Query: Standardized Extensions of High Efficiency Video Coding (HEVC)

Relevant passages:
Overview of the Stereo and Multiview Video Coding Extensions of the H.264/MPEG-4 AVC Standard <=SEP=> Significant improvements in video compression capability have been demonstrated with the introduction of the H.264/MPEG-4 advanced video coding (AVC) standard. Since developing this standard, the Joint Video Team of the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG) has also standardized an extension of that technology that is referred to as multiview video coding (MVC). MVC provides a compact representation for multiple views of a video scene, such as multiple synchronized video cameras. Stereo-paired video for 3-D viewing is an important special case of MVC. The standard enables inter-view prediction to improve compression capability, as well as supporting ordinary temporal and spatial prediction. It also supports backward compatibility with existing legacy systems by structuring the MVC bitstream to include a compatible “base view.” Each other view is encoded at the same picture resolution as the base view. In recognition of its high-quality encoding capability and support for backward compatibility, the stereo high profile of the MVC extension was selected by the Blu-Ray Disc Association as the coding format for 3-D video with high-definition resolution. This paper provides an overview of the algorithmic design used for extending H.264/MPEG-4 AVC towards MVC. The basic approach of MVC for enabling inter-view prediction and view scalability in the context of H.264/MPEG-4 AVC is reviewed. Related supplemental enhancement information (SEI) metadata is also described. Various “frame compatible” approaches for support of stereo-view video as an alternative to MVC are also discussed. A summary of the coding performance achieved by MVC for both stereo- and multiview video is also provided. Future directions and challenges related to 3-D video are also briefly discussed.

Overview of the High Efficiency Video Coding (HEVC) Standard <=SEP=> High Efficiency Video Coding (HEVC) is currently being prepared as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. The main goal of the HEVC standardization effort is to enable significantly improved compression performance relative to existing standards-in the range of 50% bit-rate reduction for equal perceptual video quality. This paper provides an overview of the technical features and characteristics of the HEVC standard.

H.264/AVC over IP <=SEP=> H.264 is the ITU-T’s new, nonbackward compatible video compression Recommendation that significantly outperforms all previous video compression standards. It consists of a video coding layer (VCL) which performs all the classic signal processing tasks and generates bit strings containing coded macroblocks, and a network adaptation layer (NAL) which adapts those bit strings in a network friendly way. The paper describes the use of H.264 coded video over best-effort IP networks, using RTP as the real-time transport protocol. After the description of the environment, the error-resilience tools of H.264 and the draft specification of the RTP payload format are introduced. Next the performance of several possible VCLand NAL-based error-resilience tools of H.264 are verified in simulations.

Comparison of the Coding Efficiency of Video Coding Standards—Including High Efficiency Video Coding (HEVC) <=SEP=> The compression capability of several generations of video coding standards is compared by means of peak signal-to-noise ratio (PSNR) and subjective testing results. A unified approach is applied to the analysis of designs, including H.262/MPEG-2 Video, H.263, MPEG-4 Visual, H.264/MPEG-4 Advanced Video Coding (AVC), and High Efficiency Video Coding (HEVC). The results of subjective tests for WVGA and HD sequences indicate that HEVC encoders can achieve equivalent subjective reproduction quality as encoders that conform to H.264/MPEG-4 AVC when using approximately 50% less bit rate on average. The HEVC design is shown to be especially effective for low bit rates, high-resolution video content, and low-delay communication applications. The measured subjective improvement somewhat exceeds the improvement measured by the PSNR metric.

The Quadtree and Related Hierarchical Data Structures <=SEP=> A tutorial survey is presented of the quadtree and related hierarchical data structures. They are based on the principle of recursive decomposition. The emphasis is on the representation of data used in applications in image processing, computer graphics, geographic information systems, and robotics. There is a greater emphasis on region data (i.e., two-dimensional shapes) and to a lesser extent on point, curvilinear, and threedimensional data. A number of operations in which such data structures find use are examined in greater detail.

