Consideration of 10kW in-wheel type axial-gap motor using ferrite permanent magnets

In general, the in-wheel type permanent magnet synchronous motor (PMSM) for electric city commuters uses powerful rare earth permanent magnets (PMs). However, the employment of the rare earth PMs should be reduced due to high prices. Therefore, it is important to develop an in-wheel PMSM that does not use the rare earth PMs. We proposed 5kW in-wheel type axial gap motor that can generate high torque density and has features such as a coreless rotor structure with ferrite PMs and a reduction gearbox in the inner side of the stator. And we have presented details of experimental results on a prototype. So, in this paper, in order to achieve further higher power output, we considered a 10 kW in-wheel motor with the proposed structure. The motor with 10-kW output was examined by means of using 3D-FEM and experimental results in order to attain the further higher power as a rare earth free motor.


A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization

Text categorization has become a key research field in the NLP community. However, most works in this area are focused on Western languages ignoring other Semitic languages like Arabic. These languages are of immense political and social importance necessitating robust categorization techniques. In this paper, we present a novel three-stage technique to efficiently classify Arabic documents into different categories based on the words they contain. We leverage the significance of root-words in Arabic and incorporate a combination of Markov clustering and Deep Belief Networks to classify Arabic words into separate groups (clusters). Our approach is tested on two public datasets giving a F-Measure of 91.02%.


Algorithms on strings, trees, and sequences

Linear-Time Construction of Suffix Trees We will present two methods for constructing suffix trees in detail, Ukkonen’s method and Weiner’s method. Weiner was the first to show that suffix trees can be built in linear time, and his method is presented both for its historical importance and for some different technical ideas that it contains. However, lJkkonen’s method is equally fast and uses far less space (i.e., memory) in practice than Weiner’s method Hence Ukkonen is the method of choice for most problems requiring the construction of a suffix tree. We also believe that Ukkonen’s method is easier to understand. Therefore, it will be presented first A reader who wishes to study only one method is advised to concentrate on it. However, our development of Weiner’s method does not depend on understanding Ukkonen’s algorithm, and the two algorithms can be read independently (with one small shared section noted in the description of Weiner’s method).


Enterprise Software System Integration : An Architectural Perspective

The present thesis is concerned with enterprise software systems of companies in the Swedish electricity industry, an industry that for the past few years has been exposed to a fairly tumultuous change process as a consequence of legislative reforms. Previously, the business operations of electric utilities – as well as those of most companies in the computerized world – were supported by a number of isolated software systems performing specific tasks. In recent years, these systems have been extended, and more importantly, integrated into a company-wide system in its own right, in this thesis referred to as the enterprise software system. As enterprise software systems have evolved, so has a need for strategies, methods and techniques for their management. Enterprise software system management involves a number of concerns; of these concerns, software integration is one of the most prominent. The discipline of software architecture is concerned with the modeling of large-scale structures of software systems. While the generated models are employed for a number of purposes , their perhaps most significant function is to serve as a base for reasoning about the represented system. Several methods for analysis of architectures have been proposed in recent years, and although software architecture analysis has displayed considerable success for a number of systems, enterprise software systems have to a large extent been ignored. In the empirical context of the Scandinavian electricity industry, this thesis explores the applicability of software architecture analysis to enterprise software system integration. Two conceptually different architectural analysis methods – deduction-and induction-based approaches – are considered, as well as the engineering process in which architectural analysis is performed. As a result of the investigations, the thesis proposes a modified process for architectural analysis, presents an evaluation of deduction-based analysis methods , and proposes an adaptation of induction-based analysis methods to the enterprise software system context.


Wireless Sensor Network for Precise Agriculture Monitoring

Precision Agriculture Monitor System (PAMS) is an intelligent system which can monitor the agricultural environments of crops and provides service to farmers. PAMS based on the wireless sensor network (WSN) technique attracts increasing attention in recent years. The purpose of such systems is to improve the outputs of crops by means of managing and monitoring the growth period. This paper presents the design of a WSN for PAMS, shares our real-world experience, and discusses the research and engineering challenges in implementation and deployments.


Synthesis and Application of Monolayer Semiconductors (June 2015)

Recently, semiconducting monolayers, such as MoS2 and WSe2, have been highlighted for their spin-valley coupling, diverse band structures, bendability, and excellent optoelectronic performances. With a subnanometer thickness of atomic layers, the transition metal dichalcogenides (TMDc) atomic layers demonstrate a significant photoresponse, considerable absorption to incident sunlight and favorable transport performances, leading to applications in the electronic circuit requiring low stand-by power, diverse optoelectronic devices, and next-generation nanoelectronics. Therefore, the class of monolayer TMDc offers a burgeoning field in materials science, fundamental physics, and optoelectronics. A feasible synthetic process to realize controlled synthesis of large area and high quality of TMDc monolayers is in demands. In this review, we will introduce the progress on synthesis and applications of the TMDc atomic layers.


Human skin as data transmission medium for improved privacy and usability in wearable electronics

Wearable electronics have multiple benefits in medical applications, such as feedback, monitoring and prevention. The number of these devices in circulation is rapidly growing, emphasizing increasing problems in their use caused by one of two standard communication types - wireless communications and wired communications. These problems include obtrusiveness and inconvenient usage, limited bandwidth, privacy and security issues and relatively high energy consumption. Wireless communications are less secure, with a more limited bandwidth but more unobtrusive and convenient than wired ones. On the other hand, wired communications don’t need so many batteries, thereby devices can be charged more rarely and provide more privacy. A third, emerging technology for wearable sensor network communication using the body itself as communication medium tries to solve these problems. The body coupled communications benefit from the communication privacy of the wired networks, while not requiring obtrusive wiring. Unfortunately this type of communication requires complex signal processing for reliable communications as the signal changes depending on the body parts connected. To solve this problem and allow more rapid adoption of body coupled communications in wearable electronics we develop a transfer function for calculating the parameters of human skin as data transmission medium, allowing more usable and privacy oriented communications between the wearable nodes. Additionally this has the potential of reducing the energy consumption of wearable devices making them more practical to use for a prolonged period of time as well as opening new applications as secure personal data exchange using touch.


Gender and Smile Classification Using Deep Convolutional Neural Networks

Facial gender and smile classification in unconstrained environment is challenging due to the invertible and large variations of face images. In this paper, we propose a deep model composed of GNet and SNet for these two tasks. We leverage the multi-task learning and the general-to-specific fine-tuning scheme to enhance the performance of our model. Our strategies exploit the inherent correlation between face identity, smile, gender and other face attributes to relieve the problem of over-fitting on small training set and improve the classification performance. We also propose the tasks-aware face cropping scheme to extract attribute-specific regions. The experimental results on the ChaLearn 16 FotW dataset for gender and smile classification demonstrate the effectiveness of our proposed methods.


Evidence-Based Psychological Assessment.

In recent years there has been increasing emphasis on evidence-based practice in psychology (EBPP), and as is true in most health care professions, the primary focus of EBPP has been on treatment. Comparatively little attention has been devoted to applying the principles of EBPP to psychological assessment, despite the fact that assessment plays a central role in myriad domains of empirical and applied psychology (e.g., research, forensics, behavioral health, risk management, diagnosis and classification in mental health settings, documentation of neuropsychological impairment and recovery, personnel selection and placement in organizational contexts). This article outlines the central elements of evidence-based psychological assessment (EBPA), using the American Psychological Association's tripartite definition of EBPP as integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences. After discussing strategies for conceptualizing and operationalizing evidence-based testing and evidence-based assessment, 6 core skills and 3 meta-skills that underlie proficiency in psychological assessment are described. The integration of patient characteristics, culture, and preferences is discussed in terms of the complex interaction of patient and assessor identities and values throughout the assessment process. A preliminary framework for implementing EBPA is offered, and avenues for continued refinement and growth are described.


Very low-voltage fully differential amplifier for switched-capacitor applications

A fully differential opamp suitable for very-low voltage switched-capacitor circuits in standard CMOS technologies is introduced. The proposed two stage opamp needs a simple low voltage CMFB switched-capacitor circuit only for the second stage. Due to the reduced supply voltage, the CMFB circuit is implemented using boot-strapped switches. Minor modifications allow to use chop-per stabilization for flicker noise reduction. Two different compensation schemes are discussed and compared using an example for 1V operation of the amplifier.


