title: Digital Image Security: Fusion of Encryption, Steganography and Watermarking

abstract: Digital images are widely communicated over the internet. The security of digital images is an essential and challenging task on shared communication channel. Various techniques are used to secure the digital image, such as encryption, steganography and watermarking. These are the methods for the security of digital images to achieve security goals, i.e. confidentiality, integrity and availability (CIA). Individually, these procedures are not quite sufficient for the security of digital images. This paper presents a blended security technique using encryption, steganography and watermarking. It comprises of three key components: (1) the original image has been encrypted using large secret key by rotating pixel bits to right through XOR operation, (2) for steganography, encrypted image has been altered by least significant bits (LSBs) of the cover image and obtained stego image, then (3) stego image has been watermarked in the time domain and frequency domain to ensure the ownership. The proposed approach is efficient, simpler and secured; it provides significant security against threats and attacks.


title: Metamaterial-Based Thin Planar Lens Antenna for Spatial Beamforming and Multibeam Massive MIMO

abstract: A metamaterial (MTM)-based thin planar lens antenna is proposed for spatial beamforming and multibeam massive multiple-input multiple-output systems. The antenna consists of a planar lens and a linear array of receive/transmit elements. To lower the insertion and reflection loss, the lens is formed by the two-layered ultrathin MTM-based surface separated with air and fed by substrate integrated waveguide-fed stacked-patch antennas. The effects of the focal-to-diameter ( f/D) on the power distribution of the lens are investigated to work out a design method. A planar lens antenna fed with seven elements is, for example, designed to operate at 28-GHz bands. The measured results show that the proposed antenna can achieve a scanning coverage of ±27° with a gain tolerance of 3.7 dB and a maximum gain of 24.2 dBi with an aperture efficiency of 24.5% over the operating bandwidth of 26.6-29 GHz. The lens antenna also features the advantages of compact size, low cost, lightweight, simple feeding network, and easy integration with other circuits for the next generation mobile communication and radar systems.


title: Affect analysis model: Novel rule-based approach to affect sensing from text

abstract: In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses) and complex–compound sentences. Affect in text is classified into nine emotion categories (or neutral). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).


title: Personalization and Customization Technologies

abstract: This chapter surveys personalization and customization technologies. It starts with an analysis of user profiling and explains how user profiles can be used to generate personalized products and services. It explains some standard personalization techniques such as rule-based filtering, collaborative filtering, and content-based filtering. Web usage mining is also explained and it is shown how data mining can be used to learn customer behavior and to provide personalized content. Several applications such as adaptive Web sites, recommender systems, adaptive Web stores, and intelligent agents are discussed. The chapter concludes by summarizing current trends. ::: ::: ::: Keywords: ::: ::: collaborative filtering; ::: content-based filtering; ::: data mining in e-commerce; ::: personalized products and services; ::: rule-based filtering; ::: user profiling; ::: web usage mining


title: Double-strand breaks in DNA caused by repair of damage due to ultraviolet light

abstract: DNA DSBs are formed in normal human IMR-90 cells during repair incubation after 100 and 300 J.m-2 of UVL. By contrast, no DSBs are formed after UVL in human XPA cells that are unable to excise pyrimidine dimers. The DSBs are not due to immediate cell death since all the cells excluded trypan blue at the time of assay and because XPA cells, which are much more UVL-sensitive than IMR-90, did not form DSBs after UVL. We suggest that these repair-induced DSBs should be potent lesions that might lead to cytotoxicity, chromosome aberrations, deletion mutations, and perhaps cellular transformation.


title: Adaptive Sliding Mode Control for Interval Type-2 Fuzzy Systems

abstract: This paper is concerned with the adaptive sliding mode control problem of uncertain nonlinear systems. Interval type-2 Takagi–Sugeno (T–S) fuzzy model is employed to represent uncertain nonlinear systems. The input matrices of the nonlinear systems are allowed to be different for the sliding mode controller design. The uncertain parameters are described by the lower and upper membership functions. An integral sliding mode surface is designed for analysis of sliding motion. Based on the sliding mode surface, a novel sliding mode controller is designed to guarantee that the closed-loop system is uniformly ultimately bounded. Some simulation results are given to illustrate the effectiveness of the presented control scheme.


title: Bidirectionally aligned sparse representation for single image super-resolution

abstract: It has been demonstrated that the sparse representation based framework is one of the most popular and promising ways to handle the single image super-resolution (SISR) issue. However, due to the complexity of image degradation and inevitable existence of noise, the coding coefficients produced by imposing sparse prior only are not precise enough for faithful reconstructions. In order to overcome it, we present an improved SISR reconstruction method based on the proposed bidirectionally aligned sparse representation (BASR) model. In our model, the bidirectional similarities are first modeled and constructed to form a complementary pair of regularization terms. The raw sparse coefficients are additionally aligned to this pair of standards to restrain sparse coding noise and therefore result in better recoveries. On the basis of fast iterative shrinkage-thresholding algorithm, a well-designed mathematic implementation is introduced for solving the proposed BASR model efficiently. Thorough experimental results indicate that the proposed method performs effectively and efficiently, and outperforms many recently published baselines in terms of both objective evaluation and visual fidelity.


title: Energy Management Strategy for a Parallel Hybrid Electric Truck

abstract: Hybrid vehicle techniques have been widely studied recently because of their potential to significantly improve the fuel economy and drivability of future ground vehicles. Due to the dual-power-source nature of these vehicles, control strategies based on engineering intuition frequently fail to fully explore the potential of these advanced vehicles. In this paper, we present a procedure for the design of a near-optimal power management strategy. The design procedure starts by defining a cost function, such as minimizing a combination of fuel consumption and selected emission species over a driving cycle. Dynamic programming (DP) is then utilized to find the optimal control actions including the gear-shifting sequence and the power split between the engine and motor while subject to a battery SOC-sustaining constraint. Through analysis of the behavior of DP control actions, near-optimal rules are extracted, which, unlike DP control signals, are implementable. The performance of this power management control strategy is studied by using the hybrid vehicle model HE-VESIM developed at the Automotive Research Center of the University of Michigan. A tradeoff study between fuel economy and emissions was performed. It was found that significant emission reduction could be achieved at the expense of a small increase in fuel consumption.


title: CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems

abstract: To overcome data sparsity problem, we propose a cross domain recommendation system named CCCFNet which can combine collaborative filtering and content-based filtering in a unified framework. We first introduce a factorization framework to tie CF and content-based filtering together. Then we find that the MAP estimation of this framework can be embedded into a multi-view neural network. Through this neural network embedding the framework can be further extended by advanced deep learning techniques.


title: GLEnc Algorithm to Secure Data in Public Cloud Environment

abstract: From Electronic Numerical Integrator And Computer (ENIAC) through desktops to mobile devices, technology has evolved throughout the years. Data storage has become transparent after the commencement of the internet. The emergence of cloud computing allows data stored at different locations with different servers using internet and central remote servers. The advantage of cloud computing has transformed the method distributed computing used in the past. Data security is totally maintained by means of encryption. Encryption and decryption are the key elements where hidden data is transformed into an undecipherable code, while decryption is the exact opposite resulting in the original data. The proposed algorithm is performed prior to the arrival of data in the cloud storage. Here the real plain text undergoes different types of splitting and dividing techniques. Three keys are employed at different stages to get highly encrypted data. This paper presents to protect data in the Public Cloud Environment.


title: A Multi-part Convolutional Attention Network for Fine-Grained Image Recognition

abstract: The goal of fine-grained image recognition is to recognize hundreds of sub-categories affiliating to the same basic-level category (e.g., bird species). It is a highly challenging task due to the large intra-class variance and small inter-class variance. Existing approaches deal with the subtle difference among object classes via learning and localizing discriminative parts. However, most of the part localization methods follow a step-to-step manner that first localizes larger parts and then generates smaller parts from the larger ones, which is not efficient. In this paper, we present a Multi-part Convolutional Attention Network (M-CAN), which simultaneously focuses on the discriminative image parts at multiple scales. In specific, a convolutional attention based part localization network is presented to localize multi-scale parts from different layers of the deep Convolutional Neural Networks (CNN). Importantly, our part localization network requires no part annotations but only the image labels, which avoids the heavy labor of complex part labeling. We conduct comprehensive experiments and the experimental results show that, our method outperforms the state-of-the-art approaches on three challenging fine-grained datasets, including CUB-Birds, Stanford-Dogs and Stanford-Cars.


title: The N400 elicited by homonyms in puns: Two primes are not better than one

abstract: To comprehend a pun involving a homonym (e.g., The prince with a bad tooth got a crown), both meanings of the homonym must be accessed and selected. Previous ERP studies have shown that the N400 reflects lexicosemantic processing, but none have directly investigated the N400 elicited by homonyms in the unique context of puns. Here, N400 priming effects showed that the dual context of puns (e.g., the primes prince and tooth) did not facilitate homonym processing in comparison to single dominant biasing (e.g., The prince with a bad leg got a crown) or subordinate biasing (e.g., The adult with a bad tooth got a crown) conditions. However, homonyms did elicit a less negative N400 (i.e., priming) in the pun condition in comparison to the neutral context condition (e.g., The adult with a bad leg got a crown). These findings are interpreted in terms of the dominant advantage and subordinate bias effect posited by the reordered access model of homonym processing, and in terms of N400 amplitude as an index of how consistently various sources of semantic featural information converge on one lexical item, even when two lexical items must be activated for comprehension.