Software developers' perceptions of productivity <=SEP=> The better the software development community becomes at creating software, the more software the world seems to demand. Although there is a large body of research about measuring and investigating productivity from an organizational point of view, there is a paucity of research about how software developers, those at the front-line of software construction, think about, assess and try to improve their productivity. To investigate software developers' perceptions of software development productivity, we conducted two studies: a survey with 379 professional software developers to help elicit themes and an observational study with 11 professional software developers to investigate emergent themes in more detail. In both studies, we found that developers perceive their days as productive when they complete many or big tasks without significant interruptions or context switches. Yet, the observational data we collected shows our participants performed significant task and activity switching while still feeling productive. We analyze such apparent contradictions in our findings and use the analysis to propose ways to better support software developers in a retrospection and improvement of their productivity through the development of new tools and the sharing of best practices.

"The part of me that you bring out": ideal similarity and the Michelangelo phenomenon. <=SEP=> This work examines the Michelangelo phenomenon, an interpersonal model of the means by which people move closer to (vs. further from) their ideal selves. The authors propose that partner similarity--similarity to the ideal self, in particular--plays an important role in this process. Across 4 studies employing diverse designs and measurement techniques, they observed consistent evidence that when partners possess key elements of one another's ideal selves, each person affirms the other by eliciting important aspects of the other's ideals, each person moves closer to his or her ideal self, and couple well-being is enhanced. Partner similarity to the actual self also accounts for unique variance in key elements of this model. The associations of ideal similarity and actual similarity with couple well-being are fully attributable to the Michelangelo process, to partner affirmation and target movement toward the ideal self. The authors also performed auxiliary analyses to rule out several alternative interpretations of these findings.

Wrong, but useful: regional species distribution models may not be improved by range‐wide data under biased sampling <=SEP=> Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence-only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species-specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point-process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor ("prior") to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias-free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data-poor regions.

Autonomous Flight for Detection, Localization, and Tracking of Moving Targets With a Small Quadrotor <=SEP=> In this letter, we address the autonomous flight of a small quadrotor, enabling tracking of a moving object. The 15-cm diameter, 250-g robot relies only on onboard sensors (a single camera and an inertial measurement unit) and computers, and can detect, localize, and track moving objects. Our key contributions include the relative pose estimate of a spherical target as well as the planning algorithm, which considers the dynamics of the underactuated robot, the actuator limitations, and the field of view constraints. We show simulation and experimental results to demonstrate feasibility and performance, as well as robustness to abrupt variations in target motion.

The Role of Search Engine Optimization in Search Marketing <=SEP=> Web sites invest significant resources in trying to influence their visibility among online search results. In addition to paying for sponsored links, they invest in methods known as search engine optimization (SEO) that improve the ranking of a site among the search results without improving its quality. We study the economic incentives of Web sites to invest in SEO and its implications on search engine and advertiser payoffs. We find that the process is equivalent to an all-pay auction with noise and headstarts. Our results show that, under certain conditions, a positive level of search engine optimization improves the search engine’s ranking quality and thus the satisfaction of its visitors. In particular, if the quality of sites coincides with their valuation for visitors then search engine optimization serves as a mechanism that improves the ranking by correcting measurement errors. While this benefits consumers and increases traffic to the search engine, sites participating in search engine optimization could be worse off due to wasteful spending unless their valuation for traffic is very high. We also investigate how search engine optimization affects the revenues from sponsored links. Surprisingly, we find that in many cases search engine revenues are increased by SEO.

Inferring Dockless Shared Bike Distribution in New Cities <=SEP=> Recently, dockless shared bike services have achieved great success and reinvented bike sharing business in China. When expanding bike sharing business into a new city, most start-ups always wish to find out how to cover the whole city with a suitable bike distribution. In this paper, we study the problem of inferring bike distribution in new cities, which is challenging. As no dockless bikes are deployed in the new city, we propose to learn insights on bike distribution from cities populated with dockless bikes. We exploit multi-source data to identify important features that affect bike distributions and develop a novel inference model combining Factor Analysis and Convolutional Neural Network techniques. The extensive experiments on real-life datasets show that the proposed solution provides significantly more accurate inference results compared with competitive prediction methods.