Optimization of 6Slots-7Poles & 12Slots-14Poles flux-switching permanent magnet machines for plug-in HEV

Plug-in hybrid electric vehicle (PHEV) is independent of the internal combustion engine but acts as the auxiliary unit mostly on the electric drive system. Main while, the Flux-switching permanent magnet machine (FSPMM) has been studied in terms of structure with its operation principle been analyzed. To achieve a higher torque with a corresponding high power density and lower torque ripple, FSPMM with 6Slots-7Poles and 12Slots-14Poles, according investigations, are optimized based on their objective functions. Moreover, it analyzes several typical performance curves, such as cogging torque, flux linkage and back-EMF. Finally, the torque and corresponding power, efficiency and rotor mechanical strength analysis of optimized designed are obtained. The results indicate that FSPMM is a viable candidate for PHEV and also has good mechanical robustness with strong flux-weakening ability.


Magnetic Equivalent Circuit Modeling of the AC Homopolar Machine for Flywheel Energy Storage

This paper develops a magnetic equivalent circuit model suitable to the design and optimization of the synchronous ac homopolar machine. The ac homopolar machine is of particular interest in the application of grid-based flywheel energy storage, where it has the potential to significantly reduce self-discharge associated with magnetic losses. The ac homopolar machine features both axial and radial magnetizing flux paths, which requires finite element analysis to be conducted in 3-D. The computation time associated with 3-D finite element modeling is highly prohibitive in the design process. The magnetic equivalent circuit model developed in this paper is shown to be a viable alternative for calculating several design performance parameters and has a computation time which is orders of magnitude less than that of 3-D finite element analysis. Results obtained from the developed model are shown to be in good agreement with finite element and experimental results for varying levels of saturation.


Analysis of brushless permanent magnet synchronous motors

A brief design review of the Permanent Magnet Synchronous Motors has been presented. A procedure has been developed to predict the steady state and dynamic performances of a brushless permanent magnet synchronous motor. Finite element analysis has been combined with a lumped parameter circuit model in order to provide satisfactory engineering information. To this end, two coordinated steps are involved. One is to develop a unified lumped parameter circuit model for both steady state and dynamic analysis. The second step is to extract the individual lumped parameters from finite element solutions based on corresponding equivalent circuits, each with a pre-determined topology. The proposed techniques have been experimentally verified in a laboratory permanent magnet synchronous motor.


Mail versus Mall: A Strategic Analysis of Competition between Direct Marketers and Conventional Retailers

Consumers now purchase several offerings from direct sellers, including catalog and Internet marketers. These direct channels exist in parallel with the conventional retail stores. The availability of multiple channels has significant implications for the performance of consumer markets. The literature in marketing and economics has, however, been dominated by a focus on the conventional retail sector. This paper is an effort toward modeling competition in the multiplechannel environment from a strategic viewpoint. At the outset, a parsimonious model that accommodates the following consumer and market characteristics is introduced. First, the relative attractiveness of retail shopping varies across consumers. Second, the fit with the direct channel varies across product categories. Third, the strength of existing retail presence in local markets moderates competition. Fourth, in contrast with the fixed location of the retail store that anchors its localized market power, the location of the direct marketer is irrelevant to the competitive outcome. The model is first applied in a setting where consumers have complete knowledge of product availability and prices in all channels. In the resulting equilibrium, the direct marketer acts as a competitive wedge between retail stores. The direct presence is so strong that each retailer competes against the remotely located direct marketer, rather than against neighboring retailers. This outcome has implications for the marketing mix of retailers, which has traditionally been tuned to attract consumers choosing between retail stores. In the context of market entry, conditions under which a direct channel can access a local market in retail entry equilibrium are derived. Our analysis suggests that the traditional focus on retail entry equilibria may not yield informative or relevant findings when direct channels are a strong presence. Next, the role of information in multiple-channel markets is modeled. This issue is particularly relevant in the context of direct marketing where the seller can typically control the level of information in the marketplace, sometimes on a customer-by-customer basis (e.g., by deciding on the mailing list for a catalog campaign). When a certain fraction of consumers does not receive information from the direct marketer, the retailers compete with each other for that fraction of the market. The retailer’s marketing mix has to be tuned, in this case, to jointly address direct and neighboring retail competition. The level of information disseminated by the direct marketer is shown to have strategic implications, and the use of market coverage as a lever to control competition is described. Even with zero information costs, providing information to all consumers may not be optimal under some circumstances. In particular, when the product is not well adapted to the direct channel, the level of market information about the direct option should ideally be lowered. The only way to compete with retailers on a larger scale with a poorly adapted product is by lowering direct prices, which lowers profits. Lowering market information levels and allowing retailers to compete more with each other facilitates a higher equilibrium retail price. In turn, this allows a higher direct price to be charged and improves overall direct profit. On the other hand, when the product is well adapted, increasing direct market presence and engaging in greater competition with the retail sector yields higher returns. The finding that high market coverage may depress profits raises some issues for further exploration. First, implementing the optimal coverage is straightforward when the seller controls the information mechanism, as in the case of catalog marketing. The Internet, in contrast, is an efficient mechanism to transmit information, but does not provide the sellers with such control over the level of market information. A key reason is that the initiative to gather information on the Internet lies largely with consumers. The design and implementation of mechanisms to control aggregate information levels in electronic markets can, therefore, be an important theme for research and managerial interest. Second, direct marketers have traditionally relied on the statistical analysis of customer records to decide on contact policies. The analysis in this paper reveals that these policies can have significant strategic implications as well. Research that integrates the statistical and strategic aspects could make a valuable contribution. The paper concludes with a discussion of issues for future research in multiple-channel markets, including avenues to model competition in settings with multiple direct marketers. (Channels of Distribution; Catalog Marketing; Direct Marketing; Electronic Marketing; Internet Marketing; Competitive Strategy; Game Theory)


How to derive priorities in AHP: a comparative study

A heated discussion has arisen over the “best” priorities derivation method. Using a Monte Carlo simulation this article compares and evaluates the solutions of four AHP ratio scaling methods: the right eigenvalue method, the left eigenvalue method, the geometric mean and the mean of normalized values. Matrices with different dimensions and degree of impurities are randomly constructed. We observe a high level of agreement between the different scaling techniques. The number of ranking contradictions increases with the dimension of the matrix and the inconsistencies. However these contradictions affect only close priorities.


An unusual case of asymptomatic spontaneous umbilical endometriosis treated with skin-sparing excision

Spontaneous umbilical endometriosis is a rare extrapelvic manifestation of endometriosis. Very few such cases have been previously reported, almost always associated with a variety of symptoms, usually during menstruation. We present a case of asymptomatic umbilical endometriosis treated with skin-sparing excision. Differential diagnoses relevant to the clinician, as well as treatment options, are also presented. Surgeons should always consider umbilical endometriosis in their diagnostic approach when confronted with atypical umbilical nodules, regardless of whether they are symptomatic or not.


Bayesian Nonparametric Methods for Learning Markov Switching Processes

In this article, we explored a Bayesian nonparametric approach to learning Markov switching processes. This framework requires one to make fewer assumptions about the underlying dynamics, and thereby allows the data to drive the complexity of the inferred model. We began by examining a Bayesian nonparametric HMM, the sticky HDPHMM, that uses a hierarchical DP prior to regularize an unbounded mode space. We then considered extensions to Markov switching processes with richer, conditionally linear dynamics, including the HDP-AR-HMM and HDP-SLDS. We concluded by considering methods for transferring knowledge among multiple related time series. We argued that a featural representation is more appropriate than a rigid global clustering, as it encourages sharing of behaviors among objects while still allowing sequence-specific variability. In this context, the beta process provides an appealing alternative to the DP.


Cetus: A Source-to-Source Compiler Infrastructure for Multicores

The Cetus tool provides an infrastructure for research on multicore compiler optimizations that emphasizes automatic parallelization. The compiler infrastructure, which targets C programs, supports source-to-source transformations, is user-oriented and easy to handle, and provides the most important parallelization passes as well as the underlying enabling techniques.


