title: Understanding the Real World: Combining Objects, Appearance, Geometry and Topology for Semantic Mapping

abstract: A cornerstone for mobile robots operating in man-made environments and interacting with humans is representing and understanding the human semantic concepts of space. In this report, we present a m ...


title: Flattening curved documents in images

abstract: Compared to scanned images, document pictures captured by camera can suffer from distortions due to perspective and page warping. It is necessary to restore a frontal planar view of the page before other OCR techniques can be applied. In this paper we describe a novel approach for flattening a curved document in a single picture captured by an uncalibrated camera. To our knowledge this is the first reported method able to process general curved documents in images without camera calibration. We propose to model the page surface by a developable surface, and exploit the properties (parallelism and equal line spacing) of the printed textual content on the page to recover the surface shape. Experiments show that the output images are much more OCR friendly than the original ones. While our method is designed to work with any general developable surfaces, it can be adapted for typical special cases including planar pages, scans of thick books, and opened books.


title: Bias in Natural Actor-Critic Algorithms

abstract: We show that several popular discounted reward natural actor-critics, including the popular NAC-LSTD and eNAC algorithms, do not generate unbiased estimates of the natural policy gradient as claimed. We derive the first unbiased discounted reward natural actor-critics using batch and iterative approaches to gradient estimation. We argue that the bias makes the existing algorithms more appropriate for the average reward setting. We also show that, when Sarsa(λ) is guaranteed to converge to an optimal policy, the objective function used by natural actor-critics has only global optima, so policy gradient methods are guaranteed to converge to globally optimal policies as well.


title: Lateral trajectory tracking control for autonomous vehicles

abstract: In this contribution a structure for high level lateral vehicle tracking control is presented. It is based on the two degrees of freedom structure that allows to separately define the command response and disturbance attenuation. The application of the disturbance observer guarantees robust compensation of the disturbances. An advantage of the presented structure is its robustness against variable vehicle parameters. Only a considerably reduced model is necessary which significantly simplifies the application process. Moreover the presented approach is characterized by its extensibility and its modularity.


title: Improving Error Correction Codes for Multiple-Cell Upsets in Space Applications

abstract: Currently, faults suffered by SRAM memory systems have increased due to the aggressive CMOS integration density. Thus, the probability of occurrence of single-cell upsets (SCUs) or multiple-cell upsets (MCUs) augments. One of the main causes of MCUs in space applications is cosmic radiation. A common solution is the use of error correction codes (ECCs). Nevertheless, when using ECCs in space applications, they must achieve a good balance between error coverage and redundancy, and their encoding/decoding circuits must be efficient in terms of area, power, and delay. Different codes have been proposed to tolerate MCUs. For instance, Matrix codes use Hamming codes and parity checks in a bi-dimensional layout to correct and detect some patterns of MCUs. Recently presented, column–line–code (CLC) has been designed to tolerate MCUs in space applications. CLC is a modified Matrix code, based on extended Hamming codes and parity checks. Nevertheless, a common property of these codes is the high redundancy introduced. In this paper, we present a series of new low-redundant ECCs able to correct MCUs with reduced area, power, and delay overheads. Also, these new codes maintain, or even improve, memory error coverage with respect to Matrix and CLC codes.


title: Human temporal-lobe response to vocal sounds

abstract: Abstract Voice is not only the vehicle of speech, it is also an ‘auditory face’ that conveys a wealth of information on a person’s identity and affective state. In contrast to speech perception, little is known about the neural bases of our ability to perceive these various types of paralinguistic vocal information. Using functional magnetic resonance imaging (fMRI), we identified regions along the superior temporal sulcus (STS) that were not only sensitive, but also highly selective to vocal sounds. In the present study, we asked how neural activity in the voice areas was influenced by (i) the presence or not of linguistic information in the vocal input (speech vs. nonspeech) and (ii) frequency scrambling. Speech sounds were found to elicit greater responses than nonspeech vocalizations in most parts of auditory cortex, including primary auditory cortex (A1), on both sides of the brain. In contrast, response attenuation due to frequency scrambling was much more pronounced in anterior STS areas than at the level of A1. Importantly, only right anterior STS regions responded more strongly to nonspeech vocal sounds than to their scrambled version, suggesting that these regions could be specifically involved in paralinguistic aspects of voice perception.


title: Rethinking Formalisms in Formal Education

abstract: I explore a belief about learning and teaching that is commonly held in education and society at large that nonetheless is deeply flawed. The belief asserts that mastery of formalisms—specialized representations such as symbolic equations and diagrams with no inherent meaning except that which is established by convention—is prerequisite to applied knowledge. A formalisms first (FF) view of learning, rooted in Western dualist philosophy, incorrectly advocates the introduction of formalisms too early in the development of learners’ conceptual understanding and can encourage a formalisms-only mind-set toward learning and instruction. I identify the prevalence of FF in curriculum and instruction and outline some of the serious problems engendered by FF approaches. I then turn to promising alternatives that support progressive formalization, problem-based learning, and inquiry learning, which capitalize on the strengths of formalisms but avoid some of the most costly problems found in FF approaches.


title: Low-Resolution Face Recognition via Coupled Locality Preserving Mappings

abstract: Practical face recognition systems are sometimes confronted with low-resolution face images. Traditional two-step methods solve this problem through employing super-resolution (SR). However, these methods usually have limited performance because the target of SR is not absolutely consistent with that of face recognition. Moreover, time-consuming sophisticated SR algorithms are not suitable for real-time applications. To avoid these limitations, we propose a novel approach for LR face recognition without any SR preprocessing. Our method based on coupled mappings (CMs), projects the face images with different resolutions into a unified feature space which favors the task of classification. These CMs are learned through optimizing the objective function to minimize the difference between the correspondences (i.e., low-resolution image and its high-resolution counterpart). Inspired by locality preserving methods for dimensionality reduction, we introduce a penalty weighting matrix into our objective function. Our method significantly improves the recognition performance. Finally, we conduct experiments on publicly available databases to verify the efficacy of our algorithm.


title: Non-Advertized does not Mean Concealed: Body Odour Changes across the Human Menstrual Cycle

abstract: Females of a number of primate species display their fertile period by behavioural and/or morphological changes. Traditionally, the fertile period in human females has been considered to be concealed. However, this presumption has rarely been tested. One of the possible mechanisms for assessing menstrual cycle phase is through the sense of smell. In this study possible changes in odour across the menstrual cycle were investigated. Samples of body odour were acquired from 12 women (aged 19–27 yr), none of whom were using hormonal contraceptives. Samples were collected using cotton pads worn in the armpit for 24 h, from the menstrual, follicular and luteal cycle phases. Our experimental sample of 42 males (age 19–34 yr) repeatedly rated these odour samples for their intensity, pleasantness, attractiveness and femininity. Raw subjective smell ratings from each man were transformed to z-scores. Subsequently, these z-scores were tested by the general linear mixed-model analysis (PROC MIXED, SAS) with the female's ID nested within the subject's ID as a random factor to account for the repeated measures of the subjects. Significant changes across the cycle were found for ratings of pleasantness [F(2,689) = 702; p = 0.001], attractiveness [F(2,546) = 6.35; p = 0.002] and intensity [F(2,530) = 3.57; p = 0.028]. Odour from women in the follicular (i.e. fertile) phase was rated as the least intense and the most attractive. Subsequent post hoc analysis revealed significant differences in intensity, pleasantness and attractiveness between the menstrual phase and the follicular phase, and in pleasantness and attractiveness between the menstrual and luteal phases. Significant difference between the follicular and the luteal phase was found only for attractiveness. Our results suggest that men can potentially use smell as a mechanism for monitoring menstrual cycle phase in current or prospective sexual partners. Therefore, the fertile period in humans should be considered non-advertized, rather than concealed.


title: Superfaces: A Super-Resolution Model for 3D Faces

abstract: Face recognition based on the analysis of 3D scans has been an active research subject over the last few years. However, the impact of the resolution of 3D scans on the recognition process has not been addressed explicitly yet being of primal importance after the introduction of a new generation of low cost 4D scanning devices. These devices are capable of combined depth/rgb acquisition over time with a low resolution compared to the 3D scanners typically used in 3D face recognition benchmarks. In this paper, we define a super-resolution model for 3D faces by which a sequence of low-resolution 3D scans can be processed to extract a higher resolution 3D face model, namely the superface model. The proposed solution relies on the Scaled ICP procedure to align the low-resolution 3D models with each other and estimate the value of the high-resolution 3D model based on the statistics of values of the low-resolution scans in corresponding points. The approach is validated on a data set that includes, for each subject, one sequence of low-resolution 3D face scans and one ground-truth high-resolution 3D face model acquired through a high-resolution 3D scanner. In this way, results of the super-resolution process are evaluated qualitatively and quantitatively by measuring the error between the superface and the ground-truth.