Design and Analysis of a 21–29-GHz Ultra-Wideband Receiver Front-End in 0.18-$\mu$ m CMOS Technology <=SEP=> This paper reports the design and analysis of 21-29-GHz CMOS low-noise amplifier (LNA), balun and mixer in a standard 0.18-μm CMOS process for ultra-wideband automotive radar systems. To verify the proposed LNA, balun, and mixer architectures, a simplified receiver front-end comprising an LNA, a double-balanced Gilbert-cell-based mixer, and two Marchand baluns was implemented. The wideband Marchand baluns can convert the single RF and local oscillator (LO) signals to nearly perfect differential signals over the 21-29-GHz band. The performance of the mixer is improved with the current-bleeding technique and a parallel resonant inductor at the differential outputs of the RF transconductance stage. Over the 21-29-GHz band, the receiver front-end exhibits excellent noise figure of 4.6±0.5 dB, conversion gain of 23.7±1.4 dB, RF port reflection coefficient lower than -8.8 dB, LO-IF isolation lower than -47 dB, LO-RF isolation lower than -55 dB, and RF-IF isolation lower than -35.5 dB. The circuit occupies a chip area of 1.25×1.06 mm2, including the test pads. The dc power dissipation is only 39.2 mW.

Load-Adaptive Modulation of a Series-Resonant Inverter for All-Metal Induction Heating Applications <=SEP=> A conventional induction heating (IH) system has been developed to heat the pot of ferromagnetic materials because the small resistance of nonferromagnetic materials induces large resonant current to the power switch of a series-resonant IH inverter. However, the heating capability for various materials is the most important to improve the functionality and the usability of IH products. In this paper, a load-adaptive modulation (LAM) method is proposed to heat the pot made from nonferromagnetic and ferromagnetic materials. The LAM method can change the magnitude of the input voltage of the IH coil and the operating frequency of the series-resonant IH inverter according to the resistance of the pot. The operational principle and the design method are analyzed to implement the proposed LAM method and its power control. The validity of the LAM method is experimentally verified using a 2-kW prototype series-resonant IH inverter.

BIM: Enabling Sustainability and Asset Management through Knowledge Management <=SEP=> Building Information Modeling (BIM) is the use of virtual building information models to develop building design solutions and design documentation and to analyse construction processes. Recent advances in IT have enabled advanced knowledge management, which in turn facilitates sustainability and improves asset management in the civil construction industry. There are several important qualifiers and some disadvantages of the current suite of technologies. This paper outlines the benefits, enablers, and barriers associated with BIM and makes suggestions about how these issues may be addressed. The paper highlights the advantages of BIM, particularly the increased utility and speed, enhanced fault finding in all construction phases, and enhanced collaborations and visualisation of data. The paper additionally identifies a range of issues concerning the implementation of BIM as follows: IP, liability, risks, and contracts and the authenticity of users. Implementing BIM requires investment in new technology, skills training, and development of new ways of collaboration and Trade Practices concerns. However, when these challenges are overcome, BIM as a new information technology promises a new level of collaborative engineering knowledge management, designed to facilitate sustainability and asset management issues in design, construction, asset management practices, and eventually decommissioning for the civil engineering industry.

Robot Collisions: A Survey on Detection, Isolation, and Identification <=SEP=> Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human–robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products.

Semi-Supervised Ensemble Ranking <=SEP=> Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ranking approaches employ supervised learning techniques to learn appropriate weights for combining multiple rankers. The main shortcoming with these approaches is that the learned weights for ranking algorithms are query independent. This is suboptimal since a ranking algorithm could perform well for certain queries but poorly for others. In this paper, we propose a novel semi-supervised ensemble ranking (SSER) algorithm that learns query-dependent weights when combining multiple rankers in document retrieval. The proposed SSER algorithm is formulated as an SVM-like quadratic program (QP), and therefore can be solved efficiently by taking advantage of optimization techniques that were widely used in existing SVM solvers. We evaluated the proposed technique on a standard document retrieval testbed and observed encouraging results by comparing to a number of state-of-the-art techniques.

Empathy and social functioning in late adulthood. <=SEP=> OBJECTIVES
Both cognitive and affective empathy are regarded as essential prerequisites for successful social functioning, and recent studies have suggested that cognitive, but not affective, empathy may be adversely affected as a consequence of normal adult aging. This decline in cognitive empathy is of concern, as older adults are particularly susceptible to the negative physical and mental health consequences of loneliness and social isolation.


METHOD
The present study compared younger (N = 80) and older (N = 49) adults on measures of cognitive empathy, affective empathy, and social functioning.