A NEW GENERALIZATION OF FIBONACCI SEQUENCE AND EXTENDED BINET’S FORMULA

Consider the Fibonacci sequence {Fn}n=0 with initial conditions F0 = 0, F1 = 1 and recurrence relation Fn = Fn−1 + Fn−2 (n ≥ 2). The Fibonacci sequence has been generalized in many ways, some by preserving the initial conditions, and others by preserving the recurrence relation. In this article, we study a new generalization {qn}, with initial conditions q0 = 0 and q1 = 1, which is generated by the recurrence relation qn = aqn−1 + qn−2 (when n is even) or qn = bqn−1 + qn−2 (when n is odd), where a and b are nonzero real numbers. Some well-known sequences are special cases of this generalization. The Fibonacci sequence is a special case of {qn} with a = b = 1. Pell’s sequence is {qn} with a = b = 2 while k-Fibonacci sequence has a = b = k. We produce an extended Binet’s formula for {qn} and, thereby, identities such as Cassini’s, Catalan’s, d’Ocagne’s, etc.


RHex: A Simple and Highly Mobile Hexapod Robot

In this paper, the authors describe the design and control of RHex, a power autonomous, untethered, compliant-legged hexapod robot. RHex has only six actuators—one motor located at each hip—achieving mechanical simplicity that promotes reliable and robust operation in real-world tasks. Empirically stable and highly maneuverable locomotion arises from a very simple clock-driven, openloop tripod gait. The legs rotate full circle, thereby preventing the common problem of toe stubbing in the protraction (swing) phase. An extensive suite of experimental results documents the robot’s significant “intrinsic mobility”—the traversal of rugged, broken, and obstacle-ridden ground without any terrain sensing or actively controlled adaptation. RHex achieves fast and robust forward locomotion traveling at speeds up to one body length per second and traversing height variations well exceeding its body clearance.


Track--Stair Interaction Analysis and Online Tipover Prediction for a Self-Reconfigurable Tracked Mobile Robot Climbing Stairs

This paper analyzes track-stair interactions and develops an online tipover prediction algorithm for a self-reconfigurable tracked mobile robot climbing stairs, which is vulnerable to tipping-over. Tipover prediction and prevention for a tracked mobile robot in stair climbing are intractable problems because of the complex track--stair interactions. Unlike the wheeled mobile robots, which are normally assumed to obey the nonholonomic constraints, slippage is unavoidable for a tracked mobile robot, especially in stair climbing. Furthermore, the track-stair interactive forces are complicated, which may take the forms of grouser-tread hooking force, track--stair edge frictional force, grouser-riser clutching force, and even their compositions. In this paper, the track--stair interactions are analyzed systematically, and tipover stability criteria are derived for a tracked mobile robot climbing stairs. An online tipover prediction algorithm is also developed, which forms an essential part for autonomous and semiautonomous stair-climbing control. The effectiveness of the proposed algorithms are verified by experiments.


Multi-Modal Locomotion Robotic Platform Using Leg-Track-Wheel Articulations

Other than from its sensing and processing capabilities, a mobile robotic platform can be limited in its use by its ability to move in the environment. Legs, tracks and wheels are all efficient means of ground locomotion that are most suitable in different situations. Legs allow to climb over obstacles and change the height of the robot, modifying its viewpoint of the world. Tracks are efficient on uneven terrains or on soft surfaces (snow, mud, etc.), while wheels are optimal on flat surfaces. Our objective is to work on a new concept capable of combining different locomotion mechanisms to increase the locomotion capabilities of the robotic platform. The design we came up with, called AZIMUT, is symmetrical and is made of four independent leg-track-wheel articulations. It can move with its articulations up, down or straight, allowing the robot to deal with three-dimensional environments. AZIMUT is also capable of moving sideways without changing its orientation, making it omnidirectional. By putting sensors on these articulations, the robot can also actively perceive its environment by changing the orientation of its articulations. Designing a robot with such capabilities requires addressing difficult design compromises, with measurable impacts seen only after integrating all of the components together. Modularity at the structural, hardware and embedded software levels, all considered concurrently in an iterative design process, reveals to be key in the design of sophisticated mobile robotic platforms.


Autonomous Stair Climbing for Tracked Vehicles

In this paper, we present an algorithm for autonomous stair climbing with a tracked vehicle. The proposed method achieves robust performance under real-world conditions, without assuming prior knowledge of the stair geometry, the dynamics of the vehicle’s interaction with the stair surface, or lighting conditions. Our approach relies on fast and accurate estimation of the robot’s heading and its position relative to the stair boundaries. An extended Kalman filter is used for quaternion-based attitude estimation, fusing rotational velocity measurements from a 3-axial gyroscope, and measurements of the stair edges acquired with an onboard camera. A twotiered controller, comprised of a centeringand a headingcontrol module, utilizes the estimates to guide the robot fast, safely, and accurately upstairs. Both the theoretical analysis and implementation of the algorithm are presented in detail, and extensive experimental results demonstrating the algorithm’s performance are described.


Dante II: Technical Description, Results, and Lessons Learned

Dante II is a unique walking robot that provides important insight into high-mobility robotic locomotion and remote robotic exploration. Dante II’s uniqueness stems from its combined legged and rappelling mobility system, its scanning-laser rangefinder, and its multilevel control scheme. In 1994 Dante II was deployed and successfully tested in a remote Alaskan volcano, as a demonstration of the fieldworthiness of these technologies. For more than five days the robot explored alone in the volcano crater using a combination of supervised autonomous control and teleoperated control. Human operators were located 120 km distant during the mission. This article first describes in detail the robot, support systems, control techniques, and user interfaces. We then describe results from the battery of field tests leading up to and including the volcanic mission. Finally, we put forth important lessons which comprise the legacy of this project. We show that framewalkers are appropriate for rappelling in severe terrain, though tether systems have limitations. We also discuss the importance of future “autonomous” systems to realize when they require human support rather than relying on humans for constant oversight. KEY WORDS—rappelling, walking robot, supervised autonomy, behavior-based control, terrain visualization, volcano exploration


Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices

Indoor localization is one of the key enablers for various application and service areas that rely on precise locations of people, goods, and assets, ranging from home automation and assisted living to increased automation of production and logistic processes and wireless network optimization. Existing solutions provide various levels of precision, which also depends on the complexity of the indoor radio environment. In this paper, we propose two methods for reducing the localization error in indoor non-line-of-sight (NLoS) conditions using raw channel impulse response (CIR) information obtained from ultra-wide band radios requiring no prior knowledge about the radio environment. The methods are based on NLoS channel classification and ranging error regression models, both using convolutional neural networks (CNNs) and implemented in the TensorFlow computational framework. We first show that NLoS channel classification using raw CIR data outperforms existing approaches that are based on derived input signal features. We further demonstrate that the predicted NLoS channel state and predicted ranging error information, used in combination with least squares (LS) and weighted LS location estimation algorithms, significantly improve indoor localization performance. We also evaluate the computational performance and suitability of the proposed CNN-based algorithms on various computing platforms with a wide range of different capabilities and show that in a distributed localization system, they can also be used on computationally restricted devices.


Emotion recognition in the wild from videos using images

This paper presents the implementation details of the proposed solution to the Emotion Recognition in the Wild 2016 Challenge, in the category of video-based emotion recognition. The proposed approach takes the video stream from the audio-video trimmed clips provided by the challenge as input and produces the emotion label corresponding to this video sequence. This output is encoded as one out of seven classes: the six basic emotions (Anger, Disgust, Fear, Happiness, Sad, Surprise) and Neutral. Overall, the system consists of several pipelined modules: face detection, image pre-processing, deep feature extraction, feature encoding and, finally, an SVM classification.
 This system achieves 59.42% validation accuracy, surpassing the competition baseline of 38.81%. With regard to test data, our system achieves 56.66% recognition rate, also improving the competition baseline of 40.47%.


Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting

Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected linguistic variables. However, the selection of these variables is theorydriven and itself insensitive to change. We use Doc2Vec on a corpus of 16.8M anonymous online posts in the German-speaking area to learn continuous document representations of cities. These representations capture continuous regional linguistic distinctions, and can serve as input to downstream NLP tasks sensitive to regional variation. By incorporating geographic information via retrofitting and agglomerative clustering with structure, we recover dialect areas at various levels of granularity. Evaluating these clusters against an existing dialect map, we achieve a match of up to 0.77 V-score (harmonic mean of cluster completeness and homogeneity). Our results show that representation learning with retrofitting offers a robust general method to automatically expose dialectal differences and regional variation at a finer granularity than was previously possible.


Sky Detection in Hazy Image

Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions.


