title: SGAN: An Alternative Training of Generative Adversarial Networks

abstract: The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results in a time-consuming and human-involved development process to use them. We consider an alternative training process, named SGAN, in which several adversarial"local"pairs of networks are trained independently so that a"global"supervising pair of networks can be trained against them. The goal is to train the global pair with the corresponding ensemble opponent for improved performances in terms of mode coverage. This approach aims at increasing the chances that learning will not stop for the global pair, preventing both to be trapped in an unsatisfactory local minimum, or to face oscillations often observed in practice. To guarantee the latter, the global pair never affects the local ones. The rules of SGAN training are thus as follows: the global generator and discriminator are trained using the local discriminators and generators, respectively, whereas the local networks are trained with their fixed local opponent. Experimental results on both toy and real-world problems demonstrate that this approach outperforms standard training in terms of better mitigating mode collapse, stability while converging and that it surprisingly, increases the convergence speed as well.


A Brief Program Robustness Survey


title: Virtual Reality in Stroke Rehabilitation: A Meta-Analysis and Implications for Clinicians

abstract: Background and Purpose— Approximately two thirds of stroke survivors continue to experience motor deficits of the arm resulting in diminished quality of life. Conventional rehabilitation provides modest and sometimes delayed effects. Virtual reality (VR) technology is a novel adjunctive therapy that could be applied in neurorehabilitation. We performed a meta-analysis to determine the added benefit of VR technology on arm motor recovery after stroke. Methods— We searched Medline, EMBASE, and Cochrane literature from 1966 to July 2010 with the terms “stroke,” “virtual reality,” and “upper arm/extremity.” We evaluated the effect of VR on motor function improvement after stroke. Results— From the 35 studies identified, 12 met the inclusion/exclusion criteria totaling 195 participants. Among them, there were 5 randomized clinical trials and 7 observational studies with a pre-/postintervention design. Interventions were delivered within 4 to 6 weeks in 9 of the studies and within 2 to 3 weeks in the remaining 3. Eleven of 12 studies showed a significant benefit toward VR for the selected outcomes. In the pooled analysis of all 5 randomized controlled trials, the effect of VR on motor impairment (Fugl-Meyer) was OR=4.89 (95% CI, 1.31 to 18.3). No significant difference was observed for Box and Block Test or motor function. Among observational studies, there was a 14.7% (95% CI, 8.7%–23.6%) improvement in motor impairment and a 20.1% (95% CI, 11.0%–33.8%) improvement in motor function after VR. Conclusions— VR and video game applications are novel and potentially useful technologies that can be combined with conventional rehabilitation for upper arm improvement after stroke.


title: An optimized design of SCMA codebook based on star-QAM signaling constellations

abstract: In this paper, an optimized codebook design for a non-orthogonal multiple access scheme, called sparse code multiple access (SCMA), is presented. Unlike the low density signature (LDS) systems, in SCMA systems, the procedure of bits to QAM symbol mapping and spreading are combined together, and the incoming bits are directly mapped to the codewords of the SCMA codebook sets. Each layer or user has its dedicated codebook and the codebooks are all different. An improved method based on star-QAM signaling constellations is proposed here for designing the SCMA codebooks. It is demonstrated that the new method can greatly improve the BER performance without sacrificing the low detection complexity, compared to the existing codebooks and LDS.


title: Quantum cryptography for IoT: APerspective

abstract: Internet of things (IoT) is going to be an integral part of our lives in the next few years. It will be found as direct service provider in our surroundings through the connected sensor based networks. Even indirectly, it will serve us in several forms as value added services over the cellular platforms. However, it is very much vulnerable from several security threats. The current security level is not very strong for the future applications of IoT. There is a need of a robust cryptosystem for IoT. In this article, we propose quantum cryptography as a security solution for IoT in the long term. We explain the motivation behind this idea and the potential applications in IoT in the near future.