Quality Assessment for Linked Data : A Survey A Systematic Literature Review and Conceptual Framework


title: Understanding dynamic capabilities through knowledge management

abstract: Purpose – This paper sets out to integrate research on knowledge management with the dynamic capabilities approach. This paper will add to the understanding of dynamic capabilities by demonstrating that dynamic capabilities can be seen as composed of concrete and well‐known knowledge management activities.Design/methodology/approach – This paper is based on a literature review focusing on key knowledge management processes and activities as well as the concept of dynamic capabilities, the paper connects these two approaches. The analysis is centered on knowledge management activities which then are compiled into dynamic capabilities.Findings – In the paper eight knowledge management activities are identified; knowledge creation, acquisition, capture, assembly, sharing, integration, leverage, and exploitation. These activities are assembled into the three dynamic capabilities of knowledge development, knowledge (re)combination, and knowledge use. The dynamic capabilities and the associated knowledge manage...


title: A reliable gyroscope-based gait-phase detection sensor embedded in a shoe insole

abstract: This paper presents results of patient experiments using a new gait-phase detection sensor (GPDS) together with a programmable functional electrical stimulation (FES) system for subjects with a dropped-foot walking dysfunction. The GPDS (sensors and processing unit) is entirely embedded in a shoe insole and detects in real time four phases (events) during the gait cycle: stance, heel off, swing, and heel strike. The instrumented GPDS insole consists of a miniature gyroscope that measures the angular velocity of the foot and three force sensitive resistors that measure the force load on the shoe insole at the heel and the metatarsal bones. The extracted gait-phase signal is transmitted from the embedded microcontroller to the electrical stimulator and used in a finite state control scheme to time the electrical stimulation sequences. The electrical stimulations induce muscle contractions in the paralyzed muscles leading to a more physiological motion of the affected leg. The experimental results of the quantitative motion analysis during walking of the affected and nonaffected sides showed that the use of the combined insole and FES system led to a significant improvement in the gait-kinematics of the affected leg. This combined sensor and stimulation system has the potential to serve as a walking aid for rehabilitation training or permanent use in a wide range of gait disabilities after brain stroke, spinal-cord injury, or neurological diseases.


title: Optimizing Ontology Alignments by Using Genetic Algorithms

abstract: In this work we present GOAL (Genetics for Ontology Alignments) a new approach to compute the optimal ontology alignment function for a given ontology input set. Although this problem could be solved by an exhaustive search when the number of similarity measures is low, our method is expected to scale better for a high number of measures. Our approach is a genetic algorithm which is able to work with several goals: maximizing the alignment precision, maximizing the alignment recall, maximizing the f-measure or reducing the number of false positives. Moreover, we test it here by combining some cutting-edge similarity measures over a standard benchmark, and the results obtained show several advantages in relation to other techniques.


title: Techniques for Image Retrieval: Deformation Insensitivity and Automatic Thumbnail Cropping

abstract: We study several problems in image retrieval systems. These problems and proposed techniques are divided into three parts. ::: Part I: This part focuses on robust object representation, which is of fundamental importance in computer vision. We target this problem without using specific object models. This allows us to develop methods that can be applied to many different problems. Three approaches are proposed that are insensitive to different kind of object or image changes. First, we propose using the inner-distance, defined as the length of shortest paths within shape boundary, to build articulation insensitive shape descriptors. Second, a deformation insensitive framework for image matching is presented, along with an insensitive descriptor based on geodesic distances on image surfaces. Third, we use a gradient orientation pyramid as a robust face image representation and apply it to the task of face verification across ages. ::: Part II1: This part concentrates on comparing histogram-based descriptors that are widely used in image retrieval. We first present an improved algorithm of the Earth Mover's Distance (EMD), which is a popular dissimilarity measure between histograms. The new algorithm is one order faster than original EMD algorithms. Then, motivated by the new algorithm, a diffusion-based distance is designed that is more straightforward and efficient. The efficiency and effectiveness of the proposed approaches are validated in experiments on both shape recognition and interest point matching tasks, using both synthetic and real data. ::: Part III2: This part studies the thumbnail generation problem that has wide application in visualization tasks. Traditionally, thumbnails are generated by shrinking the original images. These thumbnails are often illegible due to size limitation. We study the ability of computer vision systems to detect key components of images so that intelligent cropping, prior to shrinking, can render objects more recognizable. With this idea, we propose an automatic thumbnail cropping technique based on the distribution of pixel saliency in an image. The proposed approach is tested in a carefully designed user study, which shows that the cropped thumbnails are substantially more recognizable and easier to find in the context of visual search. ::: 1This work is supervised by Dr. Kazunori Okada at Siemens Corporate Research during my internship. 2Part of this work is jointly done with Bongwon Suh under supervision of Professor Ben B. Bederson.


title: Zero Voltage Vector Sampling Method for PMSM Three-Phase Current Reconstruction Using Single Current Sensor

abstract: For the purpose of reducing cost and volume, techniques of reconstructing three-phase currents through a single current sensor have been reported for permanent magnet synchronous motor vector control system. In existing studies, the reconstruction precision is largely affected by the dead zones in space vector PWM plane, which requires additional efforts to compensate the dead zones either by modifying pulse width modulation (PWM) modulation strategy or by phase-shifting of PWM signal. In this paper, a novel zero voltage vector sampling method (ZVVSM) is proposed, which can move the current reconstruction dead zones in low modulation region and sector boundary regions toward the outline of the space vector hexagon without modifying PWM signal. By arranging the single current sensor at a novel position, the proposed method is able to sample current in two zero voltage vectors (ZVV). ZVVSM avoids the complicated algorithms as well as the increase of the switching times, so that it is beneficial to the PMSM drive performance. The proposed method is validated by both simulation and experiments.


title: pigeo: A Python Geotagging Tool

abstract: We present pigeo, a Python geolocation prediction tool that predicts a location for a given text input or Twitter user. We discuss the design, implementation and application of pigeo, and empirically evaluate it. pigeo is able to geolocate informal text and is a very useful tool for users who require a free and easy-to-use, yet accurate geolocation service based on pre-trained models. Additionally, users can train their own models easily using pigeo's API.


title: Discovering Health Topics in Social Media Using Topic Models

abstract: By aggregating self-reported health statuses across millions of users, we seek to characterize the variety of health information discussed in Twitter. We describe a topic modeling framework for discovering health topics in Twitter, a social media website. This is an exploratory approach with the goal of understanding what health topics are commonly discussed in social media. This paper describes in detail a statistical topic model created for this purpose, the Ailment Topic Aspect Model (ATAM), as well as our system for filtering general Twitter data based on health keywords and supervised classification. We show how ATAM and other topic models can automatically infer health topics in 144 million Twitter messages from 2011 to 2013. ATAM discovered 13 coherent clusters of Twitter messages, some of which correlate with seasonal influenza (r = 0.689) and allergies (r = 0.810) temporal surveillance data, as well as exercise (r = .534) and obesity (r = -.631) related geographic survey data in the United States. These results demonstrate that it is possible to automatically discover topics that attain statistically significant correlations with ground truth data, despite using minimal human supervision and no historical data to train the model, in contrast to prior work. Additionally, these results demonstrate that a single general-purpose model can identify many different health topics in social media.


title: Energy-theft detection issues for advanced metering infrastructure in smart grid

abstract: With the proliferation of smart grid research, the Advanced Metering Infrastructure (AMI) has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than $25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.


title: Substrate Integrated Waveguide Cross-Coupled Filter With Negative Coupling Structure

abstract: Substrate integrated waveguide (SIW) technology provides an attractive solution to the integration of planar and nonplanar circuits by using a planar circuit fabrication process. However, it is usually difficult to implement the negative coupling structure required for the design of compact canonical folded elliptic or quasi-elliptic cross-coupled bandpass filter on the basis of a single-layer SIW. In this paper, a special planar negative coupling scheme including a magnetic coupling post-wall iris and a balanced microstrip line with a pair of metallic via-holes is studied in detail. Two -band fourth-degree cross-coupled bandpass filters without and with source-load coupling using the negative coupling structures are then proposed and designed. The two novel SIW filters having the same center frequency of 20.5 GHz and respective passband width of 700 and 800 MHz are implemented on a single-layer Rogers RT/Duroid 5880 substrate with thickness of 0.508 mm. Measured results of those filters, which exhibit a high selectivity, and a minimum in-band insertion loss of approximately 0.9 and 1.0 dB, respectively, agree well with simulated results.