A Study and Comparison of Sentiment Analysis Methods for Reputation Evaluation

The aim of this paper is to study and compare some of the methods used to evaluate the reputation of items using sentiment analysis. We explain the challenge of the increasing amount of data available on the Internet and the role of sentiment analysis in mining this information. We classify recent solutions into different categories based on techniques, document approach, and rating methods. We present six methods corresponding to different categories and analyze them based on the technique used, advances and results.


Target Detection in SFCW Ground Penetrating Radar with C3 Algorithm and Hough Transform based on GPRMAX Simulation and Experimental Data

We perform target detection in B-scan images obtained using stepped frequency continuous wave (SFCW) ground-penetrating radar (GPR) simulation with Matlab and the open-source software for electromagnetic propagation simulation gprMax and using experimental data from measurements available on the Web. We perform hyperbola detection and target depth estimation using the Hough transform. Additionally, we reduce the complexity of Hough transform using the Column-Connection Clustering (C3) algorithm. The results show that gprMax can be used with SFCW modulated signals, besides its use with conventional impulse signals (impulse GPR). The proposed combination of C3 and Hough transform shows very good detection capability. These algorithms are also tested on experimental GPR data available on the Web to show their applicability in real soil conditions. Mean subtraction method and Singular Value Decomposition (SVD) algorithms are used to reduce the effects of the ground reflected wave.


StackMap: Low-Latency Networking with the OS Stack and Dedicated NICs

StackMap leverages the best aspects of kernel-bypass networking into a new low-latency Linux network service based on the full-featured TCP kernel implementation, by dedicating network interfaces to applications and offering an extended version of the netmap API as a zero-copy, lowoverhead data path while retaining the socket API for the control path. For small-message, transactional workloads, StackMap outperforms baseline Linux by 4 to 80 % in latency and 4 to 391 % in throughput. It also achieves comparable performance with Seastar, a highly-optimized user-level TCP/IP stack for DPDK.


Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets

The long short-term memory (LSTM) network trained by gradient descent solves difficult problems which traditional recurrent neural networks in general cannot. We have recently observed that the decoupled extended Kalman filter training algorithm allows for even better performance, reducing significantly the number of training steps when compared to the original gradient descent training algorithm. In this paper we present a set of experiments which are unsolvable by classical recurrent networks but which are solved elegantly and robustly and quickly by LSTM combined with Kalman filters.


Free-form deformation of solid geometric models

A technique is presented for deforming solid geometric models in a free-form manner. The technique can be used with any solid modeling system, such as CSG or B-rep. It can deform surface primitives of any type or degree: planes, quadrics, parametric surface patches, or implicitly defined surfaces, for example. The deformation can be applied either globally or locally. Local deformations can be imposed with any desired degree of derivative continuity. It is also possible to deform a solid model in such a way that its volume is preserved.The scheme is based on trivariate Bernstein polynomials, and provides the designer with an intuitive appreciation for its effects.


Conscious and unconscious perception: Experiments on visual masking and word recognition

Five experiments are presented which explore the relation of masking to consciousness and visual word processing. In Experiment 1 a single word or blank field was followed by a pattern mask. Subjects had to make one of three decisions: Did anything precede the mask? To which of two probe words was what preceded the mask more similar graphically? To which of two probe words was it more similar semantically? As word-mask stimulus onset asynchrony (SOA) was reduced, subjects reached chance performance on the detection, graphic, and semantic decisions in that order. In Experiment 2, subjects again had to choose which of two words was more similar either graphically or semantically to a nondetectable masked word, but the forced-choice stimuli now covaried negatively on graphic and semantic similarity. Subjects were now unable to choose selectively on each dimension, suggesting that their ability to choose in Experiment 1 was passively rather than intentionally mediated. In Experiment 3 subjects had to make manual identification responses to color patches which were either accompanied or preceded by words masked to prevent awareness. Color-congruent words facilitated reaction time (RT), color-incongruent words delayed RT. Experiment 4 used a lexical decision task where a trial consisted of the critical letter string following another not requiring a response. When both were words they were either semantically associated or not. The first letter string was either left unmasked, energy masked monoptically, or pattern masked dichoptically to prevent awareness. The effect of association was equal in the unmasked and pattern masked cases, but absent with energy masking. In Experiment 5 repeating a word-plus-mask (where the SOA precluded detection) from 1 to 20 times (a) increased the association effect on a subsequent lexical decision, but had no effect on (b) detectability or(c) the semantic relatedness of forced guesses of the masked word. It is proposed that central pattern masking has little effect on visual processing itself (while peripheral energy masking does), but affects availability of records of the results of those processes to consciousness. Perceptual processing itself is unconscious and automatically proceeds to all levels of analysis and redescription available to the perceiver. The general importance of these findings is to cast doubt on the paradigm assumption that representations yielded by perceptual analysis are identical to and directly reflected by phenomenal percepts.


Automatic Tempo-based Classification of Argentine Tango Music

Tempo-based classification is a popular technique to classify music, with applications in media-art projects, systematic study of music etc. The success of this technique using dance music genres has typically been found to be over 70%, yet it was mostly evaluated using standardized music genres, such as ballroom music. Argentine tango is a music genre, originating in Buenos Aires at the end of the 19th century, which has gained a lot of popularity since its creation and is currently being danced in social settings all over the world (see side Photo taken in San Francisco, courtesy of Ref. [10]). There are three music types which define the Argentine tango played in social settings; although these types have different meters from one another, their tempi feature variations influenced by the trends of the era of composition and the composersʼ personal style. Therefore, Argentine tango seemingly makes a good, yet interesting candidate to explore for tempo-based classification schemes. In this report, we construct and evaluate an automatic, bayes tempo classifier with application to Argentine tango music, using a library with more than 2,000 pieces. We find that the accuracy of our classifier stands at 79%, which is well within the bound set by similar schemes applied to ballroom music, proving our original point that tempo is a promising feature in the classification of Argentine tango. We also come up with interesting insights about how to improve classification schemes for this particular musical genre.


A rule based bengali stemmer

One of the biggest challenges in doing word lookups is to derive the appropriate base word for any given word in Bengali. The basic concept to the solution of the problem is to eliminate inflections from a given word to derive its stem word. Stemmers attempt to reduce a word to its root form using stemming process, which reduces an inflected or derived word to its stem or root form. Existing works in the literature use lookup tables either for stem words or suffixes, increasing the overheads in terms of memory and time. This paper develops a rule-based algorithm that eliminates inflections stepwise without continuously searching for the desired root in the dictionary. To the best of our knowledge, this paper first investigates that, in Bengali morphology, for a large set of inflections, the stems can be computed algorithmically cutting down the inflections step by step. The proposed algorithm is independent of inflected word lengths and our evaluation shows around 88% accuracy.


Data quality management in the public domain: a case study within the Dutch justice system

The need for anonymity preservation within the justice domain requires the introduction of a trusted third party as an intermediary, while integrating individual databases within its boundaries. After the trusted third party encrypts records, it is no longer possible to perform checks on the data quality and correct data anomalies. Therefore, this research examines the concepts of data quality, data integration, record linkage and trusted third party and, then, combines these with four expert interviews in order to identify ways to assess and improve data quality while linking privacy-sensitive data. Next, the trusted data linkage framework (TDLF) is presented to aid data quality management while combining citizens’ privacy-sensitive data from different organisations. Finally, we evaluate the framework in a case study to demonstrate how the quality of structured judicial data can be managed prior to its encryption, while using multiple databases as sources of data and a different final recipient.


Discrete-Time Methods for the Analysis of Event Histories

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Contactless Energy Transfer to a Moving Actuator

In this paper a new topology for contactless energy transfer is proposed and tested that can transfer energy to a moving actuator using inductive coupling. The proposed topology provides long-stroke contactless energy transfer capability in a plane and a short-stroke movement of a few millimeters perpendicular to the plane. In addition, it is tolerant to small rotations. The experimental setup consists of a platform with one secondary coil, which is attached to a linear actuator and a 3-phase brushless electromotor. Underneath the platform is an array of primary coils, which are each connected to a half-bridge square wave power supply. The energy transfer to the electromotor is measured while the platform is moved over the array of primary coils by the linear actuator. The secondary coil moves with a stroke of 18 cm at speeds over 1 m/s, while up to 33 W power is transferred with 90% efficiency


UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.