title: Structured matching for phrase localization

abstract: In this paper we introduce a new approach to phrase localization: grounding phrases in sentences to image regions. We propose a structured matching of phrases and regions that encourages the semantic relations between phrases to agree with the visual relations between regions. We formulate structured matching as a discrete optimization problem and relax it to a linear program. We use neural networks to embed regions and phrases into vectors, which then define the similarities (matching weights) between regions and phrases. We integrate structured matching with neural networks to enable end-to-end training. Experiments on Flickr30K Entities demonstrate the empirical effectiveness of our approach.


title: Evolution of complex hierarchical societies

abstract: One of the greatest puzzles of human evolutionary history concerns the how and why of the transition from small-scale, ‘simple’ societies to large-scale, hierarchically complex ones. This paper reviews theoretical approaches to resolving this puzzle. Our discussion integrates ideas and concepts from evolutionary biology, anthropology, and political science. The evolutionary framework of multilevel selection suggests that complex hierarchies can arise in response to selection imposed by intergroup conflict (warfare). The logical coherency of this theory has been investigated with mathematical models, and its predictions were tested empirically by constructing a database of the largest territorial states in the world (with the focus on the preindustrial era).


title: Tricking the balance: NK cells in anti-cancer immunity

abstract: Abstract Natural Killer (NK) cells are classically considered innate immune effector cells involved in the first line of defense against infected and malignant cells. More recently, NK cells have emerged to acquire properties of adaptive immunity in response to certain viral infections such as expansion of specific NK cell subsets and long-lasting virus-specific responses to secondary challenges. NK cells distinguish healthy cells from abnormal cells by measuring the net input of activating and inhibitory signals perceived from target cells through NK cell surface receptors. Acquisition of activating ligands in combination with reduced expression of MHC class I molecules on virus-infected and cancer cells activates NK cell cytotoxicity and release of immunostimulatory cytokines like IFN-γ. In the cancer microenvironment however, NK cells become functionally impaired by inhibitory factors produced by immunosuppressive immune cells and cancer cells. Here we review recent progress on the role of NK cells in cancer immunity. We describe regulatory factors of the tumor microenvironment on NK cell function which determine cancer cell destruction or escape from immune recognition. Finally, recent strategies that focus on exploiting NK cell anti-cancer responses for immunotherapeutic approaches are outlined.


title: Popcorn-Driven Robotic Actuators

abstract: Popcorn kernels are a natural, edible, and inexpensive material that has the potential to rapidly expand with high force upon application of heat. Although this transition is irreversible, it carries potential for several robotic applications. Here, we examine relevant characteristics of three types of kernels including expansion ratio, transition temperature, popping force, compression strength, and biodegradability. We test the viability of popping by hot oil, hot air, microwaves, and direct contact with heated Nichrome wire. As kernels can change from regular to (larger) irregular shapes, we examine the change in inter-granular friction and propose their use as granular fluids in jamming actuators, without the need for a vacuum pump. Furthermore, as a proof-of-concept, we also demonstrate the use of popcorn-driven actuation in soft, compliant, and rigid-link grippers. Serving as a first introduction of popcorn into robotics, we hope this paper will inspire novel mechanisms for multi-functional designs.


title: 9.2 A scalable 0.1-to-1.7GHz spatio-spectral-filtering 4-element MIMO receiver array with spatial notch suppression enabling digital beamforming

abstract: Multiple-antenna receivers offer numerous advantages over single-antenna receivers, including sensitivity improvement, ability to reject interferers spatially and enhancement of data-rate or link reliability via MIMO. In the recent past, RF/analog phased-array receivers have been investigated [1–4]. On the other hand, digital beamforming offers far greater flexibility, including ability to form multiple simultaneous beams, ease of digital array calibration and support for MIMO. However, ADC dynamic range is challenged due to the absence of spatial interference rejection at RF/analog.


title: Operating Systems for Internet of Things: A Comparative Study

abstract: The intense growth of Internet of Things (IoTs), its multidisciplinary nature and broadcasting communication pattern made it very challenging for research community/domain. Operating systems for IoTs plays vital role in this regard. Through this research contribution, the objective is to present an analytical study on the recent developments on operating systems specifically designed or fulfilled the needs of IoTs. Starting from study and advances in the field of IoTs with focus on existing operating systems specifically for IoTs. Finally the existing operating systems for IoTs are evaluated and compared on some set criteria and facts and findings are presented.