title: Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations

abstract: BackgroundThe limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program.ResultsThe results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives.ConclusionsResults of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces.


title: Cognitive Load During Problem Solving: Effects on Learning

abstract: Considerable evidence indicates that domain specific knowledge in the form of schemas is the primary factor distinguishing experts from novices in problem-solving skill. Evidence that conventional problem-solving activity is not effective in schema acquisition is also accumulating. It is suggested that a major reason for the ineffectiveness of problem solving as a learning device, is that the cognitive processes required by the two activities overlap insufficiently, and that conventional problem solving in the form of means-ends analysis requires a relatively large amount of cognitive processing capacity which is consequently unavailable for schema acquisition. A computational model and experimental evidence provide support for this contention. Theoretical and practical implications are discussed.


title: Cardiff Conversation Database (CCDb): A Database of Natural Dyadic Conversations

abstract: We present the Cardiff Conversation Database (CCDb), a unique 2D audiovisual database containing natural conversations between pairs of people. The database currently contains 30 conversations. To date, eight conversations are fully annotated for speaker activity, facial expressions, head motion, and non-verbal utterances. In this paper we describe the data collection and annotation process. We also provide results of baseline experiments in which an SVM classifier was used to identify which parts of the recordings are from the frontchannel speaker and which are backchannel signals. We believe this database will make a useful contribution to computer vision, affective computing, and cognitive science communities by providing raw data, features, annotations and baseline comparisons.


title: USRP technology for multiband passive radar

abstract: The purpose of the paper is to show how the Universal Software Radio Peripheral (USRP) technology, typically used for implementing telecommunication systems, can be used for realizing passive radars. Specifically in this paper a multiband passive radar is considered. A preliminary study on the signals of opportunity is presented where real and simulated radar ambiguity functions have been evaluated. An experimental system has been set up and live data acquired both for UMTS and DVB-T signals in order to verify the capability of the proposed passive radar demonstrator.


title: Helmet Detection on Motorcyclists Using Image Descriptors and Classifiers

abstract: Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. The helmet is the main safety equipment of motorcyclists, however many drivers do not use it. The main goal of helmet is to protect the drivers head in case of accident. In case of accident, if the motorcyclist does not use can be fatal. This paper aims to propose a system for detection of motorcyclist without helmet. For this, we have applied the circular Hough transform and the Histogram of Oriented Gradients descriptor to extract the image attributes. Then, the MultiLayer Perceptron classifier was used and the obtained results were compared with others algorithms. Traffic images were captured by cameras from public roads and constitute a database of 255 images. Indeed, the algorithm step regarding the helmet detection accomplished an accuracy rate of 91.37%.


title: Active damping control of a high power PWM current source rectifier for line current THD reduction

abstract: The use of active damping to reduce the total harmonic distortion (THD) of the line current for medium-voltage (2.3-7.2 kV) high-power pulsewidth-modulation (PWM) current-source rectifiers is investigated. The rectifier requires an LC filter connected at its input terminals, which constitutes an LC resonant mode. The lightly damped LC filter is prone to series and parallel resonances when tuned to a system harmonic either from the utility or from the PWM rectifier. These issues are traditionally addressed at the design stage by properly choosing the filter resonant frequency. This approach may result in a limited performance since the LC resonant frequency is a function of the power system impedance, which usually varies with power system operating conditions. In this paper, an active damping control method is proposed for the reduction in line current THD of high-power current-source rectifiers operating at a switching frequency of only 540 Hz. Two types of LC resonances are investigated: the parallel resonance excited by harmonic currents drawn by the rectifier and the series resonance caused by harmonic pollution in the source voltage. It is demonstrated through simulation and experiments that the proposed active damping control can effectively reduce the line-current THD caused by both parallel and series resonances.


title: Sensor Information Networking Architecture

abstract: This article introduces a sensor information networking architecture, called SINA, that facilitates querying, monitoring, and tasking of sensor networks. SINA serves the role of middleware that abstracts a network of sensor nodes as a collection of massively distributed objects. SINA's execution environment provides a set of configuration and communication primitives that enable scalable and energy-efficient organization of and interactions among sensor objects. On top the execution environment is a programmable substrate that provides mechanisms to create associations and coordinate activities among sensor nodes. Users then access information within a sensor network using declarative queries, or perform tasks using programming script.


title: Aging, Nutritional Status and Health

abstract: The older population is increasing worldwide and in many countries older people will outnumber younger people in the near future. This projected growth in the older population has the potential to place significant burdens on healthcare and support services. Meeting the diet and nutrition needs of older people is therefore crucial for the maintenance of health, functional independence and quality of life. While many older adults remain healthy and eat well those in poorer health may experience difficulties in meeting their nutritional needs. Malnutrition, encompassing both under and over nutrition increases health risks in the older population. More recently the increase in obesity, and in turn the incidence of chronic disease in older adults, now justifies weight management interventions in obese older adults. This growing population group is becoming increasingly diverse in their nutritional requirements. Micro-nutrient status may fluctuate and shortfalls in vitamin D, iron and a number of other nutrients are relatively common and can impact on well-being and quality of life. Aging presents a number of challenges for the maintenance of good nutritional health in older adults.


title: Table Detection in Noisy Off-line Handwritten Documents

abstract: Table detection can be a valuable step in the analysis of unstructured documents. Although much work has been conducted in the domain of machine-print including books, scientific papers, etc., little has been done to address the case of handwritten inputs. In this paper, we study table detection in scanned handwritten documents subject to challenging artifacts and noise. First, we separate text components (machine-print, handwriting) from the rest of the page using an SVM classifier. We then employ a correlation-based approach to measure the coherence between adjacent text lines which may be part of the same table, solving the resulting page decomposition problem using dynamic programming. A report of preliminary results from ongoing experiments concludes the paper.


title: Deep Learning and Medical Diagnosis: A Review of Literature

abstract: In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.


title: SNARKs for C: Verifying program executions succinctly and in zero knowledge

abstract: An argument system for NP is a proof system that allows efficient verification of NP statements, given proofs produced by an untrusted yet computationally-bounded prover. Such a system is non-interactive and publicly-verifiable if, after a trusted party publishes a proving key and a verification key, anyone can use the proving key to generate non-interactive proofs for adaptively-chosen NP statements, and proofs can be verified by anyone by using the verification key.


title: Mechanically Strong Graphene/Aramid Nanofiber Composite Electrodes for Structural Energy and Power.

abstract: Structural energy and power systems offer both mechanical and electrochemical performance in a single multifunctional platform. These are of growing interest because they potentially offer reduction in mass and/or volume for aircraft, satellites, and ground transportation. To this end, flexible graphene-based supercapacitors have attracted much attention due to their extraordinary mechanical and electrical properties, yet they suffer from poor strength. This problem may be exacerbated with the inclusion of functional guest materials, often yielding strengths of <15 MPa. Here, we show that graphene paper supercapacitor electrodes containing aramid nanofibers as guest materials exhibit extraordinarily high tensile strength (100.6 MPa) and excellent electrochemical stability. This is achieved by extensive hydrogen bonding and π-π interactions between the graphene sheets and aramid nanofibers. The trade-off between capacitance and mechanical properties is evaluated as a function of aramid nanofiber loading, where it is shown that these electrodes exhibit multifunctionality superior to that of other graphene-based supercapacitors, nearly rivaling those of graphene-based pseudocapacitors. We anticipate these composite electrodes to be a starting point for structural energy and power systems that harness the mechanical properties of aramid nanofibers.


title: Predicting Stock Price Returns Using Microblog Sentiment for Chinese Stock Market

abstract: Recently there have been many efforts to study the predictability of stock market trend using post sentiments on social media sites. These studies have mainly focused on US stock market. In this paper we investigate the relationship between stock price movement and social media sentiment in China, which has a large stock capitalization and a unique social media landscape. We collect data from several different types of social media sites (microblogs, chat rooms, web forums), and find that data from these sites exhibit distinct characteristics in activity level, post length, and correlation with stock market behavior. Users in stock market related chat rooms tend to post more but much shorter blogs. The activity level is much more highly correlated with market trading hours and stock trading volumes. We then investigated several machine learning models to classify post sentiment in chat rooms, and achieved a performance similar to the state-of-the-art sentiment analysis result for short posts. We find that there is strong correlation and Granger causality between chat room post sentiment and stock price movement, indicating that post sentiments can be used to improve the prediction of stock price return over using the historic stock trading information alone. We further propose a prediction model that uses chat room sentiment to forecast the market direction, and develop a trading strategy that utilizes the prediction as trading indicators. Backtest using our strategy achieves promising portfolio returns. A total return of 19.54% is obtained at the end of the seven-month period when taking into account of slippage and commissions, compared to a loss of -25.26% by a passive buy-and-hold baseline strategy.


