconnect between increasing availability of sensor data and our ability to apply advanced learning algorithms to process that data in real time. While multitudes of very promising learning algorithms are developed in the research community, years and decades pass before these algorithms become implemented in embedded platforms. We describe a feasibility study of deploying a ground target multiple hypothesis tracker (MHT) in a system suitable for airborne deployment in platforms like JSTAR and the Global Hawk UAV. For a typical tracking scenario, an MHT algorithm requires more than 10 GFLOPs, necessitating implementation on a multicomputer. In addition, an MHT algorithm presents serious implementation challenges due to code complexity and global nature of the data. We discuss steps necessary to implement and deploy an MHT algorithm. We argue that modern high-performance computing systems that consist of general-purpose processors like Motorola PowerPCs and high-bandwidth interconnect like Mercury's RACE++, coupled with advanced development tools and libraries, provide an environment that enables rapid deployment of advanced learning algorithms.
---
paper_title: Mathematical Techniques in Multisensor Data Fusion

paper_content:

From the Publisher: ::: This invaluable reference offers the most comprehensive introduction available to the concepts of multisensor data fusion. It introduces key algorithms, provides advice on their utilization, and raises issues associated with their implementation. With a diverse set of mathematical and heuristic techniques for combining data from multiple sources, the book shows how to implement a data fusion system, describes the process for algorithm selection, functional architectures and requirements for ancillary software, and illustrates man-machine interface requirements an database issues.
---
paper_title: Dempster--Shafer Theory and Statistical Inference with Weak Beliefs

paper_content:

The Dempster-Shafer (DS) theory is a powerful tool for probabilistic reasoning based on a formal calculus for combining evi- dence. DS theory has been widely used in computer science and engi- neering applications, but has yet to reach the statistical mainstream, perhaps because the DS belief functions do not satisfy long-run fre- quency properties. Recently, two of the authors proposed an extension of DS, called the weak belief (WB) approach, that can incorporate de- sirable frequency properties into the DS framework by systematically enlarging the focal elements. The present paper reviews and extends this WB approach. We present a general description of WB in the context of inferential models, its interplay with the DS calculus, and the maximal belief solution. New applications of the WB method in two high-dimensional hypothesis testing problems are given. Simula- tions show that the WB procedures, suitably calibrated, perform well compared to popular classical methods. Most importantly, the WB ap- proach combines the probabilistic reasoning of DS with the desirable frequency properties of classical statistics.
---
paper_title: Multi-Sensor Data Fusion with MATLAB®

paper_content:

Using MATLAB examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for aerospace applications, although the methods can also be applied to systems in other areas, such as biomedicine, military defense, and environmental engineering. After presenting several useful strategies and algorithms for DF and tracking performance, the book evaluates DF algorithms, software, and systems. It next covers fuzzy logic, fuzzy sets and their properties, fuzzy logic operators, fuzzy propositions/rule-based systems, an inference engine, and defuzzification methods. It develops a new MATLAB graphical user interface for evaluating fuzzy implication functions, before using fuzzy logic to estimate the unknown states of a dynamic system by processing sensor data. The book then employs principal component analysis, spatial frequency, and wavelet-based image fusion algorithms for the fusion of image data from sensors. It also presents procedures for combing tracks obtained from imaging sensor and ground-based radar. The final chapters discuss how DF is applied to mobile intelligent autonomous systems and intelligent monitoring systems. Fusing sensors data can lead to numerous benefits in a systems performance. Through real-world examples and the evaluation of algorithmic results, this detailed book provides an understanding of MSDF concepts and methods from a practical point of view. Select MATLAB programs are available for download on www.crcpress.com
---
paper_title: Handbook of Multisensor Data Fusion

paper_content:

Multisensor Data Fusion D. L. Hall and J. Llinas Data Fusion Perspectives and Its Role in Information Processing O. Kessler and F. White Revisions to the JDL Data Fusion Model A. N. Steinberg and C. L. Bowman Introduction to the Algorithms of Data Association in Multiple-Target Tracking J. K. Uhlmann The Principles and Practice of Image and Spatial Data Fusion E. Waltz and T. Waltz Data Registration R. R. Brooks and L. Grewe Data Fusion Automation: A Top-Down Perspective R. Antony Overview of Distributed Decision Fusion M. E. Liggins Introduction to Particle Filtering: The Next Stage in Tracking M. E. Liggins and K-C. Chang Target Tracking Using Probabilistic Data Association-Based Techniques with Applications to Sonar, Radar, and EO Sensors T. Kirubarajan and Y. Bar-Shalom An Introduction to the Combinatorics of Optimal and Approximate Data Association J. K. Uhlmann A Bayesian Approach to Multiple-Target Tracking L. D. Stone Data Association Using Multiple-Frame Assignments A. B. Poore, S. Lu, and B. J. Suchomel General Decentralized Data Fusion with Covariance Intersection S. Julier and J. K. Uhlmann Data Fusion in Nonlinear Systems S. Julier and J. K. Uhlmann Random Set Theory for Multisource-Multitarget Information Fusion R. Mahler Distributed Fusion Architectures, Algorithms, and Performance within a Network-Centric Architecture M. E. Liggins and K-C. Chang Foundations of Situation and Threat Assessment A. N. Steinberg An Introduction to Level 5 Fusion: The Role of the User E. Blasch Perspectives on the Human Side of Data Fusion: Prospects for Improved Effectiveness using Advanced Human-Computer Interfaces D. L. Hall, C. M. Hall, and S. A. H. McMullen Requirements Derivation for Data Fusion Systems E. Waltz and D. L. Hall A Systems Engineering Approach for Implementing Data Fusion Systems C. L. Bowman and A. N. Steinberg Studies and Analyses within Project Correlation: An In-Depth Assessment of Correlation Problems and Solution Techniques J. Llinas, Capt. L. McConnell, C. L. Bowman, D. L. Hall, and P. Applegate Data Management Support to Tactical Data Fusion R. Antony Assessing the Performance of Multisensor Fusion Processes J. Llinas A Survey of COTS Software for Multisensor Data Fusion S. A. Hall McMullen, R. R. Sherry, and S. Miglani A Survey of Multisensor Data Fusion Systems M. L. Nichols Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems C. S. Byington and A. K. Garga Adapting Data Fusion to Chemical and Biological Sensors D. C. Swanson Fusion of Ground and Satellite Data via the Army Battle Command System S. Aungst, M. Campbell, J. Kuhns, D. Beyerle, T. Bacastow, and J. Knox Developing Information Fusion Methods for Combat Identification T. M. Schuck, J. Bockett Hunter, and D. D. Wilson
---
paper_title: Bayesian and Dempster-Shafer fusion

paper_content:

The Kalman Filter is traditionally viewed as a prediction-correction filtering algorithm. In this work we show that it can be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman-Kolmogorov prediction integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster-Shafer theory, which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster-Shafer theory and presenting the basic Dempster-Shafer fusion equation. Finally we compare the two methods, and discuss the relative merits and demerits using an illustrative example.
---
paper_title: Multi-Sensor Fuzzy Data Fusion Using Sensors with Different Characteristics

paper_content:

This paper proposes a new approach to multisensor data fusion, suggesting by considering information about the sensors' different characteristics, aggregation of data acquired by individual sensors can be done more efficient. Same as the most effective sensors' characteristics, especially in control systems, our focus is on sensors' accuracy and frequency response. A rule-based fuzzy system is presented for fusion of raw data obtained from the sensors having complement characteristics in accuracy and bandwidth. Furthermore, a fuzzy predictor system is also suggested aiming to extremely high accuracy for highly sensitive applications. The great advantages of the proposed sensor fusion system are revealed on simulation results of a control system utilizing the fusion system for output estimation.
---
paper_title: Two Target Detection Algorithms for Passive Multistatic Radar

paper_content:

This paper considers the problem of passive detection with a multistatic radar system involving a noncooperative illumi- nator of opportunity (IO) and multiple receive platforms. An un- known source signal is transmitted by the IO, which illuminates a target of interest. These receive platforms are geographically dis- persed, and collect independent target echoes due to the illumi- nation by the same IO. We consider a generalized canonical cor- relation (GCC) detector for passive detection which requires the knowledge of the noise power. We derive closed-form expressions for the probabilities of false alarm and detection of this detector. For the case where the noise power is unknown, we propose a gen- eralized likelihood ratio test (GLRT) detector to deal with the pas- sive detection problem. Moreover, a closed-form expression for the probability of false alarm of this GLRT detector is given, which shows that the proposed GLRT detector exhibits a constant false alarm rate property with respect to the noise power. Numerical simulations demonstrate that the proposed GLRT detector gen- erally outperforms the generalized coherence detector, a previous popular passive detector that neither requires the knowledge ofthe noise power. In addition, the GLRT also outperforms the GCC de- tector when the latter has an uncertainty in its knowledge of the noise power.
---
paper_title: Advanced multiple objects tracking by fusing radar and image sensor data - Application on a case study.

paper_content:

The aim of this paper is to present a multiple object tracking data fusion technique, which fuses radar, image, and ego vehicle odometry. The data are fused at a high level, which leads to reliable and stable tracking results providing also additional features as width estimation and the detection of stationary objects. A ldquorealrdquo application of these algorithms is illustrated on a specific use-case: the SASPENCE system, developed inside the Integrated Project PReVENT. The principle results show that such a data-fusion technique is actually able to improve the SASPENCE performances, especially in terms of extension of operative scenarios, a basic issue for the system functionality.
---
paper_title: Improved JPDA for fast fault detection

paper_content:

Regarding faults as dynamic modes which observe through the multi-sensors, with probabilistic data association based on multi-sensor, we obtain the fast fault detection results according to the association probability and threshold values. Joint Probabilistic Data Association (JPDA) algorithm is one of the effective ways for single-sensor multi-target tracking. Based on the analysis of JPDA algorithm, we improve the JPDA algorithm: first, we propose an approximation method for constructing the confirmation matrix through removing the small probability events using the right threshold values, and then, we present the mathematical division of the confirmation matrix according to the intersection area of the association gate of fault targets to be tracked; lastly, we compute the association probability of fault targets through attenuating the value of the public measurement. The simulation results show preliminarily that our improved JPDA algorithm saves the computing time greatly, and effectively meet the requirements of fast and real-time fault detection.
---
paper_title: Scalable Bayesian human-robot cooperation in mobile sensor networks

paper_content:

In this paper, scalable collaborative human-robot systems for information gathering applications are approached as a decentralized Bayesian sensor network problem. Human-computer augmented nodes and autonomous mobile sensor platforms are collaborating on a peer-to-peer basis by sharing information via wireless communication network. For each node, a computer (onboard the platform or carried by the human) implements both a decentralized Bayesian data fusion algorithm and a decentralized Bayesian control negotiation algorithm. The individual node controllers iteratively negotiate anonymously with each other in the information space to find cooperative search plans based on both observed and predicted information that explicitly consider the platforms (humans and robots) motion models, their sensors detection functions, as well as the target arbitrary motion model. The results of a collaborative multi-target search experiment conducted with a team of four autonomous mobile sensor platforms and five humans carrying small portable computers with wireless communication are presented to demonstrate the efficiency of the approach.
---
paper_title: Distributed vision network for multiple target tracking using a dynamic hybrid consensus filter

paper_content:

In this paper, we present a distributed estimator for solving data association problem in the scenario of multiple target tracking using vision networks. In contrast to the centralized methods, which require a central processing node to do sensor data processing and information fusion, the distributed one without fusion center is more robust to the sensor node failure, and can save data bandwidth for vision information communication. Here, we propose a dynamic hybrid consensus filter combined with joint probabilistic data association (DHCF-JPDA) for multiple target tracking. Compared to the state of the art, the proposed method can not only handle the sensor failure (naive node) problem and prior information redundancy problem, but also show a faster converge rate when a limited number of consensus iterations is performed.
---
paper_title: Recent advances in multi-INT track fusion

paper_content:

This paper provides recent experimental results on a large-scale scenario for recently-introduced MCMC-based and MHT-based solutions to the multi-INT problem, where sparse non-kinematic data is to be fused with multi-sensor kinematic data. The MCMC Data Fuser (MCMC-DF) and Asynchronous MHT (A-MHT) show promising scalability relative to classical track-oriented MHT, the leading paradigm for high-performance multi-target tracking. Additionally, we propose a number of modeling and algorithmic enhancements: (i) efficient hypothesis formation based on coarse (pairwise) track gating; (ii) a forward-backward Kalman smoother for Ornstein-Uhlenbeck (OU) processes; (iii) an OU-IMM filter for the target move-stop-move scenarios that are of interest; and (iv) fusion logic and scoring generalizations to accommodate multiple emitters per target.12
---
paper_title: Track-to-track association in the presence of sensor bias and the relative bias estimation

paper_content:

Track to track association problem is crucial for multisensor data fusion, and become complicated in the presence of sensor bias, random errors, false tracks and missed tracks. In this paper, we apply the optimal Bayes joint decision and estimation(JDE) which was proposed in [12] to the track-to-track association in the presence of sensor bias and develop a simplified JDE. The optimal Bayes JDE accounts to the possible association error when estimates the relative sensor bias and takes the estimate error into consideration when chooses the track-to-track association by the association cost having the estimation error. Hence it can enhance the accuracy of track-to-track association and reduce the relative bias estimate error. For computational simplicity, in this paper we proposed another JDE algorithm which can reduce the computation and only is slightly worse than the optimal Bayes JDE. The simulations compare the performance of the JDE association, the simplifed JDE developed in this paper, the method proposed in [8], and verify the feasibility and effectiveness of methods.
---
paper_title: Distributed Sensor Allocation for Multi-Target Tracking in Wireless Sensor Networks

paper_content:

In emerging tracking systems using large-scale wireless sensor networks, sensor management is an essential task in order to balance the tracking performance and costs subject to limited network resources in terms of energy, communication bandwidth, and sensing range. This paper considers the sensor allocation problem for multi-target tracking (MTT), in which a group of sensors are dynamically selected and allocated to track each of the multiple targets and collaborate within the group via track data fusion. The sensor assignments evolve over time as targets move, and are accomplished by solving a constrained optimization problem that is formulated to maximize the overall tracking performance for all targets, while conserving network energy and providing tracking coverage guarantee. The original integer-valued optimization problem is relaxed to a convex program for computational tractability, and then implemented in a distributed manner for network scalability and reduced communication costs. Through local one-hop communication with neighboring nodes, each sensor autonomously decides on whether to participate in data collection and how to contribute to track fusion. The proposed distributed sensor allocation algorithm, implemented via iterative subgradient search, is shown to converge to the global optimum of the centralized relaxed problem, and is near optimal for the original integer programming problem.
---
paper_title: Asynchronous Track-to-Track Fusion by Direct Estimation of Time of Sample in Sensor Networks

paper_content:

Asynchronous data fusion is inevitable in track-to-track fusion for tracking high-speed targets. For low-speed targets, e.g., the movement of clouds, synchronization is insignificant and, depending on the application, may be disregarded. Real-time asynchronous fusion is a demanding task in sensor networks when the sensors are not synchronous in sampling-rate or in sampling-phase. In the method proposed in this paper, an estimator in the fusion center estimates the actual time of the sample with respect to the time-reference of the fusion center upon receiving the data from a sensor. Then, the computer of the fusion center uses predictions to transfer all the received data to the data corresponding to the start of the next fusion period. This process synchronizes the data, which is necessary for real-time uncorrelated track-to-track fusion. Finally, the pseudo-synchronized data of all the sensors are fused with an element-wise linear minimum variance unbiased estimator algorithm before the start of the next fusion period. Simulation and comparison with some benchmark algorithms are demonstrated to verify the effectiveness of the proposed algorithm.
---
paper_title: Novel Implementation of Track-Oriented Multiple Hypothesis Tracking Algorithm

paper_content:

It is widely accepted that modern compu- tational capabilities have made the application of Multi- ple hypothesis tracking (MHT) feasible for a wide variety of applications. However, even in typical expected sce- narios, periods of unusually high target or clutter density may occur that stress the ability of MHT to operate in real-time and under the constraints of limited computer memory. The most computing burden in MHT is the best global hypothesis formation. This paper establishes a solu- tion tree and introduces the branch and bound strategy for the best global hypothesis formation. Then, a novel MHT algorithm which can be applied in practical radar imple- mentations is proposed. The algorithm is illustrated with examples of simulated missile defense scenarios and a tar- get tracking scenario with real radar data. The experiment results indicate that the algorithm is valid.
---
paper_title: Robust Target Identification Using a Modified Generalized Likelihood Ratio Test

paper_content:

In order to correctly identify a remote target, an efficient and robust target signature identification technique is required. Radar target identification based on complex natural resonances (CNRs) has drawn the interest of many researchers following the development of the singularity expansion method (SEM). As evident from the literature, statistical techniques such as the generalized likelihood ratio test (GLRT) have produced a better identification result, in the presence of noise, compared to some other SEM-based identification methods such as the extinction pulse (E-pulse) technique. However, one of the issues related to a resonance based target classifier is that it requires the commencement of the late time period for the unknown target response to be determined accurately in order to avoid false alarms during target classification process. For automatic target recognition (ATR) applications, usually such information is not known a priori. In view of this problem, a modified GLRT technique that utilizes time-frequency analysis is presented in this paper. The improved GLRT method does not require prior knowledge of the beginning of the late time period for the transient response of the unknown target. Simulation results using various targets show that our method is comparable to the original GLRT technique when the commencement of the late time period for the unknown target response is correctly determined and outperforming the original GLRT technique when the commencement of the late time period for the unknown target response is incorrectly determined.
---
paper_title: Object tracking: A survey

paper_content:

The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.
---
paper_title: Bayesian Approach with Pre- and Post-Filtering to Handle Data Uncertainty and Inconsistency in Mobile Robot Local Positioning

paper_content:

Abstract One of the important issues in mobile robots is finding the position of robots in space. This is normally achieved by using a sensor to locate the position of the robot. However, relying on more than one sensor and then using multisenor data fusion algorithms tends to be more reliable than just using a reading from a single sensor. If these sensors provide inconsistent data, catastrophic fusion may occur, and thus the estimated position of the robot obtained will be less accurate than if an individual sensor is used. This article uses an approach that relies on combining modified Bayesian fusion algorithm with Kalman filtering to estimate the position of a mobile robot. Two case studies are presented to prove the efficiency of the proposed approach in estimating the position of a mobile robot. Both scenarios show that combining fusion with filtering provides an accurate estimate of the location of the robot by handling the problem of uncertainty and inconsistency of the data provided by the sensors.
---
paper_title: Mathematical Techniques in Multisensor Data Fusion

paper_content:

From the Publisher: ::: This invaluable reference offers the most comprehensive introduction available to the concepts of multisensor data fusion. It introduces key algorithms, provides advice on their utilization, and raises issues associated with their implementation. With a diverse set of mathematical and heuristic techniques for combining data from multiple sources, the book shows how to implement a data fusion system, describes the process for algorithm selection, functional architectures and requirements for ancillary software, and illustrates man-machine interface requirements an database issues.
---
paper_title: UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies

paper_content:

The education industry around the globe is undergoing major transformations. Organizations, such as Coursera are advancing new business models for education. A number of major industries have dropped degrees from the job requirements. While the economics of higher education institutions are under threat in a continuing gloomy global economy, digital and lifelong learners are increasingly demanding new teaching and learning paradigms from educational institutions. There is an urgent need to transform teaching and learning landscape in order to drive global economic growth. The use of distance eTeaching and eLearning (DTL) is on the rise among digital natives alongside our evolution toward smart societies. However, the DTL systems today lack the necessary sophistication due to several challenges including data analysis and management, learner-system interactivity, system cognition, resource planning, agility, and scalability. This paper proposes a personalised Ubiquitous eTeaching & eLearning (UTiLearn) framework that leverages Internet of Things, big data, supercomputing, and deep learning to provide enhanced development, management, and delivery of teaching and learning in smart society settings. A proof of concept UTiLearn system has been developed based on the framework. A detailed design, implementation, and evaluation of the UTiLearn system, including its five components, are provided using 11 widely used datasets.
---
paper_title: From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

paper_content:

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user's daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).
---
paper_title: Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT)

paper_content:

Internet of Things (IoT) is set to revolutionize all aspects of our lives. The number of objects connected to IoT is expected to reach 50 billion by 2020, giving rise to an enormous amounts of valuable data. The data collected from the IoT devices will be used to understand and control complex environments around us, enabling better decision making, greater automation, higher efficiencies, productivity, accuracy, and wealth generation. Data mining and other artificial intelligence methods would play a critical role in creating smarter IoTs, albeit with many challenges. In this paper, we examine the applicability of eight well-known data mining algorithms for IoT data. These include, among others, the deep learning artificial neural networks (DLANNs), which build a feed forward multi-layer artificial neural network (ANN) for modelling high-level data abstractions. Our preliminary results on three real IoT datasets show that C4.5 and C5.0 have better accuracy, are memory efficient and have relatively higher processing speeds. ANNs and DLANNs can provide highly accurate results but are computationally expensive.
---
paper_title: Supervised Machine Learning: A Review of Classification Techniques

paper_content:

The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques. Of course, a single chapter cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.
---
paper_title: A decision fusion method based on multiple support vector machine system for fusion of hyperspectral and LIDAR data

paper_content:

Fusion of remote sensing data from multiple sensors has been remarkably increased for classification. This is because, additional sources may provide more information, and fusion of different information can produce a better understanding of the observed site. In the field of data fusion, fusion of light detection and ranging (LIDAR) and optical remote sensing data for land cover classification has attracted more attention. This paper addressed the use of a decision fusion methodology for the combination of hyperspectral and LIDAR data in land cover classification. The proposed method applied a support vector machine (SVM)-based classifier fusion system for fusion of hyperspectral and LIDAR data in the decision level. First, feature spaces are extracted on LIDAR and hyperspectral data. Then, SVM classifiers are applied on each feature data. After producing multiple of classifiers, Naive Bayes as a classifier fusion method combines the results of SVM classifiers form two data sets. A co-registered hyperspe...
---
paper_title: Automatic fusion and classification of hyperspectral and LiDAR data using random forests

paper_content:

In this paper we discuss the use of the random forest algorithm for automatic fusion and classification of hyperspectral and LiDAR data. We demonstrate how relative feature relevance can be used in random forests to perform automatic and unsupervised feature selection. This allows using a large number of features without suffering from the curse of dimensionality. The effectiveness of the proposed approach is demonstrated on two datasets. The first dataset features a combination of hyperspectral and LiDAR data for urban classification whereas the second dataset is the well-known Indian Pines dataset featuring pure hyperspectral imagery. We show that by using the proposed approach classification accuracies can be improved significantly.
---
paper_title: Data Mining for Internet of Things: A Survey

paper_content:

It sounds like mission impossible to connect everything on the Earth together via Internet, but Internet of Things (IoT) will dramatically change our life in the foreseeable future, by making many "impossibles" possible. To many, the massive data generated or captured by IoT are considered having highly useful and valuable information. Data mining will no doubt play a critical role in making this kind of system smart enough to provide more convenient services and environments. This paper begins with a discussion of the IoT. Then, a brief review of the features of "data from IoT" and "data mining for IoT' is given. Finally, changes, potentials, open issues, and future trends of this field are addressed.
---
paper_title: Evaluating KNN, LDA and QDA classification for embedded online feature fusion

paper_content:

In this paper we evaluate k-nearest neighbor (KNN), linear and quadratic discriminant analysis (LDA and QDA, respectively) for embedded, online feature fusion which poses strong limitations on computing resources and timing. These algorithms are implemented on our multisensor data fusion (MSDF) architecture and are applied to traffic monitoring, i.e., classifying vehicles using distributed image, acoustic and laser sensors. We performed several tests of the algorithms on our embedded platform and evaluated CPU performance and memory consumption for training as well as classification. The results obtained are very promising for further use, especially of LDA and QDA for embedded online fusion at feature-level.
---
paper_title: Multi-sensor data fusion using support vector machine for motor fault detection

paper_content:

Motor fault diagnosis in dynamic condition is a typical multi-sensor data fusion problem. It involves the use of information collected from multiple sensors, such as vibration, sound, current, voltage, and temperature, to detect and identify motor faults. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, the multi-sensor based motor fault diagnosis can be viewed as the problem of evidence fusion. In this article we propose and investigate a hybrid method for fault signal classification based on sensor data fusion by using the Support Vector Machine (SVM) and Short Term Fourier Transform (STFT) techniques. We report a practical application of this hybrid model and evaluate its performance. Finally, we compare the performance of the proposed system against some other standard fault classification techniques.
---
paper_title: Data Mining for the Internet of Things: Literature Review and Challenges

paper_content:

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.
---
paper_title: Distributed data fusion using support vector machines

paper_content:

The basic quantity to be estimated in the Bayesian approach to data fusion is the conditional probability density function (CPDF). Computationally efficient particle filtering approaches are becoming more important in estimating these CPDFs. In this approach, IID samples are used to represent the conditional probability densities. However, their application in data fusion is severely limited due to the fact that the information is stored in the form of a large set of samples. In all practical data fusion systems that have limited communication bandwidth, broadcasting this probabilistic information, available as a set of samples, to the fusion center is impractical. Support vector machines, through statistical learning theory, provide a way of compressing information by generating optimal kernal based representations. In this paper we use SVM to compress the probabilistic information available in the form of IID samples and apply it to solve the Bayesian data fusion problem. We demonstrate this technique on a multi-sensor tracking example.
---
paper_title: Practical travel time prediction algorithms based on neural network and data fusion for urban expressway

paper_content:

Current travel time prediction algorithms often need large amount of travel time data to identify the algorithms parameters. However, it is highly costly and time-consuming to obtain many travel time data. In this paper, we proposed an algorithm structure to calculate the travel time which utilize neural network to dynamically predict future speed and employ data fusion to integrate the speed data of different detectors in a urban expressway link. Based on the algorithm structure, two practical travel time prediction algorithms, called as Space Discretization Travel Time Calculation Algorithm (SDTCM) and Speed Integral Travel Time Calculation Method (SITCM), were developed by the discretization of the space and integral of the predicted speed. Vehicle plate recognition technology was used to collect the real travel time data in the test section on Beijing Third-Ring urban expressway to evaluate the algorithms. The obtained results show that the average prediction error of two algorithms are both less than 10% which can meet the requirement of the field applications and the algorithms are easy to be implemented as no travel time data collection are needed in advance. The two algorithms have some advantages and disadvantages over each other in accuracy and smoothness. Although SDTCM is more accurate than SITCM in general, the fluctuation of error of the SITCM is a little smoother than SDTCM.
---
paper_title: Long-Term Wind Speed Forecasting and General Pattern Recognition Using Neural Networks

paper_content:

Long-term forecasting of wind speed has become a research hot spot in many different areas such as restructured electricity markets, energy management, and wind farm optimal design. However, wind energy with unstable and intermittent characteristics entails establishing accurate predicted data to avoid inefficient and less reliable results. The proposed study in this paper may provide a solution regarding the long-term wind speed forecast in order to solve the earlier-mentioned problems. For this purpose, two fundamentally different approaches, the statistical and the neural network-based approaches, have been developed to predict hourly wind speed data of the subsequent year. The novelty of this study is to forecast the general trend of the incoming year by designing a data fusion algorithm through several neural networks. A set of recent wind speed measurement samples from two meteorological stations in Malaysia, namely Kuala Terengganu and Mersing, are used to train and test the data set. The result obtained by the proposed method has given rather promising results in view of the very small mean absolute error (MAE).
---
paper_title: A study on wind speed prediction using artificial neural network at Jeju Island in Korea II

paper_content:

Wind power is one of the most successfully utilized of renewable sources to produce electrical energy. The available wind energy depends on the wind speed, which is a random variable. For the wind-farm operator, this poses difficulty in the system scheduling and energy dispatching, as the schedule of the wind-power availability is not known in advance. This paper proposes to use the two-layered artificial neural networks for predicting the actual wind speed from the previous values of the same variable.
---
paper_title: Fuzzy logic data correlation approach in multisensor-multitarget tracking systems

paper_content:

Abstract In this paper, a fuzzy logic data correlation approach for multisensor–multitarget tracking is proposed. This fuzzy correlation approach is developed based on fuzzy clustering means algorithm. The proposed approach is applied to a two- and a four-dimensional multisensor–multitarget tracking system using Monte Carlo simulations. Fuzzy system performance evaluation is presented to demonstrate the efficiency of the new approach. The computational complexity of this approach is also analyzed and compared to that of conventional fuzzy logic data association methods. Considerable improvement in terms of computational complexity and performance is achieved.
---
paper_title: Real-life applications of fuzzy logic

paper_content:

1 Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA 2 Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9 3 Electrified Armor Lab, RDECOM TARDEC, 6501 E. 11 Mile Road, RDTA-RS, MS 263, Warren, MI 48397-5000, USA 4Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing 100190, China 5Manipal University Jaipur, Jaipur 303007, India 6Maverick Technologies America Inc., Suite 808, 1220 North Market Street, Wilmington, DE 19801, USA 7Department of Electrical Engineering and Computer Sciences, University of California Berkeley, CA 94720-1776, USA
---
paper_title: IoT based smart parking system

paper_content:

In recent times the concept of smart cities have gained grate popularity. Thanks to the evolution of Internet of things the idea of smart city now seems to be achievable. Consistent efforts are being made in the field of IoT in order to maximize the productivity and reliability of urban infrastructure. Problems such as, traffic congestion, limited car parking facilities and road safety are being addressed by IoT. In this paper, we present an IoT based cloud integrated smart parking system. The proposed Smart Parking system consists of an on-site deployment of an IoT module that is used to monitor and signalize the state of availability of each single parking space. A mobile application is also provided that allows an end user to check the availability of parking space and book a parking slot accordingly. The paper also describes a high-level view of the system architecture. Towards the end, the paper discusses the working of the system in form of a use case that proves the correctness of the proposed model.
---
paper_title: Sensor Fusion Using Fuzzy Logic Enhanced Kalman Filter for Autonomous Vehicle Guidance in Citrus Groves

paper_content:

This article discusses the development of a sensor fusion system for guiding an autonomous vehicle through citrus grove alleyways. The sensor system for path finding consists of machine vision and laser radar. An inertial measurement unit (IMU) is used for detecting the tilt of the vehicle, and a speed sensor is used to find the travel speed. A fuzzy logic enhanced Kalman filter was developed to fuse the information from machine vision, laser radar, IMU, and speed sensor. The fused information is used to guide a vehicle. The algorithm was simulated and then implemented on a tractor guidance system. The guidance system's ability to navigate the vehicle at the middle of the path was first tested in a test path. Average errors of 1.9 cm at 3.1 m s -1 and 1.5 cm at 1.8 m s -1 were observed in the tests. A comparison was made between guiding the vehicle using the sensors independently and using fusion. Guidance based on sensor fusion was found to be more accurate than guidance using independent sensors. The guidance system was then tested in citrus grove alleyways, and average errors of 7.6 cm at 3.1 m s -1 and 9.1 cm at 1.8 m s -1 were observed. Visually, the navigation in the citrus grove alleyway was as good
---
paper_title: Fuzzy Double-Threshold Track Association Algorithm Using Adaptive Threshold in Distributed Multisensor-Multitarget Tracking Systems

paper_content:

The fuzzy double-threshold track association algorithm using the adaptive threshold (AT-FDTTA) is presented in this paper. The AT-FDTTA is described in detail at first. Then, the simulations of two different cases are designed to illustrate the improved performance caused by the adaptive threshold. Both the simulation results show that the correct association rate of the AF-FDTTA proposed here is higher than that of the fuzzy double-threshold track association algorithm with the fixed threshold (FT-FDTTA) algorithm, Besides, the AT-FDTTA is with lower missing association rate. Moreover, the high performance of the AT-FDTTA is not sensitive to other parameter values in the algorithm, which are usually chosen empirically by the users.
---
paper_title: Multi-Sensor Data Fusion in Cluster based Wireless Sensor Networks Using Fuzzy Logic Method

paper_content:

Wireless sensor network (WSN) consist of a large number of sensor nodes which are limited in battery power and communication range and are having multi-modal sensing capability. One of the most significant applications of wireless sensor network is environment monitoring. In this paper, a multi-sensor data fusion algorithm in WSN using fuzzy logic for event detection application is proposed. In the proposed method, each sensor node is equipped with diverse sensors (temperature, humidity light, and carbon monoxide). The use of more than one sensor provides additional information on the environmental condition. The processing and fusion of these diverse sensor signals are carried out using proposed fuzzy rule based system. All the diverse sensor signals are collected at the cluster head and fused using fuzzy rule based method. The multiple data fusion process improves the reliability and accuracy of the sensed information and thereby minimizes the false alarm rate.
---
paper_title: Fuzzy Fusion Approach for Object Tracking

paper_content:

In multi-target object tracking, for data fusion, data in presence of noise as input must be sent to fusion center to be filtered, associated, combined and made final decision as output. In the chain, association is very important processing. In this paper, an efficient fuzzy logic data association approach for object tracking is proposed. The proposed approach is developed based on the fuzzy clustering means algorithm, which differs from many other fuzzy logic data association algorithms. Performance evaluation and results are reported, and comparisons with other fuzzy logic approaches based on the results described in other reference are also presented. The efficiency of the new approach has been demonstrated by the fuzzy system performance evaluation.
---
paper_title: Data Fusion for Magnetic Sensor Based on Fuzzy Logic Theory

paper_content:

Data fusion of sensors is a useful technique to obtain more comprehensive information about the monitoring targets in wireless sensor networks (WSN). This paper proposes a data fusion algorithm based on fuzzy logic theory to monitor a parking space in the parking lot using magnetic sensors and point out the probability of occupancy of the corresponding parking space. Simulation results show that our algorithm gathers more accurate information about the monitoring targets to support right decisions and has the ability of anti-interference.
---
paper_title: A new fuzzy clustering approach for data association and track fusion in multisensor-multitarget environment

paper_content:

This paper develops a fuzzy clustering approach to solve the problem of track-to-track association and track fusion in distributed multisensor-multitarget multiple-attribute environments in overlapping coverage scenarios. The proposed approach uses the fuzzy clustering means algorithm to reduce the number of target tracks and associate duplicate tracks by determining the degree of membership for each target track. It uses current sensor data and the known sensor resolutions for track-to-track association, track fusion, and the selection of the most accurate sensor for tracking fused targets. Numerical results based on Monte Carlo simulations are presented. The results show that the proposed approach significantly reduces the computational complexity and achieves considerable performance improvement compared to Euclidean clustering. We also show that the performance of the proposed approach is better than the performance of the Bayesian minimum mean square error criterion.12
---
paper_title: An Intelligent Fire Detection and Mitigation System Safe from Fire (SFF)

paper_content:

Safe From Fire (SFF) is an intelligent self controlled smart fire extinguisher system assembled with multiple sensors, actuators and operated by micro-controller unit (MCU). It takes input signals from various sensors placed in different position of the monitored area, and combines integrated fuzzy logic to identify fire breakout locations and severity. Data fusion algorithm facilitates the system to discard deceptive fire situations such as: cigarette smoke, welding etc. During the fire hazard SFF notifies the fire service and others by text messages and telephone calls. Along with ringing fire alarm it announces the fire affected locations and severity. To prevent fire from spreading it breaks electric circuits of the affected area, releases the extinguishing gas pointing to the exact fire locations. This paper presents how this system is built, components, and connection diagram and implementation logic. Overall performance is evaluated through experimental tests by creating real time fire hazard prototype scenarios to investigate reliability. It is observed that SFF system demonstrated its efficiency most of the cases perfectly.
---
paper_title: Hybrid Kalman filter-fuzzy logic adaptive multisensor data fusion architectures

paper_content:

In this work the recently developed fuzzy logic-based adaptive Kalman filter (FL-AKF) is used to build adaptive centralized, decentralized, and federated Kalman filters for adaptive multisensor data fusion (AMSDF). The adaptation carried out is in the sense of adaptively adjusting the measurement noise covariance matrix of each local FL-AKF to fit the actual statistics of the noise profiles present in the incoming measured data. A fuzzy inference system (FIS) based on a covariance-matching technique is used as the adaptation mechanism. The effectiveness and accuracy of the proposed AMSDF approaches is demonstrated in a simulated example.
---
paper_title: Vehicle surround sensing based on information fusion of monocular video and digital map

paper_content:

In this paper we propose a vehicle surround sensing system that combines information from a video sensor with a digital map sensor. The video sensor detects road signs and lane markers, whereas the digital map contains information on road geometry, topology and additional information attributes. We show both sensors to be complementary to each other and fuse their information to obtain driver assistance functions such as Speed Limit Assistant, Curve Warning System, and Hazard Warning System.
---
paper_title: Multi-Sensor Data Fusion with MATLAB®

paper_content:

Using MATLAB examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for aerospace applications, although the methods can also be applied to systems in other areas, such as biomedicine, military defense, and environmental engineering. After presenting several useful strategies and algorithms for DF and tracking performance, the book evaluates DF algorithms, software, and systems. It next covers fuzzy logic, fuzzy sets and their properties, fuzzy logic operators, fuzzy propositions/rule-based systems, an inference engine, and defuzzification methods. It develops a new MATLAB graphical user interface for evaluating fuzzy implication functions, before using fuzzy logic to estimate the unknown states of a dynamic system by processing sensor data. The book then employs principal component analysis, spatial frequency, and wavelet-based image fusion algorithms for the fusion of image data from sensors. It also presents procedures for combing tracks obtained from imaging sensor and ground-based radar. The final chapters discuss how DF is applied to mobile intelligent autonomous systems and intelligent monitoring systems. Fusing sensors data can lead to numerous benefits in a systems performance. Through real-world examples and the evaluation of algorithmic results, this detailed book provides an understanding of MSDF concepts and methods from a practical point of view. Select MATLAB programs are available for download on www.crcpress.com
---
paper_title: Fusion of digital map traffic signs and camera-detected signs

paper_content:

In this paper, we present several fusion approaches to merge speed limits reported in digital maps with detected speed limit signs using an onboard camera. Digital maps holding speed limits signs are required to be updated to cover speed limit changes and are unable to support variable speed limits. On the other hand, a camera system placed onboard a vehicular can detect variable speed limits as well as temporary speed limits at construction sites. However, an onboard camera cannot detect implicit speed limits. As such, a combination of digital map and camera system can provide more accurate speed limit information for driver assistance and support vehicle safety features. In this paper, the digital map and camera system are fused to obtain the desired more accurate speed limit information. The fused speed limits as well as those from the individual sources are compared with ground truth data obtained from an extensive measurement campaign spanning over 15000 km driving distance in five European countries. Specifically, five fusion approaches are defined, modeled and evaluated. Four of them are based on prioritizing information while the remaining approach is based on the classical Dempster-Shafer data fusion technique. The performance of the approaches are presented in terms of the percentage of correct speed limit detection with respect to the driving distance. The obtained results clearly show that fusion techniques can significantly increase the amount of correctly detected speed limits. A MATLAB based graphical user interface was designed to load test data, evaluate and present the results as fast and as efficient as possible.
---
paper_title: Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion Applications

paper_content:

Nowadays ontologies present a growing interest in Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances.
---
paper_title: Fusion of Airborne LiDAR With Multispectral SPOT 5 Image for Enhancement of Feature Extraction Using Dempster–Shafer Theory

paper_content:

This paper presents an application of data-driven Dempster–Shafer theory (DST) of evidence to fuse multisensor data for land-cover feature extraction. Over the years, researchers have focused on DST for a variety of applications. However, less attention has been given to generate and interpret probability, certainty, and conflict maps. Moreover, quantitative assessment of DST performance is often overlooked. In this paper, for implementation of DST, two main types of data were used: multisensor data such as Light Detection and Ranging (LiDAR) and multispectral satellite imagery [Satellite Pour l'Observation de la Terre 5 (SPOT 5)]. The objectives are to classify land-cover types from fused multisensor data using DST, to quantitatively assess the accuracy of the classification, and to examine the potential of slope data derived from LiDAR for feature detection. First, we derived the normalized difference vegetation index (NDVI) from SPOT 5 image and the normalized digital surface model (DSM) (nDSM) from LiDAR by subtracting the digital terrain model from the DSM. The two products were fused using the DST algorithm, and the accuracy of the classification was assessed. Second, we generated a surface slope from LiDAR and fused it with NDVI. Subsequently, the classification accuracy was assessed using an IKONOS image of the study area as ground truth data. From the two processing stages, the NDVI/nDSM fusion had an overall accuracy of 88.7%, while the NDVI/slope fusion had 75.3%. The result indicates that NDVI/nDSM integration performed better than NDVI/slope. Although the overall accuracy of the former is better than the latter (NDVI/slope), the contribution of individual class reveals that building extraction from fused slope and NDVI performed poorly. This study proves that DST is a time- and cost-effective method for accurate land-cover feature identification and extraction without the need for a prior knowledge of the scene. Furthermore, the ability to generate other products like certainty, conflict, and maximum probability maps for better visual understanding of the decision process makes it more reliable for applications such as urban planning, forest management, 3-D feature extraction, and map updating.
---
paper_title: A Situation context aware Dempster-Shafer fusion of digital maps and a road sign recognition system

paper_content:

Speed limit information systems solely based on one modality can hardly overcome their respective intrinsic disadvantages: Digital maps lack support for short-term changes brought by variable message signs and road works, while camera based systems cannot recognize implicit speed limits and may fail in adverse lighting scenarios. In this work we show a fusion approach that is able to overcome these limitations. It is specifically tailored to our task by adapting sensor reliability based on the perceived situation context. This enables the camera based system to easily outvote the digital map in a construction site or the digital map to veto against uncertain camera recognition results during nighttime. The proposed fusion approach was implemented and evaluated on a qualitative base showing very promising results: The situation context aware fusion is able to deduce the correct effective speed limit even when one of the sensors fails. Moreover, it reduces conflicts encountered between the sources compared to a not situation context aware fusion.
---
paper_title: Using the Dempster-Shafer method for the fusion of LIDAR data and multi-spectral images for building detection

paper_content:

Abstract A method for the detection of buildings in densely built-up urban areas by the fusion of first and last pulse laser scanner data and multi-spectral images is presented. The method attempts to achieve a classification of land cover into the classes “building”, “tree”, “grassland”, and “bare soil”, the latter three being considered relevant for the subsequent generation of a high-quality digital terrain model (DTM). Building detection is accomplished by first applying a hierarchical rule-based technique for coarse DTM generation based on morphological filtering. After that, data fusion based on the theory of Dempster–Shafer is used at two different stages of the classification process. We describe the algorithms involved, giving examples for a test site in Fairfield (New South Wales).
---
paper_title: System architecture for an intersection assistant fusing image, map, and GPS information

paper_content:

Street intersections account for a large portion of road accidents everywhere around the world. Therefore, a driver assistance system supporting the driver to negotiate intersections is highly desirable and has a tremendous potential of reducing the number of collisions at intersections. What makes intersection assistance more challenging than other traffic scenarios such as highways and rural roads? On one hand, the environment all around the vehicle must be monitored. A very large field of view for the local sensors is required. We decided to use both omnidirectional cameras for a panoramic view and active cameras to inspect smaller areas around us at a high resolution. On the other hand, intersections come in all shapes, sizes, and signal regulations. Hence, some prior knowledge of the signal mode and the intersection geometry is very helpful. This can be achieved via GPS in conjunction with an attributed digital map. We propose a system architecture for intersection assistance using image, map, and GPS information to issue the typical intersection warnings such as cross traffic warning or traffic light warning.
---
paper_title: Bayesian and Dempster-Shafer fusion

paper_content:

The Kalman Filter is traditionally viewed as a prediction-correction filtering algorithm. In this work we show that it can be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman-Kolmogorov prediction integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster-Shafer theory, which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster-Shafer theory and presenting the basic Dempster-Shafer fusion equation. Finally we compare the two methods, and discuss the relative merits and demerits using an illustrative example.
---
paper_title: Distributed data fusion algorithm for Wireless Sensor Network

paper_content:

Signal processing in Wireless Sensor Network (WSN) has a huge range of applications. Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. DKF finds applications in object tracking, environmental monitoring, surveillance, and many other applications. All algorithms proposed in the literature are based on static network. In reality, the network topology is changing. The topology change is often caused by node failure, which is due to energy depletion. In this work a DKF is proposed for such network. The simulation and the experimental results validate our proposed DKF. The experimental results show that each sensor node can run DKF with up to six neighbors.
---
paper_title: A Data Fusion Formulation for Decentralized Estimation Predictions under Communications Uncertainty

paper_content:

Uncertainty in communication channel characteristics is a significant factor for data fusion operations in wireless networks. Burst and random errors, message delays, user mobility, and link outages are significant factors that influence data fusion performance. These factors become even more significant in future mobile ad hoc networking environments. To date, however, those factors are not sufficiently addressed by formulations used for modeling and predicting data fusion performance. A stochastic-based fusion formulation that incorporates the effects of non-deterministic behaviors and stochastic communications characteristics is developed and proposed as a method for predicting estimation capabilities. The resulting stochastic fusion equations enable decentralized estimation capabilities to be evaluated in communication networks having non-idealized channel characteristics and ad hoc connectivity. The method is implemented in a simulation model for decentralized estimation in networks with time-varying ad hoc connectivity. The simulation results demonstrate the ability to closely predict expected fusion performance while greatly reducing model complexity and simulation time relative to current techniques. Those findings demonstrate the efficacy of a stochastic fusion formulation for prediction, and extending the approach to a wider range of data fusion domains and techniques is recommended.
---
paper_title: The Internet of Things: A survey

paper_content:

This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.
---
paper_title: Analytical and Computational Evaluation of Scalable Distributed Fusion Algorithms

paper_content:

The theoretical fundamentals of distributed information fusion have been developed over the past two decades and are now fairly well established. However, practical applications of these theoretical results to dynamic sensor networks have remained a challenge. There has been a great deal of work in developing distributed fusion algorithms applicable to a network centric architecture. In general, in a distributed system such as ad hoc sensor networks, the communication architecture is not fixed. In those cases, the distributed fusion approaches based on pedigree information may not scale because of limited communication bandwidth. In this paper, we focus on scalable fusion algorithms and conduct analytical performance evaluation to compare their performance. The goal is to understand the performance of these algorithms under different operating conditions. Specifically, we evaluate the performance of channel filter fusion, naive fusion, Chernoff fusion, Shannon fusion, and Bhattacharyya fusion algorithms. We also compare their performance to "optimal" centralized fusion under a specific communication pattern. The results show that the channel filter fusion, representing a first order approximation to the information graph fusion, is the only "consistent" fusion algorithm.
---
paper_title: Heterogeneous multi-metric learning for multi-sensor fusion

paper_content:

In this paper, we propose a multiple-metric learning algorithm to learn jointly a set of optimal homogenous/heterogeneous metrics in order to fuse the data collected from multiple sensors for classification. The learned metrics have the potential to perform better than the conventional Euclidean metric for classification. Moreover, in the case of heterogenous sensors, the learned multiple metrics can be quite different, which are adapted to each type of sensor. By learning the multiple metrics jointly within a single unified optimization framework, we can learn better metrics to fuse the multi-sensor data for joint classification.
---
paper_title: Fuzzy logic data correlation approach in multisensor-multitarget tracking systems

paper_content:

Abstract In this paper, a fuzzy logic data correlation approach for multisensor–multitarget tracking is proposed. This fuzzy correlation approach is developed based on fuzzy clustering means algorithm. The proposed approach is applied to a two- and a four-dimensional multisensor–multitarget tracking system using Monte Carlo simulations. Fuzzy system performance evaluation is presented to demonstrate the efficiency of the new approach. The computational complexity of this approach is also analyzed and compared to that of conventional fuzzy logic data association methods. Considerable improvement in terms of computational complexity and performance is achieved.
---
paper_title: Bayesian joint analysis of heterogeneous genomics data

paper_content:

Summary: A non-parametric Bayesian factor model is proposed for joint analysis of multi-platform genomics data. The approach is based on factorizing the latent space (feature space) into a shared component and a data-specific component with the dimensionality of these components (spaces) inferred via a beta-Bernoulli process. The proposed approach is demonstrated by jointly analyzing gene expression/ copy number variations and gene expression/methylation data for ovarian cancer patients, showing that the proposed model can potentially uncover key drivers related to cancer. Availability and implementation: The source code for this model is written in MATLAB and has been made publicly available at https://
---
paper_title: Relations Between Two Sets of Variates

paper_content:

Concepts of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions. Marksmen side by side firing simultaneous shots at targets, so that the deviations are in part due to independent individual errors and in part to common causes such as wind, provide a familiar introduction to the theory of correlation; but only the correlation of the horizontal components is ordinarily discussed, whereas the complex consisting of horizontal and vertical deviations may be even more interesting. The wind at two places may be compared, using both components of the velocity in each place. A fluctuating vector is thus matched at each moment with another fluctuating vector. The study of individual differences in mental and physical traits calls for a detailed study of the relations between sets of correlated variates. For example the scores on a number of mental tests may be compared with physical measurements on the same persons. The questions then arise of determining the number and nature of the independent relations of mind and body shown by these data to exist, and of extracting from the multiplicity of correlations in the system suitable characterizations of these independent relations. As another example, the inheritance of intelligence in rats might be studied by applying not one but s different mental tests to N mothers and to a daughter of each
---
paper_title: Heterogeneous data fusion for an adaptive training in informed virtual environment

paper_content:

This paper presents an informed virtual environment (environment including knowledge-based models and providing an action/perception coupling) for fluvial navigation training. We add an automatic guide to a driving ship simulator by displaying multimodal aids adapted to human perception for trainees. To this end, a decision-making module determines the most appropriate aids according to heterogeneous data coming from observations of the learner (his/her mistakes, the risks taken, his/her state determined by using physiological sensors, etc.). The Dempster-Shafer theory is used to merge these uncertain data. The purpose of the whole system is to manage the training almost autonomously in order to relieve trainers from controlling the whole training simulation. We intend to demonstrate the relevance of taking the learner's state into account and the relevance of the heterogeneous data fusion with the Dempster-Shafer theory for decision-making about the best learner guiding. First results, obtained with a predefined set of data, show that our decision-making module is able to propose a guiding well-adapted to the trainees, even in complex situations with uncertain data.
---
paper_title: Sparse canonical methods for biological data integration: application to a cross-platform study

paper_content:

BackgroundIn the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines.ResultsWe compare the results obtained with two other sparse or related canonical correlation approaches: CCA with Elastic Net penalization (CCA-EN) and Co-Inertia Analysis (CIA). The latter does not include a built-in procedure for variable selection and requires a two-step analysis. We stress the lack of statistical criteria to evaluate canonical correlation methods, which makes biological interpretation absolutely necessary to compare the different gene selections. We also propose comprehensive graphical representations of both samples and variables to facilitate the interpretation of the results.ConclusionsPLS and CCA-EN selected highly relevant genes and complementary findings from the two data sets, which enabled a detailed understanding of the molecular characteristics of several groups of cell lines. These two approaches were found to bring similar results, although they highlighted the same phenomenons with a different priority. They outperformed CIA that tended to select redundant information.
---
paper_title: Bayesian Consensus Clustering

paper_content:

Motivation: In biomedical research a growing number of platforms and technologies are used to measure diverse but related information, and the task of clustering a set of objects based on multiple sources of data arises in several applications. Most current approaches to multisource clustering either independently determine a separate clustering for each data source or determine a single ‘joint’ clustering for all data sources. There is a need for more flexible approaches that simultaneously model the dependence and the heterogeneity of the data sources. Results: We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence they are not independent. We describe a computationally scalable Bayesian framework for simultaneous estimation of both the consensus clustering and the source-specific clusterings. We demonstrate that this flexible approach is more robust than joint clustering of all data sources, and is more powerful than clustering each data source independently. We present an application to subtype identification of breast cancer tumor samples using publicly available data from The Cancer Genome Atlas. Availability: R code with instructions and examples is available at
---
paper_title: Multisensor Data Fusion for Wildfire Warning

paper_content:

Wildfires are highly destructive disasters that spread quickly. The use of advanced technology to achieve early warnings of wildfires is essential for the protection of wilderness resources. Nowadays, the method of using wireless sensor networks for wildfire warning has been extensively studied by many researchers. In this paper, we propose and have implemented a multi-sensor data fusion algorithm for wildfire monitoring and warning based on adaptive weighted fusion algorithm (AWFA) and Dempster -- Shafer theory (DST) of evidence. At the same time, we also have put forward some auxiliary algorithms for fire warning, including heterogeneous sensor data homogenization methods, a judgment algorithm for sensor numerical errors, and an evidence conflict solution of Dempster -- Shafer theory of evidence. Experimental results show that this algorithm can ensure the timeliness and accuracy of the wildfire warning, effectively reduce the amount of data transmission of sensor nodes and the whole network, and reduce the energy consumption, thus prolonging the network lifetime.
---
paper_title: A Reduced-Complexity Data-Fusion Algorithm Using Belief Propagation for Location Tracking in Heterogeneous Observations

paper_content:

This paper presents a low-complexity and high-accuracy algorithm to reduce the computational load of the traditional data-fusion algorithm with heterogeneous observations for location tracking. For the location-estimation technique with the data fusion of radio-based ranging measurement and speed-based sensing measurement, the proposed tracking scheme, based on the Bayesian filtering concept, is handled by a state space model. The location tracking problem is divided into many mutual-interaction local constraints with the inherent message- passing features of factor graphs. During each iteration cycle, the messages with reliable information are passed efficiently between the prediction phase and the correction phase to simplify the data-fusion implementation for tracking the location of the mobile terminal. Numerical simulations show that the proposed forward and one-step backward refining tracking approach that combines radio ranging with speed sensing measurements for data fusion not only can achieve an accurate location close to that of the traditional Kalman filtering data-fusion algorithm, but also has much lower computational complexity.
---
paper_title: Noise and Outlier Filtering in Heterogeneous Medical Data Sources

paper_content:

There is a growing interest in studying the common features from multiple data sources. Fusing information from multiple heterogenous data sources promises to identify complex multivariate relationships among the heterogeneous sources. Such relationships can provide additional connectivity across the sources. A common way to analyze the relationships between a pair of data sources based on their correlation is canonical correlation analysis (CCA). CCA seeks for linear combinations of all variables from each dataset with maximal correlation between the two linear combinations. However, the existence of non-informative data points and features makes it challenging for CCA to identify significant relationships among the examined datasets. In this paper, we propose a novel method, NORA, Noise-Outliers Removal Algorithm, that can be used to filter out the non-informative data points and features before applying the CCA. NORA was applied to preprocess two epilepsy modalities, the MRI and neuropsychology, prior to applying CCA to find the association between them. The results show that the proposed method leads to interpretable results when noise plays a significant role in the acquisition of the data.
---
paper_title: HIGHLIGHTING RELATIONSHIPS BETWEEN HETEROGENEOUS BIOLOGICAL DATA THROUGH GRAPHICAL DISPLAYS BASED ON REGULARIZED CANONICAL CORRELATION ANALYSIS

paper_content:

Biological data produced by high throughput technologies are becoming more and more abundant and are arousing many statistical questions. This paper addresses one of them; when gene expression data are jointly observed with other variables with the purpose of highlighting significant relationships between gene expression and these other variables. One relevant statistical method to explore these relationships is Canonical Correlation Analysis (CCA). Unfortunately, in the context of postgenomic data, the number of variables (gene expressions) is usually greater than the number of units (samples) and CCA cannot be directly performed: a regularized version is required. We applied regularized CCA on data sets from two different studies and show that its interpretation evidences both previously validated relationships and new hypothesis. From the first data sets (nutrigenomic study), we generated interesting hypothesis on the transcription factor pathways potentially linking hepatic fatty acids and gene expression. From the second data sets (pharmacogenomic study on the NCI-60 cancer cell line panel), we identified new ABC transporter candidate substrates which relevancy is illustrated by the concomitant identification of several known substrates. In conclusion, the use of regularized CCA is likely to be relevant to a number and a variety of biological experiments involving the generation of high throughput data. We demonstrated here its ability to enhance the range of relevant conclusions that can be drawn from these relatively expensive experiments.
---
paper_title: Heterogeneous fusion with a combined evidential, probability and OWA methods for target classification

paper_content:

Fusing multiple independent sensor measurements and human intelligence reports are essential to support critical decisions in a timely manner for today's situation awareness systems. The problem of great significance is associated with fusing Human Originated Information (HOI) with the information from other sources. Ordered Weighted Average (OWA) algorithm was proposed recently as a means to assimilate uncertain human originated information. We propose a novel method to use OWA in conjunction with Bayesian and Dempster-Shafer Theory (DST) fusion algorithms to fuse information from diverse sources and demonstrate its effectiveness using an example from literature.
---
paper_title: Heterogeneous Data Fusion via Space Alignment Using Nonmetric Multidimensional Scaling ∗

paper_content:

Heterogeneous data sets are typically represented in different feature spaces, making it difficult to analyze relationships spanning different data sets even when they are semantically related. Data fusion via space alignment can remedy this task by integrating multiple data sets lying in different spaces into one common space. Given a set of reference correspondence data that share the same semantic meaning across different spaces, space alignment attempts to place the corresponding reference data as close together as possible, and accordingly, the entire data are aligned in a common space. Space alignment involves optimizing two potentially conflicting criteria: minimum deformation of the original relationships and maximum alignment between the different spaces. To solve this problem, we provide a novel graph embedding framework for space alignment, which converts each data set into a graph and assigns zero distance between reference correspondence pairs resulting in a single graph. We propose a graph embedding method for fusion based on nonmetric multidimensional scaling (MDS). Its criteria using the rank order rather than the distance allows nonmetric MDS to effectively handle both deformation and alignment. Experiments using parallel data sets demonstrate that our approach works well in comparison to existing methods such as constrained Laplacian eigenmaps,more » Procrustes analysis, and tensor decomposition. We also present standard cross-domain information retrieval tests as well as interesting visualization examples using space alignment.« less
---
paper_title: Multisensor data fusion in nonlinear Bayesian filtering

paper_content:

In this paper, an optimal multisensor data fusion method is proposed to estimate the state of a highly nonlinear dynamic model. Data fusion from spatially distributed sensors is expressed as a semi definite program (SDP) that aims at minimizing mean-squared error (MSE) of the state estimate under total transmit power constraints. Furthermore, a Bayesian filtering technique, based on unscented transformations and linear fractional transformations (LFT), is presented under multisensor framework to implement the SDP. Extensive simulations are performed to justify effectiveness of the proposed multisensor scheme over a single sensor supplied with the same power budget as that of the entire sensor network.
---
paper_title: Nonlinear Bayesian Filtering Using the Unscented Linear Fractional Transformation Model

paper_content:

For nonlinear state space model involving random variables with arbitrary probability distributions, the state estimation given a sequence of observations is based on an appropriate criterion such as the minimum mean square error (MMSE). This leads to linear approximation in the state space of the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), which work reasonably well only for mildly nonlinear systems. We propose a Bayesian filtering technique based on the MMSE criterion in the framework of the virtual linear fractional transformation (LFT) model, which is characterized by a linear part and a simple nonlinear structure in the feedback loop. LFT is an exact representation for any differentiable nonlinear mapping, so the virtual LFT model is amenable to a wide range of nonlinear systems. Simulation results demonstrate that the proposed filtering technique gives better approximation and tracking performance than standard methods like the UKF. Furthermore, for highly nonlinear systems where UKF diverges, the LFT model estimates the conditional mean with reasonable accuracy.
---
paper_title: Iterated Unscented Kalman Filter for Passive Target Tracking

paper_content:

It is of great importance to develop a robust and fast tracking algorithm in passive localization and tracking system because of its inherent disadvantages such as weak observability and large initial errors. In this correspondence, a new algorithm referred to as the iterated unscented Kalman filter (IUKF) is proposed based on the analysis and comparison of conventional nonlinear tracking problem. The algorithm is developed from UKF but it can obtain more accurate state and covariance estimation. Compared with the traditional approaches (e.g., extended Kalman filter (EKF) and UKF) used in passive localization, the proposed method has potential advantages in robustness, convergence speed, and tracking accuracy. The correctness as well as validity of the algorithm is demonstrated through numerical simulation and experiment results.
---
paper_title: An introduction to multisensor data fusion

paper_content:

Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion.
---
paper_title: A new method for the nonlinear transformation of means and covariances in filters and estimators

paper_content:

This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parametrize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demonstrated using an example.
---
paper_title: Semi-definite programming for distributed tracking of dynamic objects by nonlinear sensor network

paper_content:

This paper discusses dynamic state estimation for nonlinear measurement model through distributed multisensor network under power constraints. For this scenario, we propose an optimized power allocation strategy based on semidefinite programming, that achieves minimum mean-squared error for the estimate subject to constraints on total transmit power. System nonlinearity is handled effectively with the help of distributed unscented Kalman filtering and linear fractional transformation. Furthermore, advantage of using multiple sensors over a single independent sensor is established through simulation results for tracking a maneuvering target.
---
paper_title: On the track-to-track correlation problem

paper_content:

The testing of the hypothesis whether two tracks represent the same target is considered. Previous works in the literature assumed that the estimates of the same target's state from two track files are uncorrelated. A test that includes their correlation is presented.
---
paper_title: Handbook of Multisensor Data Fusion

paper_content:

Multisensor Data Fusion D. L. Hall and J. Llinas Data Fusion Perspectives and Its Role in Information Processing O. Kessler and F. White Revisions to the JDL Data Fusion Model A. N. Steinberg and C. L. Bowman Introduction to the Algorithms of Data Association in Multiple-Target Tracking J. K. Uhlmann The Principles and Practice of Image and Spatial Data Fusion E. Waltz and T. Waltz Data Registration R. R. Brooks and L. Grewe Data Fusion Automation: A Top-Down Perspective R. Antony Overview of Distributed Decision Fusion M. E. Liggins Introduction to Particle Filtering: The Next Stage in Tracking M. E. Liggins and K-C. Chang Target Tracking Using Probabilistic Data Association-Based Techniques with Applications to Sonar, Radar, and EO Sensors T. Kirubarajan and Y. Bar-Shalom An Introduction to the Combinatorics of Optimal and Approximate Data Association J. K. Uhlmann A Bayesian Approach to Multiple-Target Tracking L. D. Stone Data Association Using Multiple-Frame Assignments A. B. Poore, S. Lu, and B. J. Suchomel General Decentralized Data Fusion with Covariance Intersection S. Julier and J. K. Uhlmann Data Fusion in Nonlinear Systems S. Julier and J. K. Uhlmann Random Set Theory for Multisource-Multitarget Information Fusion R. Mahler Distributed Fusion Architectures, Algorithms, and Performance within a Network-Centric Architecture M. E. Liggins and K-C. Chang Foundations of Situation and Threat Assessment A. N. Steinberg An Introduction to Level 5 Fusion: The Role of the User E. Blasch Perspectives on the Human Side of Data Fusion: Prospects for Improved Effectiveness using Advanced Human-Computer Interfaces D. L. Hall, C. M. Hall, and S. A. H. McMullen Requirements Derivation for Data Fusion Systems E. Waltz and D. L. Hall A Systems Engineering Approach for Implementing Data Fusion Systems C. L. Bowman and A. N. Steinberg Studies and Analyses within Project Correlation: An In-Depth Assessment of Correlation Problems and Solution Techniques J. Llinas, Capt. L. McConnell, C. L. Bowman, D. L. Hall, and P. Applegate Data Management Support to Tactical Data Fusion R. Antony Assessing the Performance of Multisensor Fusion Processes J. Llinas A Survey of COTS Software for Multisensor Data Fusion S. A. Hall McMullen, R. R. Sherry, and S. Miglani A Survey of Multisensor Data Fusion Systems M. L. Nichols Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems C. S. Byington and A. K. Garga Adapting Data Fusion to Chemical and Biological Sensors D. C. Swanson Fusion of Ground and Satellite Data via the Army Battle Command System S. Aungst, M. Campbell, J. Kuhns, D. Beyerle, T. Bacastow, and J. Knox Developing Information Fusion Methods for Combat Identification T. M. Schuck, J. Bockett Hunter, and D. D. Wilson
---
paper_title: Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning

paper_content:

Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.
---
paper_title: Covariance consistency methods for fault-tolerant distributed data fusion

paper_content:

Abstract This paper presents a general, rigorous, and fault-tolerant framework for maintaining consistent mean and covariance estimates in an arbitrary, dynamic, distributed network of information processing nodes. In particular, a solution is provided that addresses the information deconfliction problem that arises when estimates from two or more different nodes are determined to be inconsistent with each other, e.g., when two high precision (small covariance) estimates place the position of a particular object at very different locations. The challenge is to be able to resolve such inconsistencies without having to access and exploit global information to determine which of the estimates is spurious. The solution proposed in this paper is called Covariance Union.
---
paper_title: Advanced multiple objects tracking by fusing radar and image sensor data - Application on a case study.

paper_content:

The aim of this paper is to present a multiple object tracking data fusion technique, which fuses radar, image, and ego vehicle odometry. The data are fused at a high level, which leads to reliable and stable tracking results providing also additional features as width estimation and the detection of stationary objects. A ldquorealrdquo application of these algorithms is illustrated on a specific use-case: the SASPENCE system, developed inside the Integrated Project PReVENT. The principle results show that such a data-fusion technique is actually able to improve the SASPENCE performances, especially in terms of extension of operative scenarios, a basic issue for the system functionality.
---
paper_title: Track-to-track association in the presence of sensor bias and the relative bias estimation

paper_content:

Track to track association problem is crucial for multisensor data fusion, and become complicated in the presence of sensor bias, random errors, false tracks and missed tracks. In this paper, we apply the optimal Bayes joint decision and estimation(JDE) which was proposed in [12] to the track-to-track association in the presence of sensor bias and develop a simplified JDE. The optimal Bayes JDE accounts to the possible association error when estimates the relative sensor bias and takes the estimate error into consideration when chooses the track-to-track association by the association cost having the estimation error. Hence it can enhance the accuracy of track-to-track association and reduce the relative bias estimate error. For computational simplicity, in this paper we proposed another JDE algorithm which can reduce the computation and only is slightly worse than the optimal Bayes JDE. The simulations compare the performance of the JDE association, the simplifed JDE developed in this paper, the method proposed in [8], and verify the feasibility and effectiveness of methods.
---
paper_title: Scene-Adaptive Hierarchical Data Association for Multiple Objects Tracking

paper_content:

Obtaining reliable and discriminative target representation are two vital tasks for data association in multi-tracking. Pervious works always directly combine bunch of features for more discriminative target representation, but this is prone to error accumulation and unnecessary computational cost, which on the contrary may increase identity switches in data association. Moreover, reliability of a same feature in different scenes may vary a lot, especially for currently widespread network cameras, which have been settled in complex and various scenes, previous fixed feature selection scheme cannot meet general requirements. To address this problem, we propose a scene-adaptive hierarchical data association scheme, which adaptively selects features which have higher reliability on target representation in applied scene, and gradually combines features to the minimum requirements of discriminating ambiguous targets. Hierarchical feature space is constructed according to reliability of features in the multi-tracking system, and data association is conducted in different layers of the feature space adaptively. Our algorithm is validated on various challenging RGB-D and RGB datasets recorded in various indoor and outdoor scenes, for diversities of both features and scenes. Experimental results validate its effectiveness and efficiency.
---
paper_title: Sensor Placement Algorithms for Fusion-Based Surveillance Networks

paper_content:

Mission-critical target detection imposes stringent performance requirements for wireless sensor networks, such as high detection probabilities and low false alarm rates. Data fusion has been shown as an effective technique for improving system detection performance by enabling efficient collaboration among sensors with limited sensing capability. Due to the high cost of network deployment, it is desirable to place sensors at optimal locations to achieve maximum detection performance. However, for sensor networks employing data fusion, optimal sensor placement is a nonlinear and nonconvex optimization problem with prohibitively high computational complexity. In this paper, we present fast sensor placement algorithms based on a probabilistic data fusion model. Simulation results show that our algorithms can meet the desired detection performance with a small number of sensors while achieving up to seven-fold speedup over the optimal algorithm.
---
paper_title: Distributed Sensor Allocation for Multi-Target Tracking in Wireless Sensor Networks

paper_content:

In emerging tracking systems using large-scale wireless sensor networks, sensor management is an essential task in order to balance the tracking performance and costs subject to limited network resources in terms of energy, communication bandwidth, and sensing range. This paper considers the sensor allocation problem for multi-target tracking (MTT), in which a group of sensors are dynamically selected and allocated to track each of the multiple targets and collaborate within the group via track data fusion. The sensor assignments evolve over time as targets move, and are accomplished by solving a constrained optimization problem that is formulated to maximize the overall tracking performance for all targets, while conserving network energy and providing tracking coverage guarantee. The original integer-valued optimization problem is relaxed to a convex program for computational tractability, and then implemented in a distributed manner for network scalability and reduced communication costs. Through local one-hop communication with neighboring nodes, each sensor autonomously decides on whether to participate in data collection and how to contribute to track fusion. The proposed distributed sensor allocation algorithm, implemented via iterative subgradient search, is shown to converge to the global optimum of the centralized relaxed problem, and is near optimal for the original integer programming problem.
---
paper_title: Asynchronous Track-to-Track Fusion by Direct Estimation of Time of Sample in Sensor Networks

paper_content:

Asynchronous data fusion is inevitable in track-to-track fusion for tracking high-speed targets. For low-speed targets, e.g., the movement of clouds, synchronization is insignificant and, depending on the application, may be disregarded. Real-time asynchronous fusion is a demanding task in sensor networks when the sensors are not synchronous in sampling-rate or in sampling-phase. In the method proposed in this paper, an estimator in the fusion center estimates the actual time of the sample with respect to the time-reference of the fusion center upon receiving the data from a sensor. Then, the computer of the fusion center uses predictions to transfer all the received data to the data corresponding to the start of the next fusion period. This process synchronizes the data, which is necessary for real-time uncorrelated track-to-track fusion. Finally, the pseudo-synchronized data of all the sensors are fused with an element-wise linear minimum variance unbiased estimator algorithm before the start of the next fusion period. Simulation and comparison with some benchmark algorithms are demonstrated to verify the effectiveness of the proposed algorithm.
---
paper_title: Target modeling and dynamic classification for adaptive sensor data fusion

paper_content:

Volkswagen research has developed a sensor fusion system for integrating different sensor data of the environment into a combined description for use with advanced driver assistance systems. Sensor models allow a detailed characterization of sensor properties. Due to the fused extensive information about the detected objects a classification into different dynamic classes, such as stationary or vehicle like moving objects, is reasonable. The classification switches between different dynamic models and is based on statistical tests that evaluate the fused data. The technique is tested with recorded sequences from stop and go scenes and shows robust and well-fitting classification results including reduced uncertainties compared to the usage of a universal model.
---
paper_title: On the track-to-track correlation problem

paper_content:

The testing of the hypothesis whether two tracks represent the same target is considered. Previous works in the literature assumed that the estimates of the same target's state from two track files are uncorrelated. A test that includes their correlation is presented.
---
paper_title: Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

paper_content:

This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.
---
paper_title: Handbook of Multisensor Data Fusion

paper_content:

Multisensor Data Fusion D. L. Hall and J. Llinas Data Fusion Perspectives and Its Role in Information Processing O. Kessler and F. White Revisions to the JDL Data Fusion Model A. N. Steinberg and C. L. Bowman Introduction to the Algorithms of Data Association in Multiple-Target Tracking J. K. Uhlmann The Principles and Practice of Image and Spatial Data Fusion E. Waltz and T. Waltz Data Registration R. R. Brooks and L. Grewe Data Fusion Automation: A Top-Down Perspective R. Antony Overview of Distributed Decision Fusion M. E. Liggins Introduction to Particle Filtering: The Next Stage in Tracking M. E. Liggins and K-C. Chang Target Tracking Using Probabilistic Data Association-Based Techniques with Applications to Sonar, Radar, and EO Sensors T. Kirubarajan and Y. Bar-Shalom An Introduction to the Combinatorics of Optimal and Approximate Data Association J. K. Uhlmann A Bayesian Approach to Multiple-Target Tracking L. D. Stone Data Association Using Multiple-Frame Assignments A. B. Poore, S. Lu, and B. J. Suchomel General Decentralized Data Fusion with Covariance Intersection S. Julier and J. K. Uhlmann Data Fusion in Nonlinear Systems S. Julier and J. K. Uhlmann Random Set Theory for Multisource-Multitarget Information Fusion R. Mahler Distributed Fusion Architectures, Algorithms, and Performance within a Network-Centric Architecture M. E. Liggins and K-C. Chang Foundations of Situation and Threat Assessment A. N. Steinberg An Introduction to Level 5 Fusion: The Role of the User E. Blasch Perspectives on the Human Side of Data Fusion: Prospects for Improved Effectiveness using Advanced Human-Computer Interfaces D. L. Hall, C. M. Hall, and S. A. H. McMullen Requirements Derivation for Data Fusion Systems E. Waltz and D. L. Hall A Systems Engineering Approach for Implementing Data Fusion Systems C. L. Bowman and A. N. Steinberg Studies and Analyses within Project Correlation: An In-Depth Assessment of Correlation Problems and Solution Techniques J. Llinas, Capt. L. McConnell, C. L. Bowman, D. L. Hall, and P. Applegate Data Management Support to Tactical Data Fusion R. Antony Assessing the Performance of Multisensor Fusion Processes J. Llinas A Survey of COTS Software for Multisensor Data Fusion S. A. Hall McMullen, R. R. Sherry, and S. Miglani A Survey of Multisensor Data Fusion Systems M. L. Nichols Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems C. S. Byington and A. K. Garga Adapting Data Fusion to Chemical and Biological Sensors D. C. Swanson Fusion of Ground and Satellite Data via the Army Battle Command System S. Aungst, M. Campbell, J. Kuhns, D. Beyerle, T. Bacastow, and J. Knox Developing Information Fusion Methods for Combat Identification T. M. Schuck, J. Bockett Hunter, and D. D. Wilson
---
paper_title: Covariance consistency methods for fault-tolerant distributed data fusion

paper_content:

Abstract This paper presents a general, rigorous, and fault-tolerant framework for maintaining consistent mean and covariance estimates in an arbitrary, dynamic, distributed network of information processing nodes. In particular, a solution is provided that addresses the information deconfliction problem that arises when estimates from two or more different nodes are determined to be inconsistent with each other, e.g., when two high precision (small covariance) estimates place the position of a particular object at very different locations. The challenge is to be able to resolve such inconsistencies without having to access and exploit global information to determine which of the estimates is spurious. The solution proposed in this paper is called Covariance Union.
---
paper_title: Track-to-Track Fusion With Asynchronous Sensors Using Information Matrix Fusion for Surround Environment Perception

paper_content:

Driver-assistance systems and automated driving applications in the future will require reliable and flexible surround environment perception. Sensor data fusion is typically used to increase reliability and the observable field of view. In this paper, a novel approach to track-to-track fusion in a high-level sensor data fusion architecture for automotive surround environment perception using information matrix fusion (IMF) is presented. It is shown that IMF produces the same good accuracy in state estimation as a low-level centralized Kalman filter, which is widely known to be the most accurate method of fusion. Additionally, as opposed to state-of-the-art track-to-track fusion algorithms, the presented approach guarantees a globally maintained track over time as an object passes in and out of the field of view of several sensors, as required in surround environment perception. As opposed to the often-used cascaded Kalman filter for track-to-track fusion, it is shown that the IMF algorithm has a smaller error and maintains consistency in the state estimation. The proposed approach using IMF is compared with other track-to-track fusion algorithms in simulation and is shown to perform well using real sensor data in a prototype vehicle with a 12-sensor configuration for surround environment perception in highly automated driving applications.
---
paper_title: Advanced multiple objects tracking by fusing radar and image sensor data - Application on a case study.