title: Pathogenesis of bovine brucellosis.

abstract: Bovine brucellosis is one of the most important zoonotic diseases worldwide, and is of particular significance in developing countries. The disease, which results in serious economic losses due to late term abortion, stillborn and weakly calves, is caused by Gram negative coccobacilli bacteria of the genus Brucella. Lesions consist of necrotic placentitis and interstitial mastitis in pregnant cows, and fibrinous pleuritis with interstitial pneumonia in aborted fetuses and newborn calves. This article considers the pathogenesis of Brucella abortus and reviews the ability of the pathogen to invade phagocytic and non-phagocytic host cells, resist the acidified intraphagosomal environment, and inhibit phagosome-lysosome fusion. Significant aspects of innate and adaptive immunity against brucellosis are also discussed.


title: A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation

abstract: Dental X-ray image segmentation has an important role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoretically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solutions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and compared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.


Future directions in learning to rank


title: Dielectric notch radiator antennas with integrated filtering for 5G and IoT access

abstract: The development of dielectric notch radiator (DNR) antennas is described with integrated filters for 5G and IoT systems. The DNR is a type of tapered slot antenna (TSA) and is similar to the familiar Vivaldi antenna except that the length of the DNR is much shorter than a Vivaldi. The five step design process for the DNR is given and expands upon prior exponential taper design methods. The optimization and analysis of the microstrip to slot line section uses the finite element method (FEM). The DNR is used for a X-band antenna and line array. The single element simulated and measured return loss and antenna gain pattern are shown. The gain pattern for a four element line array is shown. The DNR is then used for an antenna system operating in the mobile phone and Wi-Fi bands. The antenna is integrated with filters to form a solution with application to 5G and internet of things (IoT) over Zigbee or Wi-Fi systems. The results demonstrate how the DNR antenna can be applied to solve wide band antenna design requirements where antenna length is an important design constraint.


title: A Survey on License Plate Recognition Systems

abstract: License Plate Recognition (LPR) is a well known image processing technology. LPR system consists of four steps: capture the image from digital camera, pre-processing, character segmentation and character recognition. License plates are available in various styles and colors in various countries. Every country has their own license plate format. So each country develops the LPR system appropriate for the vehicle license plate format. Difficulties that the LPR systems face are the environmental and non-uniform outdoor illumination conditions. Therefore, most of the systems work under restricted environmental conditions like fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Each LPR system use different combination of algorithms. From the papers being surveyed, it is realized that a good success rate of 93.7% is obtained by the combination of fuzzy logic for license plate detection and Self Organizing (SO) neural network for character recognition. Comparisons of different LPR systems are discussed in this paper.


title: Design and characterization of a novel high-compliance spring for robots with soft joints

abstract: Low stiffness elements have a number of applications in Soft Robotics, from Series Elastic Actuators (SEA) to torque sensors for compliant systems.


title: Automatic Migration and Deployment of Cloud Services for Healthcare Application Development in FIWARE

abstract: Over the latest years due to the emergency of new requirements in the area of healthcare domain, the software to data cloud model attracted significant attention by bridging the gap between cloud service deployment and sensitive data storage and manipulation. In particular, in FI-STAR FP7 we develop innovative cloud services for the healthcare domain using the FIWARE platform. This offers new opportunities for building innovative applications that utilize FIWARE Generic Enablers as the building blocks of Future Internet (FI) applications. In this work we focus on (a) the automatic migration requirements in FIWARE systems and (b) on the automatic shipment and deployment of FI-STAR services among service providers infrastructures that is the FIWARE cloud nodes and service consumer infrastructures (FI-STAR use cases). We describe the design and implementation enabling the automatic shipment and deployment to support the software to data paradigm and to allow marketplace development of the FI-STAR catalogue. The deployment automation solution offers less error-prone and highly repeatable processes, efficient deployment and management of updates and new releases.


