query: Novel rotor design optimization of Synchronous Reluctance Machine for low torque ripple

positive: Torque ripple improvement for synchronous reluctance motor using an asymmetric flux barrier arrangement An interior permanent-magnet synchronous motor (IPMSM) is a highly efficient motor and operates in a wide speed range; therefore, it is used in many industrial and home appliance applications. However, the torque ripple of synchronous motors such as the IPMSM and synchronous reluctance motor is very large. The variation of magnetic resistance between the flux barriers and teeth causes the torque ripple. In this paper, flux barriers are asymmetrically designed so that the relative positions between the outer edges of the flux barriers and the teeth do not correspond. As a result, torque ripple can be reduced dramatically.

positive: Analysis of Rotor Core Eddy-Current Losses in Interior Permanent-Magnet Synchronous Machines This paper presents the results of an investigation focused on the rotor core eddy-current losses of interior permanent-magnet (IPM) synchronous machines. First, analytical insight into the rotor core eddy-current losses of IPM machines is developed. Next, major design parameters that have the most significant impact on the rotor core eddy-current losses of IPM machines are identified. Finite-element analysis results are then presented to compare the predicted eddy-current losses in the machine core of IPM machines with one- and two-layer rotors coupled with concentrated- and distributed-winding stators. It is shown that the lowest total eddy-current losses in the machine core are achieved using a combination of distributed stator windings and two magnet layers per rotor pole, whereas minimizing only the rotor core eddy-current losses favors replacement of the rotor with a single-layer configuration.

positive: Optimization of average and cogging torque in 3-phase IPM motor drives In this paper, an interior permanent magnet (IPM) brushless DC motor for traction applications is analyzed. The effect of magnetization direction, number of stator slots and current waveform on torque pulsation are examined. A three-phase, four-pole IPM motor is considered for the study. The finite element method is used to calculate the torque, reluctance torque, back iron flux density, tooth flux density, detent torque and back-EMF of the motor. It is shown that because of the reluctance torque resulting from rotor saliency, the peak point in the torque-angle curve is shifted to the left. Therefore, it is not possible to find the switching instants just by considering the direction of stator and rotor MMF and keeping the right angle between them. A Matlab program has been developed to rind the switching intervals, which will produce the maximum average torque and minimum cogging torque. Experimental results to support the simulation findings are included in the paper.

positive: Iron loss analysis of interior permanent-magnet synchronous motors-variation of main loss factors due to driving condition In this paper, the authors investigate the iron loss of interior permanent magnet motors driven by pulsewidth modulation (PWM) inverters from both results of the experiments and the finite-element analysis. In the analysis, the iron loss of the motor is decomposed into several components due to their origins, for instance, the fundamental field, carrier of the PWM inverter, slot ripples, and harmonic magnetomotive forces of the permanent magnet in order to clarify the main loss factors. The Fourier transformation and the finite-element method considering the carrier harmonics are applied to this calculation. The calculated iron loss is compared with the measurement at each driving condition. The measured and the calculated results agree well. It is clarified that the iron loss caused by the carrier of the PWM inverter is the largest component at low-speed condition under the maximum torque control, whereas the loss caused by the harmonic magnetomotive forces of the permanent magnet remarkably increase at high-speed condition under the flux-weakening control

positive: Pulsating torque minimization techniques for permanent magnet AC motor drives-a review AbstructPermanent magnet ac (PMAC) motor drives are finding expanded use in high-performance applications where torque smoothness is essential. This paper reviews a wide range of motorand controller-based design techniques that have been described in the literature for minimizing the generation of cogging and ripple torques in both sinusoidal and trapezoidal PMAC motor drives. Sinusoidal PMAC drives generally show the greatest potential for pulsating torque minimization using wellknown motor design techniques such as skewing and fractional slot pitch windings. In contrast, trapezoidal PMAC drives pose more difficult tradeoffs in both the motor and controller design which may require compromises in drive simplicity and cost to improve torque smoothness. Controller-based techniques for minimizing pulsating torque typically involve the use of active cancellation algorithms which depend on either accurate tuning or adaptive control schemes for effectiveness. In the end, successful suppression of pulsating torque ultimately relies on an orchestrated systems approach to all aspects of the PMAC machine and controller design which often requires a carefully selected combination of minimization techniques.

negative: A novel wavelet transform technique for on-line partial discharge measurements. 1. WT de-noising algorithm Medium and high voltage power cables are widely used in the electrical industry with substantial growth over the last 20-30 years ago, particular in the use of XLPE insulated systems. Ageing of the cable insulation is becoming an increasing problem that requires development of reliable methods for on-line condition assessment. For insulation condition assessment of MV and HV cables, partial discharge (PD) monitoring is one of the most effective techniques. However on-site and on-line PD measurements are affected by electromagnetic interference (EMI) that makes sensitive PD detection very difficult, if not impossible. This paper describes implementation of wavelet transform techniques to reject noise from on-line partial discharge measurements on cables. A new wavelet threshold determination method is proposed with the technique. With implementation of this novel de-noising method, PD measurement sensitivity has been greatly improved. In addition, a full AC cycle data recovery can be achieved instead of focusing only on recovering individual PD pulses. Other wavelet threshold de-noising methods are discussed and examined under a noisy environment to compare their performance with the new method proposed here. The method described here has been found to be superior to the other wavelet-based methods

negative: THE EXPERIENCE FACTORY This article presents an infrastructure, called the experience factory , aimed at capitalization and reuse of life cycle experience and products. The experience factory is a logical and physical organization, and its activities are independent from the ones of the development organization. The activities of the development organization and of the experience factory can be outlined in the following way:

negative: Articulatory and spectrum features integration using generalized distillation framework It has been shown that by combining the acoustic and articulatory information significant performance improvements in automatic speech recognition (ASR) task can be achieved. In practice, however, articulatory information is not available during recognition and the general approach is to estimate it from the acoustic signal. In this paper, we propose a different approach based on the generalized distillation framework, where acoustic-articulatory inversion is not necessary. We trained two DNN models: one called “teacher” learns from both acoustic and articulatory features and the other one called “student” is trained on acoustic features only, but its training process is guided by the “teacher” model and can reach a better performance that can't be obtained by regular training even without articulatory feature inputs during test time. The paper is organized as follows: Section 1 gives the introduction and briefly discusses some related works. Section 2 describes the distillation training process, Section 3 describes ASR system used in this paper. Section 4 presents the experiments and the paper is concluded by Section 5.

negative: Invertible Conditional GANs for image editing Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes. Additionally, we evaluate the design of cGANs. The combination of an encoder with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real images with deterministic complex modifications.

negative: Machine Learning Approaches for Mood Classification of Songs toward Music Search Engine Human often wants to listen to music that fits best his current emotion. A grasp of emotions in songs might be a great help for us to effectively discover music. In this paper, we aimed at automatically classifying moods of songs based on lyrics and metadata, and proposed several methods for supervised learning of classifiers. In future, we plan to use automatically identified moods of songs as metadata in our music search engine. Mood categories in a famous contest about Audio Music Mood Classification (MIREX 2007) are applied for our system. The training data is collected from a LiveJournal blog site in which each blog entry is tagged with a mood and a song. Then three kinds of machine learning algorithms are applied for training classifiers: SVM, Naive Bayes and Graph-based methods. The experiments showed that artist, sentiment words, putting more weight for words in chorus and title parts are effective for mood classification. Graph-based method promises a good improvement if we have rich relationship information among songs.