title: Brief Announcement: ZeroBlock: Timestamp-Free Prevention of Block-Withholding Attack in Bitcoin

abstract: Bitcoin was recently introduced as a peer-to-peer electronic currency in order to facilitate transactions outside the traditional financial system. The core of Bitcoin, the Blockchain, is the history of the transactions in the system maintained by all miners as a distributed shared register. New blocks in the Blockchain contain the last transactions in the system and are added by miners after a block mining process that consists in solving a resource consuming proof-of-work (cryptographic puzzle). The reward is a motivation for mining process but also could be an incentive for attacks such as selfish mining. In this paper we propose a solution for one of the major problems in Bitcoin : selfish mining or block-withholding attack. This attack is conducted by adversarial or selfish miners in order to either earn undue rewards or waste the computational power of honest miners. Contrary to recent solutions, our solution, ZeroBlock, prevents block-withholding using a technique free of timestamp that can be forged. Moreover, we show that our solution is compliant with nodes churn.


Petabit Optical Switch for Data Center Networks


title: Machine-learning models for combinatorial catalyst discovery

abstract: A variety of machine learning algorithms, including hierarchical clustering, decision trees, k-nearest neighbours, support vector machines and bagging, were applied to construct models to predict the molecular weight of the polymers produced by a set of 96 homogeneous catalysts. The goal of the study was to develop models that could be used to screen large virtual libraries of catalysts in order to suggest candidates for further synthesis and screening. The descriptors used to represent the catalysts did not require detailed information about the catalysts themselves; they could be calculated using only the topology of the ligands. Using an initial set of five descriptors, model accuracies of about 70% were observed from each learning algorithm. A larger descriptor set (with ten descriptors) allowed bag classifiers that were 80% accurate to be built. All models were carefully evaluated to detect overfitting (memorization of the training data) and one example of the effects of overfitting is provided. Because the descriptors used in this study can be calculated very rapidly and the models themselves are very efficient, these bag classifiers are well suited to screening very large virtual libraries.


title: A 103.125-Gb/s Reverse Gearbox IC in 40-nm CMOS for Supporting Legacy 10- and 40-GbE Links

abstract: This paper presents the first 103.125-Gb/s multilink gearbox (MLG) IC, which facilitates the transport of independent 10and 40-GbE signals to 4 × 25.78 Gb/s physical layers, such as 100GBASE-xR4. The IC consumes only 1.37 W while implementing complicated reverse gearbox functionality. The measured TX jitter from 10 and 25-G lanes is 0.407 and 0.448 ps ::: rms ::: , respectively. The measured input sensitivities for a BER of 10 ::: -12 ::: of the 10 and 25-G RXs are 20 and 42 mV ::: ppd ::: , respectively. The proposed gearbox IC, fabricated in a 40-nm CMOS process, occupies 3.7 × 3.4 mm ::: 2 ::: . The power consumption of RX and TX in a 25-G interface is 50.9 and 52 mW, respectively, and those of a 10-G interface are 29 and 24.4 mW, respectively. MLG functionality is verified using embedded self-test logics.


title: Co-Clustering based Dual Prediction for Cargo Pricing Optimization

abstract: This paper targets the problem of cargo pricing optimization in the air cargo business. Given the features associated with a pair of origination and destination, how can we simultaneously predict both the optimal price for the bid stage and the outcome of the transaction (win rate) in the decision stage? In addition, it is often the case that the matrix representing pairs of originations and destinations has a block structure, i.e., the originations and destinations can be co-clustered such that the predictive models are similar within the same co-cluster, and exhibit significant variation among different co-clusters. How can we uncover the co-clusters of originations and destinations while constructing the dual predictive models for the two stages? We take the first step at addressing these problems. In particular, we propose a probabilistic framework to simultaneously construct dual predictive models and uncover the co-clusters of originations and destinations. It maximizes the conditional probability of observing the responses from both the quotation stage and the decision stage, given the features and the co-clusters. By introducing an auxiliary distribution based on the co-clustering assumption, such conditional probability can be converted into an objective function. To minimize the objective function, we propose the cocoa algorithm, which will generate both the suite of predictive models for all the pairs of originations and destinations, as well as the co-clusters consisting of similar pairs. Experimental results on both synthetic data and real data from cargo price bidding demonstrate the effectiveness and efficiency of the proposed algorithm.


title: Design of a Novel Compliant Safe Robot Joint With Multiple Working States

abstract: This paper presents the design and development of a novel compliant safe joint (CSJ). The principle of operation of the CSJ is based on a cam-like transmission mechanism, which we call the bridge. The cam profile on the stationary bridge is designed to have multiple regions, which allow the CSJ to passively achieve multiple working states. The proposed joint behaves as a normal rigid joint when the load is smaller than a preset threshold, shifts into the flexible state when the load exceeds the threshold, and switches into the free state in which the motion cannot be transmitted from the motor to the output shaft. In order to achieve a compact modular assembly, the joint's components are designed to have the shape of a hollow cylinder and to be installed around the motor. We analyze how the key dimension parameters affect the torque threshold and the joint's stiffness. Experiments are conducted to validate the working principle and verify the performance of the joint.


title: Development of open humanoid platform DARwIn-OP

abstract: This paper presents the design method for a humanoid which has a network based modular structure and a standard PC architecture. Based on the proposed method, we developed DARwIn-OP which meets the requirements for an open humanoid platform. DARwIn-OP has an expandable system structure, high performance, simple maintenance, familiar development environment, and affordable prices. All resources of DARwIn-OP including source codes, circuit diagrams, mechanical CAD files, and parts information will be opened to the public.


title: PPGSecure: Biometric Presentation Attack Detection Using Photopletysmograms

abstract: Authentication of users by exploiting face as a biometric is gaining widespread traction due to recent advances in face detection and recognition algorithms. While face recognition has made rapid advances in its performance, such facebased authentication systems remain vulnerable to biometric presentation attacks. Biometric presentation attacks are varied and the most common attacks include the presentation of a video or photograph on a display device, the presentation of a printed photograph or the presentation of a face mask resembling the user to be authenticated. In this paper, we present PPGSecure, a novel methodology that relies on camera-based physiology measurements to detect and thwart such biometric presentation attacks. PPGSecure uses a photoplethysmogram (PPG), which is an estimate of vital signs from the small color changes in the video observed due to minor pulsatile variations in the volume of blood flowing to the face. We demonstrate that the temporal frequency spectra of the estimated PPG signal for real live individuals are distinctly different than those of presentation attacks and exploit these differences to detect presentation attacks. We demonstrate that PPGSecure achieves significantly better performance than existing state of the art presentation attack detection methods.


title: Using GLCM and Gabor filters for classification of PAN images

abstract: In the present research we have used GLCM and Gabor filters to extract texture features in order to classify PAN images. The main drawback of GLCM algorithm is its time-consuming nature. In this work, we proposed a fast GLCM algorithm to overcome the mentioned weakness of traditional GLCM. The fast GLCM is capable of extracting approximately the same features as the traditional GLCM does, but in a really much less time (in the best case, 180 times faster, and in the worst case, 30 times faster). The other weakness of the traditional GLCM is its lower accuracies in the region near the class borders. As Gabor filters are more powerful in border regions, we have tried to combine Gabor features with GLCM features. In this way we would compensate the latter mentioned weakness of GLCM. Experimental results show good capabilities of the proposed fast GLCM and the feature fusion method in classification of PAN images.


BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches


title: Eigenface-domain super-resolution for face recognition

abstract: Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that is later passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose to transfer the super-resolution reconstruction from pixel domain to a lower dimensional face space. Such an approach has the advantage of a significant decrease in the computational complexity of the super-resolution reconstruction. The reconstruction algorithm no longer tries to obtain a visually improved high-quality image, but instead constructs the information required by the recognition system directly in the low dimensional domain without any unnecessary overhead. In addition, we show that face-space super-resolution is more robust to registration errors and noise than pixel-domain super-resolution because of the addition of model-based constraints.


title: Industrial Big Data Analysis in Smart Factory: Current Status and Research Strategies

abstract: Under the background of cyber-physical systems and Industry 4.0, intelligent manufacturing has become an orientation and produced a revolutionary change. Compared with the traditional manufacturing environments, the intelligent manufacturing has the characteristics as highly correlated, deep integration, dynamic integration, and huge volume of data. Accordingly, it still faces various challenges. In this paper, we summarize and analyze the current research status in both domestic and aboard, including industrial big data collection, modeling of the intelligent product lines based on ontology, the predictive diagnosis based on industrial big data, group learning of product line equipment and the product line reconfiguration of intelligent manufacturing. Based on the research status and the problems, we propose the research strategies, including acquisition schemes of industrial big data under the environment of intelligent, ontology modeling and deduction method based intelligent product lines, predictive diagnostic methods on production lines based on deep neural network, deep learning among devices based on cloud supplements and 3-D self-organized reconfiguration mechanism based on the supplements of cloud. In our view, this paper will accelerate the implementation of smart factory.


title: Flying Swarm of Drones Over Circulant Digraph

abstract: Drones are critical to military collaborative engagement, surveillance, and monitoring of areas of interest. They also play an increasingly influential role in commercial applications. We describe a new innovative method for collisionless, survivable, and persistent flying of given $k$ drones over $2n$ waypoints that results in dispersed coverage of a given area of interest, where $n > k \geq 3$ and $k$ divides $n$ . Our method is fundamentally different than any known to date since it does not need sensors to avoid collisions between drones. In addition, the flight of drones does not have to be coordinated.


title: Deep Self-Taught Learning for Weakly Supervised Object Localization

abstract: Most existing weakly supervised localization (WSL) approaches learn detectors by finding positive bounding boxes based on features learned with image-level supervision. However, those features do not contain spatial location related information and usually provide poor-quality positive samples for training a detector. To overcome this issue, we propose a deep self-taught learning approach, which makes the detector learn the object-level features reliable for acquiring tight positive samples and afterwards re-train itself based on them. Consequently, the detector progressively improves its detection ability and localizes more informative positive samples. To implement such self-taught learning, we propose a seed sample acquisition method via image-to-object transferring and dense subgraph discovery to find reliable positive samples for initializing the detector. An online supportive sample harvesting scheme is further proposed to dynamically select the most confident tight positive samples and train the detector in a mutual boosting way. To prevent the detector from being trapped in poor optima due to overfitting, we propose a new relative improvement of predicted CNN scores for guiding the self-taught learning process. Extensive experiments on PASCAL 2007 and 2012 show that our approach outperforms the state-of-the-arts, strongly validating its effectiveness.


title: PARADIS: An Efficient Parallel Algorithm for In-place Radix Sort

abstract: In-place radix sort is a popular distribution-based sorting algorithm for short numeric or string keys due to its linear run-time and constant memory complexity. However, efficient parallelization of in-place radix sort is very challenging for two reasons. First, the initial phase of permuting elements into buckets suffers read-write dependency inherent in its in-place nature. Secondly, load balancing of the recursive application of the algorithm to the resulting buckets is difficult when the buckets are of very different sizes, which happens for skewed distributions of the input data. In this paper, we present a novel parallel in-place radix sort algorithm, PARADIS, which addresses both problems: a) "speculative permutation" solves the first problem by assigning multiple non-continuous array stripes to each processor. The resulting shared-nothing scheme achieves full parallelization. Since our speculative permutation is not complete, it is followed by a "repair" phase, which can again be done in parallel without any data sharing among the processors. b) "distribution-adaptive load balancing" solves the second problem. We dynamically allocate processors in the context of radix sort, so as to minimize the overall completion time. Our experimental results show that PARADIS offers excellent performance/scalability on a wide range of input data sets.


title: A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles

abstract: In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.


title: Digital anthropometry: a critical review

abstract: Anthropometry, Greek for human measurement, is a tool widely used across many scientific disciplines. Clinical nutrition applications include phenotyping subjects across the lifespan for assessing growth, body composition, response to treatments, and predicting health risks. The simple anthropometric tools such as flexible measuring tapes and calipers are now being supplanted by rapidly developing digital technology devices. These systems take many forms, but excitement today surrounds the introduction of relatively low cost three-dimensional optical imaging methods that can be used in research, clinical, and even home settings. This review examines this transformative technology, providing an overview of device operational details, early validation studies, and potential applications. Digital anthropometry is rapidly transforming dormant and static areas of clinical nutrition science with many new applications and research opportunities.


title: Addiction is a Reward Deficit and Stress Surfeit Disorder

abstract: Drug addiction can be defined by a three-stage cycle—binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation—that involves allostatic changes in the brain reward and stress systems. Two primary sources of reinforcement, positive and negative reinforcement, have been hypothesized to play a role in this allostatic process. The negative emotional state that drives negative reinforcement is hypothesized to derive from dysregulation of key neurochemical elements involved in the brain reward and stress systems. Specific neurochemical elements in these structures include not only decreases in reward system function (within-system opponent processes) but also recruitment of the brain stress systems mediated by corticotropin-releasing factor (CRF) and dynorphin-κ opioid systems in the ventral striatum, extended amygdala, and frontal cortex (both between-system opponent processes). CRF antagonists block anxiety-like responses associated with withdrawal, block increases in reward thresholds produced by withdrawal from drugs of abuse, and block compulsive-like drug taking during extended access. Excessive drug taking also engages the activation of CRF in the medial prefrontal cortex, paralleled by deficits in executive function that may facilitate the transition to compulsive-like responding. Neuropeptide Y, a powerful anti-stress neurotransmitter, has a profile of action on compulsive-like responding for ethanol similar to a CRF1 antagonist. Blockade of the κ opioid system can also block dysphoric-like effects associated with withdrawal from drugs of abuse and block the development of compulsive-like responding during extended access to drugs of abuse, suggesting another powerful brain stress system that contributes to compulsive drug seeking. The loss of reward function and recruitment of brain systems provide a powerful neurochemical basis that drives the compulsivity of addiction.


title: The Overall Heat Transfer Characteristics of a Double-Pipe Heat Exchanger

abstract: Heat exchangers are used in industrial processes to recover heat between two process fluids. The purpose of this paper is to develop analytical solutions using mathematical techniques to work out the two-dimensional (2D) temperature changes of flow in the passages of a double-pipe heat exchanger in parallel flow arrangement. Although the necessary equations for heat transfer in a double-pipe heat exchanger are available, using these equations the optimization of the system cost is laborious. Also, the solution of the problem yields the heat-transfer coefficient in inner and outer flows of double-pipe heat exchangers. The results are then compared with the experimental data available in other literature.


title: Multi-Core, Main-Memory Joins: Sort vs. Hash Revisited

abstract: In this paper we experimentally study the performance of main-memory, parallel, multi-core join algorithms, focusing on sort-merge and (radix-)hash join. The relative performance of these two join approaches have been a topic of discussion for a long time. With the advent of modern multi-core architectures, it has been argued that sort-merge join is now a better choice than radix-hash join. This claim is justified based on the width of SIMD instructions (sort-merge outperforms radix-hash join once SIMD is sufficiently wide), and NUMA awareness (sort-merge is superior to hash join in NUMA architectures). We conduct extensive experiments on the original and optimized versions of these algorithms. The experiments show that, contrary to these claims, radix-hash join is still clearly superior, and sort-merge approaches to performance of radix only when very large amounts of data are involved. The paper also provides the fastest implementations of these algorithms, and covers many aspects of modern hardware architectures relevant not only for joins but for any parallel data processing operator.


title: There's More to Touch Than Meets the Eye: The Salience of Object Attributes for Haptics With and Without Vision

abstract: SUMMARY The availability and salience of object attributes under haptic exploration, with and without vision, were assessed by two tasks in which subjects sorted objects that varied factorially in size, shape, texture, and hardness. In the directed-discrimination task, subjeets were instructed to sort along a particular dimension. Although levels on all dimensions were easily discriminated, shape was relatively less so for haptic explorers without vision, as was hardness for those using vision and haptics. Size was least discriminable for both groups. In thefiee-sorting task, subjects were to sort objects by similarity. Three groups used haptic exploration only; these were differentiated by the experimenters' definition of object similarity: unbiased haptics (no particular definition of similarity), haptically biased hapties (similarity = objects feel similar), haptics plus visual imagery (similarity = objects' visual inmges are similar). A fourth group used vision as well as haptics, with instructions like those of the unbiased haptics group. Dimensional salience was measured by the extent to which levels on a dimension were differentiated in free sorting (more differentiation indicating higher salience). The unbiased haptics and haptically biased haptics groups were highly similar; both found the substance dimensions (hardness and texture) relatively salient. The haptics plus visual imagery group showed shape to be overwhelmingly salient, even more so when they were instructed to use two hands, but less so when they had just seen the objects. The haptics plus vision group showed salience to be more evenly distributed over the dimensions. Exploratory hand movements were videotaped and scored into four categories of exploratory procedure (I.,derman & Klatzky, 1987): lateral motion, pressure, contour following, and enclosure (related to texture, hardness, shape, and size, respectively). The distribution of exploratory procedures was found to be directly related to both the designated dimension in the directed-discrimination task, and the salient dimension in the free-sorting task. The results support our contention that the haptic and visual systems have distinct encoding pathways, with haptics oriented toward the encoding of substance rather than shape. This may reflect a direct influence of haptic exploratory procedures: The procedures that are executed under unbiased haptic encoding are those that are generally found to be rapid and accurate (high "ease of encoding"), and the execution of these procedures determines which object properties become salient.