Bayesian Estimation of Beta Mixture Models with Variational Inference

Bayesian estimation of the parameters in beta mixture models (BMM) is analytically intractable. The numerical solutions to simulate the posterior distribution are available, but incur high computational cost. In this paper, we introduce an approximation to the prior/posterior distribution of the parameters in the beta distribution and propose an analytically tractable (closed form) Bayesian approach to the parameter estimation. The approach is based on the variational inference (VI) framework. Following the principles of the VI framework and utilizing the relative convexity bound, the extended factorized approximation method is applied to approximate the distribution of the parameters in BMM. In a fully Bayesian model where all of the parameters of the BMM are considered as variables and assigned proper distributions, our approach can asymptotically find the optimal estimate of the parameters posterior distribution. Also, the model complexity can be determined based on the data. The closed-form solution is proposed so that no iterative numerical calculation is required. Meanwhile, our approach avoids the drawback of overfitting in the conventional expectation maximization algorithm. The good performance of this approach is verified by experiments with both synthetic and real data.


Action recognition in depth videos using hierarchical gaussian descriptor

In this paper, we propose a new approach based on distribution descriptors for action recognition in depth videos. Our local features are computed from binary patterns which incorporate the shape and motion cues for effective action recognition. Given pixel-level features, our approach estimates video local statistics in a hierarchical manner, where the distribution of pixel-level features and that of frame-level descriptors are modeled using single Gaussians. In this way, our approach constructs video descriptors directly from low-level features without resorting to codebook learning required by Bag-of-features (BoF) based approaches. In order to capture the spatial geometry and temporal order of a video, we use a spatio-temporal pyramid representation for each video. Our approach is validated on six benchmark datasets, i.e. MSRAction3D, MSRGesture3D, DHA, SKIG, UTD-MHAD and CAD-120. The experimental results show that our approach gives good performance on all the datasets. In particular, it achieves state-of-the-art accuracies on DHA, SKIG and UTD-MHAD datasets.


Experiences of integration and performance testing of multilingual OCR for printed Indian scripts

This paper presents integration and testing scheme for managing a large Multilingual OCR Project. The project is an attempt to implement an integrated platform for OCR of different Indian languages. Software engineering, workflow management and testing processes have been discussed in this paper. The OCR has now been experimentally deployed for some specific applications and currently is being enhanced for handling the space and time constraints, achieving higher recognition accuracies and adding new functionalities.


A 5GHz Digital Fractional- $N$ PLL Using a 1-bit Delta–Sigma Frequency-to-Digital Converter in 65 nm CMOS

A highly digital two-stage fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> phase-locked loop (PLL) architecture utilizing a first-order 1-bit <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> frequency-to-digital converter (FDC) is proposed and implemented in a 65nm CMOS process. Performance of the first-order 1-bit <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC is improved by using a phase interpolator-based fractional divider that reduces phase quantizer input span and by using a multiplying delay-locked loop that increases its oversampling ratio. We also describe an analogy between a time-to-digital converter (TDC) and a <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC followed by an accumulator that allows us to leverage the TDC-based PLL analysis techniques to study the impact of <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC characteristics on <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC-based fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> PLL (FDCPLL) performance. Utilizing proposed techniques, a prototype PLL achieves 1 MHz bandwidth, −101.6 dBc/Hz in-band phase noise, and <inline-formula> <tex-math notation="LaTeX">$ {\textrm {1.22 ps}_{\textrm {rms}}}$ </tex-math></inline-formula> (1 kHz–40 MHz) jitter while generating 5.031GHz output from 31.25MHz reference clock input. For the same output frequency, the stand-alone second-stage fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> FDCPLL achieves 1MHz bandwidth, −106.1dBc/Hz in-band phase noise, and <inline-formula> <tex-math notation="LaTeX">$ {\textrm {403 fs}_{\textrm {rms}}}$ </tex-math></inline-formula> jitter with a 500MHz reference clock input. The two-stage PLL consumes 10.1mW power from a 1V supply, out of which 7.1 mW is consumed by the second-stage FDCPLL.


Terra: a virtual machine-based platform for trusted computing

We present a flexible architecture for trusted computing, called Terra, that allows applications with a wide range of security requirements to run simultaneously on commodity hardware. Applications on Terra enjoy the semantics of running on a separate, dedicated, tamper-resistant hardware platform, while retaining the ability to run side-by-side with normal applications on a general-purpose computing platform. Terra achieves this synthesis by use of a trusted virtual machine monitor (TVMM) that partitions a tamper-resistant hardware platform into multiple, isolated virtual machines (VM), providing the appearance of multiple boxes on a single, general-purpose platform. To each VM, the TVMM provides the semantics of either an "open box," i.e. a general-purpose hardware platform like today's PCs and workstations, or a "closed box," an opaque special-purpose platform that protects the privacy and integrity of its contents like today's game consoles and cellular phones. The software stack in each VM can be tailored from the hardware interface up to meet the security requirements of its application(s). The hardware and TVMM can act as a trusted party to allow closed-box VMs to cryptographically identify the software they run, i.e. what is in the box, to remote parties. We explore the strengths and limitations of this architecture by describing our prototype implementation and several applications that we developed for it.


Breaking up is hard to do: security and functionality in a commodity hypervisor

Cloud computing uses virtualization to lease small slices of large-scale datacenter facilities to individual paying customers. These multi-tenant environments, on which numerous large and popular web-based applications run today, are founded on the belief that the virtualization platform is sufficiently secure to prevent breaches of isolation between different users who are co-located on the same host. Hypervisors are believed to be trustworthy in this role because of their small size and narrow interfaces.
 We observe that despite the modest footprint of the hypervisor itself, these platforms have a large aggregate trusted computing base (TCB) that includes a monolithic control VM with numerous interfaces exposed to VMs. We present Xoar, a modified version of Xen that retrofits the modularity and isolation principles used in micro-kernels onto a mature virtualization platform. Xoar breaks the control VM into single-purpose components called service VMs. We show that this componentized abstraction brings a number of benefits: sharing of service components by guests is configurable and auditable, making exposure to risk explicit, and access to the hypervisor is restricted to the least privilege required for each component. Microrebooting components at configurable frequencies reduces the temporal attack surface of individual components. Our approach incurs little performance overhead, and does not require functionality to be sacrificed or components to be rewritten from scratch.


Principles of remote attestation

Remote attestation is the activity of making a claim about properties of a target by supplying evidence to an appraiser over a network. We identify five central principles to guide development of attestation systems. We argue that (i) attestation must be able to deliver temporally fresh evidence; (ii) comprehensive information about the target should be accessible; (iii) the target, or its owner, should be able to constrain disclosure of information about the target; (iv) attestation claims should have explicit semantics to allow decisions to be derived from several claims; and (v) the underlying attestation mechanism must be trustworthy. We illustrate how to acquire evidence from a running system, and how to transport it via protocols to remote appraisers. We propose an architecture for attestation guided by these principles. Virtualized platforms, which are increasingly well supported on stock hardware, provide a natural basis for our attestation architecture.


seL4: formal verification of an OS kernel

Complete formal verification is the only known way to guarantee that a system is free of programming errors.
 We present our experience in performing the formal, machine-checked verification of the seL4 microkernel from an abstract specification down to its C implementation. We assume correctness of compiler, assembly code, and hardware, and we used a unique design approach that fuses formal and operating systems techniques. To our knowledge, this is the first formal proof of functional correctness of a complete, general-purpose operating-system kernel. Functional correctness means here that the implementation always strictly follows our high-level abstract specification of kernel behaviour. This encompasses traditional design and implementation safety properties such as the kernel will never crash, and it will never perform an unsafe operation. It also proves much more: we can predict precisely how the kernel will behave in every possible situation.
 seL4, a third-generation microkernel of L4 provenance, comprises 8,700 lines of C code and 600 lines of assembler. Its performance is comparable to other high-performance L4 kernels.


Linux kernel integrity measurement using contextual inspection

This paper introduces the Linux Kernel Integrity Monitor (LKIM) as an improvement over conventional methods of software integrity measurement. LKIM employs contextual inspection as a means to more completely characterize the operational integrity of a running kernel. In addition to cryptographically hashing static code and data in the kernel, dynamic data structures are examined to provide improved integrity measurement. The base approach examines structures that control the execution flow of the kernel through the use of function pointers as well as other data that affect the operation of the kernel. Such structures provide an efficient means of extending the kernel operations, but they are also a means of inserting malicious code without modifying the static parts. The LKIM implementation is discussed and initial performance data is presented to show that contextual inspection is practical


Person Re-Identification by Iterative Re-Weighted Sparse Ranking

In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second.