negative: A Study on the Customer-Based Brand Equity of Online Retailers Different from traditional firms, online retailers do not often produce common goods on their own. On the contrary, they sell merchandises that traditional firms produce. Furthermore, online retailers are also different from traditional retailers because the former do not often have frontline employees, physical outlets, or displaying layouts. Therefore, when building a strong online retailer brand, it is not enough to only consider the traditional dimensions of customer-based brand equity, namely brand awareness, perceived quality, brand associations, and brand loyalty. Some dimensions, i.e. trust associations and emotional connection need to be distinguished or included in the context of online business. Besides, the willingness to pay a price premium is always being viewed as a good indicator to measure brand equity, but for online retailers, it is conditional on some premises. By structural equation modeling, it was verified that for online retailers, the importance of brand awareness and trust associations decreased but perceived quality remained its strong influence. Brand loyalty was the better indicator for measuring brand equity, and emotional connection became so critical that if without it, customers would lack willingness to pay a price premium.

negative: Automatic Fingerprint Recognition Systems This article summarizes the major developments in the history of efforts to use fingerprint patterns to identify individuals, from the earliest fingerprint classification systems of Vucetich and Henry in the 1890s through the advent of automated fingerprint identification. By chronicling the history of “manual” systems for recording storing, matching, and retrieving fingerprints, the article puts advances in automatic fingerprint recognition in historical context and highlights their historical and social significance.

negative: Document Modeling with Gated Recurrent Neural Network for Sentiment Classification Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural network model to learn vector-based document representation in a unified, bottom-up fashion. The model first learns sentence representation with convolutional neural network or long short-term memory. Afterwards, semantics of sentences and their relations are adaptively encoded in document representation with gated recurrent neural network. We conduct document level sentiment classification on four large-scale review datasets from IMDB and Yelp Dataset Challenge. Experimental results show that: (1) our neural model shows superior performances over several state-of-the-art algorithms; (2) gated recurrent neural network dramatically outperforms standard recurrent neural network in document modeling for sentiment classification.1

negative: Re-embedding words We present a fast method for re-purposing existing semantic word vectors to improve performance in a supervised task. Recently, with an increase in computing resources, it became possible to learn rich word embeddings from massive amounts of unlabeled data. However, some methods take days or weeks to learn good embeddings, and some are notoriously difficult to train. We propose a method that takes as input an existing embedding, some labeled data, and produces an embedding in the same space, but with a better predictive performance in the supervised task. We show improvement on the task of sentiment classification with respect to several baselines, and observe that the approach is most useful when the training set is sufficiently small.

negative: Reference table based k-anonymous private blocking Privacy Preserving Record Linkage is an emerging field of research which attempts to deal with the classical linkage problem from a privacy preserving point of view. In this paper we propose a novel approach for performing Privacy Preserving Blocking in order to minimize the computational cost of Privacy Preserving Record Linkage. We achieve this without compromising privacy by using Nearest Neighbors clustering, a well-known clustering algorithm and by using a reference table. A reference table is a publicly known table the contents of which are used as intermediate references. The combination of Nearest Neighbors and a reference table offers our approach k-anonymity characteristics.

negative: Pedestrian Behaviour Monitoring: Methods and Experiences The investigation of pedestrian spatio-temporal behaviour is of particular interest in many different research fields. Disciplines like travel behaviour research and tourism research, social sciences, artificial intelligence, geoinformation and many others have approached this subject from different perspectives. Depending on the particular research questions, various methods of data collection and analysis have been developed and applied in order to gain insight into specific aspects of human motion behaviour and the determinants influencing spatial activities. In this contribution, we provide a general overview about most commonly used methods for monitoring and analysing human spatio-temporal behaviour. After discussing frequently used empirical methods of data collection and emphasising related advantages and limitations, we present seven case studies concerning the collection and analysis of human motion behaviour following different purposes.

negative: ANALYSIS OF SEGMENTATION PARAMETERS IN ECOGNITION SOFTWARE USING HIGH RESOLUTION QUICKBIRD MS IMAGERY For objectoriented classification approaches, main step is the segmentation part of the imagery. In eCognition v.4.0.6 software, with the segmentation process, meaningful objects can be created for following steps. In the experimental imagery with 2.4m ground sampling distance (GSD) has been used and several different parameters e.g. scale, color/shape and smoothness/compactness parameters have been tested accordingly. Additionally, segmentation parameters were set to low and high values and thus, dissimilarity of segmentation results were examined.

negative: Towards Query Efficient Black-box Attacks: An Input-free Perspective Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack algorithms need to make a huge amount of queries to perform attacks, which is not practical in the real world. We note one of the main reasons for the massive queries is that the adversarial example is required to be visually similar to the original image, but in many cases, how adversarial examples look like does not matter much. It inspires us to introduce a new attack called input-free attack, under which an adversary can choose an arbitrary image to start with and is allowed to add perceptible perturbations on it. Following this approach, we propose two techniques to significantly reduce the query complexity. First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model. Then we shrink the dimension of the attack space by perturbing a small region and tiling it to cover the input image. To make our algorithm more effective, we stabilize a projected gradient ascent algorithm with momentum, and also propose a heuristic approach for region size selection. Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack algorithms need to make a huge amount of queries to perform attacks, which is not practical in the real world. We note one of the main reasons for the massive queries is that the adversarial example is required to be visually similar to the original image, but in many cases, how adversarial examples look like does not matter much. It inspires us to introduce a new attack called input-free attack, under which an adversary can choose an arbitrary image to start with and is allowed to add perceptible perturbations on it. Following this approach, we propose two techniques to significantly reduce the query complexity. First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model. Then we shrink the dimension of the attack space by perturbing a small region and tiling it to cover the input image. To make our algorithm more effective, we stabilize a projected gradient ascent algorithm with momentum, and also propose a heuristic approach for region size selection. Through extensive experiments, we show that with only 1,701 queries on average, we can perturb a gray image to any target class of ImageNet with a 100% success rate on InceptionV3. Besides, our algorithm has successfully defeated two real-world systems, the Clarifai food detection API and the Baidu Animal Identification API.

negative: Model Learning for Look-ahead Exploration in Continuous Control We propose an exploration method that incorporates look-ahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies . Our skills are multi-goal policies learned in isolation in simpler environments using existing multigoal RL formulations, analogous to options or macroactions. Coarse skill dynamics, i.e., the state transition caused by a (complete) skill execution, are learnt and are unrolled forward during lookahead search. Policy search benefits from temporal abstraction during exploration, though itself operates over low-level primitive actions, and thus the resulting policies does not suffer from suboptimality and inflexibility caused by coarse skill chaining. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parametrized skills as building blocks of the policy itself, as opposed to guiding exploration. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parameterized skills as building blocks of the policy itself, as opposed to guiding exploration.

negative: A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation Speech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial preprocessing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this paper, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: 1 the acoustic impulse response model, 2 the spatial filter design criterion, 3 the parameter estimation algorithm, and 4 optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.