title: A Low-Power, Process-and- Temperature- Compensated Ring Oscillator With Addition-Based Current Source

abstract: The design of a 1.8 GHz 3-stage current-starved ring oscillator with a process- and temperature- compensated current source is presented. Without post-fabrication calibration or off-chip components, the proposed low variation circuit is able to achieve a 65.1% reduction in the normalized standard deviation of its center frequency at room temperature and 85 ppm/ ° C temperature stability with no penalty in the oscillation frequency, the phase noise or the start-up time. Analysis on the impact of transistor scaling indicates that the same circuit topology can be applied to improve variability as feature size scales beyond the current deep submicron technology. Measurements taken on 167 test chips from two different lots fabricated in a standard 90 nm CMOS process show a 3x improvement in frequency variation compared to the baseline case of a conventional current-starved ring oscillator. The power and area for the proposed circuitry is 87 μW and 0.013 mm2 compared to 54 μ W and 0.01 mm 2 in the baseline case.


title: Split-beam mode four-arm slot sinuous antenna

abstract: A coplanar waveguide (CPW) center-fed four-arm slot sinuous antenna is introduced in this letter. The antenna demonstrates broadband characteristics in terms of its split-beam radiation pattern and angle of maximum gain as well as multiband characteristics in terms of axial ratio (AR), omnidirectionality, polarization, and return loss. It is observed experimentally and computationally that regions of low AR with alternating polarization handedness, good omnidirectionality, low return loss, and high antenna gain/efficiency appear in narrow frequency bands. Measured and simulated results are presented to discuss the principles behind the antenna operation and venues for future performance optimization.


title: Transfer-Learning-Based Online Mura Defect Classification

abstract: Flat panel displays, such as the thin film transistor liquid crystal display, the organic light-emitting diode, and the polymer light-emitting diode, have been widely applied in many fields in recent decades. To ensure the quality of these displays, defect inspection is crucial. Mura defects, which are phenomena of uneven screen displays, are the most challenging visual defects to detect. This paper presents an online sequential classifier and transfer learning (OSC-TL) method for the online training and classification of Mura defects. OSC-TL is a new method that combines a deep convolutional feature extractor and a sequential extreme learning machine classifier. It makes online sequential training in a production line possible. To demonstrate the performance of the OSC-TL method, several experiments are performed to compare the results of this method with those of other popular classification algorithms. The experimental results show that the computational resources and time consumed by OSC-TL are well below those of other common methods because of the feature transfer and the online sequential classification strategies. Consequently, the OSC-TL method has been implemented in our automated optical inspection equipment to perform online Mura defect classification. It is able to learn and recognize a Mura defect image within 1.5 milliseconds.


title: PebblesDB: Building Key-Value Stores using Fragmented Log-Structured Merge Trees

abstract: Key-value stores such as LevelDB and RocksDB offer excellent write throughput, but suffer high write amplification. The write amplification problem is due to the Log-Structured Merge Trees data structure that underlies these key-value stores. To remedy this problem, this paper presents a novel data structure that is inspired by Skip Lists, termed Fragmented Log-Structured Merge Trees (FLSM). FLSM introduces the notion of guards to organize logs, and avoids rewriting data in the same level. We build PebblesDB, a high-performance key-value store, by modifying HyperLevelDB to use the FLSM data structure. We evaluate PebblesDB using micro-benchmarks and show that for write-intensive workloads, PebblesDB reduces write amplification by 2.4-3x compared to RocksDB, while increasing write throughput by 6.7x. We modify two widely-used NoSQL stores, MongoDB and HyperDex, to use PebblesDB as their underlying storage engine. Evaluating these applications using the YCSB benchmark shows that throughput is increased by 18-105% when using PebblesDB (compared to their default storage engines) while write IO is decreased by 35-55%.


title: Return of the killer fish: accidental choking death on a bluegill (Lepomis macrochirus).

abstract: An unusual case of choking is presented. In this case, a 45-year-old man, while fishing and drinking with friends, attempted to swallow a whole fish. He subsequently asphyxiated as a result of upper airway obstruction.


FastSLAM : An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data Association


title: Denial-of-Service detection in 6LoWPAN based Internet of Things

abstract: Smart objects connected to the Internet, constituting the so called Internet of Things (IoT), are revolutionizing human beings' interaction with the world. As technology reaches everywhere, anyone can misuse it, and it is always essential to secure it. In this work we present a denial-of-service (DoS) detection architecture for 6LoWPAN, the standard protocol designed by IETF as an adaptation layer for low-power lossy networks enabling low-power devices to communicate with the Internet. The proposed architecture integrates an intrusion detection system (IDS) into the network framework developed within the EU FP7 project ebbits. The aim is to detect DoS attacks based on 6LoWPAN. In order to evaluate the performance of the proposed architecture, preliminary implementation was completed and tested against a real DoS attack using a penetration testing system. The paper concludes with the related results proving to be successful in detecting DoS attacks on 6LoWPAN. Further, extending the IDS could lead to detect more complex attacks on 6LoWPAN.


title: Leveraging Multi-modal Prior Knowledge for Large-scale Concept Learning in Noisy Web Data

abstract: Learning video concept detectors automatically from the big but noisy web data with no additional manual annotations is a novel but challenging area in the multimedia and the machine learning community. A considerable amount of videos on the web is associated with rich but noisy contextual information, such as the title and other multi-modal information, which provides weak annotations or labels about the video content. To tackle the problem of large-scale noisy learning, We propose a novel method called Multi-modal WEbly-Labeled Learning (WELL-MM), which is established on the state-of-the-art machine learning algorithm inspired by the learning process of human. WELL-MM introduces a novel multi-modal approach to incorporate meaningful prior knowledge called curriculum from the noisy web videos. We empirically study the curriculum constructed from the multi-modal features of the Internet videos and images. The comprehensive experimental results on FCVID and YFCC100M demonstrate that WELL-MM outperforms state-of-the-art studies by a statically significant margin on learning concepts from noisy web video data. In addition, the results also verify that WELL-MM is robust to the level of noisiness in the video data. Notably, WELL-MM trained on sufficient noisy web labels is able to achieve a better accuracy to supervised learning methods trained on the clean manually labeled data.


title: Empirical evaluation of a technology acceptance model for mobile policing

abstract: Technology acceptance in policing is under-researched, yet mobile devices are widely implemented across UK police forces. The paper validates a mobile technology acceptance model (M-TAM) developed in a single police force. It shows that the M-TAM is transferrable to other UK police forces, and potentially worldwide. The influence of local supervision and fit of technology to roles and tasks are shown to be the most influential factors. Factors beyond the technology itself, such as the influence of peers and involvement of operational officers in technology investment decisions, must be considered to accommodate the strong cultural barriers in policing.


title: Research Paper No. 2006/66 Widening Gap of Educational Opportunity? A Longitudinal Study of Educational Inequality

abstract: This study attempts to convey an accurate and dynamic account of educational inequality in China during the last decade. The study finds that there is clear evidence of rapid expansion of education, and younger students all over China are benefiting from


title: Comparison between Genetic Algorithms and Particle Swarm Optimization

abstract: This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from one paradigm into the other.


title: Rocky 7: a next generation Mars rover prototype

abstract: This paper provides a system overview of a new Mars rover prototype, Rocky 7. We describe all system aspects: mechanical and electrical design, computer and software infrastructure, algorithms for navigation and manipulation, science data acquisition, and outdoor rover testing. In each area, the improved or added functionality is explained in a context of its path to flight and within the constraints of desired science missions.


title: The role of budgets in organizations facing strategic change: an exploratory study

abstract: Abstract Recent attention in the general management literature has focused on mechanisms and processes used by organizations to respond and adapt to changes in their operating environment. There is, however, very little broad-based empirical research examining the role that management accounting control systems can play in shaping organizational change. Much of the empirical research to date has focused on the role of accounting as a diagnostic tool for assessing and rewarding managerial performance despite the recognition that accounting can serve as a dialogue, learning and idea creation machine ( Burchell et al., 1980 . Accounting Organisations and Society 5,5–27). The purpose of this study is to explore how accounting can serve this alternative role. We use (Simons, R. 1990.) Accounting Organisations and Society 15, 127–143). interactive/diagnostic classification of management control systems to capture how accounting can be used as a learning machine in the formulation and implementation of strategic change. A theoretical model is developed to examine the relationship between strategic change, style of budget use and performance. It is argued that an interactive style of budget use can mitigate the disruptive performance effects of the strategic change process. The data, collected from Chief Executive Officers in 63 public hospitals, provide results that are consistent with our expectations.