paper_content:

The aim of this paper is to present a multiple object tracking data fusion technique, which fuses radar, image, and ego vehicle odometry. The data are fused at a high level, which leads to reliable and stable tracking results providing also additional features as width estimation and the detection of stationary objects. A ldquorealrdquo application of these algorithms is illustrated on a specific use-case: the SASPENCE system, developed inside the Integrated Project PReVENT. The principle results show that such a data-fusion technique is actually able to improve the SASPENCE performances, especially in terms of extension of operative scenarios, a basic issue for the system functionality.
---
paper_title: Pushing the Limits of Deep CNNs for Pedestrian Detection

paper_content:

Compared with other applications in computer vision, convolutional neural networks (CNNs) have underperformed on pedestrian detection. A breakthrough was made very recently using sophisticated deep CNN (DCNN) models, with a number of handcrafted features or explicit occlusion handling mechanism. In this paper, we show that by reusing the convolutional feature maps of a DCNN model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. We empirically identify and disclose important implementation details. We also show that pixel labeling may be simply combined with a detector to boost the detection performance. By adding complementary handcrafted features such as optical flow, the DCNN-based detector can be further improved. We advance the state-of-the-art results by lowering the log-average miss rate from 11.7% to 8.9% on the Caltech data set and from 11.2% to 8.6% on the Inria data set. We also achieve a comparable result to state-of-the-art approaches on the KITTI data set.
---
paper_title: Road structure based scene understanding for intelligent vehicle systems

paper_content:

We address the topic of intelligent vehicle systems and especially systems aiming at high level scene understanding. Our goal is to build an on-line Behavior Map of surrounding environment. In this case, the road structure becomes important in order to understand entities behavior. From a general view-point, this paper demonstrates two important concepts: 1) the smooth integration of global, absolute but partial information (the Navi Map) in a local and relative map (the Behavior Map). This is demonstrated by the building of the Behavior Map composed of the roads structure and pedestrian/vehicle's position and trajectory. 2) the use of both global and local information to improve the understanding of the environment. This is demonstrated by the proposed “Pedestrian Warning System”. From a technical viewpoint, the main contributions are: 1) the dual ground plane/image plane approach which avoid introduction of errors in both the cost function evaluation and the constraints for minimization, 2) the smooth and efficient integration of prior data in a realistic road model computation when they are available.
---
paper_title: Fusion of laser and monocular camera data in object grid maps for vehicle environment perception

paper_content:

Occupancy grid maps provide a reliable vehicle environmental model and usually process data from range finding sensors. Object grid maps additionally contain information about the classes of objects, which is crucial for applications like autonomous driving. Unfortunately, they lack the precision of occupancy grid maps, since they mostly process classification results from camera data by projecting the corresponding images onto the ground plane. This paper proposes a modular framework to create precise object grid maps. The presented algorithm creates classical occupancy grid maps and object grid maps. In a combination step, it transforms both maps into the same frame of discernment based on the Dempster-Shafer theory of evidence. This allows fusing the maps to one object grid map, which contains valuable object information and at the same time benefits from the precision of the occupancy grid map.
---
paper_title: A multi-sensor fusion system for moving object detection and tracking in urban driving environments

paper_content:

A self-driving car, to be deployed in real-world driving environments, must be capable of reliably detecting and effectively tracking of nearby moving objects. This paper presents our new, moving object detection and tracking system that extends and improves our earlier system used for the 2007 DARPA Urban Challenge. We revised our earlier motion and observation models for active sensors (i.e., radars and LIDARs) and introduced a vision sensor. In the new system, the vision module detects pedestrians, bicyclists, and vehicles to generate corresponding vision targets. Our system utilizes this visual recognition information to improve a tracking model selection, data association, and movement classification of our earlier system. Through the test using the data log of actual driving, we demonstrate the improvement and performance gain of our new tracking system.
---
paper_title: Obstacle detection for self-driving cars using only monocular cameras and wheel odometry

paper_content:

Mapping the environment is crucial to enable path planning and obstacle avoidance for self-driving vehicles and other robots. In this paper, we concentrate on ground-based vehicles and present an approach which extracts static obstacles from depth maps computed out of multiple consecutive images. In contrast to existing approaches, our system does not require accurate visual inertial odometry estimation but solely relies on the readily available wheel odometry. To handle the resulting higher pose uncertainty, our system fuses obstacle detections over time and between cameras to estimate the free and occupied space around the vehicle. Using monocular fisheye cameras, we are able to cover a wider field of view and detect obstacles closer to the car, which are often not within the standard field of view of a classical binocular stereo camera setup. Our quantitative analysis shows that our system is accurate enough for navigation purposes of self-driving cars and runs in real-time.
---
paper_title: Multimodal information fusion for urban scene understanding

paper_content:

This paper addresses the problem of scene understanding for driver assistance systems. To recognize the large number of objects that may be found on the road, several sensors and decision algorithms have to be used. The proposed approach is based on the representation of all available information in over-segmented image regions. The main novelty of the framework is its capability to incorporate new classes of objects and to include new sensors or detection methods while remaining robust to sensor failures. Several classes such as ground, vegetation or sky are considered, as well as three different sensors. The approach was evaluated on real publicly available urban driving scene data.
---
paper_title: Pedestrian detection combining RGB and dense LIDAR data

paper_content:

Why is pedestrian detection still very challenging in realistic scenes? How much would a successful solution to monocular depth inference aid pedestrian detection? In order to answer these questions we trained a state-of-theart deformable parts detector using different configurations of optical images and their associated 3D point clouds, in conjunction and independently, leveraging upon the recently released KITTI dataset. We propose novel strategies for depth upsampling and contextual fusion that together lead to detection performance which exceeds that of the RGB-only systems. Our results suggest depth cues as a very promising mid-level target for future pedestrian detection approaches.
---
paper_title: Environmental Perception and Sensor Data Fusion for Unmanned Ground Vehicle

paper_content:

Unmanned Ground Vehicles (UGVs) that can drive autonomously in cross-country environment have received a good deal of attention in recent years. They must have the ability to determine whether the current terrain is traversable or not by using onboard sensors. This paper explores new methods related to environment perception based on computer image processing, pattern recognition, multisensors data fusion, and multidisciplinary theory. Kalman filter is used for low-level fusion of physical level, thus using the D-S evidence theory for high-level data fusion. Probability Test and Gaussian Mixture Model are proposed to obtain the traversable region in the forward-facing camera view for UGV. One feature set including color and texture information is extracted from areas of interest and combined with a classifier approach to resolve two types of terrain (traversable or not). Also, three-dimension data are employed; the feature set contains components such as distance contrast of three-dimension data, edge chain-code curvature of camera image, and covariance matrix based on the principal component method. This paper puts forward one new method that is suitable for distributing basic probability assignment (BPA), based on which D-S theory of evidence is employed to integrate sensors information and recognize the obstacle. The subordination obtained by using the fuzzy interpolation is applied to calculate the basic probability assignment. It is supposed that the subordination is equal to correlation coefficient in the formula. More accurate results of object identification are achieved by using the D-S theory of evidence. Control on motion behavior or autonomous navigation for UGV is based on the method, which is necessary for UGV high speed driving in cross-country environment. The experiment results have demonstrated the viability of the new method.
---
paper_title: Real-time classification and sensor fusion with a spiking deep belief network

paper_content:

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input.
---
paper_title: Deep Multimodal Fusion: Combining Discrete Events and Continuous Signals

paper_content:

Multimodal datasets often feature a combination of continuous signals and a series of discrete events. For instance, when studying human behaviour it is common to annotate actions performed by the participant over several other modalities such as video recordings of the face or physiological signals. These events are nominal, not frequent and are not sampled at a continuous rate while signals are numeric and often sampled at short fixed intervals. This fundamentally different nature complicates the analysis of the relation among these modalities which is often studied after each modality has been summarised or reduced. This paper investigates a novel approach to model the relation between such modality types bypassing the need for summarising each modality independently of each other. For that purpose, we introduce a deep learning model based on convolutional neural networks that is adapted to process multiple modalities at different time resolutions we name deep multimodal fusion. Furthermore, we introduce and compare three alternative methods (convolution, training and pooling fusion) to integrate sequences of events with continuous signals within this model. We evaluate deep multimodal fusion using a game user dataset where player physiological signals are recorded in parallel with game events. Results suggest that the proposed architecture can appropriately capture multimodal information as it yields higher prediction accuracies compared to single-modality models. In addition, it appears that pooling fusion, based on a novel filter-pooling method provides the more effective fusion approach for the investigated types of data.
---
paper_title: Learning Deep Architectures for AI

paper_content:

Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae. Each level of the architecture represents features at a different level of abstraction, defined as a composition of lower-level features. Searching the parameter space of deep architectures is a difficult task, but new algorithms have been discovered and a new sub-area has emerged in the machine learning community since 2006, following these discoveries. Learning algorithms such as those for Deep Belief Networks and other related unsupervised learning algorithms have recently been proposed to train deep architectures, yielding exciting results and beating the state-of-the-art in certain areas. Learning Deep Architectures for AI discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed and discussed, highlighting challenges and suggesting avenues for future explorations in this area.
---
paper_title: A tutorial survey of architectures, algorithms, and applications for deep learning

paper_content:

In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent developments in deep learning. The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of non-linear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial survey, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the recent deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures – deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) – one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed.
---
paper_title: A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

paper_content:

This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On the other hand, we will also trace back to see the evolution history of deep generative models, to see how researchers balance the representation power and computation complexity to reach Restricted Boltzmann Machine and eventually reach Deep Belief Nets. Further, we will also look into the development history of modelling time series data with neural networks. We start with Time Delay Neural Networks and move further to currently famous model named Recurrent Neural Network and its extension Long Short Term Memory. We will also briefly look into how to construct deep recurrent neural networks. Finally, we will conclude this report with some interesting open-ended questions of deep neural networks.
---
paper_title: A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws

paper_content:

As ball screw has complex structure and long range of distribution, single signal collected by one sensor is difficult to express its condition fully and accurately. Multi-sensor data fusion usually has a better effect compared with single signal. Multi-sensor data fusion based on neural network(BP) is a commonly used multi-sensor data fusion method, but its application is limited by local optimum problem. Aiming at this problem, a multi-sensor data fusion method based on deep learning for ball screw is proposed in this paper. Deep learning, which consists of unsupervised learning and supervised learning, is the development and evolution of traditional neural network. It can effectively alleviate the optimization difficulty. Parallel superposition on frequency spectra of signals is directly done in the proposed deep learning-based multi-sensor data fusion method, and deep belief networks(DBN) are established by using fused data to adaptively mine available fault characteristics and automatically identify the degradation condition of ball screw. Test is designed to collect vibration signals of ball screw in 7 different degradation conditions by using 5 acceleration sensors installed on different places. The proposed fusion method is applied in identifying the degradation degree of ball screw in the test to demonstrate its efficacy. Finally, the multi-sensor data fusion based on neural network is also applied in degradation degree monitoring. The monitoring accuracy of deep learning-based multi-sensor data fusion is higher compared with that of neural network-based multi-sensor data fusion, which means the proposed method has more superiority.
---
paper_title: Combining multiple sources of knowledge in deep CNNs for action recognition

paper_content:

Although deep convolutional neural networks (CNNs) have shown remarkable results for feature learning and prediction tasks, many recent studies have demonstrated improved performance by incorporating additional handcrafted features or by fusing predictions from multiple CNNs. Usually, these combinations are implemented via feature concatenation or by averaging output prediction scores from several CNNs. In this paper, we present new approaches for combining different sources of knowledge in deep learning. First, we propose feature amplification, where we use an auxiliary, hand-crafted, feature (e.g. optical flow) to perform spatially varying soft-gating on intermediate CNN feature maps. Second, we present a spatially varying multiplicative fusion method for combining multiple CNNs trained on different sources that results in robust prediction by amplifying or suppressing the feature activations based on their agreement. We test these methods in the context of action recognition where information from spatial and temporal cues is useful, obtaining results that are comparable with state-of-the-art methods and outperform methods using only CNNs and optical flow features.
---
paper_title: Multimodal Deep Learning

paper_content:

Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning.
---
paper_title: Effective sensor fusion with event-based sensors and deep network architectures

paper_content:

The use of spiking neuromorphic sensors with state-of-art deep networks is currently an active area of research. Still relatively unexplored are the pre-processing steps needed to transform spikes from these sensors and the types of network architectures that can produce high-accuracy performance using these sensors. This paper discusses several methods for preprocessing the spiking data from these sensors for use with various deep network architectures. The outputs of these preprocessing methods are evaluated using different networks including a deep fusion network composed of Convolutional Neural Networks and Recurrent Neural Networks, to jointly solve a recognition task using the MNIST (visual) and TIDIGITS (audio) benchmark datasets. With only 1000 visual input spikes from a spiking hardware retina, the classification accuracy of 64.5% achieved by a particular trained fusion network increases to 98.31% when combined with inputs from a spiking hardware cochlea.
---
paper_title: Deep learning

paper_content:

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
---
paper_title: Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

paper_content:

This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: (1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; (2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; (3) we perform data fusion from heterogeneous sensors (optical and laser) using residual correction. Our framework improves state-of-the-art accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.
---
paper_title: A Big Network Traffic Data Fusion Approach Based on Fisher and Deep Auto-Encoder

paper_content:

Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be built by using both labeled and unlabeled data, than using each one alone. In this paper, a novel network traffic data fusion approach based on Fisher and deep auto-encoder (DFA-F-DAE) is proposed to reduce the data dimensions and the complexity of computation. The experimental results show that the DFA-F-DAE improves the generalization ability of the three classification algorithms (J48, back propagation neural network (BPNN), and support vector machine (SVM)) by data dimensionality reduction. We found that the DFA-F-DAE remarkably improves the efficiency of big network traffic classification.
---
paper_title: Deep data fusion model for risk perception and coordinated control of smart grid

paper_content:

This paper presents a deep data fusion model for risk perception and coordinated control in a regional power system control center. A knowledge learning data fusion approach has been used to find an efficient state representation based on prior knowledge from cross-domain energy management systems. In particular, a kernel principal components analysis technique is presented for nonlinear dimensionality reduction of knowledge learning. The control strategy we study is based on cross-domain global optimization approach, which regards the contingencies and control actions of mutual backup systems as constraints. The objective function is defined as the product of cross-domain assessment and control factors. The method for obtaining optimal solution is given by interior point code. To show the applicability, different machine learning method has been studied. The experimental results show that the proposed knowledge learning approach consistently outperforms the traditional machine learning method. In addition, the proposed coordinated control approach is verified effective on large-scale smart grid decision support system for East China project.
---
paper_title: End to End Wireless Multimedia Service Modelling over a Metropolitan Area Network

paper_content:

Multimedia applications have been the key driving force in converging fixed, mobile and IP networks. A major hurdle in the realisation of this convergence is obtaining Quality of Service from a heterogeneous, best-effort service network. Interactive voice requires strict bounds on delay, jitter and packet losses, video adds significant bandwidth requirements to the network, while internet only makes its best effort to deliver a packet. Hence, the end-to-end QoS management of heterogeneous networks supporting multimedia services is of paramount importance. This paper presents a performance study of multimedia applications over 802.11 networks within metropolitan area networking environment. Specifically, we study QoS performance of VoIP applications over 802.11, while sharing the network resources with HTTP and video applications. Using the OPNET simulator, we simulate a number of realistic application traffic scenarios and study the network performance by analysing QoS parameters including delay, jitter, MOS, and packet loss ratio.
---
paper_title: UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies

paper_content:

The education industry around the globe is undergoing major transformations. Organizations, such as Coursera are advancing new business models for education. A number of major industries have dropped degrees from the job requirements. While the economics of higher education institutions are under threat in a continuing gloomy global economy, digital and lifelong learners are increasingly demanding new teaching and learning paradigms from educational institutions. There is an urgent need to transform teaching and learning landscape in order to drive global economic growth. The use of distance eTeaching and eLearning (DTL) is on the rise among digital natives alongside our evolution toward smart societies. However, the DTL systems today lack the necessary sophistication due to several challenges including data analysis and management, learner-system interactivity, system cognition, resource planning, agility, and scalability. This paper proposes a personalised Ubiquitous eTeaching & eLearning (UTiLearn) framework that leverages Internet of Things, big data, supercomputing, and deep learning to provide enhanced development, management, and delivery of teaching and learning in smart society settings. A proof of concept UTiLearn system has been developed based on the framework. A detailed design, implementation, and evaluation of the UTiLearn system, including its five components, are provided using 11 widely used datasets.
---
paper_title: LocPriS: a security and privacy preserving location based services development framework

paper_content:

With the ever increasing pervasiveness of devices with functionality to provide location based services comes the increased importance and reliance upon those services to provide user privacy and security. Many techniques to facilitate privacy and security in mobile and fixed networks have been developed, but surveys of user's show that this area still has a lot of work left to do to satisfy privacy fears and help developers of such services to choose the best techniques to use. In this paper we propose a security and privacy preserving location based services development framework. The framework will allow for future development, visualisation, comparison and analysis of location based services that preserve security and privacy in order to improve user confidence in such technologies.
---
paper_title: Vehicular ad hoc and grid networks: discussion, design and evaluation

paper_content:

This paper presents an overview of ongoing research on fixed (conventional), ad hoc, and grid networking technologies for advanced road safety applications and traffic management systems. Integration of fixed and ad hoc network infrastructure with grid networks is considered in order to address the enormous amount of data aggregation and processing requirements posed by the ubiquitous traffic monitoring and management systems. The discussions in this paper draw on experiences from a collaborative project at BT Research on Ubiquitous Traffic Telematics (Traffimatics) and various strands of research in telematics and ITS at Swansea University. For the covering abstract see ITRD E140665.
---
paper_title: Multimedia applications over metropolitan area networks (MANs)

paper_content:

Multimedia applications have been the key driving force in converging fixed, mobile and IP networks. A major hurdle in the realisation of this convergence is obtaining Quality of Service from a heterogeneous, best-effort service network. Interactive voice requires strict bounds on delay, jitter and packet losses, for Different Network Traffic Intensity, whereas video adds significant bandwidth requirements to the network, while Internet only makes its best effort to deliver a packet. Hence, the end-to-end QoS management of heterogeneous networks supporting multimedia services is of paramount importance. We present an empirical performance study of multimedia applications over 802.11 networks within metropolitan area networking environments. Specifically, we study the QoS performance of Voice over IP (VoIP) applications over 802.11-based networks, while sharing the network resources with HTTP and video applications. Using the OPNET simulator, we simulate several realistic application traffic scenarios, and we investigate the performance of VoIP applications by analyzing QoS parameters, such as delay, jitter, MOS, and packet loss ratio. Subsequently, the performance characteristics data of the network, which we obtain through simulations, are used to build a Markov model of the network performance to extend our analysis and gain further insight into the network performance dynamics.
---
paper_title: Intelligent Mobility Systems: Some Socio-technical Challenges and Opportunities

paper_content:

Analysis of socio-technical challenges and opportunities around contemporary mobilities suggests new interpretations and visions for intelligent transport systems. Multiple forms of intelligence are required (but not easily compatible), transport is too narrow a term, and innovation results in new socio-technical systems. An exploration of cumulative, collective and collaborative aspects of mobility systems, allows us to sketch challenges and opportunities in relation to practices of collaboration, communication and coordination, literacies for creativity, comfort and control, citizenship and (lack of) a sense of crisis, concluding with a discussion of methodological implications.
---
paper_title: IoT-based collaborative reputation system for associating visitors and artworks in a cultural scenario

paper_content:

We design a CRS to establish the people reputation within Cultural spaces.The system is able to classify visitor behaviours.Our approach is suitable for both nonprofit and business oriented organizations. In this paper, starting from a comprehensive mathematical model of a Collaborative Reputation Systems (CRSes), we present a research study within the Cultural Heritage domain. The main goal of this study has been the evaluation and classification of the visitors behaviour during a cultural event. By means of mobile technological instruments, opportunely deployed within the environment, it is possible to collect data representing the knowledge to be inferred and give a reliable rate for both visitors and exposed artworks. Discussed results, confirm the reliability and the usefulness of CRSes for deeply understand dynamics related to people visiting styles.
---
paper_title: An Associative Engines Based Approach Supporting Collaborative Analytics in the Internet of Cultural Things

paper_content:

In this paper we illustrate an integrated approach supporting an information system which combines Business Intelligence, Big Data, Internet of Things, GeoSpatial information processing, multimedia resources, structured and unstructured content analysis with Semantic techniques, and Social Network Analysis. This system exploits the specific opportunities provided by Associative in-memory technologies in the context of Cloud Computing and Collaborative processes and experiments the use of semantic technologies to mitigate the efforts involved in merging, and analyzing information incoming from different sources. The primary aim is supporting Cultural Heritage (CH) Asset crowdsourcing, promotion, publication, management and usage. Preliminary results are reported from ongoing experimentation, focused on visitors interests and behaviour analysis performed by monitoring, comparing and combining information from different types of populations and visits: on-site ad-hoc (exhibitions, museums, cultural events, etc.), territorial (eg. Historical downtown, touristic areas including relevant CH resources) and Virtual - Worldwide Internet based visits.
---
paper_title: Big data for smart cities with KNIME a real experience in the SmartSantander testbed

paper_content:

Big data techniques are conceived as the powerful tool to exploit all the potential of the Internet of Things and the smart cities. A new dimension of understanding about the human behaviours is expected to be reached through all the gathered data in the emerging smart environment. The described potential, so-called Human Dynamics, pursues to describe in real-time the human behaviours and activities. This work presents our experiences for big data analytics in smart cities, in terms of sensors data management, data fusion and knowledge discovery from the data. The data used is from the European Project SmartSantander, where the traffic behaviour has been correlated with respect to the temperature in the Santander City. The evolution of both flows present a similar behaviour, in detail, a fine grain correlation is discovered. On the one hand, the traffic distribution, aggregated by temperature bins, follows up a Poisson distribution model. The Poisson modelling allows to interpolate and predict complex behaviours based on simple measures such as the temperature. At the same time, on the other hand, the isolated traffic density distribution, without taking into account the temperature-based aggregation has been analysed. The traffic distribution has presented a burst behaviour, which presents a closer model to the human dynamics. Therefore, this work presents as the smart cities data can be modelled as Poisson or Human Dynamics burst models. Finally, reference data analytics process, data sets and models are offered for the Open Source Data analytics platform Konstanz Information Miner KNIME. Copyright © 2014 John Wiley & Sons, Ltd.
---
paper_title: Iot-based smart cities: A survey

paper_content:

Due to the growing developments in advanced metering and digital technologies, smart cities have been equipped with different electronic devices on the basis of Internet of Things (IoT), therefore becoming smarter than before. The aim of this article is that of providing a comprehensive review on the concepts of smart cities and on their motivations and applications. Moreover, this survey describes the IoT technologies for smart cities and the main components and features of a smart city. Furthermore, practical experiences over the world and the main challenges are explained.
---
paper_title: The strategic prototype “crime-sourcing” and the science/science fiction behind it

paper_content:

Science fiction prototyping (SFP) and crowdsourcing are among a range of promising methodologies that are set to transform all spheres of human landscape be it science, business or social. This paper exploits the SFP and crowdsourcing methodologies and presents “crime-sourcing”, a prototype designed to explore futuristic crowdsourcing ideas. Its purpose is to provide a fictional scenario that speculates how crowdsourcing could be blended with future technology to develop a “crime-to-conviction” model. This scenario takes the cyber-space concept of crowdsourcing and then transfers it to a hybrid cyber/physical business model context. The story aligns itself with Zuckerman's doctrine that while there is human tendency to “flock together” in crowds, most of our social ties, online or offline, are only with a small set of people with whom we have much in common. The paper concludes with a discussion on contribution of this paper to crowdsourcing theory and the challenges that need to be overcome if prototyping is to become an established foresight methodology.
---
paper_title: Intelligent sensor with data fusion to improve the care and management of water

paper_content:

The problem of the scarce of drinking water is increasing every day around the world and is required of new monitoring systems in order to improve the care and management of water. The intelligent sensors with function of data fusion are a good alternative to build intelligent monitoring systems. This paper presents a data fusion method based on least squares technique to design one intelligent sensor using a microcontroller of low computational capability. Three variables are used by the data fusion model to estimate the water consumption; the result is compared with a real value of water consumption. The intelligent sensor proposed is able to detect if the water consumption is normal or not and its performance was simulated using real data of water consumption of an Instituto Tecnologico de Chihuahua facility. Intelligent water consumption monitoring systems can be built for warning about improper water use and improve its care and management.
---
paper_title: Big Data Logistics: A health-care Transport Capacity Sharing Model

paper_content:

The growth of cities in the 21st century has put more pressure on resources and conditions of urban life. There are several reasons why the health-care industry is the focus of this investigation. For instance, in the UK various studies point to the lack of failure of basic quality control procedures and misalignment between customer needs and provider services and duplication of logistics practices. The development of smart cities and big data present unprecedented challenges and opportunities for operations managers; they need to develop new tools and techniques for network planning and control. Our paper aims to make a contribution to big data and city operations theory by exploring how big data can lead to improvements in transport capacity sharing. We explore using Markov models the integration of big data with future city (health-care) transport sharing. A mathematical model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services. The results from our analysis of 13 different sharing/demand scenarios are presented. A key finding is that the probability for system failure and performance variance tends to be highest in a scenario of high demand/zero sharing.
---
paper_title: ICDMS: An Intelligent Cloud Based Disaster Management System for Vehicular Networks

paper_content:

The importance of emergency response systems cannot be overemphasized today due to the many manmade and natural disasters in the recent years such as September 2001 and the recent Japan earthquake and tsunami disaster. The overall cost of the Japan disaster alone is estimated to have exceeded 300 billion USD. Transportation and telecommunications play a critical role in disaster response and management in order to minimize loss of human life, economic cost and disruptions. Our research is concerned with developing emergency response systems for disasters of various scales with a focus on transportation systems, which exploit ICT developments. ::: ::: In this paper, we leverage Intelligent Transportation Systems including Vehicular Ad hoc Networks, mobile and Cloud Computing technologies to propose an intelligent disaster management system. The system is intelligent because it is able to gather information from multiple sources and locations, including from the point of incident, and is able to let vehicles make effective strategies and decisions of communication protocols usage. Hybrid vehicular communications based on vehicle-to-vehicle and vehicle-to-infrastructure protocols are opportunistically exploited. The effectiveness of our system is demonstrated through modelling the impact of a disaster on a real city transport environment and comparing it with the case where our disaster management system was in place. We report great benefits derived from the adoption of our proposed system in terms of improved and balanced traffic flow and smooth evacuation.
---
paper_title: Framework for an Autonomic Transport System in Smart Cities

paper_content:

AbstractToday the concept of smart cities is discussed in scientific society and politics. A core function of smart cities is transportation. This paper gives a short overview on the concepts for Intelligent Transport Systems ITS for smart cities and proposes a framework for the design of an autonomic transportation system that provides personalized mobility services to its users in a smart city setting. The transportation poses extreme environment for ICT systems due to fast moving vehicles and users, requiring real-time acquisition and high performance processing of large scale data, and rapidly changing communication networks topologies and node densities. The aim of this paper is to propose a framework that will act as a reference for the design of future transportation systems that are able to cope with the ever rising system complexities and users' demands. Therefore, a backbone system providing information at different levels was designed following the principles of a corporative ICT that were proposed in [1]. The framework fulfills the main requirement providing suitable information about the local decision engines in vehicles and infrastructure interacting in smart cities traffic systems.
---
paper_title: A scalable multimedia QoS architecture for ad hoc networks

paper_content:

Communication demands have grown from separate data and voice to integrated multimedia, paving the way to converging fixed, mobile and IP networks. Supporting Multimedia is a challenging task for wireless ad hoc network designers. Multimedia forms high data rate traffic with stringent Quality of Service (QoS) requirements. Wireless ad hoc networks are characterized by frequent topology changes, unreliable wireless channel, network congestion and resource contention. Providing scalable QoS is the most important challenge for multimedia delivery over ad hoc networks. We introduce here a provisioning and routing architecture for ad hoc networks which scales well while provisioning QoS. The proposed architecture is analysed using a mix of HTTP, voice and video streaming applications over 54 Mbps 802.11 g-based ad hoc networks. The architecture is simulated and compared to well-known routing protocols using the OPNET Modeller. The results show that our architecture scales well with increase in the network size, and outperforms well-known routing protocols.
---
paper_title: Internet of Things for Smart Cities

paper_content:

The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.
---
paper_title: Social big data: Recent achievements and new challenges

paper_content:

The paper presents the methodologies on information fusion for social media.The methodologies, frameworks, and software used to work with big data are given.The state of the art in the data analytic techniques on social big data is provided.Social big data applications for various domains are described and analyzed. Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms.
---
paper_title: On Discovering Road Traffic Information Using Virtual Reality Simulations

paper_content:

Transport systems play critical role inmodern day economies and are prime example of highlydynamic complex systems. Current traffic monitoringsystems do not provide timely and granular informationwhich could sufficiently meet system analysisrequirements. Simulation augmented with VirtualReality (VR) has emerged as an important methodologyto model and analyze transport systems. This paperreports our ongoing work on developing an open sourceroad traffic VR simulator based on microscopic and carfollowingmodels, designed using Java Monkey Engine.Subsequent to a brief literature review on trafficsimulations, we give a description of the simulatordesign, its functionality, and the driver behaviourmodels used in the simulator. We then simulate fewinteresting traffic scenarios and analyse traffic flow,speed, occupancy and other results produced by thesimulator. Finally, we conclude and outline the futurework.
---
paper_title: An event-based data fusion algorithm for smart cities

paper_content:

The last decade has seen a considerable increase in the number of sensors we interact with on a daily basis. However, it is not always possible for a single sensing system to capture the complete story. While statically mounted infrastructure sensors typically capture the what, where, how much etc aspects of a detected event, e.g. (what appliance was used, how much energy did it consume), they do not always answer the who question. On the other hand, the advent of wearables has helped answer the what and who aspects - e.g. (who used the appliance). Fusing such sensor streams that observe the same event but different attributes of it, opens up many interesting applications. In this paper, we present a globally optimal data fusion algorithm for such pairs of systems, and show why traditional bipartite algorithms do not work. We evaluate our algorithm against two greedy baselines and show that our algorithm has lesser variance in the presence of time skew, false positives and false negatives.
---
paper_title: Next Generation of Journey Planner in a Smart City

paper_content:

Journey planning is the key to an efficient andsustainable transportation system in a smart city. A good journeyplanner is expected to help commuters travel safely, comfortablyand quickly, as well as keep the whole transportation networkrunning efficiently. In modern cities, it should be able to combinea wide range of private and public transport modes, and moreimportantly, react to real-time events that are impactful on thetopology of the transport network. In this paper, we present ourmulti-modal journey planner, JPlanner developed for the cityof Singapore. JPlanner leverages on more comprehensive urbandata, i.e., traffic network data and real-time traffic speed data, aiming to provide more accurate and effective recommendationsto commuters. With respect to functionality, JPlanner supportsthe combination of multiple transport modes, such as "Park andRide" for the switch between private car driving and publictransport riding. Other travel modes supported by JPlanner include walking, cycling and taxi. We highlight that the keytechnology enabling the accurate journey planning in JPlanner isthe Speed Fusion, which infers real-time traffic speed by fusingdifferent data sources. Finally we use a case study to compare thejourney recommendation results between JPlanner and the othertwo popular journey planners to demonstrate the advantages ofour system.
---
paper_title: Multi-sensor Acquisition, Data Fusion, Criteria Mining and Alarm Triggering for Decision Support in Urban Water Infrastructure Systems

paper_content:

Frequent adjustment of the drinking water treatment process as a simultaneous response to climate variations, and the impact those variations have on water quality, has been a grand challenge in water resource management in recent years. An early warning system with the aid of satellite remote sensing and local sensor networks, which provides timely and quantitative knowledge to monitor the quality of water, may be a soluition to this challenge. The development of such an early warning system is addressed to discover and evaluate the severity in a discrete event mode in this paper. The early warning system in the current study is able to empower the urban water ifrastructure systems with the integration of advanced data science, environmental monitoring, computational intelligence, and satellite remote sensing data. By developing a graphical user interface, end-users who do not have knowledge or skill in the field of integrated sensing, monitoring, networking, modeling can take advantage of the user-friendly early warning system. Practical implementation of the proposed early warning system was assessed at the largest resrvoir, Lake Mead, in Las Vegas in the United States. It uniquely demonstrates how such a system can benefit the drinking water treatment plant throughout decision support actions via multi-sensor acquisition, data fusion, criteria mining and alarm trigerring.
---
paper_title: Towards smart traffic management systems: Vacant on-street parking spot detection based on video analytics

paper_content:

Smart Cities rely on the use of ICTs for a more efficient and intelligent use of resources, whilst improving citizens' quality of life and reducing the environmental footprint. As far as the livability of cities is concerned, traffic is one of the most frequent and complex factors directly affecting citizens. Particularly, drivers in search of a vacant parking spot are a non-negligible source of atmospheric and acoustic pollution. Although some cities have installed sensor-based vacant parking spot detectors in some neighbourhoods, the cost of this approach makes it unfeasible at large scale. As an approach to implement a sustainable solution to the vacant parking spot detection problem in urban environments, this work advocates fusing the information from small-scale sensor-based detectors with that obtained from exploiting the widely-deployed video surveillance camera networks. In particular, this paper focuses on how video analytics can be exploited as a prior step towards Smart City solutions based on data fusion. Through a set of experiments carefully planned to replicate a real-world scenario, the vacant parking spot detection success rate of the proposed system is evaluated through a critical comparison of local and global visual features (either alone or fused at feature level) and different classifier systems applied to the task. Furthermore, the system is tested under setup scenarios of different complexities, and experimental results show that while local features are best when training with small amounts of highly accurate on-site data, they are outperformed by their global counterparts when training with more samples from an external vehicle database.
---
paper_title: A distributed quaternion Kalman filter with applications to fly-by-wire systems

paper_content:

The introduction of automated flight control and management systems have made possible aircraft designs that sacrifice arodynamic stability in order to incorporate stealth technology intro their shape, operate more efficiently, and are highly maneuverable. Therefore, modern flight management systems are reliant on multiple redundant sensors to monitor and control the rotations of the aircraft. To this end, a novel distributed quaternion Kalman filtering algorithm is developed for tracking the rotation and orientation of an aircraft in the three-dimensional space. The algorithm is developed to distribute computation among the sensors in a manner that forces them to consent to a unique solution while being robust to sensor and link failure, a desirable characteristic for flight management systems. In addition, the underlying quaternion-valued state space model allows to avoid problems associated with gimbal lock. The performance of the developed algorithm is verified through simulations.
---
paper_title: Greener and Smarter Phones for Future Cities: Characterizing the Impact of GPS Signal Strength on Power Consumption

paper_content:

Smart cities appear as the next stage of urbanization aiming to not only exploit physical and digital infrastructure for urban development but also the intellectual and social capital as its core ingredient for urbanization. Smart cities harness the power of data from sensors in order to understand and manage city systems. The most important of these sensing devices are smartphones as they provide the most important means to connect the smart city systems with its citizens, allowing personalization n and cocreation. The battery lifetime of smartphones is one of the most important parameters in achieving good user experience for the device. Therefore, the management and the optimization of handheld device applications in relation to their power consumption are an important area of research. This paper investigates the relationship between the energy consumption of a localization application and the strength of the global positioning system (GPS) signal. This is an important focus, because location-based applications are among the top power-hungry applications. We conduct experiments on two android location-based applications, one developed by us, and the other one, off the shelf. We use the results from the measurements of the two applications to derive a mathematical model that describes the power consumption in smartphones in terms of SNR and the time to first fix. The results from this study show that higher SNR values of GPS signals do consume less energy, while low GPS signals causing faster battery drain (38% as compared with 13%). To the best of our knowledge, this is the first study that provides a quantitative understanding of how the poor strength (SNR) of satellite signals will cause relatively higher power drain from a smartphone’s battery.
---
paper_title: Exploring future cityscapes through urban logistics prototyping: a technical viewpoint

paper_content:

Purpose – The purpose of this technical viewpoint is to provide a commentary of how we went about using logistics prototyping as a method to engage citizens, science fiction (SF) writers and small- to medium- sized enterprises (SME’s). Six urban logistic prototypes built on the themes of future cities, community resilience and urban supply chain management (SCM) are summarized, together with details of the data collection procedure and the methodological challenges encountered. Our investigation aimed to explore the potential of logistics prototyping to develop “user-driven” and “SME” approaches to future city design and urban supply chain decision-making. Design/methodology/approach – This Boston field experiment was a case study investigation conducted between May and August 2013. Qualitative data was collected using a “mixed-method” approach combining together focus groups (MIT faculty), scenarios, prototyping workshops, interviews and document analysis. These story-creators could use the prototype met...
---
paper_title: A smart disaster management system for future cities

paper_content:

Smart cities have recently become the mainstream approaches for urbanisation. Environmental, social and economic sustainability, digital inclusion and high quality of life are considered important elements in smart cities design. Emergency response system and resilience are among the most crucial dimensions of smart and future cities design due to the increase in various disruptions caused by frequent manmade and natural disasters such as September 2001 and Philippines Typhoon Haiyan 2013. Disasters cause great economic and human losses each year throughout the world. Transportations and Telecommunications play a crucial role in disaster response and management. Our research is focused on developing emergency response systems for disasters of various scales with a focus on transportation systems. We have proposed and evaluated a disaster management system that uses Intelligent Transportation Systems including Vehicular Ad hoc Networks (VANETs), mobile and Cloud Computing technologies. In this paper, we report our recent work on two major evacuation strategies, Demand Strategies (DS) and Speed Strategies (SS), which provide better evacuation results in smart cities settings.
---
paper_title: Understanding Customer Experience Diffusion on Social Networking Services by Big Data Analytics

paper_content:

Social networking services (e.g., Facebook and Twitter) are playing a significant role of interacting with customers. In particular, most of businesses are trying to exploit such social networking services for more profit, since it has dramatically become an information carrier for customers who are disseminating latest information about products and services. Thus, this study examines how information shared by companies is distributed and what the important factors in understanding information dissemination are. More importantly, this study classifies the types of tweets posted by a company and then to investigate the effect of these types of tweets on diffusion. By using content analysis, this study defined three types, which are i) information provision (IF), ii) advertisement (AD), and iii) both (IFAD), with 8 specific concepts. These results indicate that the differences are significant for all three types of information content. It shows that companies can spread information more quickly by providing the IFAD type rather than the AD type.
---
paper_title: From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

paper_content:

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user's daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).
---
paper_title: Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications

paper_content:

Mobile devices are increasingly becoming an indispensable part of people’s daily life, facilitating to perform a variety of useful tasks. Mobile cloud computing integrates mobile and cloud computing to expand their capabilities and benefits and overcomes their limitations, such as limited memory, CPU power, and battery life. Big data analytics technologies enable extracting value from data having four Vs: volume, variety, velocity, and veracity. This paper discusses networked healthcare and the role of mobile cloud computing and big data analytics in its enablement. The motivation and development of networked healthcare applications and systems is presented along with the adoption of cloud computing in healthcare. A cloudlet-based mobile cloud-computing infrastructure to be used for healthcare big data applications is described. The techniques, tools, and applications of big data analytics are reviewed. Conclusions are drawn concerning the design of networked healthcare systems using big data and mobile cloud-computing technologies. An outlook on networked healthcare is given.
---
paper_title: Algorithms for Smartphone and Tablet Image Analysis for Healthcare Applications

paper_content:

Smartphones and tablets are finding their way into healthcare delivery to the extent that mobile health (mHealth) has become an identifiable field within eHealth. In prior work, a mobile app to document chronic wounds and wound care, specifically pressure ulcers (bedsores) was developed for Android smartphones and tablets. One feature of the mobile app allowed users to take images of the wound using the smartphone or tablet's integrated camera. In a user trial with nurses at a personal care home, this feature emerged as a key benefit of the mobile app. This paper developed image analysis algorithms that facilitate noncontact measurements of irregularly shaped images (e.g., wounds), where the image is taken with a sole smartphone or tablet camera. The image analysis relies on the sensors integrated in the smartphone or tablet with no auxiliary or add-on instrumentation on the device. Three approaches to image analysis were developed and evaluated: 1) computing depth using autofocus data; 2) a custom sensor fusion of inertial sensors and feature tracking in a video stream; and 3) a custom pinch/zoom approach. The pinch/zoom approach demonstrated the strongest potential and thus developed into a fully functional prototype complete with a measurement mechanism. While image analysis is a very well developed field, this paper contributes to image analysis applications and implementation in mHealth, specifically for wound care.
---
paper_title: Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis

paper_content:

This paper presents a PhD project related to the identification of a set of Activities of Daily Living (ADLs) using different techniques applied to the sensors available in off-the-shelf mobile devices. This project consists on the creation of new methodologies, to identify ADLs, and to present some concepts, such as definition of the set of ADLs relevant to be identified, the mobile device as a multi-sensor system, review of the best techniques for data acquisition, data processing, data validation, data imputation, and data fusion processes, and creation of the methods for the identification of ADLs with data mining, pattern recognition and/or machine learning techniques. However, mobile devices present several limitations, therefore techniques at each stage have to be adapted. As result of this study, we presented a brief review of the state-of-the-art related to the several parts of a mobile-system for the identification of the ADLs. Currently, the main focus consists on the study for the creation of a new method based on the analysis of audio fingerprinting samples in some Ambient Assisted Living (AAL) scenarios.
---
paper_title: Open traffic data platform for scenario-based control

paper_content:

Integrating formally separated control systems of a city by means of Information and Communication Technology (ICT) is well known under the term “Smart City”. The benefit is a more energy and cost efficient city. In the domain of road traffic an open traffic data platform shall be the base for smart road traffic infrastructure and services. The idea is to pool available data from road side sensors as well as aggregated data from sensor fusion units at a single place. Due to a highly scalable technology the platform can provide data to any system. The results are that fewer sensors are required for more accurate and efficient services.
---

Title: Data Fusion and IoT for Smart Ubiquitous Environments: A Survey

Section 1: INTRODUCTION
Description 1: Introduction to the Internet of Things (IoT) and the significance of data fusion in enhancing IoT's capabilities for smart ubiquitous environments.

Section 2: CONTRIBUTION OF THIS WORK
Description 2: Description of the contributions made by this survey compared to previous surveys and the uniqueness of the focus on mathematical methods and specific IoT environments.

Section 3: PAPER STRUCTURE
Description 3: Overview of the paper's structure and a guide to the sections discussing data fusion opportunities, challenges, mathematical methods, specific IoT environments, and emerging trends.

Section 4: DATA FUSION OPPORTUNITIES AND CHALLENGES
Description 4: Examination of the opportunities that data fusion presents in improving the precision and reliability of information in IoT and the challenges involved in achieving effective data fusion.

Section 5: MATHEMATICAL METHODS FOR DATA FUSION
Description 5: Review of the literature on various mathematical methods used in data fusion, including probabilistic methods, artificial intelligence (AI) approaches, and theory of belief.

Section 6: PROBABILISTIC DATA FUSION ALGORITHMS
Description 6: Detailed analysis of probabilistic data fusion algorithms, their applications, benefits, and limitations.

Section 7: ARTIFICIAL INTELLIGENCE BASED DATA FUSION ALGORITHMS
Description 7: Overview and applications of AI-based data fusion algorithms, including supervised machine learning, Artificial Neural Networks (ANNs), and fuzzy logic.

Section 8: THEORY OF EVIDENCE BASED DATA FUSION ALGORITHMS
Description 8: Discussion of data fusion algorithms based on the theory of evidence, particularly the Dempster-Shafer Theory (DST), and their applications and challenges.

Section 9: DATA FUSION METHODS FOR IoT-SPECIFIC ENVIRONMENTS
Description 9: Review of data fusion challenges in specific IoT environments, including distributed, heterogeneous, nonlinear, and tracking environments.

Section 10: DISTRIBUTED ENVIRONMENT
Description 10: Analysis of data fusion algorithms in distributed environments, focusing on practical implementation challenges and solutions in IoT.

Section 11: HETEROGENEOUS ENVIRONMENT
Description 11: Examination of data fusion in heterogeneous IoT environments and strategies to manage diverse data sets and devices.

Section 12: NONLINEAR ENVIRONMENT
Description 12: Exploration of the challenges and solutions for data fusion in nonlinear environments within IoT.

Section 13: OBJECT TRACKING
Description 13: Focus on multi-target tracking and the challenges of data fusion for tracking applications in IoT environments.

Section 14: EMERGING DATA FUSION TRENDS
Description 14: Discussion of the latest trends in data fusion, including autonomous and connected vehicles, deep learning for data fusion, and applications in smart cities and societies.

Section 15: CONCLUSION
Description 15: Summarization of the paper’s findings, emphasizing the importance of data fusion for enabling efficient, reliable, and accurate decision-making in IoT environments.