negative: Real-time 3D scene reconstruction with dynamically moving object using a single depth camera Online 3D reconstruction of real-world scenes has been attracting increasing interests from both the academia and industry, especially with the consumer-level depth cameras becoming widely available. Recent most online reconstruction systems take live depth data from a moving Kinect camera and incrementally fuse them to a single high-quality 3D model in real time. Although most real-world scenes have static environment, the daily objects in a scene often move dynamically, which are non-trivial to reconstruct especially when the camera is also not still. To solve this problem, we propose a single depth camera-based real-time approach for simultaneous reconstruction of dynamic object and static environment, and provide solutions for its key issues. In particular, we first introduce a robust optimization scheme which takes advantage of raycasted maps to segment moving object and background from the live depth map. The corresponding depth data are then fused to the volumes, respectively. These volumes are raycasted to extract views of the implicit surface which can be used as a consistent reference frame for the next iteration of segmentation and tracking. Particularly, in order to handle fast motion of dynamic object and handheld camera in the fusion stage, we propose a sequential 6D pose prediction method which largely increases the registration robustness and avoids registration failures occurred in conventional methods. Experimental results show that our approach can reconstruct moving object as well as static environment with rich details, and outperform conventional methods in multiple aspects.

negative: Monadic Parsing in Haskell This paper is a tutorial on defining recursive descent parsers in Haskell. In the spirit of one-stop shopping , the paper combines material from three areas into a single source. The three areas are functional parsers (Burge, 1975; Wadler, 1985; Hutton, 1992; Fokker, 1995), the use of monads to structure functional programs (Wadler, 1990, 1992a, 1992b), and the use of special syntax for monadic programs in Haskell (Jones, 1995; Peterson et al., 1996). More specifically, the paper shows how to define monadic parsers using do notation in Haskell. Of course, recursive descent parsers defined by hand lack the efficiency of bottomup parsers generated by machine (Aho et al., 1986; Mogensen, 1993; Gill and Marlow, 1995). However, for many research applications, a simple recursive descent parser is perfectly sufficient. Moreover, while parser generators typically offer a fixed set of combinators for describing grammars, the method described here is completely extensible: parsers are first-class values, and we have the full power of Haskell available to define new combinators for special applications. The method is also an excellent illustration of the elegance of functional programming. The paper is targeted at the level of a good undergraduate student who is familiar with Haskell, and has completed a grammars and parsing course. Some knowledge of functional parsers would be useful, but no experience with monads is assumed. A Haskell library derived from the paper is available on the web from:

negative: Model of Customer Churn Prediction on Support Vector Machine To improve the prediction abilities of machine learning methods, a support vector machine (SVM) on structural risk minimization was applied to customer churn prediction. Researching customer churn prediction cases both in home and foreign carries, the method was compared with artifical neural network, decision tree, logistic regression, and naive bayesian classifier. It is found that the method enjoys the best accuracy rate, hit rate, covering rate, and lift coefficient, and therefore, provides an effective measurement for customer churn prediction.

negative: On the Design of Adaptive Automation for Complex Systems This article presents a constrained review of human factors issues relevant to adaptive automation (AA), including designing complex system interfaces to support AA, facilitating human–computer interaction and crew interactions in adaptive system operations, and considering workload associated with AA management in the design of human roles in adaptive systems. Unfortunately, these issues have received limited attention in earlier reviews of AA. This work is aimed at supporting a general theory of human-centered automation advocating humans as active information processors in complex system control loops to support situation awareness and effective performance. The review demonstrates the need for research into user-centered design of dynamic displays in adaptive systems. It also points to the need for discretion in designing transparent interfaces to facilitate human awareness of modes of automated systems. Finally, the review identifies the need to consider critical human–human interactions in designing adaptive systems. This work describes important branches of a developing framework of AA research and contributes to the general theory of human-centered automation.

negative: Android based Portable Hand Sign Recognition System These days mobile devices like phones or tablets are very common among people of all age. They are connected with network and provide seamless communications through internet or cellular services. These devices can be a big help for the people who are not able to communicatie properly and even in emergency conditions. A disabled person who is not able to speak or a person who speak a different language, these devices can be a boon for them as understanding, translating and speaking systems for these people. This chapter discusses a portable android based hand sign recognition system which can be used by disabled people. This chapter shows a part of on-going project. Computer Vision based techniques were used for image analysis and PCA was used after image tokenizer for recognition. This method was tested with webcam results to make system more robust.

negative: Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks Five billion people in the world lack access to quality surgical care. Surgeon skill varies dramatically, and many surgical patients suffer complications and avoidable harm. Improving surgical training and feedback would help to reduce the rate of complications—half of which have been shown to be preventable. To do this, it is essential to assess operative skill, a process that currently requires experts and is manual, time consuming, and subjective. In this work, we introduce an approach to automatically assess surgeon performance by tracking and analyzing tool movements in surgical videos, leveraging region-based convolutional neural networks. In order to study this problem, we also introduce a new dataset, m2cai16-tool-locations, which extends the m2cai16-tool dataset with spatial bounds of tools. While previous methods have addressed tool presence detection, ours is the first to not only detect presence but also spatially localize surgical tools in real-world laparoscopic surgical videos. We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection. We further demonstrate the ability of our method to assess surgical quality through analysis of tool usage patterns, movement range, and economy of motion.

negative: A data-driven approach to predict the success of bank telemarketing We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DT), neural network (NN) and support vector machine. Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation phase, using the most recent data (after July 2012) and a rolling windows scheme. The NN presented the best results (AUC=0.8 and ALIFT=0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers. Preprint submitted to Elsevier 19 February 2014

negative: Enhancing metacognitive reinforcement learning using reward structures and feedback How do we learn to think better, and what can we do to promote such metacognitive learning? Here, we propose that cognitive growth proceeds through metacognitive reinforcement learning. We apply this theory to model how people learn how far to plan ahead and test its predictions about the speed of metacognitive learning in two experiments. In the first experiment, we find that our model can discern a reward structure that promotes metacognitive reinforcement learning from one that hinders it. In the second experiment, we show that our model can be used to design a feedback mechanism that enhances metacognitive reinforcement learning in an environment that hinders learning. Our results suggest that modeling metacognitive learning is a promising step towards promoting cognitive growth.

negative: An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring In this paper, we investigate the performance of several systems based on ensemble of classifiers for bankruptcy prediction and credit scoring. The obtained results are very encouraging, our results improved the performance obtained using the stand-alone classifiers. We show that the method ‘‘Random Subspace” outperforms the other ensemble methods tested in this paper. Moreover, the best stand-alone method is the multi-layer perceptron neural net, while the best method tested in this work is the Random Subspace of Levenberg–Marquardt neural net. In this work, three financial datasets are chosen for the experiments: Australian credit, German credit, and Japanese credit. 2008 Elsevier Ltd. All rights reserved.

negative: PyThinSearch: A Simple Web Search Engine We describe a simple, functioning web search engine for indexing and searching online documents using python programming language. Python was chosen because it is an elegant language with simple syntax, is easy to learn and debug, and supports main operating systems almost evenly. The remarkable characteristics of this program are an adjustable search function that allows users to rank documents with several combinations of score functions and the focus on anchor text analysis as we provide four additional schemes to calculate scores based on anchor text. We also provide an additional ranking algorithm based on link addition process in network motivated by PageRank and HITS as an experimental tool. This algorithm is the original contribution of this paper.

negative: Induction and synchronous reluctance motors comparison The aim of this paper is to investigate and compare the performances of two induction motors and two transverse laminated synchronous reluctance motors (induction motor nameplate data: 2.2 and 4 kW, 380 V, 50 Hz, 4 pole). Each induction motor is compared with a synchronous reluctance motor, that has the same stator lamination and winding, but, obviously, different rotor. The analysis has been based both on an analytical and experimental approach. The results have shown that the synchronous reluctance motor, compared to the induction motor, is capable of around a 10% to 15% larger rated torque for a given frame size. The direct comparison of the performances of the motor types has been made both for constant load and constant dissipated power conditions.