title: Low-cost single-fed circularly polarized stacked patch antenna for UHF RFID reader applications

abstract: A single-fed circularly polarized (CP) Radio Frequency Identification (RFID) reader antenna with a simple structure has been proposed for worldwide Ultra-High Frequency (UHF) band applications. The purpose of the antenna was to provide a low-cost and easy-fabricated solution with wide bandwidth and high performance. The proposed antenna is composed of a corner-truncated square driven patch with a stacked square parasitic patch and a microstrip match stub suspended above the ground plane. Good impedance matching and good CP radiation characteristics have been obtained. Experimental results of a fabricated prototype with the center operating frequency at 900MHz showed that the antenna has the desirable performance with a gain level of about 9 dBi, VSWR of lower than 1.5 and axial ratio of less than 3 dB within the frequency range of 840 ∼ 960 MHz.


title: Social media in the workplace: Key drivers for inclusive innovation

abstract: The recent decade has witnessed a mass proliferation of information systems enabled, community-based, social networking. Such proliferation has contributed to seismic social and political movements around the globe, but is yet to make a noticeable imprint in business organisations. While many researchers and practitioners have advocated the transition of social media to the organisational sphere, the actuality of this transition is still deficient, necessitating thorough investigation. Consequently, this study addresses this pressing issue by first, presenting a vantage point on the theoretical and practical underpinnings of social media and the revolutionising role they stand to play in organisations. An empirical case study is then presented highlighting the actual diffusion and utilisation of social media in a regional branch of a global consultancy and audit firm. The findings hold important implications as they identify key drivers contributing to the successful diffusion of social media in organisations, and their corresponding utilisation for enabling an inclusive and innovative environment in the workplace.


title: Linear time algorithm for computing a small biclique in graphs without long induced paths

abstract: The biclique problem asks, given a graph G and a parameter k, whether G has a complete bipartite subgraph of k vertices in each part (a biclique of order k). Fixed-parameter tractability of this problem is a longstanding open question in parameterized complexity that received a lot of attention from the community. In this paper we consider a restricted version of this problem by introducing an additional parameter s and assuming that G does not have induced (i.e. chordless) paths of length s. We prove that under this parameterization the problem becomes fixed-parameter linear. The main tool in our proof is a Ramsey-type theorem stating that a graph with a long (not necessarily induced) path contains either a long induced path or a large biclique.


title: Definition and validation of model transformations

abstract: With model transformations becoming more widely used, there is an increasing need for approaches focussing on a systematic development of model transformations. Although a number of approaches for specifying model transformations exist, none of them focusses on systematically validating model transformations with respect to termination and confluence. Termination and confluence ensure that a model transformation always produces a unique result. Also called functionality, these properties are important requirements for practical applications of model transformations. In this paper, we introduce our approach to model transformation. Using and extending results from the theory of graph transformation, we investigate termination and confluence properties of model transformations specified in our approach. We establish a set of criteria for termination and confluence to be checked at design time by static analysis of the transformation rules and the underlying metamodels. Moreover, the criteria are formulated in such a way that they require less experience with the theory of graph transformation. Our concepts are illustrated by a running example of a model tranformation from statecharts to the process algebra Communicating Sequential Processes.


title: Decentralized Stochastic Control with Partial History Sharing: A Common Information Approach

abstract: A general model of decentralized stochastic control called partial history sharing information structure is presented. In this model, at each step the controllers share part of their observation and control history with each other. This general model subsumes several existing models of information sharing as special cases. Based on the information commonly known to all the controllers, the decentralized problem is reformulated as an equivalent centralized problem from the perspective of a coordinator. The coordinator knows the common information and selects prescriptions that map each controller's local information to its control actions. The optimal control problem at the coordinator is shown to be a partially observable Markov decision process (POMDP) which is solved using techniques from Markov decision theory. This approach provides 1) structural results for optimal strategies and 2) a dynamic program for obtaining optimal strategies for all controllers in the original decentralized problem. Thus, this approach unifies the various ad-hoc approaches taken in the literature. In addition, the structural results on optimal control strategies obtained by the proposed approach cannot be obtained by the existing generic approach (the person-by-person approach) for obtaining structural results in decentralized problems; and the dynamic program obtained by the proposed approach is simpler than that obtained by the existing generic approach (the designer's approach) for obtaining dynamic programs in decentralized problems.


title: Visual Object Networks: Image Generation with Disentangled 3D Representation

abstract: Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an understanding of our underlying 3D world. We present a new generative model, Visual Object Networks (VON), synthesizing natural images of objects with a disentangled 3D representation. Inspired by classic graphics rendering pipelines, we unravel our image formation process into three conditionally independent factors---shape, viewpoint, and texture---and present an end-to-end adversarial learning framework that jointly models 3D shapes and 2D images. Our model first learns to synthesize 3D shapes that are indistinguishable from real shapes. It then renders the object's 2.5D sketches (i.e., silhouette and depth map) from its shape under a sampled viewpoint. Finally, it learns to add realistic texture to these 2.5D sketches to generate natural images. The VON not only generates images that are more realistic than state-of-the-art 2D image synthesis methods, but also enables many 3D operations such as changing the viewpoint of a generated image, editing of shape and texture, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.


title: Corona Discharge Surface Treater Without High Voltage Transformer

abstract: A corona discharge generator for surface treatment without the use of a step-up transformer with a high-voltage secondary is presented. The oil bath for high-voltage components is eliminated and still a reasonable volume, efficiency, and reliability of the generator are obtained. The voltage multiplication is achieved by an LC series resonant circuit. The resonant circuit is driven by a bridge type voltage-source resonant inverter. First, feasibility of the proposed method is proved by calculations. Closed form design expressions for key components of the electronic generator are provided. Second, a prototype of the electronic generator is built and efficiency measurements are performed. For power measurement, Lissajous figures and direct averaging of the instantaneous voltage-current product are used. The overall efficiency achieved is in the range between 80% and 90%.


title: Dynamic frequency scaling on android platforms for energy consumption reduction

abstract: Dynamic Voltage and Frequency Scaling (DVFS) is an efficient energy saving technique for processing units. This paper evaluates the impact of the DVFS on the energy consumption, when it is applied to adjust the operational frequency of an Android based smartphone during common mobile activities, such as 3G and Wi-Fi communications. An experimental infrastructure was defined to carry out energy measurements, using a simulated environment and a Samsung Galaxy SII smartphone. After defining the best operational frequency for each activity, we identified the amount of energy that could be saved if the smartphone was using an optimized strategy to adjust the frequency. Results of the proposed approach were compared to the performance mode of this smartphone and an average energy reduction of about 23.4% was obtained.


title: Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms

abstract: This paper examine the Euler-Lagrange equations for the solution of the large deformation diffeomorphic metric mapping problem studied in Dupuis et al. (1998) and Trouve (1995) in which two images I 0, I 1 are given and connected via the diffeomorphic change of coordinates I 0???1=I 1 where ?=?1 is the end point at t= 1 of curve ? t , t?[0, 1] satisfying .? t =v t (? t ), t? [0,1] with ?0=id. The variational problem takes the form ::: ::: $$\mathop {\arg {\text{m}}in}\limits_{\upsilon :\dot \phi _t = \upsilon _t \left( {\dot \phi } \right)} \left( {\int_0^1 {\left\| {\upsilon _t } \right\|} ^2 {\text{d}}t + \left\| {I_0 \circ \phi _1^{ - 1} - I_1 } \right\|_{L^2 }^2 } \right),$$ ::: ::: where ?v t? V is an appropriate Sobolev norm on the velocity field v t(·), and the second term enforces matching of the images with ?·?L 2 representing the squared-error norm. ::: ::: In this paper we derive the Euler-Lagrange equations characterizing the minimizing vector fields v t, t?[0, 1] assuming sufficient smoothness of the norm to guarantee existence of solutions in the space of diffeomorphisms. We describe the implementation of the Euler equations using semi-lagrangian method of computing particle flows and show the solutions for various examples. As well, we compute the metric distance on several anatomical configurations as measured by ?0 1?v t? V dt on the geodesic shortest paths.


title: Trust management system design for the Internet of Things: A context-aware and multi-service approach

abstract: This work proposes a new trust management system (TMS) for the Internet of Things (IoT). The wide majority of these systems are today bound to the assessment of trustworthiness with respect to a single function. As such, they cannot use past experiences related to other functions. Even those that support multiple functions hide this heterogeneity by regrouping all past experiences into a single metric. These restrictions are detrimental to the adaptation of TMSs to today's emerging M2M and IoT architectures, which are characterized with heterogeneity in nodes, capabilities and services. To overcome these limitations, we design a context-aware and multi-service trust management system fitting the new requirements of the IoT. Simulation results show the good performance of the proposed system and especially highlight its ability to deter a class of common attacks designed to target trust management systems.