Convolutional LSTM Networks for Video-based Person Re-identification

In this paper, we study the problem of video-based person re-identification. This is more challenging, and of greater practical interest, than conventional image-based person re-identification. To address this problem, we propose the use of convolutional Long Short Term Memory (LSTM) based networks to learn a video-based representation for person re-identification. To this end, we propose to jointly leverage deep Convolutional Neural Networks (CNNs) and LSTM networks. Given sequential video frames of a person, the spatial information encoded in the frames is first extracted by a set of CNNs. An encoderdecoder framework derived from LSTMs is employed to encode the resulting temporal of CNN outputs. This approach leads to a refined feature representation that is able to explicitly model the video as an ordered sequence, while preserving the spatial information. Comparative experiments demonstrate that our approach achieves the state-of-the-art performance for video-based person re-identification on iLIDS-VID and PRID 2011, the two primary public datasets for this purpose.


A Review of Ensemble Methods in Bioinformatics

Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this article is two-fold. First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein sequences. Second, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising directions such as ensemble of support vector machine, meta-ensemble, and ensemble based feature selection are discussed.


Integration of independent component analysis and neural networks for ECG beat classification

In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG. 2007 Elsevier Ltd. All rights reserved.


Union simulation on lower limbs rehabilitation robot based on MATLAB and ADAMS

For people who suffers the lower limbs motor dysfunction, using lower limbs exoskeleton rehabilitation devices is a very effective method to restore the motor ability of lower limbs. However, it works only if the device has a suitable mechanical structure and appropriate design parameters for the specific individual. To get this work completed, we carry out the modeling and simulation of a new proposed lower limbs rehabilitation robot with joint help of two kinds of software, i.e. the MATLAB and the ADAMS. Firstly, the three-dimensional model of the robot is created with the SolidWorks software. After importing the model into ADAMS, some essential kinematic pairs are added into the movable joints. Meanwhile, the control system model is established using the MATLAB/SIMULINK software. Then, the union simulation of the lower limbs rehabilitation robot is carried out by using the interface modular ADAMS/CONTROL. The results show that the proposed rehabilitation robot has a good kinematic performance, which means that the device can be used in the actual rehabilitation process. Besides, the results also indicate that the control method is of good trajectory tracking ability. These simulated results will be significant for the development of physical prototype of lower limbs rehabilitation robot.


Efficient and Consistent Flow Update for Software Defined Networks

Software defined network (SDN) provides flexible and scalable routing by separating control plane and data plane. With centralized control, SDN has been widely used in traffic engineering, link failure recovery, and load balancing. This work considers the flow update problem, where a set of flows need to be migrated or rearranged due to change of network status. During flow update, efficiency and consistency are two main challenges. Efficiency refers to how fast these updates are completed, while consistency refers to prevention of blackholes, loops, and network congestions during updates. This paper proposes a scheme that maintains all these properties. It works in four phases. The first phase partitions flows into shorter routing segments to increase update parallelism. The second phase generates a global dependency graph of these segments to be updated. The third phase conducts actual updates and then adjusts dependency graphs accordingly. The last phase deals with deadlocks, if any, and then loops back to phase three if necessary. Through simulations, we validate that our scheme not only ensures freedom of blackholes, loops, congestions, and deadlocks during flow updates, but is also faster than existing schemes.


Realization of three-dimensional walking of a cheetah-modeled bio-inspired quadruped robot

Adaptability of quadruped animals is not solely reached by brain control, but by the interaction between its body, environment, and control. Especially, morphology of the body is supposed to contribute largely to the adaptability. We have tried to understand quadrupedal locomotion by building a bio-inspired quadruped robot named “Pneupard”, which has a feline-like muscular-skeletal structure. In our previous study, we successfully realized alternative gait of hindlimbs by reflex control based on the sole touch information, which is called an unloading rule, and that of forelimbs as well. In this paper, we finally connect forelimbs and hindlimbs by a rigid spine, and conduct 3D walking experiments only with the simple unloading rule. Through several preliminary experiments, we realize that the touch information on the sole is the most critical for stable 3D walking.


Development of an exoskeleton haptic interface for virtual task training

An exoskeleton haptic interface is developed for functional training in virtual environments. A composite control scheme enables a variety of tasks to be implemented, and a “Qt” graphics library is used to generate the virtual environment for the haptic interface at the hand and graphical user interfaces for input and telemetry. Inter-process communications convert telemetry from the exoskeleton into motion commands for objects in the virtual environment. A second haptic interface at the upper arm is used to control the elbow orbit self-motion of the arm during tasks. Preliminary results are reviewed for a wall-painting task in which the virtual wall stiffness and viscosity are generated using an admittance controller.


Centrifugal fan design for permanent magnet synchronous motor in a traction application

The requirements of high torque density and high efficiency, which are particularly pronounced in electric traction applications, often result in substantial thermal loading of electric machines for driving trams, electric multiple units (EMU) or electric cars. Permanent magnet synchronous machines are suitable candidates for traction applications due to their inherently high torque density and high efficiency. At the same time they are sensitive to temperature rise, especially in permanent magnets, highlighting the need for implementation of efficient cooling system. The performance of the cooling system and its ability to remove heat directly affect the attainable torque and efficiency of the electric machine. In this paper, the selection and sizing of the cooling system for an interior permanent magnet motor designed to drive a low-floor tram is presented. The procedure for selecting the basic dimensions of the centrifugal fan according to the analytical formulas in combination with computational fluid dynamics (CFD) analysis are explained. In addition to the geometry of the centrifugal fan itself, the geometry of the passive system components (e.g. air flow router) which have a significant impact on the performance of the cooling system, are also considered. The results of computer aided CFD analysis, which is taken as a benchmark of system performance in the design stage of the cooling system, have been confirmed with measurements on the machine prototype.


Sentiment Analysis using Deep Convolutional Neural Networks with Distant Supervision Master Thesis

This thesis addresses the problem of predicting message-level sentiments of English micro-blog messages from Twitter. Convolutional neural networks (CNN) have shown great promise in the task of sentiment classification. Here we expand the CNN proposed by [31, 32] and perform an in-depth analysis to deepen the understanding of these systems. In a first step we compare the performance of different architectures with focus on deeper architectures. We show how different choices of hyper-parameters impact the results and present the benefits and challenges of using deeper architectures. Second, we introduce the procedure for achieving a high quality initialization of the CNN and show the massive impact it has on its predictive capabilities. This procedure consists of creating custom word embeddings followed by pre-training the network using 90M tweets where the polarity of a tweet is inferred by noisy labels. Third, we present an in-depth analysis of the supervised phase. We show the impact of the pre-training on the supervised phase. We show how the choice of the optimization algorithm and the step size impact the variance of the score during the supervised phase. The analysis is concluded by an inspection of the errors made by the system. This gives us insights into the inner workings of the CNN and hints as to what is needed for further improvement. The CNN presented in this thesis is part of an ensemble which ranked 1st in the SemEval-2016 competition, Task 4a [22]. The task consists of classifying a given tweet as either positive, negative or neutral. This further demonstrates the potential these systems have in the field of sentiment analysis.


Climbing gaits of a modular biped climbing robot

For high-rise work in fields such as agriculture, forestry and architecture, and inspired by the climbing motion of inchworms, chimpanzees, and sloths, we have developed a biped climbing robot - Climbot. Consisting of five 1-DOF joint modules in series and two special grippers at the ends, Climbot is capable of grasping objects and climbing poles, trees and trusses. In this paper, we first introduce this novel robot, and then present three climbing gaits. We perform simulation to illustrate, verify and compare the proposed climbing gaits.


Efficient algorithm ?