negative: 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.

negative: A Novel Variable Reluctance Resolver for HEV/EV Applications In order to simplify the manufacturing process of variable reluctance (VR) resolvers for hybrid electric vehicle/electric vehicle (HEV/EV) applications, a novel VR resolver with nonoverlapping tooth-coil windings is proposed in this paper. A comparison of the winding configurations is first carried out between the existing and the proposed designs, followed by the description of the operating principle. Furthermore, the influence of actual application conditions is investigated by finite-element (FE) analyses, including operating speed and assembling eccentricity. In addition, identical stator and windings of the novel design can be employed in three resolvers of different rotor saliencies. The voltage difference among the three rotor combinations, as well as the detecting accuracy, is further investigated. Finally, prototypes are fabricated and tested to verify the analyses.

negative: Dual-Rotor Multiphase Permanent Magnet Machine With Harmonic Injection to Enhance Torque Density Torque density of a novel dual-rotor, radial-flux multiphase permanent magnet (PM) machine is enhanced by injecting stator current harmonics without increasing stator yoke thickness. In order to enhance a regular PM machine torque capability by injecting harmonic currents, the no-load air-gap flux density should be designed to be trapezoidal, which increases the flux per pole, and results in a thicker stator yoke. However, this drawback can be avoided in a dual-rotor, radial-flux PM machine. In such a dual-rotor PM machine, the main flux can be designed to go directly from the inner rotor poles, to stator inner and outer teeth, then to the outer rotor poles, or reverse, without through the stator yoke. Therefore, the stator yoke thickness is not affected by air-gap flux at all and can actually be reduced to minimum for mechanical reasons. This paper applies stator current harmonic injection technique to a dual-rotor, radial-flux PM machine. Sizing equations of the multiphase PM machines are deduced and used for initial design and theoretical comparison. The torque performance of the proposed dual-rotor, radial-flux multiphase PM machine with stator harmonic current injection is evaluated using finite element simulation, and compared to a traditional three-phase dual-rotor, radial-flux PM machine. Simulation results validate the feasibility of torque density enhancement. The same technique is proven that it is also suitable in a dual-rotor axial-flux PM machine topology.

negative: Variable flux permanent magnet synchronous machine (VF-PMSM) design to meet electric vehicle traction requirements with reduced losses Variable flux permanent magnet synchronous machines (VF-PMSMs) in which the magnetization state (MS) of low coercive force (low-Hc) permanent magnets can be actively controlled to reduce losses in applications that require wide-speed operation have been proposed recently. While prior focus has been on achieving MS manipulation without over-sizing the inverter and obtaining higher torque capability, this paper extends the design objectives to include the power requirements of an electric vehicle traction motor over its entire speed range. Finite element methods are used to study the effect of combinations of low-Hc and high-Hc permanent magnets arranged in either series or parallel on the performance of VF-PMSMs. It is shown that while both configurations help improve the torque density, only the series configuration can help improve the high speed power capability. Experimental results showing the variable MS property, torque-speed capability and loss reduction capability of a series magnet configuration VF-PMSM test machine are presented.

negative: Performance Evaluation of a High-Power-Density PMASynRM With Ferrite Magnets Although motors that use rare-earth permanent magnets (PMs) typically exhibit high performance, high costs and concerns about the stability of raw material supplies are leading to their decreased production. However, the performance of such motors is not easily matched without the use of rare-earth PMs. This paper proposes and examines a PM-assisted synchronous reluctance motor with ferrite magnets that has the same power density as rare-earth PM synchronous motors. A suitable rotor structure for high torque and high power is discussed with respect to the demagnetization of ferrite magnets and the mechanical strength.

negative: Design and Sensorless Control of a Novel Axial-Flux Permanent Magnet Machine for In-Wheel Applications This paper proposes an axial-flux-modulation permanent magnet (PM) machine and its sensorless control strategy for in-wheel application in electrical vehicles. A Vernier structure is integrated with the axial-flux PM machine to include the magnetic gear effect and improve output torque. The sensorless control strategy of the proposed machine, including initial rotor position estimation and rotating position estimation, is proposed for flux-modulation motors in this paper. The initial rotor position estimation is based on the technique of rectangular pulse voltage injection and the rotating rotor position estimation is based on the sliding mode observer (SMO). The saturation effect on inductances, which is the theoretical basis of the rectangular pulse voltage injection, makes the stator parameter variation in different loads and affects the SMO estimation. To overcome this problem, a novel online parameter self-adjustment procedure for the SMO is introduced. The machine design and its sensorless control performance are verified by simulation and prototype experiments.


query: A 0.6-V 800-MHz All-Digital Phase-Locked Loop With a Digital Supply Regulator

positive: All-digital PLL and transmitter for mobile phones We present the first all-digital PLL and polar transmitter for mobile phones. They are part of a single-chip GSM/EDGE transceiver SoC fabricated in a 90 nm digital CMOS process. The circuits are architectured from the ground up to be compatible with digital deep-submicron CMOS processes and be readily integrateable with a digital baseband and application processor. To achieve this, we exploit the new paradigm of a deep-submicron CMOS process environment by leveraging on the fast switching times of MOS transistors, the fine lithography and the precise device matching, while avoiding problems related to the limited voltage headroom. The transmitter architecture is fully digital and utilizes the wideband direct frequency modulation capability of the all-digital PLL. The amplitude modulation is realized digitally by regulating the number of active NMOS transistor switches in accordance with the instantaneous amplitude. The conventional RF frequency synthesizer architecture, based on a voltage-controlled oscillator and phase/frequency detector and charge-pump combination, has been replaced with a digitally controlled oscillator and a time-to-digital converter. The transmitter performs GMSK modulation with less than 0.5/spl deg/ rms phase error, -165 dBc/Hz phase noise at 20 MHz offset, and 10 /spl mu/s settling time. The 8-PSK EDGE spectral mask is met with 1.2% EVM. The transmitter occupies 1.5 mm/sup 2/ and consumes 42 mA at 1.2 V supply while producing 6 dBm RF output power.

positive: A Design Procedure for All-Digital Phase-Locked Loops Based on a Charge-Pump Phase-Locked-Loop Analogy In this brief, a systematic design procedure for a second-order all-digital phase-locked loop (PLL) is proposed. The design procedure is based on the analogy between a type-II second-order analog PLL and an all-digital PLL. The all-digital PLL design inherits the frequency response and stability characteristics of the analog prototype PLL

positive: Analysis and modeling of bang-bang clock and data recovery circuits A large-signal piecewise-linear model is proposed for bang-bang phase detectors that predicts characteristics of clock and data recovery circuits such as jitter transfer, jitter tolerance, and jitter generation. The results are validated by 1-Gb/s and 10-Gb/s CMOS prototypes using an Alexander phase detector and an LC oscillator.

positive: Analysis of charge-pump phase-locked loops In this paper, we present an exact analysis for third-order charge-pump phase-locked loops using state equations. Both the large-signal lock acquisition process and the small-signal linear tracking behavior are described using this analysis. The nonlinear state equations are linearized for the small-signal condition and the z-domain noise transfer functions are derived. A comparison to some of the existing analysis methods such as the impulse-invariant transformation and s-domain analysis is provided. The effect of the loop parameters and the reference frequency on the loop phase margin and stability is analyzed. The analysis is verified using behavioral simulations in MATLAB and SPECTRE.