RESULTS
Whilst older adults' self-reported and performance-based cognitive empathy was significantly reduced relative to younger adults, there were no age-related differences in affective empathy. Older adults also reported involvement in significantly fewer social activities than younger adults, and cognitive empathy functioned as a partial mediator of this relationship.


CONCLUSION
These findings are consistent with theoretical models that regard cognitive empathy as an essential prerequisite for good interpersonal functioning. However, the cross-sectional nature of the study leaves open the question of causality for future studies.

Study of the therapeutic effects of a hippotherapy simulator in children with cerebral palsy: a stratified single-blind randomized controlled trial. <=SEP=> OBJECTIVE
To investigate whether hippotherapy (when applied by a simulator) improves postural control and balance in children with cerebral palsy.


DESIGN
Stratified single-blind randomized controlled trial with an independent assessor. Stratification was made by gross motor function classification system levels, and allocation was concealed.


SUBJECTS
Children between 4 and 18 years old with cerebral palsy.


INTERVENTIONS
Participants were randomized to an intervention (simulator ON) or control (simulator OFF) group after getting informed consent. Treatment was provided once a week (15 minutes) for 10 weeks.


MAIN MEASURES
Gross Motor Function Measure (dimension B for balance and the Total Score) and Sitting Assessment Scale were carried out at baseline (prior to randomization), end of intervention and 12 weeks after completing the intervention.


RESULTS
Thirty-eight children participated. The groups were balanced at baseline. Sitting balance (measured by dimension B of the Gross Motor Function Measure) improved significantly in the treatment group (effect size = 0.36; 95% CI 0.01-0.71) and the effect size was greater in the severely disabled group (effect size = 0.80; 95% CI 0.13-1.47). The improvements in sitting balance were not maintained over the follow-up period. Changes in the total score of the Gross Motor Function Measure and the Sitting Assessment Scale were not significant.


CONCLUSION
Hippotherapy with a simulator can improve sitting balance in cerebral palsy children who have higher levels of disability. However, this did not lead to a change in the overall function of these children (Gross Motor Function Classification System level V).

Pricing in Agent Economies Using Multi-Agent Q-Learning <=SEP=> This paper investigates how adaptive software agents may utilize reinforcement learning algorithms such as Q-learning to make economic decisions such as setting prices in a competitive marketplace. For a single adaptive agent facing fixed-strategy opponents, ordinary Q-learning is guaranteed to find the optimal policy. However, for a population of agents each trying to adapt in the presence of other adaptive agents, the problem becomes non-stationary and history dependent, and it is not known whether any global convergence will be obtained, and if so, whether such solutions will be optimal. In this paper, we study simultaneous Q-learning by two competing seller agents in three moderately realistic economic models. This is the simplest case in which interesting multi-agent phenomena can occur, and the state space is small enough so that lookup tables can be used to represent the Q-functions. We find that, despite the lack of theoretical guarantees, simultaneous convergence to self-consistent optimal solutions is obtained in each model, at least for small values of the discount parameter. In some cases, exact or approximate convergence is also found even at large discount parameters. We show how the Q-derived policies increase profitability and damp out or eliminate cyclic price “wars” compared to simpler policies based on zero lookahead or short-term lookahead. In one of the models (the “Shopbot” model) where the sellers' profit functions are symmetric, we find that Q-learning can produce either symmetric or broken-symmetry policies, depending on the discount parameter and on initial conditions.

Generating Synthetic Missing Data: A Review by Missing Mechanism <=SEP=> The performance evaluation of imputation algorithms often involves the generation of missing values. Missing values can be inserted in only one feature (univariate configuration) or in several features (multivariate configuration) at different percentages (missing rates) and according to distinct missing mechanisms, namely, missing completely at random, missing at random, and missing not at random. Since the missing data generation process defines the basis for the imputation experiments (configuration, missing rate, and missing mechanism), it is essential that it is appropriately applied; otherwise, conclusions derived from ill-defined setups may be invalid. The goal of this paper is to review the different approaches to synthetic missing data generation found in the literature and discuss their practical details, elaborating on their strengths and weaknesses. Our analysis revealed that creating missing at random and missing not at random scenarios in datasets comprising qualitative features is the most challenging issue in the related work and, therefore, should be the focus of future work in the field.