title: Deep networks with stochastic depth for acoustic modelling

abstract: Training very deep neural networks is very difficult because of gradient degradation. However, the incomparable expressiveness of the many deep layers is highly desirable at testing time and usually leads to better performance. Recently, training techniques such as residual networks that enable us to train very deep networks have proved to be a great success. In this paper, we studied the application of the recently proposed deep networks with stochastic depth (DNSD) to train deeper acoustic models for speech recognition. By randomly dropping a subset of layers during training, the studied stochastic depth training method helps reduce the training time substantially, yet the networks trained are much deeper since all the layers are kept during testing. We investigated this approach on the TIMIT data set. Our preliminary experimental results show that when training data are limited, deep networks with stochastic depth helps very little. However, when more training data are available, DNSD significantly improves the recognition accuracy, compared with a conventional deep neural networks.


title: List sphere decoding of polar codes

abstract: Polar codes have gained a lot of attention during the past few years, because they can provably achieve the capacity of a memoryless channel. The design of efficient polar code decoders has been an active topic of research. The simple Successive Cancellation (SC) decoding algorithm yields poor error correction performance on short polar codes: the SC- List (SCL) algorithm overcomes this problem, but its hardware implementation requires a large amount of memory. Sphere Decoding (SD) is an alternative decoding technique that has been shown to work well for short polar codes, but it is burdened by undesirable characteristics. The performance of SD strongly depends on the choice of a suitable sphere radius, whose value must be selected according to the conditions of the channel. Channel conditions also affect the algorithm's time complexity, that is consequently variable. In this paper, we introduce a List- SD algorithm for short polar codes. It has a fixed time complexity and does not make use of a radius: thus, no knowledge of the channel noise level is required. It is shown that the error correction performance of List-SD can match that of SC and SCL with as low as 72% of their memory requirements.


title: Authenticated and persistent skip graph: a data structure for cloud based data-centric applications

abstract: Cloud computing has evolved as a popular computing environment. In data centric applications hosted on the cloud, data is accessed and updated in a purely distributed manner. The distributed data structures used for dynamic storage of the data for such applications require two fundamental qualities, authentication and persistence, which are not completely met by existing distributed data structures. Authentication is a crucial requirement for data structures used on the cloud, as users need to be convinced about the validity of the data they receive. Moreover the data structure has to be persistent, so that changes can be made to the data structure without losing old data, or old versions of the data structure, which may be required by different users in the distributed environment. In this paper we present authenticated and persistent skip graph, an enhanced variant of the existing skip graph. It is based on an event based model, to correspond to the distributed data centric application requirements. Skip graphs are randomized data structures based on skip lists, proposed for use in distributed applications. But skip graphs are neither persistent nor authenticated. The fat node technique is adapted here for providing persistence, and a role based access control, with a cryptographic scheme is used to ensure valid data update and verify the validity of the retrieved data. Our data structure is proved to be efficient in terms of time and space complexities.


Document Image Dewarping Contest


title: Demo: Real-time Ethernet in-car backbones: First insights into an automotive prototype

abstract: The communication infrastructure of today's automobiles forms a complex composition of heterogeneously interconnected components. At the same time, demands for higher bandwidth and low-latency communication are emerging from chassis control, camera based driver assistance and infotainment that cannot be accommodated by established technologies. A new approach towards a flexible highly scalable network is real-time Ethernet. The RECBAR research project develops and evaluates concepts and technologies for next-generation in-car backbones. In this demo we show a prototype based on a 2014 Volkswagen Golf 7 series car additionally equipped with high-bandwidth sensors, such as HD cameras and 3D laser scanners. The car uses a backbone network utilising time-triggered real-time Ethernet for the deterministic transmission of messages with hard realtime as well as rate-limiting and best-effort frames for messages with relaxed timing requirements. On the physical layer the setup utilises the OPEN Alliance 100Mbit/s BroadR-Reach (OABR or 100 BASE-T1) in addition to 100BASE-TX.


title: Eigen-PEP for Video Face Recognition

abstract: To effectively solve the problem of large scale video face recognition, we argue for a comprehensive, compact, and yet flexible representation of a face subject. It shall comprehensively integrate the visual information from all relevant video frames of the subject in a compact form. It shall also be flexible to be incrementally updated, incorporating new or retiring obsolete observations. In search for such a representation, we present the Eigen-PEP that is built upon the recent success of the probabilistic elastic part (PEP) model. It first integrates the information from relevant video sources by a part-based average pooling through the PEP model, which produces an intermediate high dimensional, part-based, and pose-invariant representation. We then compress the intermediate representation through principal component analysis, and only a number of principal eigen dimensions are kept (as small as 100). We evaluate the Eigen-PEP representation both for video-based face verification and identification on the YouTube Faces Dataset and a new Celebrity-1000 video face dataset, respectively. On YouTube Faces, we further improve the state-of-the-art recognition accuracy. On Celebrity-1000, we lead the competing baselines by a significant margin while offering a scalable solution that is linear with respect to the number of subjects.


title: Acceleration of Frequent Itemset Mining on FPGA using SDAccel and Vivado HLS

abstract: Frequent itemset mining (FIM) is a widely-used data-mining technique for discovering sets of frequently-occurring items in large databases. However, FIM is highly time-consuming when datasets grow in size. FPGAs have shown great promise for accelerating computationally-intensive algorithms, but they are hard to use with traditional HDL-based design methods. The recent introduction of Xilinx SDAccel development environment for the C/C++/OpenCL languages allows developers to utilize FPGA's potential without long development periods and extensive hardware knowledge. This paper presents an optimized implementation of an FIM algorithm on FPGA using SDAccel and Vivado HLS. Performance and power consumption are measured with various datasets. When compared to state-of-the-art solutions, this implementation offers up to 3.2× speedup over a 6-core CPU, and has a better energy efficiency as compared with a GPU. Our preliminary results on the new XCKU115 FPGA are even more promising: they demonstrate a comparable performance with a state-of-the-art HDL FPGA implementation and better performance compared to the GPU.


Evolution Of Ocean Surface Features Observed with SAR During The July 90 Gulf Stream Experiment


title: Shapley Value and Monopoly Power In A Two-Sector Model

abstract: This paper considers the measurement of monopoly power in light of recent advances in game theory, in particular those relating to the Shapley value [2, 3, 6]. Traditional measures of monopoly power (e.g., 17]) were all based on Cournot's model of monopoly [5], where the monopolist, knowing his demand curve and knowing that it is Independent of his action, maximizes his profits. Indeed, without the passivity of buyers, the hypothesis of profit maximization cannot operate, for then the situation is that of a game. The essential problem with this model, as pointed out by Aumann [1], is answering when buyers in a monopolized market become passive. Surely it takes more than one buyer, but will even a continuum of buyers be enough? Some examples in the literature [4, 9] suggest that with a very large number of buyers, monopoly will indeed be advantageous, in so far as a monopoly's imputation equals its Shapley value. Testing this hypothesis is the aim of the paper.


title: ANALYSING B2B ELECTRONIC PROCUREMENT BENEFITS – INFORMATION SYSTEMS PERSPECTIVE

abstract: This paper presents electronic procurement benefits identified in four case companies. The benefits achieved in the case companies were classified according to taxonomies from the Information Systems discipline. Existing taxonomies were combined into a new taxonomy which allows evaluation of the complex e-procurement impact. Traditional financial-based methods failed to capture the nature of e-procurement benefits. In the new taxonomy, e-procurement benefits are classified using scorecard dimensions (strategic, tactical and operational), which allows the identification of areas of e-procurement impact, in addition the benefits characteristic is captured (tangible, intangible, financial and non-financial).