title: Potential Point-of-Care Microfluidic Devices to Diagnose Iron Deficiency Anemia

abstract: Over the past 20 years, rapid technological advancement in the field of microfluidics has produced a wide array of microfluidic point-of-care (POC) diagnostic devices for the healthcare industry. However, potential microfluidic applications in the field of nutrition, specifically to diagnose iron deficiency anemia (IDA) detection, remain scarce. Iron deficiency anemia is the most common form of anemia, which affects billions of people globally, especially the elderly, women, and children. This review comprehensively analyzes the current diagnosis technologies that address anemia-related IDA-POC microfluidic devices in the future. This review briefly highlights various microfluidics devices that have the potential to detect IDA and discusses some commercially available devices for blood plasma separation mechanisms. Reagent deposition and integration into microfluidic devices are also explored. Finally, we discuss the challenges of insights into potential portable microfluidic systems, especially for remote IDA detection.


title: A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection

abstract: Using wireless mobile terminals has become the mainstream of Internet transactions, which can verify the identity of users by passwords, fingerprints, sounds, and images. However, once these identity data are stolen, traditional information security methods will not avoid online transaction fraud. The existing convolutional neural network model for fraud detection needs to generate many derivative features. This paper proposes a fraud detection model based on the convolutional neural network in the field of online transactions, which constructs an input feature sequencing layer that implements the reorganization of raw transaction features to form different convolutional patterns. Its significance is that different feature combinations entering the convolution kernel will produce different derivative features. The advantage of this model lies in taking low dimensional and nonderivative online transaction data as the input. The whole network consists of a feature sequencing layer, four convolutional layers and pooling layers, and a fully connected layer. Verifying with online transaction data from a commercial bank, the experimental results show that the model achieves excellent fraud detection performance without derivative features. And its precision can be stabilized at around 91% and recall can be stabilized at around 94%, which increased by 26% and 2%, respectively, comparing with the existing CNN for fraud detection.


title: An improved power quality induction heater using zeta converter

abstract: This paper presents an induction heater (IH) with power factor correction (PFC) for domestic induction heating applications. A half bridge voltage fed series resonant inverter (VFSRI) based IH is fed from the front end PFC corrected zeta converter. The PFC-zeta converter operating in continuous inductor conduction mode (CICM) is used to regulate the DC link voltage. The switch current stress of PFC-zeta converter is controlled by operating zeta converter in CICM mode using a well-established internal current loop and outer voltage loop approach. The PFC-zeta converter based voltage fed IH converts intermediate DC bus voltage to high frequency (30 kHz) voltage suitable for domestic induction heating. A 2 kW PFC-zeta converter based IH is designed and its performance is simulated in MATLAB with power quality indices within an IEC 61000-3-2 standard.


title: Application-driven dynamic vertical scaling of virtual machines in resource pools

abstract: Most modern hypervisors offer powerful resource control primitives such as reservations, limits, and shares for individual virtual machines (VMs). These primitives provide a means to dynamic vertical scaling of VMs in order for the virtual applications to meet their respective service level objectives (SLOs). VMware DRS offers an additional resource abstraction of a resource pool (RP) as a logical container representing an aggregate resource allocation for a collection of VMs. In spite of the abundant research on translating application performance goals to resource requirements, the implementation of VM vertical scaling techniques in commercial products remains limited. In addition, no prior research has studied automatic adjustment of resource control settings at the resource pool level. In this paper, we present AppRM, a tool that automatically sets resource controls for both virtual machines and resource pools to meet application SLOs. AppRM contains a hierarchy of virtual application managers and resource pool managers. At the application level, AppRM translates performance objectives into the appropriate resource control settings for the individual VMs running that application. At the resource pool level, AppRM ensures that all important applications within the resource pool can meet their performance targets by adjusting controls at the resource pool level. Experimental results under a variety of dynamically changing workloads composed by multi-tiered applications demonstrate the effectiveness of AppRM. In all cases, AppRM is able to deliver application performance satisfaction without manual intervention.