A Quantitative Comparison of Calibration Methods for RGB-D Sensors Using Different Technologies

RGB-D (Red Green Blue and Depth) sensors are devices that can provide color and depth information from a scene at the same time. Recently, they have been widely used in many solutions due to their commercial growth from the entertainment market to many diverse areas (e.g., robotics, CAD, etc.). In the research community, these devices have had good uptake due to their acceptable levelofaccuracyformanyapplicationsandtheirlowcost,butinsomecases,theyworkatthelimitof their sensitivity, near to the minimum feature size that can be perceived. For this reason, calibration processes are critical in order to increase their accuracy and enable them to meet the requirements of such kinds of applications. To the best of our knowledge, there is not a comparative study of calibration algorithms evaluating its results in multiple RGB-D sensors. Speciﬁcally, in this paper, a comparison of the three most used calibration methods have been applied to three different RGB-D sensors based on structured light and time-of-ﬂight. The comparison of methods has been carried out by a set of experiments to evaluate the accuracy of depth measurements. Additionally, an object reconstruction application has been used as example of an application for which the sensor works at the limit of its sensitivity. The obtained results of reconstruction have been evaluated through visual inspection and quantitative measurements.


Interactive ramp merging planning in autonomous driving: Multi-merging leading PGM (MML-PGM)

Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also manage these situations by behaving cooperatively and naturally. The challenge of cooperative driving is estimating other vehicles' intentions. In this paper, we present a novel method to estimate other human-driven vehicles' intentions with the aim of achieving a natural and amenable cooperative driving behavior, without using wireless communication. The new approach allows the autonomous vehicle to cooperate with multiple observable merging vehicles on the ramp with a leading vehicle ahead of the autonomous vehicle in the same lane. To avoid calculating trajectories, simplify computation, and take advantage of mature Level-3 components, the new method reacts to merging cars by determining a following target for an off-the-shelf distance keeping module (ACC) which governs speed control of the autonomous vehicle. We train and evaluate the proposed model using real traffic data. Results show that the new approach has a lower collision rate than previous methods and generates more human driver-like behaviors in terms of trajectory similarity and time-to-collision to leading vehicles.


Religious meaning and subjective well-being in late life.

OBJECTIVES
The purpose of this study is to examine the relationship between religious meaning and subjective well-being. A major emphasis is placed on assessing race differences in the relationship between these constructs.

METHODS
Interviews were conducted with a nationwide sample of older White and older Black adults. Survey items were administered to assess a sense of meaning in life that is derived specifically from religion. Subjective well-being was measured with indices of life satisfaction, self-esteem, and optimism.

RESULTS
The findings suggest that older adults who derive a sense of meaning in life from religion tend to have higher levels of life satisfaction, self-esteem, and optimism. The data further reveal that older Black adults are more likely to find meaning in religion than older White adults. In addition, the relationships among religious meaning, life satisfaction, self-esteem, and optimism tend to be stronger for older African Americans persons than older White persons.

DISCUSSION
Researchers have argued for some time that religion may be an important source of resilience for older Black adults, but it is not clear how these beneficial effects arise. The data from this study suggest that religious meaning may be an important factor.


A New Rectenna With All-Polarization-Receiving Capability for Wireless Power Transmission

This letter presents a new rectenna with all-polarization-receiving capability for wireless power transmission applications. By utilizing a dual linearly-polarized antenna, the incident wave of arbitrary polarization can be totally collected at its two ports. To be connected with the antenna as a rectenna, a dual-input rectifier is designed so that the RF power supplied by the two ports of the antenna can be efficiently rectified. With a proper polarization of the incident wave, the proposed rectenna exhibits a maximum efficiency of 78% under the input power density of 295.3 μW/cm2. Moreover, under the same power density, the rectenna's efficiency can always stay higher than 61% regardless of the incident wave's polarization.


Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles

Precise trajectory tracking is a crucial property for Micro Air Vehicles (MAVs) to operate in cluttered environment or under disturbances. In this paper we present a detailed comparison between two state-of-the-art model-based control techniques for MAV trajectory tracking. A classical Linear Model Predictive Controller (LMPC) is presented and compared against a more advanced Nonlinear Model Predictive Controller (NMPC) that considers the full system model. In a careful analysis we show the advantages and disadvantages of the two implementations in terms of speed and tracking performance. This is achieved by evaluating hovering performance, step response, and aggressive trajectory tracking under nominal conditions and under external wind disturbances.


M.Sc. in Computer Science Cum Laude, advised by Shuly Wintner. 2007-2010 • Research topics: semi-supervised multiword expression extraction from parallel and monolingual corpora. • Thesis: “Extraction of Multi-word Expressions from Small Parallel Corpora”. • Awards: Gutwirth award for excellence (awarded top 50 graduate students in Israel); excellence scholarship in honor of Captain Uri Akavia.


Optically transparent conductive polymer RFID meandering dipole antenna

In this paper, we present optically transparent flexible conductive polymer antennas for radio frequency identification systems. The designs for these antennas are presented along with simulated and measured results of antenna radiating properties. These conductive polymer antennas are compared to antennas with the same design fabricated out of copper. Finally, we include an analysis of the optical transparency of the conductive polymer antennas.


Cryptonite: a programmable crypto processor architecture for high bandwidth applications

Cryptographic methods are widely used within networking and digital rights management. Numerous algorithms exist, e.g. spanning VPNs or distributing sensitive data over a shared network infrastructure. While these algorithms can be run with moderate performance on general purpose processors, such processors do not meet typical embedded systems requirements (e.g. area, cost and power consumption). Instead, specialized cores dedicated to one or a combination of algorithms are typically used. These cores provide very high bandwidth data transmission and meet the needs of embedded systems. However, with such cores changing the algorithm is not possible without replacing the hardware. This paper describes a fully programmable processor architecture which has been tailored for the needs of a spectrum of cryptographic algorithms and has been explicitly designed to run at high clock rates while maintaining a significantly better performance/area/power tradeoff than general purpose processors. Both the architecture and instruction set have been developed to achieve a bits-per-clock rate of greater than one, even with complex algorithms. This performance will be demonstrated with standard cryptographic algorithms (AES and DES) and a widely used hash algorithm (MD5).


Potentials and Challenges of C-RAN Supporting Multi-RATs Toward 5G Mobile Networks

This paper presents an overview of the cloud radio access network (C-RAN), which is a key enabler for future mobile networks in order to meet the explosive capacity demand of mobile traffic, and reduce the capital and operating expenditure burden faced by operators. We start by reviewing the requirements of future mobile networks, called 5G, followed by a discussion on emerging network concepts for 5G network architecture. Then, an overview of C-RAN and related works are presented. As a significant scenario of a 5G system, the ultra dense network deployment based on C-RAN is discussed with focuses on flexible backhauling, automated network organization, and advanced mobility management. Another import feature of a 5G system is the long-term coexistence of multiple radio access technologies (multi-RATs). Therefore, we present some directions and preliminary thoughts for future C-RAN-supporting Multi-RATs, including joint resource allocation, mobility management, as well as traffic steering and service mapping.


Depth-Based Hand Pose Estimation: Methods, Data, and Challenges

Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation [required for immersive virtual reality and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.


Towards an intelligent framework for multimodal affective data analysis

An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human-computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate.


Opensmile: the munich versatile and fast open-source audio feature extractor

We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.


A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks

Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network’s baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.


Convolutional recurrent neural networks: Learning spatial dependencies for image representation

In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have been taken into consideration. Such dependencies represent useful spatial structure information in images. Whereas recurrent neural networks (RNNs) are designed for learning contextual dependencies among sequential data by using the recurrent (feedback) connections. In this work, we propose the convolutional recurrent neural network (C-RNN), which learns the spatial dependencies between image regions to enhance the discriminative power of image representation. The C-RNN is trained in an end-to-end manner from raw pixel images. CNN layers are firstly processed to generate middle level features. RNN layer is then learned to encode spatial dependencies. The C-RNN can learn better image representation, especially for images with obvious spatial contextual dependencies. Our method achieves competitive performance on ILSVRC 2012, SUN 397, and MIT indoor.


Traffic Light Control System for Emergency Vehicles Using Radio Frequency

Traffic congestion problem is a phenomena which contributed huge impact to the transportation system in country. This causes many problems especially when there are emergency cases at traffic light intersections which are always busy with many vehicles. A traffic light controller system is designed in order to solve these problems. This system was designed to be operated when it received signal from emergency vehicles based on radio frequency (RF) transmission and used the Programmable Integrated Circuit (PIC) 16F877A microcontroller to change the sequence back to the normal sequence before the emergency mode was triggered. This system will reduce accidents which often happen at the traffic light intersections because of other vehicle had to huddle for given a special route to emergency vehicle. As the result, this project successful analyzing and implementing the wireless communication; the radio frequency (RF) transmission in the traffic light control system for emergency vehicles. The prototype of this project is using the frequency of 434 MHz and function with the sequence mode of traffic light when emergency vehicles passing by an intersection and changing the sequence back to the normal sequence before the emergency mode was triggered. In future, this prototype system can be improved by controlling the real traffic situation, in fact improving present traffic light system technology.