title: �Human Brain Mapping 14:140–151(2001) � AMethod for Making Group Inferences from Functional MRI Data Using Independent Component Analysis

abstract: Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression analysis, Kolmogorov–Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA analysis revealed task-related components in left and right visual cortex, a transiently task-related component in bilateral occipital/parietal cortex, and a non-task-related component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI analysis method such as: (1) How many components should be calculated? (2) How are these components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using independent component analysis. Hum. Brain Mapping 14:140–151, 2001. © 2001 Wiley-Liss, Inc.


title: Large Scale Graph Processing in a Distributed Environment

abstract: Large graphs are widely used in real world graph analytics. Memory available in a single machine is usually inadequate to process these graphs. A good solution is to use a distributed environment. Typical programming styles used in existing distributed environment frameworks are different from imperative programming and difficult for programmers to adapt. Moreover, some graph algorithms having a high degree of parallelism ideally run on an accelerator cluster. Error prone and lower level programming methods (memory and thread management) available for such systems repel programmers from using such architectures. Existing frameworks do not deal with the accelerator clusters.


title: Safe Medicine Recommendation via Medical Knowledge Graph Embedding

abstract: Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in the systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.


title: Distribution of myocilin and extracellular matrix components in the juxtacanalicular tissue of human eyes. Invest Ophthalmol Vis Sci

abstract: PURPOSE ::: To examine ultrastructurally the composition of major extracellular matrix (ECM) components and the distribution of myocilin in the trabecular lamellae of corneoscleral (CS) meshwork in normal human eyes. The codistribution of myocilin with ECM components was also investigated. ::: ::: ::: METHODS ::: Postembedding immunoelectron microscopic studies were performed with antibodies against myocilin and other ECM components, including fibronectin, laminin, vitronectin, tenascin, elastin, fibrillin-1, microfibril-associated glycoprotein (MAGP)-1, decorin, versican, hyaluronic acid, and five types of collagen (I, III, IV, V, and VI). Double labeling of myocilin with other ECM components was performed with different sized gold particles. ::: ::: ::: RESULTS ::: In the trabecular beams of CS meshwork, fibronectin, laminin, and collagen type IV were associated with basement membranes, whereas elastin was specifically localized to the core of elastic-like fibers. Several types of collagens, glycoproteins, proteoglycans, and hyaluronic acid were detected both in the collagen fibers and ground substances. Myocilin predominantly localized in the long-spacing collagens and sheath materials surrounding elastic-like fibers, codistributed with fibronectin, fibrillin-1, MAGP-1, decorin, and type VI collagen. ::: ::: ::: CONCLUSIONS ::: This study illustrated the composition of ECM materials in the trabecular lamellae of CS meshwork. Myocilin was specifically localized to long-spacing collagens and the surrounding sheath of elastic-like fibers interacting with microfibril-associated elements where changes have been documented to occur in glaucomatous and aging eyes.


title: Psychopathy and fear: Specific impairments in judging behaviors that frighten others

abstract: Psychopathy is a disorder associated with antisocial behavior and deficits in responding to emotional stimuli, particularly fear-related stimuli. This research demonstrates that these deficits extend to judgments about behaviors that cause fear in others. We assessed whether psychopathy is associated with the ability to identify the emotional consequences of social behaviors and with judgments about these behaviors' acceptability. We found that psychopathy, as indexed by the Psychopathic Personality Inventory, is associated with impairments in identifying behaviors that cause fear and in judging the moral acceptability of these behaviors. Ratings of emotional consequences and moral acceptability were also correlated, such that individuals who less accurately identified behaviors that cause fear also judged these behaviors to be more morally acceptable. Psychopathy scores mediated the relationship between these two variables. These findings suggest that understanding that frightening others is unacceptable relies on understanding this type of behavior's emotional consequences, and have significance for understanding the relationship between psychopathy, empathy, and antisocial behavior. (PsycINFO Database Record (c) 2012 APA, all rights reserved). Language: en


title: Accurate latency-based congestion feedback for datacenters

abstract: The nature of congestion feedback largely governs the behavior of congestion control. In datacenter networks, where RTTs are in hundreds of microseconds, accurate feedback is crucial to achieve both high utilization and low queueing delay. Proposals for datacenter congestion control predominantly leverage ECN or even explicit in-network feedback (e.g., RCP-type feedback) to minimize the queuing delay. In this work we explore latency-based feedback as an alternative and show its advantages over ECN. Against the common belief that such implicit feed-back is noisy and inaccurate, we demonstrate that latency-based implicit feedback is accurate enough to signal a single packet's queuing delay in 10 Gbps networks. ::: ::: DX enables accurate queuing delay measurements whose error falls within 1.98 and 0.53 microseconds using software-based and hardware-based latency measurements, respectively. This enables us to design a new congestion control algorithm that performs fine-grained control to adjust the congestion window just enough to achieve very low queuing delay while attaining full utilization. Our extensive evaluation shows that 1) the latency measurement accurately reflects the one-way queuing delay in single packet level; 2) the latency feedback can be used to perform practical and fine-grained congestion control in high-speed datacenter networks; and 3) DX outperforms DCTCP with 5.33× smaller median queueing delay at 1 Gbps and 1.57× at 10 Gbps.


title: Age differences in exploratory learning from a health information website

abstract: An empirical study was conducted to investigate how older and younger users learned by performing exploratory search of health information using an interface that recommended relevant links based on browsing histories. While older and younger users gained both factual and structural knowledge about the health topics, significant age differences were observed. Our results showed that processing of recommended and regular Web links imposed distinct demands on cognitive abilities, which at least partially explained the observed age differences in the search process. The use of recommended links was positively associated with general knowledge, while the use of regular Web links was positively associated with processing capacity. Results also showed that the recommended links benefited both younger and older adults by broadening the exploration of information, which led to better learning. Implications on designs of health information interfaces that facilitate exploratory search and learning for different age groups were discussed.


title: Hybrid FxRLS-FxNLMS Adaptive Algorithm for Active Noise Control in fMRI Application

abstract: A hybrid adaptive algorithm is developed for an active noise control system that leverages the stability of the filtered-input normalized least mean squares (FxNLMS) adaptive algorithm, with the high convergence speed of the filtered-input recursive least squares (FxRLS) adaptive algorithm. This algorithm is motivated by practical issues in implementing a real-time active noise control system. It leads to fast initial convergence with low, stable steady-state error while being limited by the computational capability of hardware. It gives better convergence speed than either the FxNLMS or FxRLS algorithm individually, lower residual error, and a lower overall computational complexity than the FxRLS algorithm, when appropriate filter lengths are chosen. Experimental results are presented for the implementation of the hybrid algorithm to cancel functional magnetic resonance imaging (fMRI) acoustic noise in an fMRI test-bed.


Computational modeling of mood and feeling from emotion


title: Knowledge Discovery in Medical Systems Using Differential Diagnosis, LAMSTAR, and $k$-NN

abstract: Medical data are an ever-growing source of information generated from the hospitals in the form of patient records. When mined properly, the information hidden in these records is a huge resource bank for medical research. As of now, these data are mostly used only for clinical work. These data often contain hidden patterns and relationships, which can lead to better diagnosis, better medicines, better treatment, and overall, a platform to better understand the mechanisms governing almost all aspects of the medical domain. Unfortunately, discovery of these hidden patterns and relationships often goes unexploited. However, there is on-going research in medical diagnosis which can predict the diseases of the heart, lungs, and various tumours based on the past data collected from the patients. They are mostly limited to domain-specific systems that predict diseases restricted to their area of operation like heart, brain, and various other domains. These are not applicable to the whole medical dataset. The system proposed in this paper uses this vast storage of information so that diagnosis based on these historical data can be made. It focuses on computing the probability of occurrence of a particular ailment from the medical data by mining it using a unique algorithm which increases accuracy of such diagnosis by combining the key points of neural networks, Large Memory Storage, and Retrieval, k-NN, and differential diagnosis all integrated into one single algorithm. The system uses a service-oriented architecture wherein the system components of diagnosis, information portal, and other miscellaneous services are provided. This algorithm can be used in solving a few common problems that are encountered in automated diagnosis these days, which include diagnosis of multiple diseases showing similar symptoms, diagnosis of a person suffering from multiple diseases, receiving faster and more accurate second opinion, and faster identification of trends present in the medical records.


title: Digitally controlled distributed multiphase DC-DC converters

abstract: This paper describes a distributed master-slave multiphase DC-DC converter architecture and related digital control and communication techniques. In the distributed master-slave architecture, all converter modules are identical, but one module operates as the master. The master module communicates its voltage error and current data over a single-wire serial bus to remove the voltage sensing mismatch and to accomplish active current sharing among the modules. Experimental results are shown for a 5 V-to-12 V input, 1.3 V, 20 A output two-phase synchronous buck converter.