title: Cyber Intrusion Prediction and Taxonomy System Using Deep Learning And Distributed Big Data Processing

abstract: The issue of cybersecurity is becoming more and more serious every day at all levels and in all domains. Cyber-attacks threaten the national security of every country and nation. Furthermore, cyber-attacks can significantly harm the economy. With the rapid and continuous growth of the cyber-universe, more software is being created, more data is being generated, and cybersecurity breaches and defense strategies are getting more complex. For such a problem, considering the size and complexity of the cyber-universe, big data mining techniques and advanced machine learning solutions will be most suitable to use for predicting brand-new attacks. This is because traditional machine learning methods would not help combat today’s cybersecurity issues. Anomaly-based intrusion detection systems are receiving tremendous attention nowadays. This is because of the vast improvement and development in big data solutions. This paper utilizes highly imbalanced real-life benchmark network traffic datasets of multiple types of attacks. After resolving the class imbalance issue in our datasets by applying oversampling approach, our study becomes twofold. First, we are building prediction models for each type of attacks separately and optimizing the model with the highest accuracy. Then, we build a prediction model for all attacks together using deep learning with the smallest number of features and we optimize the model to achieve the highest accuracy. Our developed model can accurately predict the threat and the type of attack.


title: Microstrip antenna array with beamforming network for WLAN applications

abstract: A beamforming network with a multi-narrow-beam antenna array for WLAN applications is presented. The antenna array has four inputs and is excited by the Butler matrix feeding network to steer the beams electronically in the desired directions. The architecture of the Butler matrix beamforming network is analyzed with consideration of the coupling effects between fundamental elements of the network and the antenna array. The prototype is fabricated on a Rogers RT/duroid 5880 substrate with a relative permittivity of 2.2. Details of the simulated and measured results are investigated and analyzed.


title: Signature schemes with bounded leakage resilience

abstract: A leakage-resilient cryptosystem remains secure even if arbitrary, but bounded, information about the secret key (and possibly other internal state information) is leaked to an adversary. Denote the length of the secret key by n . We show: ::: A full-fledged signature scheme tolerating leakage of n *** n *** bits of information about the secret key (for any constant *** > 0), based on general assumptions. ::: A one-time signature scheme, based on the minimal assumption of one-way functions, tolerating leakage of $(\frac{1}{4}-\epsilon) \cdot n$ bits of information about the signer's entire state. ::: A more efficient one-time signature scheme, that can be based on several specific assumptions, tolerating leakage of $(\frac{1}{2}-\epsilon) \cdot n$ bits of information about the signer's entire state. ::: ::: ::: ::: The latter two constructions extend to give leakage-resilient t -time signature schemes. All the above constructions are in the standard model.


title: On Creative Self-Driving Cars: Hire the Computational Logicians, Fast

abstract: There can be no denying that it is entirely possible for a car-manufacturing company like Daimler to build and the deploy self-driving cars without hiring a single computational logician. However, for reasons we explain herein, a logician-less approach to engineering self-driving automobiles and, for that matter, self-moving vehicles of any consequence, in general is a profoundly unwise one. As we shall see, the folly of leaving aside logic has absolutely nothing to do with the standard and stale red-herring concern that self-driving cars will face exotic human-style ethical dilemmas the philosophers have a passionate penchant for.


title: Visitor Satisfaction at the Museum: Italian versus Foreign Visitors

abstract: Trying to understand what comprises and influences visitor satisfaction is one of the most relevant areas of research for the tourism sector. The aim of this study is to analyze which factors influence overall satisfaction with the visit at the museum, with particular interest in origin, feelings, and motivations. The research is based on 1038 questionnaires collected from June to December 2011 among the visitors of the South Tyrol Museum of Archaeology (OIˆTZI) in Bolzano, Northern Italy. Logit models were used in order to estimate the set of independent variables that significantly influence both the overall satisfaction of the sample and the eventually differences between Italian and foreign visitors in the perception of satisfaction. The results suggested that the overall satisfaction was related to the origin of the respondent, their feelings, and motivations. Furthermore, opinion about showrooms and other services received during the visit appears to be positively and significantly related with overall satisfaction. From this knowledge, museum managers can profit in their willingness to offer the optimal museum experience, as well as museum marketers in their advertisement campaigns in Italy and abroad.


title: Privatization and subsidization of adoption services from foster care: Empirical evidence

abstract: This paper analyzes empirical differences in adoption services of public and private agencies. The empirical investigation includes cross-sectional time series aggregated data for the 50 states within the United States from 1996 to 2010 with detailed statistical analysis of the period from 2000 through 2010 for which consistent and comprehensive data exists. Under private agencies, only 11.6months elapse from the time the courts terminate the natural parents' custody until the child is adopted, while with public agencies the same process lasts for 16months. Furthermore, during the decade from 1996 to 2006 private agencies completed more adoptions than public agencies. However, the performance gap in favor of private agencies was eliminated in 2006 and in the following years. The results suggest that privatization of adoption of young and healthy children did not show an advantage for private services. However, transitioning adoption services to private agencies for older children or children with complex special needs, improves the adoption services compared with those of public agencies. Subsidization especially improves the adoption of older children and of all children with special needs while it appears to be statistically insignificant or implied as unnecessary for healthy babies.


title: Feedback Convolutional Neural Network for Visual Localization and Segmentation

abstract: Feedback is a fundamental mechanism existing in the human visual system, but has not been explored deeply in designing computer vision algorithms. In this paper, we claim that feedback plays a critical role in understanding convolutional neural networks (CNNs), e.g., how a neuron in CNNs describes an object's pattern, and how a collection of neurons form comprehensive perception to an object. To model the feedback in CNNs, we propose a novel model named Feedback CNN and develop two new processing algorithms, i.e., neural pathway pruning and pattern recovering. We mathematically prove that the proposed method can reach local optimum. Note that Feedback CNN belongs to weakly supervised methods and can be trained only using category-level labels. But it possesses a powerful capability to accurately localize and segment category-specific objects. We conduct extensive visualization analysis, and the results reveal the close relationship between neurons and object parts in Feedback CNN. Finally, we evaluate the proposed Feedback CNN over the tasks of weakly supervised object localization and segmentation, and the experimental results on ImageNet and Pascal VOC show that our method remarkably outperforms the state-of-the-art ones.


title: Deep Learning in the Wild

abstract: Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research \&development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice.


title: Anatomical Location of the Mesencephalic Locomotor Region and Its Possible Role in Locomotion, Posture, Cataplexy, and Parkinsonism

abstract: The mesencephalic (or midbrain) locomotor region (MLR) was first described in 1966 by Shik and colleagues who demonstrated that electrical stimulation of this region induced locomotion in decerebrate (intercollicular transection) cats. The pedunculopontine tegmental nucleus (PPT) cholinergic neurons and midbrain extrapyramidal area (MEA) have been suggested to form the neuroanatomical basis for the MLR, but direct evidence for the role of these structures in locomotor behavior has been lacking. Here we tested the hypothesis that the MLR is composed of non-cholinergic spinally-projecting cells in the lateral pontine tegmentum (LPT). Our results showed that putative MLR neurons medial to the PPT and MEA in rats were non-cholinergic, glutamatergic and express the orexin (hypocretin) type 2 receptors. Fos mapping correlated with motor behaviors revealed that the dorsal and ventral MLR are activated, respectively, in association with locomotion and an erect posture. Consistent with these findings, chemical stimulation of the dorsal MLR produced locomotion whereas stimulation of the ventral MLR caused standing. Lesions of the MLR (dorsal and ventral regions together) resulted in cataplexy and episodic immobility of gait. Finally, trans-neuronal tracing with pseudorabies virus (PRV) demonstrated disynaptic input to the MLR from the substantia nigra via the MEA. These findings offer a new perspective on the neuroanatomic basis of the MLR, and suggest that MLR dysfunction may contribute to the postural and gait abnormalities in Parkinsonism.


title: Sentiment analysis on microblog utilizing appraisal theory

abstract: People and companies selling goods or providing services have always desired to know what people think about their products. The number of opinions on the Web has significantly increased with the emergence of microblogs. In this paper we present a novel method for sentiment analysis of a text that allows the recognition of opinions in microblogs which are connected to a particular target or an entity. This method differs from other approaches in utilizing appraisal theory, which we employ for the analysis of microblog posts. The results of the experiments we performed on Twitter showed that our method improves sentiment classification and is feasible even for such specific content as presented on microblogs.


title: Autism spectrum disorders: developmental disconnection syndromes

abstract: Autism is a common and heterogeneous childhood neurodevelopmental disorder. Analogous to broad syndromes such as mental retardation, autism has many etiologies and should be considered not as a single disorder but, rather, as ‘the autisms’. However, recent genetic findings, coupled with emerging anatomical and functional imaging studies, suggest a potential unifying model in which higher-order association areas of the brain that normally connect to the frontal lobe are partially disconnected during development. This concept of developmental disconnection can accommodate the specific neurobehavioral features that are observed in autism, their emergence during development, and the heterogeneity of autism etiology, behaviors and cognition.