positive: Digitally Controlled Oscillator ( DCO )-Based Architecture for RF Frequency Synthesis in a Deep-Submicrometer CMOS Process A novel digitally controlled oscillator (DCO)-based architecture for frequency synthesis in wireless RF applications is proposed and demonstrated. It deliberately avoids any use of an analog tuning voltage control line. Fine frequency resolution is achieved through high-speed dithering. Other imperfections of analog circuits are compensated through digital means. The presented ideas enable the employment of fully-digital frequency synthesizers using sophisticated signal processing algorithms, realized in the most advanced deep-submicrometer digital CMOS processes which allow almost no analog extensions. They also promote costeffective integration with the digital back-end onto a single silicon die. The demonstrator test chip has been fabricated in a digital 0.13m CMOS process together with a DSP, which acts as a digital baseband processor with a large number of digital gates in order to investigate noise coupling. The phase noise is112 dBc/Hz at 500-kHz offset. The close-in spurious tones are below 62 dBc, while the far-out spurs are below 80 dBc. The presented ideas have been incorporated in a commercial Bluetooth transceiver.

negative: Exploiting the use of recurrent neural networks for driver behavior profiling Driver behavior affects traffic safety, fuel/energy consumption and gas emissions. The purpose of driver behavior profiling is to understand and have a positive influence on driver behavior. Driver behavior profiling tasks usually involve an automated collection of driving data and the application of computer models to classify what characterizes the aggressiveness of drivers. Different sensors and classification methods have been employed for this task, although low-cost solutions, high performance and collaborative sensing remain open questions for research. This paper makes an investigation with different Recurrent Neural Networks (RNN), aiming to classify driving events employing data collected by smartphone accelerometers. The results show that specific configurations of RNN upon accelerometer data provide high accuracy results, being a step towards the development of safer transportation systems.

negative: Spelling Correction for Morphologically Rich Language: a Case Study of Russian We present an algorithm for automatic correction of spelling errors on the sentence level, which uses noisy channel model and feature-based reranking of hypotheses. Our system is designed for Russian and clearly outperforms the winner of SpellRuEval-2016 competition. We show that language model size has the greatest influence on spelling correction quality. We also experiment with different types of features and show that morphological and semantic information also improves the accuracy of spellchecking. The task of automatic spelling correction has applications in different areas including correction of search queries, spellchecking in browsers and text editors etc. It attracted intensive attention in early era of modern NLP. Many researchers addressed both the problems of effective candidates generation (Kernighan et al., 1990; Brill and Moore, 2000) and their adequate ranking (Golding and Roth, 1999; Whitelaw et al., 2009). Recently, the focus has moved to close but separate areas of text normalization (Han et al., 2013) and grammar errors correction (Ng et al., 2014), though the task of spellchecking is far from being perfectly solved. Most of early works were conducted for English for which NLP tasks are usually easier than for other languages due to simplicity of its morphology and strict word order. Also there were studies for Arabic (papers of QALB-2014 Shared Task (Ng et al., 2014)) and Chinese (Wu et al., 2013), but for most languages the problem still is open. In context of Slavic languages, there were just a few works including Sorokin and Shavrina (2016) for Russian, Richter et al. (2012) for Czech and Hladek et al. (2013) for Slovak. However, spelling correction becomes actual again due to intensive growth of social media. Indeed, corpora of Web texts including blogs, microblogs, forums etc. become the main sources for corpus studies. Most of these corpora are very large so they are collected and processed automatically with only limited manual correction. Hence, most texts in such corpora contain various types of spelling variation, from mere typos and orthographic errors to dialectal and sociolinguistic peculiarities. Moreover, orthographic errors are unavoidable since the more social media texts we have, the higher is the fraction of those, whose authors are not well-educated and therefore tend to make mistakes. That increases the percentage of out-of-vocabulary words in text, which affects the quality of any further NLP task from lemmatization to any kind of parsing or information extraction. Summarizing, it is desirable to detect and correct at least undoubtable misspellings in Web texts with high precision. Unfortunately, there were very few studies dealing with spellchecking for real-world Web texts, e.g. LiveJournal or Facebook. Most authors investigated spelling correction in a rather restricted fashion. They focused on selecting a correct word from a small pre-defined confusion set (e.g., adopt/adapt), skipping a problem of detecting misprints or generating the set of possible corrections. Often researchers did not deal with real-world errors just randomly introducing typos in every word with some probability. Therefore, spelling correction has no “intelligent baseline” algorithm such as trigram HMM-models for morphological parsing or CBOW vectors for distributional similarity. One of the goals of our work is to propose such a baseline. The principal feature of our approach is that it works with entire sentences, not on the level of separate words. A serious problem for research on spellcheck45 ing is the lack of publicly available datasets for spelling correction in different languages. Fortunately, recently such a corpus was created for Russian during SpellRuEval-2016 competition (Sorokin et al., 2016). Russian is rather complex for NLP tasks because of its developed nominal and verb morphology and free word order. Therefore it is well-suited for extensive testing of spelling correction algorithms, although our results are applicable to any other language having similar properties. We propose a reranking algorithm for automatic spelling correction and evaluate it on SpellRuEval2016 dataset. The paper is organized as follows: Section 1 summarizes previous work on automatic spelling correction focusing on context-sensitive approaches, Section 2 contains our algorithm, Section 3 describes test data, Section 4 analyzes the performance of our system depending on different settings and we conclude in Section 5.

negative: Automotive Ethernet: In-vehicle networking and smart mobility This paper discusses novel communication network topologies and components and describes an evolutionary path of bringing Ethernet into automotive applications with focus on electric mobility. For next generation in-vehicle networking, the automotive industry identified Ethernet as a promising candidate besides CAN and FlexRay. Ethernet is an IEEE standard and is broadly used in consumer and industry domains. It will bring a number of changes for the design and management of in-vehicle networks and provides significant re-use of components, software, and tools. Ethernet is intended to connect inside the vehicle high-speed communication requiring sub-systems like Advanced Driver Assistant Systems (ADAS), navigation and positioning, multimedia, and connectivity systems. For hybrid (HEVs) or electric vehicles (EVs), Ethernet will be a powerful part of the communication architecture layer that enables the link between the vehicle electronics and the Internet where the vehicle is a part of a typical Internet of Things (IoT) application. Using Ethernet for vehicle connectivity will effectively manage the huge amount of data to be transferred between the outside world and the vehicle through vehicle-to-x (V2V and V2I or V2I+I) communication systems and cloud-based services for advanced energy management solutions. Ethernet is an enabling technology for introducing advanced features into the automotive domain and needs further optimizations in terms of scalability, cost, power, and electrical robustness in order to be adopted and widely used by the industry.

negative: Root Gap Correction with a Deep Inpainting Model Imaging roots of growing plants in a non-invasive and affordable fashion has been a long-standing problem in image-assisted plant breeding and phenotyping. One of the most affordable and diffuse approaches is the use of mesocosms, where plants are grown in soil against a glass surface that permits the roots visualization and imaging. However, due to soil and the fact that the plant root is a 2D projection of a 3D object, parts of the root are occluded. As a result, even under perfect root segmentation, the resulting images contain several gaps that may hinder the extraction of finely grained root system architecture traits. We propose an effective deep neural network to recover gaps from disconnected root segments. We train a fully supervised encoder-decoder deep CNN that, given an image containing gaps as input, generates an inpainted version, recovering the missing parts. Since in real data ground-truth is lacking, we use synthetic root images [10] that we artificially perturb by introducing gaps to train and evaluate our approach. We show that our network can work both in dicot and monocot cases in reducing root gaps. We also show promising exemplary results in real data from chickpea root architectures.