The Ease of Doing Business Index (EDBI) uses 41 variables to compare the business environment of different countries. It is widely used by policy makers, researchers and multinational companies. This paper aims to assess EDBI’s consistency and validity in representing the business environment by using factor analysis. It is found that the EDBI presents a limited consistency and descriptive power of a country’s business environment. The consequence of these findings is that multinational firms should handle carefully the EDBI in their investment decisions. GEE The Ease of Doing Business Index as a tool for Investment location decisions – João Zambujal-Oliveira, Ricardo Pinheiro-Alves


Literature Review on Automatic Text Summarization: Single and Multiple Summarizations

The online information available on world wide web is in enormous amount. Search engines like Google, Yahoo were developed to retrieve information from the databases. But actual results were not obtained as the electronic information is increasing day by day. Thus automatic summarization came into demand. Automatic summarization gathers several documents as input and provides the shorter summarized version as output which is informative, unambiguous, save valuable time. Research was done on a single document and moved towards multiple documents. This review categorizes single and multiple summarization methods.


AN OVERVIEW OF MASSIVE MIMO SYSTEM IN 5 G

4G is proving good speeds up to 1Gbps. Then why do we need anything more. The problem is that it is not able to provide real time applications. 5G is the name given to the next generation of mobile data connectivity. It will definitely provide great speeds between 10Gbps to 100Gbps and it will have enough capacity. But the thing that separated 5G from 4G is latency; the latency provided by 4G is between 40ms to 60ms, whereas in 5G it will provide ultra latency between 1ms to 10ms. The standards for 5G will be set till 2020 and it will be applicable by 2022/23. In this paper we have discussed about 5G and its advantages, a probable architecture for 5G and some challenges in 5G. The main technology that maybe used in 5G are massive MIMO, millimetre wave communication, device to device communication, beam division multiple access etc. In this paper we have discussed about massive MIMO, channel estimate in massive MIMO, beam division multiple access technique to be used in massive MIMO, antenna selection in massive MIMO, capacity and energy efficiency in massive MIMO. In future 5G is going to be a technology which will be invisible, I will be just there everywhere just like electricity. It is a very good area for research as standards and frequency band for 5G are yet to be standardised.


Contextual Dueling Bandits

We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context. Roughly, the learner’s goal is to find the best policy, or way of behaving, in some space of policies, although “best” is not always so clearly defined. Here, we propose a new and natural solution concept, rooted in game theory, called a von Neumann winner, a randomized policy that beats or ties every other policy. We show that this notion overcomes important limitations of existing solutions, particularly the Condorcet winner which has typically been used in the past, but which requires strong and often unrealistic assumptions. We then present three efficient algorithms for online learning in our setting, and for approximating a von Neumann winner from batch-like data. The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space. The other two algorithms require time and space only logarithmic in the size of the policy space when provided access to an oracle for solving classification problems on the space.


Beyond Slideware: How a Free-form Presentation Medium Stimulates Free-form Thinking in the Classroom

We investigate how presentation in a free-form medium stimulates free-form thinking and discussion in the classroom. Most classroom presentations utilize slideware (e.g. PowerPoint). Yet, slides add intrusive segregations that obstruct the flow of information. In contrast, in a free-form medium of presentation, content is not separated into rigid slide compartments. Instead, it is visually arranged and transformed in a continuous space.
 We develop a case study that investigates student experiences authoring, presenting, viewing, and discussing free-form presentations in a graduate seminar class. We analyze interviews, present a sampling of student presentations, and develop findings: free-form presentation stimulates free-form thinking, spontaneous discussion, and emergent ideation.


Efficient Restoration Method for Images Corrupted with Impulse Noise

This paper proposes a two-stage adaptive method for restoration of images corrupted with impulse noise. In the first stage, the pixels which are most likely contaminated by noise are detected based on their intensity values. In the second stage, an efficient average filtering algorithm is used to remove those noisy pixels from the image. Only pixels which are determined to be noisy in the first stage are processed in the second stage. The remaining pixels of the first stage are not processed further and are just copied to their corresponding locations in the restored image. The experimental results for the proposed method demonstrate that it is faster and simpler than even median filtering, and it is very efficient for images corrupted with a wide range of impulse noise densities varying from 10% to 90%. Because of its simplicity, high speed, and low computational complexity, the proposed method can be used in realtime digital image applications, e.g., in consumer electronic products such as digital televisions and cameras.


A Simple Parallel Algorithm for the Maximal Independent Set Problem

Simple parallel algorithms for the maximal independent set (MIS) problem are presented. The first algorithm is a Monte Carlo algorithm with a very local property. The local property of this algorithm may make it a useful protocol design tool in distributed computing environments and artificial intelligence. One of the main contributions of this paper is the development of powerful and general techniques for converting Monte Carlo algorithms into deterministic algorithms. These techniques are used to convert the Monte Carlo algorithm for the MIS problem into a simple deterministic algorithm with the same parallel running time.


Interruptions on software teams: a comparison of paired and solo programmers

This study explores interruption patterns among software developers who program in pairs versus those who program solo. Ethnographic observations indicate that interruption length, content, type, occurrence time, and interrupter and interruptee strategies differed markedly for radically collocated pair programmers versus the programmers who primarily worked alone. After presenting an analysis of 242 interruptions drawn from more than 40 hours of observation data, we discuss how team configuration and work setting influenced how and when developers handled interruptions. We then suggest ways that CSCW systems might better support pair programming and, more broadly, provide interruption-handling support for workers in knowledge-intensive occupations.


Metric Learning and Manifolds Metric Learning and Manifolds : Preserving the Intrinsic Geometry

A variety of algorithms exist for performing non-linear dimension reduction, but these algorithms do not preserve the original geometry of the data except in special cases. In general, in the low-dimensional representations obtained, distances are distorted, as well as angles, areas, etc. This paper proposes a generic method to estimate the distortion incurred at each point of an embedding, and subsequently to correct distances and other intrinsic geometric quantities back to their original values (up to sampling noise). Our approach is based on augmenting the output of an embedding algorithm with geometric information embodied in the Riemannian metric of the manifold. The Riemannian metric allows one to compute geometric quantities (such as angle, length, or volume) for any coordinate system or embedding of the manifold. In this work, we provide an algorithm for estimating the Riemannian metric from data, consider its consistency, and demonstrate the uses of our approach in a variety of examples.


Gaze- vs. hand-based pointing in virtual environments

This paper contributes to the nascent body of literature on pointing performance in Virtual Environments (VEs), comparing gaze- and hand-based pointing. Contrary to previous findings, preliminary results indicate that gaze-based pointing is slower than hand-based pointing for distant objects.


Toward preprototype user acceptance testing of new information systems: implications for software project management

Errors in requirements specifications have been identified as a major contributor to costly software project failures. It would be highly beneficial if information systems developers could verify requirements by predicting workplace acceptance of a new system based on user evaluations of its specifications measured during the earliest stages of the development project, ideally before building a working prototype. However, conventional wisdom among system developers asserts that prospective users must have direct hands-on experience with at least a working prototype of a new system before they can provide assessments that accurately reflect future usage behavior after workplace implementation. The present research demonstrates that this assumption is only partially true. Specifically, it is true that stable and predictive assessments of a system's perceived ease of use should be based on direct behavioral experience using the system. However, stable and behaviorally predictive measures of perceived usefulness can be captured from target users who have received information about a system's functionality, but have not had direct hands-on usage experience. This distinction is key because, compared to ease of use, usefulness is generally much more strongly linked to future usage intentions and behaviors in the workplace. Two longitudinal field experiments show that preprototype usefulness measures can closely approximate hands-on based usefulness measures, and are significantly predictive of usage intentions and behavior up to six months after workplace implementation. The present findings open the door toward research on how user acceptance testing may be done much earlier in the system development process than has traditionally been the case. Such preprototype user acceptance tests have greater informational value than their postprototype counterparts because they are captured when only a relatively small proportion of project costs have been incurred and there is greater flexibility to modify a new system's design attributes. Implications are discussed for future research to confirm the robustness of the present findings and to better understand the practical potential and limitations of preprototype user acceptance testing.