title: Efficiently exploring architectural design spaces via predictive modeling

abstract: Architects use cycle-by-cycle simulation to evaluate design choices and understand tradeoffs and interactions among design parameters. Efficiently exploring exponential-size design spaces with many interacting parameters remains an open problem: the sheer number of experiments renders detailed simulation intractable. We attack this problem via an automated approach that builds accurate, confident predictive design-space models. We simulate sampled points, using the results to teach our models the function describing relationships among design parameters. The models produce highly accurate performance estimates for other points in the space, can be queried to predict performance impacts of architectural changes, and are very fast compared to simulation, enabling efficient discovery of tradeoffs among parameters in different regions. We validate our approach via sensitivity studies on memory hierarchy and CPU design spaces: our models generally predict IPC with only 1-2% error and reduce required simulation by two orders of magnitude. We also show the efficacy of our technique for exploring chip multiprocessor (CMP) design spaces: when trained on a 1% sample drawn from a CMP design space with 250K points and up to 55x performance swings among different system configurations, our models predict performance with only 4-5% error on average. Our approach combines with techniques to reduce time per simulation, achieving net time savings of three-four orders of magnitude.


title: A 100V reconfigurable synchronous gate driver with comparator-based dynamic dead-time control for high-voltage high-frequency DC-DC converters

abstract: This paper presents an integrated reconfigurable high-voltage (HV) synchronous gate driver for driving eGaN FET and Si-MOSFET power switches that have different turn-on-voltage requirements. The proposed gate driver has a comparator-based dynamic dead-time controller to enable zero-voltage-switching operation of both high- and low-side power FETs with minimal body diode conduction loss. Thanks to the proposed HV level shifter, large transient current can be generated under high dV/dt at the converter switching node for reliability enhancement. Implemented in a 0.5µm HV CMOS process, the proposed 100V synchronous gate driver can enable 2MHz and 1MHz ZVS operations for driving commercial eGaN FETs and MOSFETs, respectively. The dynamic dead-time controller enables ZVS operation of power switches for a wider input range from 40V to 100V. The proposed gate driver is capable of handling 100V/ns slew rate at the switching node.


title: A Balanced Scorecard Approach to Measure the Value of Enterprise Architecture

abstract: The value returned by Enterprise Architecture (EA) is being viewed with increasing consideration by researchers and practitioners. They struggle to justify the EA investments made. Quantifying the Enterprise Architecture benefits has always been a challenge because measurements and real value delivered can not often be expressed in simple technical oriented metrics only.In this paper we suggest a multi-perspective framework, based on the concept of Balanced Scorecard (BSC), for providing guidance about where to identify and quantify the value of EA from the perspective of management. A derived methodology is used to localize a few selected Key Performance Indicators (KPI's) for measuring the benefits of Enterprise Architecture that help practitioners to design and implement the suggested framework.


title: How the Cucumber Tendril Coils and Overwinds

abstract: The helical coiling of plant tendrils has fascinated scientists for centuries, yet the underlying mechanism remains elusive. Moreover, despite Darwin’s widely accepted interpretation of coiled tendrils as soft springs, their mechanical behavior remains unknown. Our experiments on cucumber tendrils demonstrate that tendril coiling occurs via asymmetric contraction of an internal fiber ribbon of specialized cells. Under tension, both extracted fiber ribbons and old tendrils exhibit twistless overwinding rather than unwinding, with an initially soft response followed by strong strain-stiffening at large extensions. We explain this behavior using physical models of prestrained rubber strips, geometric arguments, and mathematical models of elastic filaments. Collectively, our study illuminates the origin of tendril coiling, quantifies Darwin’s original proposal, and suggests designs for biomimetic twistless springs with tunable mechanical responses.


Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network


title: Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics

abstract: We propose a novel approach for training deep convolutional neural networks (DCNNs) that allows us to tradeoff complexity and accuracy to learn lightweight models suitable for robotic platforms such as AgBot II (which performs automated weed management). Our approach consists of three stages, the first is to adapt a pre-trained model to the task at hand. This provides state-of-the-art performance but at the cost of high computational complexity resulting in a low frame rate of just 0.12 frames per second (fps). Second, we use the adapted model and employ model compression techniques to learn a lightweight DCNN that is less accurate but has two orders of magnitude fewer parameters. Third, $K$ lightweight models are combined as a mixture model to further enhance the performance of the lightweight models. Applied to the challenging task of weed segmentation, we improve the accuracy from 85.9%, using a traditional approach, to 93.9% by adapting a complicated pre-trained DCNN with 25M parameters (Inception-v3). The downside to this adapted model, Adapted-IV3, is that it can only process 0.12 fps. To make this approach fast while still retaining accuracy, we learn lightweight DCNNs which when combined can achieve accuracy greater than 90% while using considerably fewer parameters capable of processing between 1.07 and 1.83 fps, up to an order of magnitude faster and up to an order of magnitude fewer parameters.


title: An implementation of SLAM with extended Kalman filter

abstract: This paper discusses an implementation of Extended Kalman filter (EKF) in performing Simultaneous Localization and Mapping (SLAM). The implementation is divided into software and hardware phases. The software implementation applies EKF using Python on a library dataset to produce a map of the supposed environment. The result was verified against the original map and found to be relatively accurate with minor inaccuracies. In the hardware implementation stage, real life data was gathered from an indoor environment via a laser range finder and a pair of wheel encoders placed on a mobile robot. The resulting map shows at least five marked inaccuracies but the overall form is passable.


title: FPGA-Based Intrusion Detection System for 10 Gigabit Ethernet

abstract: The present paper describes an implementation of an intrusion detection system (IDS) on an FPGA for 10 Gigabit Ethernet. The system includes an exact string matching circuit for 1,225 Snort rules on a single device. A number of studies have examined string matching circuits for IDS. However, implementing a circuit that processes a large rule set at high throughput is difficult. In a previous study, we proposed a method for generating an NFA-based string matching circuit that has expandability of processing data width and drastically reduced resource requirements. In the present paper, we implement an IDS circuit that processes 1,225 Snort rules at 10 Gbps with a single Xilinx Virtex-II Pro xc2vp-100 using the NFA-based method. The proposed circuit also provides packet filtering for an intrusion protection system (IPS). In addition, we developed a tool for automatically generating the Verilog HDL source code of the IDS circuit from a Snort rule set. Using the FPGA and the IDS circuit generator, the proposed system is able to update the matching rules corresponding to new intrusions and attacks. We implemented the IDS circuit on an FPGA board and evaluated its accuracy and throughput. As a result, we confirmed in a test that the circuit detects attacks perfectly at the wire speed of 10 Gigabit Ethernet.


title: Targeted Cyberattacks: A Superset of Advanced Persistent Threats

abstract: Targeted cyberattacks play an increasingly significant role in disrupting the online social and economic model, not to mention the threat they pose to nation-states. A variety of components and techniques come together to bring about such attacks.


title: A low-cost dual-polarized 28 GHz phased array antenna for 5G communications

abstract: A dual-polarized patch array antenna operating at 28 GHz is designed for 5G communication applications. Based on the conventional PCB process, the antenna is suitable for the phased array module integration, and it can support both horizontal and vertical polarizations. For the single antenna element, it achieves simulated average return loss of less than 10 dB, gain of about 6 dBi from 25 to 30 GHz. In addition, the isolation between the horizontal and vertical polarizations is lower than −20 dB. For the phased array antenna, the simulated peak gain at broadside and 30 degree is 17.6 and 14.6 dBi, respectively. Furthermore, 4 identical 2×2 element subarrays are arranged with special configurations, improving the antenna arrays' cross-polarization suppression and avoiding undesired sidelobes.


title: Comparing Fairness Criteria Based on Social Outcome

abstract: Fairness in algorithmic decision-making processes is attracting increasing concern. When an algorithm is applied to human-related decision-making an estimator solely optimizing its predictive power can learn biases on the existing data, which motivates us the notion of fairness in machine learning. while several different notions are studied in the literature, little studies are done on how these notions affect the individuals. We demonstrate such a comparison between several policies induced by well-known fairness criteria, including the color-blind (CB), the demographic parity (DP), and the equalized odds (EO). We show that the EO is the only criterion among them that removes group-level disparity. Empirical studies on the social welfare and disparity of these policies are conducted.


title: The Shadow Nemesis: Inference Attacks on Efficiently Deployable, Efficiently Searchable Encryption

abstract: Encrypting Internet communications has been the subject of renewed focus in recent years. In order to add end-to-end encryption to legacy applications without losing the convenience of full-text search, ShadowCrypt and Mimesis Aegis use a new cryptographic technique called "efficiently deployable efficiently searchable encryption" (EDESE) that allows a standard full-text search system to perform searches on encrypted data. Compared to other recent techniques for searching on encrypted data, EDESE schemes leak a great deal of statistical information about the encrypted messages and the keywords they contain. Until now, the practical impact of this leakage has been difficult to quantify. In this paper, we show that the adversary's task of matching plaintext keywords to the opaque cryptographic identifiers used in EDESE can be reduced to the well-known combinatorial optimization problem of weighted graph matching (WGM). Using real email and chat data, we show how off-the-shelf WGM solvers can be used to accurately and efficiently recover hundreds of the most common plaintext keywords from a set of EDESE-encrypted messages. We show how to recover the tags from Bloom filters so that the WGM solver can be used with the set of encrypted messages that utilizes a Bloom filter to encode its search tags. We also show that the attack can be mitigated by carefully configuring Bloom filter parameters.