title: Deep Bayesian Active Learning with Image Data

abstract: Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).


title: Alcohol Abuse as a Rite of Passage: The Effect of Beliefs about Alcohol and the College Experience on Undergraduates' Drinking Behaviors

abstract: Qualitative studies of alcohol's ritual influences indicate that college undergraduates who drink heavily tend to view alcohol use as integral to the student role and feel entitled to drink irresponsibly. Our analyses, based on a standardized measure of these beliefs administered to approximately 300 students, confirmed these findings. Among our sample, beliefs about alcohol and the college experience had an effect on levels of alcohol consumption similar in magnitude to that of other variables commonly associated with a risk for heavy drinking. Moreover, the alcohol beliefs index moderated the effects of three risk factors—gender, high school drinking, and friends' use of alcohol—on respondents' drinking behaviors. These findings are discussed within the context of the anthropological literature on liminality and rites of passage and with regard to strategies for intervention that address the structural roots of the widespread abuse of alcohol on college campuses.


title: Working for one penny: Understanding why people would like to participate in online tasks with low payment

abstract: The development of Internet technology has facilitated the emergence of online marketplace for various kinds of tasks (e.g., Amazon's Mechanical Turk in USA and Taskcn.com in China). Although the payment is relatively low, numerous people participate in the tasks in these online marketplaces. Drawing upon shopping value literature, we develop a research model by considering various value perceptions so as to better understand this interesting phenomenon. Specifically, it is proposed that hedonic value and utilitarian value (including benefit-cost ratio and net benefit) influence satisfaction and continuance intention. A field survey with 205 participants in an online working website in China was carried out to examine the proposed research model and hypotheses. The key findings are: (1) benefit-cost ratio significantly influences satisfaction, while net benefit does not; (2) there is a significant substitutive effect or negative interaction effect between benefit-cost ratio and net benefit; (3) hedonic value directly influences continuance intention, as well indirectly influences continuance intention via satisfaction. Implications, limitations, and future research directions are discussed.


title: Comparison and evaluation of newest failure rate prediction models: FIDES and RIAC 217Plus

abstract: The failure rate of an example avionics control unit with approximately 7000 electronic components is calculated with the latest state-of-the-art prediction models FIDES Guide 2004 and RIAC-Handbook-217Plus (2006). To allow comparison of the component prediction models a standard civil avionics profile was defined and used for both calculations. Results are compared supported by analysis of the influence of component and application specific parameters such as temperature, temperature cycles, humidity, and vibration. In addition predicted failure rates are compared to field data that has been collected for this unit during 15 years.


title: RoboCrane and EMMA Applied to Waste Storage Tank Remediation

abstract: AbstractThe RoboCrane is a revolutionary concept in crane automation. The RoboCrane, based on the Stewart platform parallel link manipulator design, consists of cables as the parallel links and winches as the actuators. The result is a work platform that can maneuver tools and/or manipulators with full six degree-of-freedom control. EMMA, a serpentine manipulator, is currently being developed for sev-eral applications, including the delivery and orien-tation of end-effectors throughout nuclear waste storage tanks. The combination of Robo- Crane and EMMA can provide an adaptable and highly effective means for waste storage tank remedia-tion.Keywords: RoboCrane, EMMA, parallel link manipulator, Stewart platform devices, cable-based robots, cranes. 1.0 RoboCrane Since the late 1980's, the National Institute of Standards and Technology (NIST) has been work-ing on the RoboCrane ® . The RoboCrane is a cable driven, multi-purpose manipulator based on the Stewart platform parallel link manipulator [1]. It provides full six degree-of-freedom (6 DoF) load control via tele-operative, semi-autonomous and autonomous control modes. Originally, the RoboCrane was developed under a Defense Advanced Research Project Agency (DARPA) contract to stabilize crane loads [2]. Currently, the conﬁguration has advanced to include air-lifted, land, sea, and space applications. It can be designed for high lift-to-weight ratio, stable gantry conﬁgurations, ﬂexibility, precise maneuverability, and mobility over a variety of surfaces including very rough terrain. As part of the NIST mission, the Intelligent Sys-tems Division (ISD) provides "... research in, develops methods for, and applies intelligent sys-tems technology to improve U.S. industry compet-itiveness.” The RoboCrane provides an intelligent machine enhancement to current large scale manu-facturing methods. It also provides a means for manipulating tools and equipment, such as long-range manipulators and end-effectors, into waste storage tanks for inspection and remediation.Multiple RoboCrane prototypes have been devel-oped to study a variety of applications. A 6 m octa-hedral system (see FIGURE 1) has performed heavy load maneuverability, sawing, grinding, gripping, welding, and assembly tasks under tele-operative, preprogrammed, off-line, and/or hybrid control modes. The 6 m RoboCrane prototype has been subjected to a variety of performance measurements and computer simulations [3]. Experimental tests were conducted to verify its functional work volume, static loading capability, and load positioning accuracy. These experimental results compared favorably to associated computer analysis. Posi-tioning uncertainty of this 6 m prototype has been measured at up to 1 mm in translation and angular precision of approximately 0.5° throughout a min-imum 100 m


CitationAS: A Summary Generation Tool Based on Clustering of Retrieved Citation Content


title: On the electrodeposition of nickel-zinc alloys from a eutectic-based ionic liquid

abstract: The electrodeposition behavior of Ni-Zn alloys upon Cu was investigated in a choline chloride-urea (1:2 molar ratio) eutectic-based ionic liquid (1:2 ChCl-urea IL) bath containing 0.1 M NiCl 2 and 0.4 M ZnCl 2. Cyclic voltammetry reveals that the more noble metal, Ni, deposits preferentially to Zn. Ni-Zn co-deposition was achieved via deposition of Zn upon deposited Ni, at potentials more negative than that of Ni deposition, resulting in a Zn content always less than 50 at.%. Chronoamperometric investigations combined with SEM reveal the nucleation mechanism changes from three-dimensional progressive to instantaneous, followed diffusion-limited growth during Zn-codeposition. The Ni content of electrodeposits could be controlled by deposition potential and current density. Ni-Zn deposits were solid solutions, and deposits containing Ni > ∼87 at.% display best corrosion resistance due to a dense and crack-free structure. © 2011 Elsevier Ltd. All rights reserved.


Adolescent development from an agentic perspective


title: Microstrip diplexers design with common resonator sections for compact size, but high isolation

abstract: High isolation and compact size microstrip diplexers designed with common resonator sections have been proposed. By exploiting the variable frequency response of the stepped-impedance resonator, resonators can be shared by the two filter channels of the desired diplexer if their fundamental and the first spurious resonant frequency are properly assigned. Size reduction are, therefore, achieved by introducing a few common resonator sections in the circuit. This concept has been verified by the experimental results of two diplexer circuits. One of the diplexers is composed of two three-pole parallel-coupled bandpass filters and the other is composed of two four-pole cross-coupled bandpass filters, which are formed by only five and six resonators, respectively. Both of them occupy extremely small areas while still keeping good isolations. Good agreements are also achieved between measurement and simulation.


title: A novel method for vegetation encroachment monitoring of transmission lines using a single 2D camera

abstract: The dangerous, overgrown vegetation/trees under high voltage transmission lines right-of-ways (ROWs) have caused severe blackouts/flashovers due to interference with power lines which leads to short circuiting among the conductors. Therefore, these dangerous encroachments are monitored periodically along the electrical distribution networks ROWs through visual inspection, or by airborne system. Each of these methods has its own attributes and limitations and have proved to be costly, time consuming and not much accurate. In these circumstances, it is necessary for the electrical utilities to review their vegetation management practices so as to avoid incidents of unintended encroachments. In tropical countries, overgrown vegetation is a common cause for power line failure. This paper proposes an innovative concept of utilizing a single camera for monitoring dangerous vegetation (trees, shrubs and plants, etc.) under transmission lines ROWs. The main focus is on using an imaging device (camera) integrated on each transmission pole to automate inspection for the vegetation encroachments endangering the transmission lines. These cameras are envisioned to be connected wirelessly to each other, forming a series of wireless camera networks that can be monitored remotely. A single camera mounted on power poles acquires images and sends them wirelessly to the base station. At base station, algorithm (software) trained by image processing and pattern recognition techniques is used to identify (height, depth, and width of encroached vegetation, etc.) excess vegetation encroachments within and outside ROWs. The performance evaluation of a real time developed test-bed scenario proves the feasibilities of integrating the method for transmission line maintenance.