negative: Punching shear capacity of continuous slabs Provisions for punching shear design of reinforced concrete slabs are usually calibrated on the basis of results from tests on isolated specimens that simulate the slab zone within the points of contraflexure around a column. However, the punching behavior of interior slab-column connections in actual continuous slabs without transverse reinforcement may be influenced by the effects of moment redistribution and compressive membrane action, which can lead to higher punching strengths and lower deformation capacities compared to those in isolated specimens. This paper discusses these behavioral differences on the basis of experiments performed on symmetric edge-restrained slabs and investigates available test data by means of a numerical model. A simplified calculation method (based on the Critical Shear Crack Theory) that accounts for these effects is also proposed. The calculation model shows consistent agreement with the results of the numerical evaluation and is sufficiently simple to be used in design and assessment.

negative: Digital Dead Time Auto-Tuning for Maximum Efficiency Operation of Isolated DC-DC Converters The rapidly growing use of digital control in Distributed Power Architectures (DPA) has been primarily driven by the increasing need of sophisticated Power Management functions. However, specifically designed control algorithms can also be exploited for optimizing the system performance. In this paper we present a new auto-tuning method and system that makes it possible to operate an isolated DC-DC converter at maximum efficiency. The auto-tuning performs an optimum adjustment of both primary side dead time and secondary side conduction times based on the measurement of the input current. It does not require any additional external components since current sensing functions are already implemented for power management purposes. Experimental measurements performed on an asymmetrical driven half-bridge DC-DC converter demonstrate the effectiveness of our solution and its robustness to component variations.

negative: A Hybrid Model to Detect Malicious Executables We present a hybrid data mining approach to detect malicious executables. In this approach we identify important features of the malicious and benign executables. These features are used by a classifier to learn a classification model that can distinguish between malicious and benign executables. We construct a novel combination of three different kinds of features: binary n-grams, assembly n-grams, and library function calls. Binary features are extracted from the binary executables, whereas assembly features are extracted from the disassembled executables. The function call features are extracted from the program headers. We also propose an efficient and scalable feature extraction technique. We apply our model on a large corpus of real benign and malicious executables. We extract the above mentioned features from the data and train a classifier using support vector machine. This classifier achieves a very high accuracy and low false positive rate in detecting malicious executables. Our model is compared with other feature-based approaches, and found to be more efficient in terms of detection accuracy and false alarm rate.

negative:
Scalable knowledge harvesting with high precision and high recall Harvesting relational facts from Web sources has received great attention for automatically constructing large knowledge bases. Stateof-the-art approaches combine pattern-based gathering of fact candidates with constraint-based reasoning. However, they still face major challenges regarding the trade-offs between precision, recall, and scalability. Techniques that scale well are susceptible to noisy patterns that degrade precision, while techniques that employ deep reasoning for high precision cannot cope with Web-scale data.
 This paper presents a scalable system, called PROSPERA, for high-quality knowledge harvesting. We propose a new notion of ngram-itemsets for richer patterns, and use MaxSat-based constraint reasoning on both the quality of patterns and the validity of fact candidates.We compute pattern-occurrence statistics for two benefits: they serve to prune the hypotheses space and to derive informative weights of clauses for the reasoner. The paper shows how to incorporate these building blocks into a scalable architecture that can parallelize all phases on a Hadoop-based distributed platform. Our experiments with the ClueWeb09 corpus include comparisons to the recent ReadTheWeb experiment. We substantially outperform these prior results in terms of recall, with the same precision, while having low run-times.

negative: Manufacturing Analytics and Industrial Internet of Things Over the last two decades, manufacturing across the globe has evolved to be more intel-ligent and data driven. In the age of industrial Internet of Things, a smart production unit can be perceived as a large connected industrial system of materials, parts, machines, tools, inventory, and logistics that can relay data and communicate with each other. While, traditionally, the focus has been on machine health and predictive maintenance, the manufacturing industry has also started focusing on analyzing data from the entire production line. These applications bring a new set of analytics challenges. Unlike tradi-tional data mining analysis, which consists of lean datasets (that is, datasets with few fea-tures), manufacturing has fat datasets. In addition, previous approaches to manufacturing analytics restricted themselves to small time periods of data. The latest advances in big data analytics allows researchers to do a deep dive into years of data. Bosch collects and utilizes all available information about its products to increase its understanding of complex linear and nonlinear relationships between parts, machines, and assembly lines. This helps in use cases such as the discovery of the root cause of internal defects. This article presents a case study and provides detail about challenges and approaches in data extraction, modeling, and visualization.

negative: Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL? Traditionally, MPI runtimes have been designed for clusters with a large number of nodes. However, with the advent of MPI+CUDA applications and dense multi-GPU systems, it has become important to design efficient communication schemes. This coupled with new application workloads brought forward by Deep Learning frameworks like Caffe and Microsoft CNTK pose additional design constraints due to very large message communication of GPU buffers during the training phase. In this context, special-purpose libraries like NCCL have been proposed. In this paper, we propose a pipelined chain (ring) design for the MPI_Bcast collective operation along with an enhanced collective tuning framework in MVAPICH2-GDR that enables efficient intra-/internode multi-GPU communication. We present an in-depth performance landscape for the proposed MPI_Bcast schemes along with a comparative analysis of NCCL Broadcast and NCCL-based MPI_Bcast. The proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement, compared to NCCL-based solutions, for intra- and internode broadcast latency, respectively. In addition, the proposed designs provide up to 7% improvement over NCCL-based solutions for data parallel training of the VGG network on 128 GPUs using Microsoft CNTK. The proposed solutions outperform the recently introduced NCCL2 library for small and medium message sizes and offer comparable/better performance for very large message sizes.

negative: Hesitant fuzzy analytic hierarchy process Ordinary fuzzy sets have been recently extended to intuitionistic and hesitant fuzzy sets, which are frequently used for the solution of decision-making problems. All these extensions aim at better defining the membership values or functions of the considered parameters. In this paper we develop a hesitant fuzzy analytic hierarchy process method involving multi-experts' linguistic evaluations aggregated by ordered weighted averaging (OWA) operator. The developed hesitant fuzzy AHP method is applied to a multicriteria supplier selection problem.

negative: Multi-clip video editing from a single viewpoint We propose a framework for automatically generating multiple clips suitable for video editing by simulating pan-tilt-zoom camera movements within the frame of a single static camera. Assuming important actors and objects can be localized using computer vision techniques, our method requires only minimal user input to define the subject matter of each sub-clip. The composition of each sub-clip is automatically computed in a novel L1-norm optimization framework. Our approach encodes several common cinematographic practices into a single convex cost function minimization problem, resulting in aesthetically pleasing sub-clips which can easily be edited together using off-the-shelf multi-clip video editing software. We demonstrate our approach on five video sequences of a live theatre performance by generating multiple synchronized subclips for each sequence.

negative: Outcome prediction of DOTA2 based on Naïve Bayes classifier Although DOTA2 is a popular game around the world, no clear algorithm or software are designed to forecast the winning probability by analyzing the lineups. However, the author finds that Naive Bayes classifier, one of the most common classification algorithm, can analyze the lineups and predict the outcome according to the lineups and gives an improved Naive Bayes classifier. Using the DOTA2 data set published in the UCI Machine Learning Repository, we test Naive Bayes classifier's prediction of respective winning probability of both sides in the game. The results show that Naive Bayes classifier is a practical tool to analyze the lineups and predict the outcome based on players' choices.