Computational Intelligence Algorithms Applied to the Pre-diagnosis of Chronic Diseases


title: The effects of music intervention on burn patients during treatment procedures: a systematic review and meta-analysis of randomized controlled trials

abstract: The treatment of burn patients is very challenging because burn injuries are one of the most severe traumas that can be experienced. The effect of music therapy on burn patients has been widely reported, but the results have been inconsistent. Thus, we performed a systematic review and meta-analysis of randomized controlled trials in burn patients to determine the effect of music during treatments. We searched a variety of electronic databases, including MEDLINE (via PubMed), EMBASE, Cochrane Library, Psychinfo, VIP Database for Chinese Technical Periodicals (VIP) and China National Knowledge Infrastructure (CNKI) for relevant trials on the basis of predetermined eligibility criteria. from their first available date through February 2016. Our search focused on two key concepts: music interventions (including music, music therapy and music medicine) and physical activity outcomes (including pain, anxiety, burn characteristics, dressing changes, wound care, debridement and rehabilitation). Two reviewers independently screened records and extracted data from all eligible studies. Statistical heterogeneity was determined using Q-test and the I2 statistic. The endpoints included standardized mean differences (SMDs) and 95% confidence intervals (CIs). Publication bias was tested by Begg’s funnel plot and Egger’s test. A total of 17 studies met the inclusion criteria, for a total of 804 patients. A statistically significant difference in pain relief was demonstrated between music and non-music interventions (SMD = −1.26, 95% CI [−1.83, −0.68]), indicating that music intervention has a positive effect on pain alleviation for burn patients. The results indicated that music interventions markedly reduced anxiety in individuals compared to non-music interventions (SMD = −1.22, 95% CI [−1.75, −0.69]). Correspondingly, heart rate decreases were found after treatments that included music interventions (SMD = −0.60, 95% CI [−0.84, −0.36]). In summary, a positive correlation was found between treatments including music interventions and pain alleviation, anxiety relief, and heart rate reduction in burn patients. However, additional high-quality studies with carefully considered music interventions for burn patients are still needed.


title: Secure Attribute-Based Signature Scheme With Multiple Authorities for Blockchain in Electronic Health Records Systems

abstract: Electronic Health Records (EHRs) are entirely controlled by hospitals instead of patients, which complicates seeking medical advices from different hospitals. Patients face a critical need to focus on the details of their own healthcare and restore management of their own medical data. The rapid development of blockchain technology promotes population healthcare, including medical records as well as patient-related data. This technology provides patients with comprehensive, immutable records, and access to EHRs free from service providers and treatment websites. In this paper, to guarantee the validity of EHRs encapsulated in blockchain, we present an attribute-based signature scheme with multiple authorities, in which a patient endorses a message according to the attribute while disclosing no information other than the evidence that he has attested to it. Furthermore, there are multiple authorities without a trusted single or central one to generate and distribute public/private keys of the patient, which avoids the escrow problem and conforms to the mode of distributed data storage in the blockchain. By sharing the secret pseudorandom function seeds among authorities, this protocol resists collusion attack out of $N$ from $N-1$ corrupted authorities. Under the assumption of the computational bilinear Diffie-Hellman, we also formally demonstrate that, in terms of the unforgeability and perfect privacy of the attribute-signer, this attribute-based signature scheme is secure in the random oracle model. The comparison shows the efficiency and properties between the proposed method and methods proposed in other studies.


title: Combat Models for RTS Games

abstract: Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or "simulator") of the game at hand. However, in some games such forward model is not readily available. This paper presents three forward models for two-player attrition games, which we call "combat models", and show how they can be used to simulate combat in RTS games. We also show how these combat models can be learned from replay data. We use StarCraft as our application domain. We report experiments comparing our combat models predicting a combat output and their impact when used for tactical decisions during a real game.


title: New design of SIW leaky wave antenna based on a T-shaped slot with controllable scanning rate from backward to forward direction

abstract: Various travelling-wave antennas such as leaky-wave antennas and waveguide slot antennas achieve frequency scanning inherently. In additional, high antenna gain is achieved due especially to their electrically large structure. This paper presents a detailed study of a substrate integrated waveguide leaky wave antenna radiating in Ku Band with a periodic set of T shaped slot based on TE10 mode. The direction of the main lobe of the antenna radiation pattern can be steered by changing the frequency from backward to forward direction. Modelling and performance evaluation of the resulting antenna is carried out using the commercially available EM simulator HFSS, from Ansoft Corporation. Furthermore, the proposed antenna exhibits good radiation characteristics at the resonant bands. The simulated results of S-parameters and radiation patterns are presented. All the results show that the proposed design serves as a suitable candidate for applications in millimetre-wave road vehicle system or indoor wireless system.


title: The Pictorial Scale of Perceived Competence and Social Acceptance for Young Children

abstract: A new pictorial scale of perceived competence and social acceptance for young children, a downward extension of the Perceived Competence Scale for Children, is described. There are 2 versions of this instrument, 1 for preschoolers and kindergartners and a second for first and second graders, each tapping 4 domains: cognitive competence, physical competence, peer acceptance, and maternal acceptance. Factor analyses reveal a 2-factor solution. The first factor, general competence, is defined by the cognitive and physical competence subscales. The second factor, social acceptance, comprises the peer and maternal acceptance subscales. The psychometric properties were found to be acceptable. Weak correlations between children's and teachers' judgments are discussed in terms of the young child's tendency to confuse the wish to be competent or accepted with reality. It is urged that this instrument not be viewed as a general self-concept scale but be treated as a measure of 2 separate constructs, perceived competence and social acceptance.


Coordinate to cooperate or compete: Abstract goals and joint intentions in social interaction.


title: High voltage pulse generator based on DC-to-DC boost converter with capacitor-diode voltage multipliers for bacterial decontamination

abstract: High voltage pulse generators can be used effectively in water treatment applications, as applying a pulsed electric field on the infected sample guarantees killing of harmful germs and bacteria. In this paper, a new high voltage pulse generator with closed loop control on its output voltage is proposed. The proposed generator is based on DC-to-DC boost converter in conjunction with capacitor-diode voltage multiplier (CDVM), and can be fed from low-voltage low-frequency AC supply, i.e. utility mains. The proposed topology provides transformer-less operation which reduces size and enhances the overall efficiency. A Detailed design of the proposed pulse generator has been presented as well. The proposed approach is validated by simulation as well as experimental results.


title: On the duality of twist/wrench distributions in serial and parallel chain robot manipulators

abstract: Duality and reciprocity between twist and wrench systems is the essence of screw theory. The power of these concepts lies in the idea that once a result is obtained for one type of system it can immediately be applied to the dual system provided the appropriate physical interpretation is also translated. The interrelation between serial and parallel mechanisms is a consequence of this twist/wrench duality. The purpose of this investigation is to focus on some fundamental configurations of serial and parallel robot manipulators in an attempt to further elucidate the various correspondences that exist between their geometries and their kinematic equations. The focus is on zero-pitch screw systems, namely revolute joints for serial manipulators and force producing sub-chains in parallel manipulators. Solutions for twist/wrench distributions will be discussed for both proper and redundant systems.


title: Vacant Parking Space Detection Based on Plane-Based Bayesian Hierarchical Framework

abstract: In this paper, we propose a vacant parking space detection system that operates day and night. In the daytime, the major challenges of the system include dramatic lighting variations, shadow effect, inter-object occlusion, and perspective distortion. In the nighttime, the major challenges include insufficient illumination and complicated lighting conditions. To overcome these problems, we propose a plane-based method which adopts a structural 3-D parking lot model consisting of plentiful planar surfaces. The plane-based 3-D scene model plays a key part in handling inter-object occlusion and perspective distortion. On the other hand, to alleviate the interference of unpredictable lighting changes and shadows, we propose a plane-based classification process. Moreover, by introducing a Bayesian hierarchical framework to integrate the 3-D model with the plane-based classification process, we systematically infer the parking status. Last, to overcome the insufficient illumination in the nighttime, we also introduce a preprocessing step to enhance image quality. The experimental results show that the proposed framework can achieve robust detection of vacant parking spaces in both daytime and nighttime.


title: The Promise of Heart Rate Variability Biofeedback: Evidence-Based Applications

abstract: Heart rate variability biofeedback has enjoyed increased popularity in recent years. In this review, empirical evidence from multiple sources is presented from the point of view of possible mechanisms of effect. While more research is clearly needed, the data thus far are certainly promising.


title: A waveform representation framework for high-quality statistical parametric speech synthesis

abstract: State-of-the-art statistical parametric speech synthesis (SPSS) generally uses a vocoder to represent speech signals and parameterize them into features for subsequent modeling. Magnitude spectrum has been a dominant feature over the years. Although perceptual studies have shown that phase spectrum is essential to the quality of synthesized speech, it is often ignored by using a minimum phase filter during synthesis and the speech quality suffers. To bypass this bottleneck in vocoded speech, this paper proposes a phase-embedded waveform representation framework and establishes a magnitude-phase joint modeling platform for high-quality SPSS. Our experiments on waveform reconstruction show that the performance is better than that of the widely-used STRAIGHT. Furthermore, the proposed modeling and synthesis platform outperforms a leading-edge, vocoded, deep bidirectional long short-term memory recurrent neural network (DBLSTM-RNN)-based baseline system in various objective evaluation metrics conducted.