title: Ternary cache: Three-valued MLC STT-RAM caches

abstract: Spin-transfer torque random access memory (STT-RAM) has become a promising non-volatile memory technology for cache memories. Recently, 2-bit multi-level cell (MLC) STT-RAM has been proposed to enhance data density, but it suffers from low reliability of its read and write operations. In this paper, we propose a novel cache design called Ternary cache. In Ternary cache, a memory cell can store three values (i.e., 0,1,2) while MLC STT-RAM can store four values. In this way, Ternary cache achieves much higher read stability than MLC STT-RAM-based caches. To enhance writability, a write operation is performed with high current and terminated as soon as the data is written. Evaluation results show that Ternary cache achieves the data density benefit of MLC STT-RAM and the reliability benefit of SLC STT-RAM.


title: An analysis of ontology engineering methodologies : a literature review.

abstract: It is now widely accepted that ontologies play a critical role in achieving the goal of machine understandable web, also known as semantic web. In order to develop ontologies, several methodologies have been proposed during the last two decades. Despite the fact, that quite a number of ontology engineering methodologies have been proposed, still the field lacks widely accepted and mature methodologies. Most methodologies lack sufficient details of techniques and activities employed in them. However, some methodologies provide sufficient details including METHONTOLOGY. This article discusses and reports a critical analysis and comparison of these methodologies. The analysis is performed based on a criterion, derived from related literature, trends and needs which evolved over the years. The results of the analysis showed that there is no completely mature methodology. Therefore, this research may act as a preliminary guide to come with a state of art ontology engineering methodology, bridging up the existing gaps and shortfalls.


title: Measurement and monitoring of urban sprawl in a rapidly growing region using entropy

abstract: Rapid urban development and dramatic change of landscape have been recently witnessed in some developing countries as a result of rapid economic development. The measurement and monitoring of land-use changes in these areas are crucial to government officials and planners who urgently need updated information for planning and management purposes. This paper examines the use of entropy in the measurement and monitoring of urban sprawl by the integration of remote sensing and GI~. The advantages of the entropy method are its simplicity and easy integration with GIs. The measurement of entropy is devised based on two locational factors-distances from town centers and roads-to capture and reveal spatial patterns of urban sprawl. The entropy space can be conveniently used to differentiate various kinds of urban growth patterns. The application of the method in the Pearl River Delta, one of the fastest growing regions in China, has demonstmted that it is very useful and effective for the monitoring of urban sprawl. It provides a useful tool for the quantitative measurement that is much needed for rapidly growing regions in identifying the spatial variations and temporal changes of urban sprawl patterns. lntroductlon


Neural sentence generation from formal semantics


title: Identifying social roles in reddit using network structure

abstract: As social networks and the user-generated content that populates them continue to grow in prevalence, size, and influence, understanding how users interact and produce this content becomes increasingly important. Insight into these community dynamics could prove valuable for measuring content trust, providing role-based group recommendations, or evaluating group stability and growth. To this end, we explore user posting behavior on reddit, a large social networking site comprised of many sub-communities in which a user may participate simultaneously. We demonstrate that the well-known "answer-person" role is present in the reddit community, provide an exposition on an automated method for identifying this role based solely on user interactions (foregoing expensive content analysis), and show that users rarely exhibit significant participation in more than one communities.


title: Comparing adult and adolescent transsexuals: An MMPI-2 and MMPI-A study

abstract: Abstract Sex, sexual orientation and age have been shown to be important in relation to psychological functioning in transsexuals. However, only few studies to date took these factors into account and not earlier have adolescent transsexuals participated. In this study the Minnesota Multiphasic Personality Inventory (MMPI-2 or MMPI-A, respectively) was administered to 293 adults (207 male to female transsexuals (MtFs), mean age 38.04 (range 18.56–65.62) and 86 female to male transsexuals (FtMs), mean age 33.26 (range 18.95–64.30)) and 83 adolescents (43 MtFs, mean age 15.70 (range 13.16–18.70) and 40 FtMs, mean age 15.64 (range 13.05–18.56)) with a gender identity disorder (GID). Of adult MtFs, 33% were categorized as “homosexuals” and 66% as “non-homosexuals”. Of adult FtMs, 77% were categorized as “homosexuals” and 33% as “non-homosexuals”. Adult FtMs functioned significantly better than MtFs on three clinical scales. Contrary to what is often assumed, no differences in psychological functioning were found in the adult transsexuals with regard to sexual orientation, except on one clinical scale. Most remarkably, significantly more adults with GID scored in the clinical range on two or more clinical scales than adolescents with GID. Therefore, early medical intervention may be recommendable for adolescents with GID.