negative: Forma Track: Tracking People based on Body Shape Knowledge of a person’s whereabouts in the home is key to context-aware applications, but many people do not want to carry or wear a tag or mobile device in the home. Therefore, many tracking systems are now using so-called weak biometrics such as height, weight, and width. In this paper, we propose to use body shape as a weak biometric, differentiating people based on features such as head size, shoulder size, or torso size. The basic idea is to scan the body with a radar sensor and to compute the reflection profile: the amount of energy that reflects back from each part of the body. Many people have different body shapes even if they have the same height, weight, or width, which makes body shape a stronger biometric. We built a proof-of-concept system called FormaTrack to test this approach, and evaluate using eight participants of varying height and weight. We collected over 2800 observations while capturing a wide range of factors such as clothing, hats, shoes, and backpacks. Results show that FormaTrack can achieve a precision, recall, direction and identity accuracy (over all possible groups of 2 people) of 100%, 99.86%, 99.7% and 95.3% respectively. Results indicate that FormaTrack can achieve over 99% tracking accuracy with 2 people in a home with 5 or more rooms.

negative:
Benchmarking distributed data warehouse solutions for storing genomic variant information Database URL
https://github.com/ZSI-Bio/variantsdwh.

negative: In vivo evaluation of bacterial cellulose/acrylic acid wound dressing hydrogel containing keratinocytes and fibroblasts for burn wounds The healing of wounds, including those from burns, currently exerts a burden on healthcare systems worldwide. Hydrogels are widely used as wound dressings and in the field of tissue engineering. The popularity of bacterial cellulose-based hydrogels has increased owing to their biocompatibility. Previous study demonstrated that bacterial cellulose/acrylic acid (BC/AA) hydrogel increased the healing rate of burn wound. This in vivo study using athymic mice has extended the use of BC/AA hydrogel by the addition of human epidermal keratinocytes and human dermal fibroblasts. The results showed that hydrogel loaded with cells produces the greatest acceleration on burn wound healing, followed by treatment with hydrogel alone, compared with the untreated group. The percentage wound reduction on day 13 in the mice treated with hydrogel loaded with cells (77.34 ± 6.21%) was significantly higher than that in the control-treated mice (64.79 ± 6.84%). Histological analysis, the expression of collagen type I via immunohistochemistry, and transmission electron microscopy indicated a greater deposition of collagen in the mice treated with hydrogel loaded with cells than in the mice administered other treatments. Therefore, the BC/AA hydrogel has promising application as a wound dressing and a cell carrier.

negative: Expert and exceptional performance: evidence of maximal adaptation to task constraints. Expert and exceptional performance are shown to be mediated by cognitive and perceptual-motor skills and by domain-specific physiological and anatomical adaptations. The highest levels of human performance in different domains can only be attained after around ten years of extended, daily amounts of deliberate practice activities. Laboratory analyses of expert performance in many domains such as chess, medicine, auditing, computer programming, bridge, physics, sports, typing, juggling, dance, and music reveal maximal adaptations of experts to domain-specific constraints. For example, acquired anticipatory skills circumvent general limits on reaction time, and distinctive memory skills allow a domain-specific expansion of working memory capacity to support planning, reasoning, and evaluation. Many of the mechanisms of superior expert performance serve the dual purpose of mediating experts' current performance and of allowing continued improvement of this performance in response to informative feedback during practice activities.

negative: AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which is usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly models content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large-scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.

negative: wUbi-Pen: windows graphical user interface interacting with haptic feedback stylus In this work, we designed stylus type haptic interfaces interacting with a touch screen. There are two kinds of haptic styli termed wUbi-Pen. The type I has functions of providing vibration, impact, texture and sound and it is a stand alone system including own battery and wireless communication module. The type II does not include the texture display module and it is a miniaturized version. We present a new interaction scheme on the Windows graphical user interface based on pointer movement haptic feedback events such as clicking, drag, drop, moving and etc. In addition, a simple interactive digital sketchbook has been implemented, which is providing haptic and auditory feedback while drawing and touching objects. We also introduce a tactile image display method on a touch screen with the wUbi-Pen and a simple fitting puzzle utilizing haptic feedback events.

negative: OpenARC: open accelerator research compiler for directive-based, efficient heterogeneous computing This paper presents Open Accelerator Research Compiler (OpenARC): an open-source framework that supports the full feature set of OpenACC V1.0 and performs source-to-source transformations, targeting heterogeneous devices, such as NVIDIA GPUs. Combined with its high-level, extensible Intermediate Representation (IR) and rich semantic annotations, OpenARC serves as a powerful research vehicle for prototyping optimization, source-to-source transformations, and instrumentation for debugging, performance analysis, and autotuning. In fact, OpenARC is equipped with various capabilities for advanced analyses and transformations, as well as built-in performance and debugging tools. We explain the overall design and implementation of OpenARC, and we present key analysis techniques necessary to efficiently port OpenACC applications. Porting various OpenACC applications to CUDA GPUs using OpenARC demonstrates that OpenARC performs similarly to a commercial compiler, while serving as a general research framework.

negative: Order patterns recurrence plots in the analysis of ERP data Recurrence quantification analysis (RQA) is an established tool for data analysis in various behavioural sciences. In this article we present a refined notion of RQA based on order patterns. The use of order patterns is commonplace in time series analysis. Exploiting this concept in combination with recurrence plots (RP) and their quantification (RQA) allows for advances in contemporary EEG research, specifically in the analysis of event related potentials (ERP), as the method is known to be robust against non-stationary data. The use of order patterns recurrence plots (OPRPs) on EEG data recorded during a language processing experiment exemplifies the potentials of the method. We could show that the application of RQA to ERP data allows for a considerable reduction of the number of trials required in ERP research while still maintaining statistical validity.

negative: On Symmetric and Asymmetric LSHs for Inner Product Search We consider the problem of designing locality sensitive hashes (LSH) for inner product similarity, and of the power of asymmetric hashes in this context. Shrivastava and Li argue that there is no symmetric LSH for the problem and propose an asymmetric LSH based on different mappings for query and database points. However, we show there does exist a simple symmetric LSH that enjoys stronger guarantees and better empirical performance than the asymmetric LSH they suggest. We also show a variant of the settings where asymmetry is in-fact needed, but there a different asymmetric LSH is required.

negative: CORONET: Fault tolerance for Software Defined Networks Software Defined Networking, or SDN, based networks are being deployed not only in testbed networks, but also in production networks. Although fault-tolerance is one of the most desirable properties in production networks, there are not much study in providing fault-tolerance to SDN-based networks. The goal of this work is to develop a fault tolerant SDN architecture that can rapidly recover from faults and scale to large network sizes. This paper presents CORONET, a SDN fault-tolerant system that recovers from multiple link failures in the data plane. We describe a prototype implementation based on NOX that demonstrates fault recovery for emulated topologies using Mininet. We also discuss possible extensions to handle control plane and controller faults.

negative:
Alteration of the platelet serotonin transporter in romantic love. BACKGROUND
The evolutionary consequences of love are so important that there must be some long-established biological process regulating it. Recent findings suggest that the serotonin (5-HT) transporter might be linked to both neuroticism and sexual behaviour as well as to obsessive-compulsive disorder (OCD). The similarities between an overvalued idea, such as that typical of subjects in the early phase of a love relationship, and obsession, prompted us to explore the possibility that the two conditions might share alterations at the level of the 5-HT transporter.