BRUTE: A High Performance and Extensibile Traffic Generator


Machine Learning with Selective Word Statistics for Automated Classification of Citation Subjectivity in Online Biomedical Articles


title: Hospital social workers and self-help groups.

abstract: Self-help has developed into a significant trend in health care, and the relationship between professional services and self-help has begun to be explored. This article describes a survey that investigated hospital social workers' involvement with self-help groups and reports the need for a dual focus on research and clinical training to prepare social workers for the practice challenges presented by the self-help movement.


title: Towards Ontology-based E-mail Fraud Detection

abstract: The European FF POIROT project (IST-2001-38248) aims at developing applications for tackling financial fraud, using formal ontological repositories as well as multilingual terminological resources. In this article, we want to focus on the development cycle towards an application recognizing several types of e-mail fraud, such as phishing, Nigerian advance fee fraud and lottery scam. The development cycle covers four tracks of development - language engineering, terminology engineering, knowledge engineering and system engineering. These development tracks are preceded by a problem determination phase and followed by a deployment phase. Each development track is supported by a methodology. All methodologies and phases in the development cycle will be discussed in detail


title: LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices

abstract: Recent developments in speech synthesis have produced systems capable of providing intelligible speech, and researchers now strive to create models that more accurately mimic human voices. One such development is the incorporation of multiple linguistic styles in various languages and accents. HMM-based speech synthesis is of great interest to researchers, due to its ability to produce sophisticated features with a small footprint. Despite such progress, its quality has not yet reached the level of the current predominant unit-selection approaches, that select and concatenate recordings of real speech. Recent efforts have been made in the direction of improving HMM-based systems. In this paper, we present the application of long short-term memory deep neural networks as a postfiltering step in HMM-based speech synthesis. Our motivation stems from a desire to obtain spectral characteristics closer to those of natural speech. The results described in the paper indicate that HMM-voices can be improved using this approach.


title: Approximate Models for General Cache Networks

abstract: Many systems employ caches to improve performance. While isolated caches have been studied in-depth, multi-cache systems are not well understood, especially in networks with arbitrary topologies. In order to gain insight into and manage these systems, a low-complexity algorithm for approximating their behavior is required. We propose a new algorithm, termed a-Net, that approximates the behavior of multi-cache networks by leveraging existing approximation algorithms for isolated LRU caches. We demonstrate the utility of a-Net using both per- cache and network-wide performance measures. We also perform factor analysis of the approximation error to identify system parameters that determine the precision of a-Net.


title: BLEU Evaluation of Machine-Translated English-Croatian Legislation

abstract: This paper presents work on the evaluation of online available machine translation (MT) service, i.e. Google Translate, for English- Croatian language pair in the domain of legislation. The total set of 200 sentences, for which three reference translations are provided, is divided into short and long sentences. Human evaluation is performed by native speakers, using the criteria of adequacy and fluency. For measuring the reliability of agreement among raters, Fleiss' kappa metric is used. Human evaluation is enriched by error analysis, in order to examine the influence of error types on fluency and adequacy, and to use it in further research. Translation errors are divided into several categories: non- translated words, word omissions, unnecessarily translated words, morphological errors, lexical errors, syntactic errors and incorrect punctuation. The automatic evaluation metric BLEU is calculated with regard to a single and multiple reference translations. System level Pearson’s correlation between BLEU scores based on a single and multiple reference translations is given, as well as correlation between short and long sentences BLEU scores, and correlation between the criteria of fluency and adequacy and each error category.


title: Coupling dense and landmark-based approaches for nonrigid registration

abstract: We investigate the introduction of cortical constraints for non rigid intersubject brain registration. We extract sulcal patterns with the active ribbon method, presented by Le Goualher et al. (1997). An energy based registration method (Hellier et al., 2001), which will be called photometric registration method in this paper, makes it possible to incorporate the matching of cortical sulci. The local sparse similarity and the photometric similarity are, thus, expressed in a unified framework. We show the benefits of cortical constraints on a database of 18 subjects, with global and local assessment of the registration. This new registration scheme has also been evaluated on functional magnetoencephalography data. We show that the anatomically constrained registration leads to a substantial reduction of the intersubject functional variability.


title: Performance comparison of EKF and particle filtering methods for maneuvering targets

abstract: Online tracking of maneuvering targets is a highly nonlinear and challenging problem that involves, at every time instant, the estimation not only of the unknown state in the dynamic model describing the evolution of the target, but also the underlying model accounting for the regime of movement. In this paper we review and compare several sequential estimation procedures, that use appropriate strategies for coping with various models that account for the different modes of operation. We focus on the application of the recently proposed cost-reference particle filtering (CRPF) methodology, which aims at the estimation of the system state without using probability distributions. The resulting method has a more robust performance when compared to standard particle filtering (SPF) algorithms or the interactive multiple model (IMM) algorithm based on the use of the well known extended Kalman filter (EKF). Advantages and disadvantages of the considered algorithms are illustrated and discussed through computer simulations.


title: An Algebraic Approach for Data-Centric Scientific Workflows

abstract: workflows have emerged as a basic abstraction for structuring and executing scientific experiments in computational environments. In many situations, these workflows are computationally and data intensive, thus requiring execution in large-scale parallel computers. However, parallelization of scientific workflows remains low-level, ad-hoc and labor- intensive, which makes it hard to exploit optimization opportunities. To address this problem, we propose an algebraic approach (inspired by relational algebra) and a parallel execution model that enable automatic optimization of scientific workflows. We conducted a thorough validation of our approach using both a real oil exploitation application and synthetic data scenarios. The experiments were run in Chiron, a data-centric scientific workflow engine implemented to support our algebraic approach. Our experiments demonstrate performance improvements of up to 226% compared to an ad-hoc workflow implementation.


title: Validation of the multiple language versions of the hopkins symptom checklist-37 for refugee adolescents

abstract: The objective of this study is to provide preliminary psychometric properties of the Hopkins Symptom Checklist-37 (HSCL-37A) for refugee adolescents. The HSCL-37A is a modification of the well-known HSCL-25 and assesses symptoms of internalizing and externalizing problems that have been associated with reactions to trauma. Four independent heterogeneous samples (N = 3,890) of unaccompanied refugee minors, immigrants, and native Dutch and Belgian adolescents were assessed at school. The confirmative factor analyses, per language version, support the two-factor structure of internalizing and externalizing behavior. The total and subscales show good internal consistency and acceptable test-retest reliability in spite of the heterogeneous sample populations. The construct, content, and criterion validity of the HSCL-37A were also examined and found to be good. The findings of this study suggest that the HSCL-37A is a reliable and valid instrument to be used among culturally diverse refugee adolescents to assess emotional distress and maladaptive behaviors.


Supplementary Materials for ‘Discovering Model Structure for Partially Linear Model’


title: LINDEN: Linking Named Entities with Knowledge Base via Semantic Knowledge

abstract: Integrating the extracted facts with an existing knowledge base has raised an urgent need to address the problem of entity linking. Specifically, entity linking is the task to link the entity mention in text with the corresponding real world entity in the existing knowledge base. However, this task is challenging due to name ambiguity, textual inconsistency, and lack of world knowledge in the knowledge base. Several methods have been proposed to tackle this problem, but they are largely based on the co-occurrence statistics of terms between the text around the entity mention and the document associated with the entity. In this paper, we propose LINDEN, a novel framework to link named entities in text with a knowledge base unifying Wikipedia and WordNet, by leveraging the rich semantic knowledge embedded in the Wikipedia and the taxonomy of the knowledge base. We extensively evaluate the performance of our proposed LINDEN over two public data sets and empirical results show that LINDEN significantly outperforms the state-of-the-art methods in terms of accuracy.