title: Forecasting Nonlinear Time Series with Feed-Forward Neural Networks : A Case Study of Canadian Lynx Data

abstract: The forecasting capabilities of feed-forward neural network (FFNN) models are compared to those of other competing time series models by carrying out forecasting experiments. As demonstrated by the detailed forecasting results for the Canadian lynx data set, FFNN models perform very well, especially when the series contains nonlinear and non-Gaussian characteristics. To compare the forecasting accuracy of a FFNN model with an alternative model, Pitman's test is employed to ascertain if one model forecasts significantly better than another when generating one-step-ahead forecasts. Moreover, the residual-fit spread plot is utilized in a novel fashion in this paper to compare visually out-of-sample forecasts of two alternative forecasting models. Finally, forecasting findings on the lynx data are used to explain under what conditions one would expect FFNN models to furnish reliable and accurate forecasts. Copyright © 2005 John Wiley & Sons, Ltd.


title: A Compact Broadband Horizontally Polarized Omnidirectional Antenna Using Planar Folded Dipole Elements

abstract: A compact horizontally polarized (HP) omnidirectional antenna with broadband characteristic is presented. The antenna is composed of four planar folded dipole antennas (PFDAs) as radiation elements of a square array and a broadband microstrip feeding structure composed of baluns and matching networks. The folded dipole antennas are chosen in order to achieve wide bandwidth operation within a compact size. The folded dipole elements are modified to compensate for the mutual coupling effects and also to improve the bandwidth and radiation performance of the antenna. The antenna is fabricated and its input impedance and radiation pattern are measured. The measured 10 dB return loss bandwidth is about 53.2% (1.19–2 GHz) and the gain variation in all directions of azimuth plane is less than 2 dB over the frequency band 1.2–1.9 GHz, and is increased to 2.8 dB at 2 GHz. The gain is almost constant over the entire band with a peak gain about 1.2 dB. The co-pol and cross-pol isolation is more than 20 dB for all azimuth directions and frequencies over the entire band.


title: Levels of ICT Integration among Teacher Educators in a Teacher Education Academic College

abstract: This article examines the perspective of teacher educators and academic officials in an academic teacher education program regarding the integration of ICT in the teacher education program. The study portrays the current state of the ICT integration process and the implementation of the program for “Adapting Teacher Training Colleges to 21st Century Education” in a specific academic college in one of Israel’s outlying areas. This mixed methods study combined quantitative and qualitative methods. Data was collected by means of a closed questionnaire, an open-ended questionnaire for the teacher educators (N = 68), and semi-structured interviews conducted with the academic officials (N = 12). Findings revealed a hierarchical range of ICT integration in teaching, which reflects different profiles of teacher educators who integrate innovative pedagogies. The three integration levels (the basic level, the focused level, and the creative level) reflect the scope of ICT integration in the context of teacher training creating a continuum of integration and implementation, which can serve as an infrastructure for the effective adoption and integration of this innovative pedagogy by teacher educators and academic officials in academic teacher training colleges.


title: Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

abstract: Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method called deep Taylor decomposition efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets. HighlightsA novel method to explain nonlinear classification decisions in terms of input variables is introduced.The method is based on Taylor expansions and decomposes the output of a deep neural network in terms of input variables.The resulting deep Taylor decomposition can be applied directly to existing neural networks without retraining.The method is tested on two large-scale neural networks for image classification: BVLC CaffeNet and GoogleNet.