METHODS
Twenty subjects who had recently (within the previous 6 months) fallen in love, 20 unmedicated OCD patients and 20 normal controls, were included in the study. The 5-HT transporter was evaluated with the specific binding of 3H-paroxetine (3H-Par) to platelet membranes.

RESULTS
The results showed that the density of 3H-Par binding sites was significantly lower in subjects who had recently fallen in love and in OCD patients than in controls.

DISCUSSION
The main finding of the present study is that subjects who were in the early romantic phase of a love relationship were not different from OCD patients in terms of the density of the platelet 5-HT transporter, which proved to be significantly lower than in the normal controls. This would suggest common neurochemical changes involving the 5-HT system, linked to psychological dimensions shared by the two conditions, perhaps at an ideational level.

negative: Message authentication in driverless cars Driverless cars and driver-assisted vehicles are becoming prevalent in today's transportation systems. Companies like TESLA, Audi, and Google are among the leaders in this new era of the automotive industry. Modern vehicles, equipped with sophisticated navigation devices, complex driver-assisted systems, and a whole lot of safety features, bring broader impacts to our quality of life, economic development, and environmental sustainability. Instant safety messages such as pre-collision warnings, blind-spot detection, pedestrian and object awareness significantly improve the safety for drivers, passengers, and pedestrians. As a result, vehicles would be able to travel closely yet safely together, forming a platoon, thus resulting in a reduction of traffic congestion and fuel consumption. Driverless cars also have non-safety-related applications: which are used to facilitate traffic management and infotainment dissemination for drivers and passengers. Vehicular Ad hoc Network (VANET), the wireless communication technology enabling driverless cars, features not only dynamic topology but also high mobility. The vehicle nodes move rapidly on streets and highways. Their movements also depend on but not limited to road traffic, speed limits, and behavior of nearby vehicles. With massive amount of messages exchanged among driverless cars that command the cars' movements at high speeds and in close distances, any malicious alternation could lead to disastrous accidents. Message authentication to ensure data integrity is paramount to attack preparation. This paper presents a novel message authentication scheme that protects cars from bogus messages and makes VANET resilient to Denial-of-Service (DoS) attacks. The work includes a simulation framework that integrates vehicle and data traffic models to validate the effectiveness of the proposed message authentication scheme.

negative: On-Chip Compensation of Ring VCO Oscillation Frequency Changes Due to Supply Noise and Process Variation A novel circuit technique that stabilizes the oscillation frequency of a ring-type voltage-controlled oscillator (RVCO) is demonstrated. The technique uses on-chip bias-current and voltage-swing controllers, which compensate RVCO oscillation frequency changes caused by supply noise and process variation. A prototype phase-locked loop (PLL) having the RVCO with the compensation circuit is fabricated with 0.13-μm CMOS technology. At the operating frequency of 4 GHz, the measured PLL rms jitter improves from 20.11 to 5.78 ps with 4-MHz RVCO supply noise. Simulation results show that the oscillation frequency difference between FF and SS corner is reduced from 63% to 6% of the NN corner oscillation frequency.

negative: A digitally compensated 1.5 GHz CMOS/FBAR frequency reference A temperature-compensated 1.5 GHz film bulk acoustic wave resonator (FBAR)-based frequency reference implemented in a 0.35 ¿m CMOS process is presented. The ultra-small form factor (0.79 mm × 1.72 mm) and low power dissipation (515 ¿A with 2 V supply) of a compensated FBAR oscillator present a promising alternative for the replacement of quartz crystal frequency references. The measured post-compensation frequency drift over a 0-100°C temperature range is <±10 ppm. The measured oscillator phase noise is -133 dBc/ Hz at 100 kHz offset from the 1.5 GHz carrier.

negative: Delta-Sigma FDC Based Fractional-N PLLs Fractional-N phase-locked loop frequency synthesizers based on time-to-digital converters (TDC-PLLs) have been proposed to reduce the area and linearity requirements of conventional PLLs based on delta-sigma modulation and charge pumps (ΔΣ-PLLs). Although TDC-PLLs with good performance have been demonstrated, TDC quantization noise has so far kept their phase noise and spurious tone performance below that of the best comparable ΔΣ-PLLs. An alternative approach is to use a delta-sigma frequency-to-digital converter (ΔΣ FDC) in place of a TDC to retain the benefits of TDC-PLLs and ΔΣ-PLLs. This paper proposes a practical ΔΣ FDC based PLL in which the quantization noise is equivalent to that of a ΔΣ-PLL. It presents a linearized model of the PLL, design criteria to avoid spurious tones in the ΔΣFDC quantization noise, and a design methodology for choosing the loop parameters in terms of standard PLL target specifications.

negative: 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.

negative: A 0.5ps 1.4mW 50MS/s Nyquist bandwidth time amplifier based two-step flash-ΔΣ time-to-digital converter We propose a 50-MS/s two-step flash-ΔΣ time-to-digital converter (TDC) using stable time amplifiers (TAs). The TDC demonstrates low-levels of shaped quantization noise. The system is simulated in 40-nm CMOS and consumes 1.3 mA from a 1.1 V supply. The bandwidth is broadened to Nyquist rate. At frequencies below 25 MHz, the integrated TDC error is as low as 143 fsrms, which is equal to an equivalent TDC resolution of 0.5 ps.

negative: A Calibration Technique for Bang-Bang ADPLLs Using Jitter Distribution Monitoring This brief presents a built-in self-calibration (BISC) technique for minimization of the total jitter in bang-bang all-digital phase-locked loops (ADPLLs). It is based on the addition of a monitoring phase-frequency detector (PFD) with tunable delay cells for the reference clock and the divider clock and a counter for this PFD output signal. This allows for on-chip binary comparison of the jitter distribution widths at the ADPLL PFD input, when ADPLL filter parameters are altered. Since only a relative comparison is performed, no accurate delay calibration is required. The statistical properties of this comparison of two random distributions are analyzed theoretically, and guidelines for circuit dimensioning are derived. The proposed method is used for BISC by adaption of the ADPLL filter coefficients. This allows for jitter minimization under process, voltage and temperature variations as well as gain and period jitter of the digitally controlled oscillator. The proposed calibration technique is verified by system simulations and measurements of a silicon prototype implementation in 28-nm CMOS technology.

negative: A Chopper Current-Feedback Instrumentation Amplifier With a 1 mHz $1/f$  Noise Corner and an AC-Coupled Ripple Reduction Loop This paper presents a chopper instrumentation amplifier for interfacing precision thermistor bridges. For high CMRR and DC gain, the amplifier employs a three-stage current-feedback topology with nested-Miller compensation. By chopping both the input and intermediate stages of the amplifier, a 1 mHz 1/f noise corner was achieved at an input-referred noise power spectral density (PSD) of 15 nV/¿Hz. To reduce chopper ripple, the amplifier employs a continuous-time AC-coupled ripple reduction loop. Due to its continuous-time nature, the loop causes no noise folding to DC and hence offers improved noise performance over auto-zeroed amplifiers. The loop reduces chopper ripple by more than 60 dB, to levels below the amplifier's own input-referred noise. Furthermore, a maximum input referred offset of 5 ¿ V and a CMRR greater than 120 dB were measured at a supply current of 230 ¿A at 5 V.