Notes on the Wesley rigidity scale; the development of a short form.


title: Magnetic Bearing Sets for a Flywheel System

abstract: This paper presents a magnetic bearing set developed to work in a flywheel energy storage system. The bearing set is composed of a permanent magnetic bearing (PMB) and a superconducting magnetic bearing (SMB). A new configuration of a PMB having Nd-Fe-B magnet rings and a back yoke is proposed and compared with an existing one. Finite element method (FEM) simulations were used to design two different PMB configurations. Measurement results of axial and radial forces were carried out in zero field cooling (ZFC) and field cooling (FC) processes in two SMB topologies. These measured force results are presented and discussed in order to discover which configuration is more promising for the proposed application.


title: Does the early bird move the polls?: the use of the social media tool 'Twitter' by U.S. politicians and its impact on public opinion

abstract: Social media tools such as Twitter and Facebook have been hailed as politically transformative communications technologies, on the level of previous politically significant technological innovations, such as radio and television. In this paper we estimate the impact of the use of the social media tool 'Twitter' by American politicians on changes in public opinion of those politicians over time, and we find little evidence that the political use of Twitter has eitther a positive or negative impact on public opinion. We run simple regressions of changes in widely used U. S. public opinion polls, estimating the association between favorable impressions of selected politicians and the social media usage levels (number of 'Tweets') of those politicians. In order to get a reliable estimate, we ran the same regressions for public opinion polls conducted by three different companies (CNN/Opinion Research, Gallup, and ABC/Washington Post) and we find that the estimated coefficients are not significant for any of the tested opinion polls.


title: Reinforcement Learning for Blackjack

abstract: This paper explores the development of an Artificial Intelligence system for an already existing framework of card games, called SKCards, and the experimental results obtained from this. The current Artificial intelligence in the SKCards Blackjack is highly flawed. Reinforcement Learning was chosen as the method to be employed. Reinforcement Learning attempts to teach a computer certain actions, given certain states, based on past experience and numerical rewards gained. The agent either assigns values to states, or actions in states. This will initially be developed for Blackjack, with possible extensions to other games. Blackjack is one of the simpler games and the only current game in the SKCards package which needs an Artificial Intelligence agent. All the other games are single player. To test the performance of the Reinforcement Learning agent, several experiments were devised and run.


Generating Natural Video Descriptions via Multimodal Processing


title: Improved musical onset detection with Convolutional Neural Networks

abstract: Musical onset detection is one of the most elementary tasks in music analysis, but still only solved imperfectly for polyphonic music signals. Interpreted as a computer vision problem in spectrograms, Convolutional Neural Networks (CNNs) seem to be an ideal fit. On a dataset of about 100 minutes of music with 26k annotated onsets, we show that CNNs outperform the previous state-of-the-art while requiring less manual preprocessing. Investigating their inner workings, we find two key advantages over hand-designed methods: Using separate detectors for percussive and harmonic onsets, and combining results from many minor variations of the same scheme. The results suggest that even for well-understood signal processing tasks, machine learning can be superior to knowledge engineering.


title: A Lightweight Intelligent Virtual Cinematography System for Machinima Production

abstract: Machinima is a low-cost alternative to full production filmmaking. However, creating quality cinematic visualizations with existing machinima techniques still requires a high degree of talent and effort. We introduce a lightweight artificial intelligence system, Cambot, that can be used to assist in machinima production. Cambot takes a script as input and produces a cinematic visualization. Unlike other virtual cinematography systems, Cambot favors an offline algorithm coupled with an extensible library of specific modular and reusable facets of cinematic knowledge. One of the advantages of this approach to virtual cinematography is a tight coordination between the positions and movements of the camera and the actors.


title: Context Sensitive Topic Models for Author Influence in Document Networks

abstract: In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.


title: Supporting Reasoning with Different Types of Evidence in Intelligence Analysis

abstract: The aim of intelligence analysis is to make sense of information that is often conflicting or incomplete, and to weigh competing hypotheses that may explain a situation. This imposes a high cognitive load on analysts, and there are few automated tools to aid them in their task. In this paper, we present an agent-based tool to help analysts in acquiring, evaluating and interpreting information in collaboration with others. Agents assist analysts in reasoning with different types of evidence to identify what happened and why, what is credible, and how to obtain further evidence. Argumentation schemes lie at the heart of the tool, and sensemaking agents assist analysts in structuring evidence and identifying plausible hypotheses. A crowdsourcing agent is used to reason about structured information explicitly obtained from groups of contributors, and provenance is used to assess the credibility of hypotheses based on the origins of the supporting information.


Progress in Tourism Management Tourism supply chain management : A new research agenda


title: Trust me, I'm a Root CA! Analyzing SSL Root CAs in Modern Browsers and Operating Systems

abstract: The security and privacy of our online communications heavily relies on the entity authentication mechanisms provided by SSL. Those mechanisms in turn heavily depend on the trustworthiness of a large number of companies and governmental institutions for attestation of the identity of SSL services providers. In order to offer a wide and unobstructed availability of SSL-enabled services and to remove the need to make a large amount of trust decisions from their users, operating systems and browser manufactures include lists of certification authorities which are trusted for SSL entity authentication by their products. This has the problematic effect that users of such browsers and operating systems implicitly trust those certification authorities with the privacy of their communications while they might not even realize it. The problem is further complicated by the fact that different software vendors trust different companies and governmental institutions, from a variety of countries, which leads to an obscure distribution of trust. To give insight into the trust model used by SSL this thesis explains the various entities and technical processes involved in establishing trust when using SSL communications. It furthermore analyzes the number and origin of companies and governmental institutions trusted by various operating systems and browser vendors and correlates the gathered information to a variety of indexes to illustrate that some of these trusted entities are far from trustworthy. Furthermore it points out the fact that the number of entities we trust with the security of our SSL communications keeps growing over time and displays the negative effects this might have as well as shows that the trust model of SSL is fundamentally broken.


title: An AUC-based permutation variable importance measure for random forests

abstract: BackgroundThe random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance.ResultsWe investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the new AUC-based permutation VIM outperforms the standard permutation VIM for unbalanced data settings while both permutation VIMs have equal performance for balanced data settings.ConclusionsThe standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html


title: A phylogenetic study of human respiratory syncytial viruses group A and B strains isolated in two cities in Japan from 1980-2002.

abstract: The circulation pattern and genetic evolution of respiratory syncytial virus (RSV) in Japan were examined based on 109 RSV field strains isolated over 20 seasons (1980-2002) in two cities, Sapporo and Tokyo. The second hypervariable region of the large glycoprotein (G) gene was amplified by RT-PCR and the products sequenced directly. The nucleotide sequences were compared to those representatives of RSV genotypes identified previously. Japanese group A and B isolates clustered into five and four genotypes defined previously, respectively. Another one group A and one group B genotypes, which could not be assigned to previous genotypes, were also identified. Although different genotypes usually co-circulated in each season, the isolates in proximate seasons from two communities were usually located in the same branches. Moreover, the strains with genotypes defined previously were usually isolated at the same time as each reference strain of Western countries. Several mutant group B strains with 1-20 longer amino acid G proteins were newly identified in Sapporo. These findings suggest that Japanese RSV strains underwent geographical and also temporal clustering while participating in RSV genetic evolution in a global setting. In addition, Japanese strains, especially group B, might have evolved individually in each community, sometimes changing the length of the G protein.


title: Some additional insights on applying differential privacy for numeric data

abstract: Recently Sarathy and Muralidhar (2009) provided the first attempt at illustrating the implementation of differential privacy for numerical data. In this paper, we attempt to provide further insights on the results that are observed when Laplace based noise addition is used to protect numerical data in order to satisfy differential privacy. Our results raise serious concerns regarding the viability of differential privacy and Laplace noise addition as appropriate procedures for protecting numerical data.


title: An eye detection method robust to eyeglasses for mobile iris recognition

abstract: An eye detection method that is robust to eyeglass interference is proposed for mobile iris recognition system.Multi-scale window mask consisting of 2ź×ź3 subblocks is used to generate eye candidates.Eye validation is applied to select the true eye image among the eye candidates.The proposed method performs more accurately and quickly than competing methods in mobile environment. Finding the accurate position of an eye is crucial for mobile iris recognition system in order to extract the iris region quickly and correctly. Unfortunately, this is very difficult to accomplish when a person is wearing eyeglasses because of the interference from the eyeglasses. This paper proposes an eye detection method that is robust to eyeglass interference in mobile environment. The proposed method comprises two stages: eye candidate generation and eye validation. In the eye candidate generation stage, multi-scale window masks consisting of 2ź×ź3 subblocks are used to generate all image blocks possibly containing an eye image. In the ensuing eye validation stage, two methods are employed to determine which blocks actually contain true eye images and locate their precise positions as well: the first method searches for the glint of an NIR illuminator on the pupil region. If this first method fails, the next method computes the intensity difference between the assumed pupil and its surrounding region using multi-scale 3ź×ź3 window masks. Experimental results show that the proposed method detects the eye position more accurately and quickly than competing methods in the presence of interference from eyeglass frames.


title: Boost Converter With Coupled Inductors and Buck–Boost Type of Active Clamp

abstract: This paper proposes an improved boost converter with coupled inductors and buck-boost type of active-clamp feature, PWM control and zero-voltage switching in both main and auxiliary switches. In the converter, the active-clamp circuit is used to eliminate voltage spike induced from the leakage inductor of the coupled inductors. The active switch of the converter can still sustain a proper duty cycle when it operates with a high step-up voltage ratio, reducing voltage stress significantly. A set of passive-clamping circuit is adopted to eliminate undesired resonance between leakage inductor of the coupled inductors and stray capacitor of the boost diode, recovering trapped energy. Thus, conversion efficiency can be improved significantly. A 200 W prototype of the proposed boost converter was built from which experimental results have shown that efficiency can reach as high as 92% and surge can be suppressed effectively.