Driver fatigue detection based on eye tracking and dynamk, template matching <=SEP=> A vision-based real-time driver fatigue detection system is proposed for driving safely. The driver's face is located, from color images captured in a car, by using the characteristic of skin colors. Then, edge detection is used to locate the regions of eyes. In addition to being used as the dynamic templates for eye tracking in the next frame, the obtained eyes' images are also used for fatigue detection in order to generate some warning alarms for driving safety. The system is tested on a Pentium III 550 CPU with 128 MB RAM. The experiment results seem quite encouraging andpromising. The system can reach 20 frames per second for eye tracking, and the average correct rate for eye location and tracking can achieve 99.1% on four test videos. The correct rate for fatigue detection is l00%, but the average precision rate is 88.9% on the test videos.

Effectiveness of Credit Management System on Loan Performance : Empirical Evidence from Micro Finance Sector in Kenya <=SEP=> Microfinance institutions in Kenya experience high levels of non-performing loans. This trend threatens viability and sustainability of MFIs and hinders the achievement of their goals. This study was aimed at assessing the effectiveness of credit management systems on loan performance of microfinance institutions. Specifically we sought to establish the effect of credit terms, client appraisal, credit risk control measures and credit collection policies on loan performance. We adopted a descriptive research design. The respondents were the credit officers of the MFIs in Meru town. Collection policy was found to have a higher effect on loan repayment with =12.74, P=0.000 at 5% significance level. Further research is recommended on the effectiveness of credit referencing on loan performance of MFIs. This study is informative in terms of public policy adjustments and firm level competences required for better operation of MFIs and it also contributes to credit management literature.

Fast Cryptographic Computation on Intel ® Architecture Processors Via Function Stitching <=SEP=> Cryptographic applications often run more than one independent algorithm such as encryption and authentication. This fact provides a high level of parallelism which can be exploited by software and converted into instruction level parallelism to improve overall performance on modern super-scalar processors. We present fast and efficient methods of computing such pairs of functions on IA processors using a method called " function stitching ". Instead of computing pairs of functions sequentially as is done today in applications/libraries, we replace the function calls by a single call to a composite function that implements both algorithms. The execution time of this composite function can be made significantly shorter than the sums of the execution times for the individual functions and, in many cases, close to the execution time of the slower function. Function stitching is best done at a very fine grain, interleaving the code for the individual algorithms at an instruction-level granularity. This results in excellent utilization of the execution resources in the processor core with a single thread. We show how stitching pairs of functions together in a fine-grained manner results in excellent performance on IA processors. Currently, applications perform the functions sequentially. We demonstrate performance gains of 1.4X-1.9X with stitching over the best sequential function performance. We show performance results achieved by this method on the Intel ® processors based on the Westmere architecture.

Dual Quaternion Variational Integrator for Rigid Body Dynamic Simulation <=SEP=> We introduce a symplectic dual quaternion variational integrator(DQVI) for simulating single rigid body motion in all six degrees of freedom. Dual quaternion is used to represent rigid body kinematics and one-step Lie group variational integrator is used to conserve the geometric structure, energy and momentum of the system during the simulation. The combination of these two becomes the first Lie group variational integrator for rigid body simulation without decoupling translations and rotations. Newton-Raphson method is used to solve the recursive dynamic equation. This method is suitable for real-time rigid body simulations with high precision under large time step. DQVI respects the symplectic structure of the system with excellent long-term conservation of geometry structure, momentum and energy. It also allows the reference point and 6-by-6 inertia matrix to be arbitrarily defined, which is very convenient for a variety of engineering problems.

Artificial error generation for translation-based grammatical error correction <=SEP=> Automated grammatical error correction for language learners has attracted a lot of attention in recent years, especially after a number of shared tasks that have encouraged research in the area. Treating the problem as a translation task from 'incorrect' into 'correct' English using statistical machine translation has emerged as a state-of-the-art approach but it requires vast amounts of corrected parallel data to produce useful results. Because manual annotation of incorrect text is laborious and expensive, we can generate artificial error-annotated data by injecting errors deliberately into correct text and thus produce larger amounts of parallel data with much less effort. In this work, we review previous work on artificial error generation and investigate new approaches using random and probabilistic methods for constrained and general error correction. Our methods use error statistics from a reference corpus of learner writing to generate errors in native text that look realistic and plausible in context. We investigate a number of aspects that can play a part in the error generation process, such as the origin of the native texts, the amount of context used to find suitable insertion points, the type of information encoded by the error patterns and the output error distribution. In addition, we explore the use of linguistic information for characterising errors and train systems using different combinations of real and artificial data. Results of our experiments show that the use of artificial errors can improve system performance when they are used in combination with real learner errors, in line with previous research. These improvements are observed for both constrained and general correction, for which probabilistic methods produce the best results. We also demonstrate that systems trained on a combination of real and artificial errors can beat other highly-engineered systems and be more robust, showing that performance can be improved by focusing on the data rather than tuning system parameters. Part of our work is also devoted to the proposal of the I-measure, a new evaluation scheme that scores corrections in terms of improvement on the original text and solves known issues with existing evaluation measures. To my family and L.A. In memory of Kika.

Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection <=SEP=> Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and fine details. Then it adaptively learns to combine these feature maps at each resolution and predict saliency maps with the combined features. Finally, the predicted results are efficiently fused to generate the final saliency map. In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework. By aggregating multi-level convolutional features in this efficient and flexible manner, the proposed saliency model provides accurate salient object labeling. Comprehensive experiments demonstrate that our method performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.

2-MHz GaN PWM isolated SEPIC converters <=SEP=> The eGaN HEMTs with ZVS technique have emerged in high frequency converters to reduce the switching loss. This paper investigates an eGaN HEMT ZVS isolated SEPIC converter running up to 2 MHz. Furthermore, an inductor optimization method is proposed to ensure ZVS and reduce reverse conduction loss of the SR. The efficiency curve is optimized based on the trade-off method between the switching and conduction losses. Moreover, the embedded inductor is proposed to reduce the conduction loss of the inductor. Finally, two 2 MHz eGaN HEMT prototypes with the commercial and embedded inductor with 18–36 V input and 5 V/2 A output are built respectively to verify the effectiveness of the proposed methods. The converters achieve a peak efficiency of over 88.2% and power density of 52.9 W/in3, outperforming the state-of-art power module products.

Vehicle Tracking, Monitoring and Alerting System: A Review <=SEP=> The goal of this paper is to review the past work of vehicle tracking, monitoring and alerting system, to categorize various methodologies and identify new trends. Vehicle tracking, monitoring and alerting system is challenging problem. There are various challenges encounter in vehicle tracking, monitoring and alerting due to deficiency in proper real time vehicle location and problem of alerting system. GPS (Global Positioning System) is most widely used technology for vehicle tracking and keep regular monitoring of vehicle. The objective of tracking system is to manage and control the transport using GPS transreceiver to know the current location of vehicle. In number of system, RFID (Radio Frequency Identification) is chosen as one of technology implemented for bus monitoring system. GSM (Global System for Mobile Communication) is most widely used for alerting system. Alerting system is essential for providing the location and information about vehicle to passenger, owner or user.

Recognition of Emotional Face Expressions and Amygdala Pathology * <=SEP=> The amygdala is often damaged in patients with temporal lobe epilepsy, either because of the primary epileptogenic disease (e.g. sclerosis or encephalitis) or because of secondary effects of surgical interventions (e.g. lobectomy). In humans, the amygdala has been associated with a range of important emotional and social functions, in particular with deficits in emotion recognition from faces. Here we review data from recent neuropsychological research illustrating the amygdala role in processing facial expressions. We describe behavioural findings subsequent to focal lesions and possible factors that may influence the nature and severity of deficits in patients. Both bilateral and unilateral amygdala damage can impair the recog nition of facial emotions, especially fear, but such deficits are not always present and not always specific. Moreover, not all components of emotion or fear processing are impaired after amygdala damage. Dissociations have been reported between deficits in the recognition of emotions from the face of other people, and intact ability in the production of emotions in one's own facial expression [...] CRISTINZIO PERRIN, Chiara, SANDER, David, VUILLEUMIER, Patrik. Recognition of emotional face expressions and amygdala pathology. Epileptologie, 2007, vol. 3, p. 130-138

FPGA Implementations of Neural Networks - A Survey of a Decade of Progress <=SEP=> The first successful FPGA implementation [1] of artificial neural networks (ANNs) was published a little over a decade ago. It is timely to review the progress that has been made in this research area. This brief survey provides a taxonomy for classifying FPGA implementations of ANNs. Different implementation techniques and design issues are discussed. Future research trends are also presented.