title: Unsupervised segmentation of synthetic aperture Radar sea ice imagery using a novel Markov random field model

abstract: Environmental and sensor challenges pose difficulties for the development of computer-assisted algorithms to segment synthetic aperture radar (SAR) sea ice imagery. In this research, in support of operational activities at the Canadian Ice Service, images containing visually separable classes of either ice and water or multiple ice classes are segmented. This work uses image intensity to discriminate ice from water and uses texture features to identify distinct ice types. In order to seamlessly combine image spatial relationships with various image features, a novel Bayesian segmentation approach is developed and applied. This new approach uses a function-based parameter to weight the two components in a Markov random field (MRF) model. The devised model allows for automatic estimation of MRF model parameters to produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to successfully segment various SAR sea ice images and achieve improvement over existing published methods including the standard MRF-based method, finite Gamma mixture model, and K-means clustering.


title: An Automatic Equalizer Based on Forward–Flyback Converter for Series-Connected Battery Strings

abstract: This paper proposes an automatic any-cells-to-any-cells battery equalizer, which merges the forward and flyback converters through a common multiwinding transformer. The windings of the transformer are divided into two groups, which have opposite polarities. The principles of the proposed equalizer are that the equalization in one group is achieved based on forward conversion and the balancing between the two different groups is based on flyback conversion, by which the magnetic energy stored in the transformer can be automatically reset without using additional demagnetizing circuits. Moreover, only one MOSFET and one primary winding are required for each cell, resulting in smaller size and lower cost. One pair of complementary control signals is employed for all MOSFETs, and energy can be automatically and directly delivered from any high-voltage cells to any low-voltage cells without the requirement of cell monitoring circuits, thereby leading to a high balancing efficiency and speed. The proposed topology can achieve the global equalization for a long battery string through connecting the secondary sides of transformers without the need of additional components for the equalization among modules, which also overcomes the mismatching problem of multiple windings. The validity of the proposed equalizer is verified through experiments, and the balancing efficiency can reach up to 89.4% over a wide range of conditions.


title: Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure

abstract: The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.


Web Audio Evaluation Tool: A Browser-based Listening Test Environment


title: Sexuality of Male-to-Female Transsexuals

abstract: Blanchard’s (J Nerv Ment Dis 177:616–623, 1989) theory of autogynephilia suggests that male-to-female transsexuals can be categorized into different types based on their sexuality. Little previous research has compared the sexuality of male-to-female transsexuals to biological females. The present study examined 15 aspects of sexuality among a non-clinical sample of 234 transsexuals and 127 biological females, using either an online or a paper questionnaire. The results showed that, overall, transsexuals tended to place more importance on partner’s physical attractiveness and reported higher scores on Blanchard’s Core Autogynephilia Scale than biological females. In addition, transsexuals classified as autogynephilic scored significantly higher on Attraction to Feminine Males, Core Autogynephilia, Autogynephilic Interpersonal Fantasy, Fetishism, Preference for Younger Partners, Interest in Uncommitted Sex, Importance of Partner Physical Attractiveness, and Attraction to Transgender Fiction than other transsexuals and biological females. In accordance with Blanchard’s theory, autogynephilia measures were positively correlated to Sexual Attraction to Females among transsexuals. In contrast to Blanchard’s theory, however, those transsexuals classified as autogynephilic scored higher on average on Sexual Attraction to Males than those classified as non-autogynephilic, and no transsexuals classified as autogynephilic reported asexuality.


title: Superresolution of Images Based on Local Correlations

abstract: An adaptive two-step paradigm for the super-resolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image data. First, an unsupervised feature extraction is performed on local neighborhood information from a training image. These features are then used to cluster the neighborhoods into disjoint sets for which an optimal mapping relating homologous neighborhoods across scales can be learned in a supervised manner. A super-resolved image is obtained through the convolution of a low-resolution test image with the established family of kernels. Results demonstrate the effectiveness of the approach.


Sox 9 upregulation in breast cancer is correlated with poor prognosis and the CD 44 + / CD 24-/ low phenotype


title: The self and social behavior in differing cultural contexts

abstract: Three aspects of the self(private, public, collective) with different probabilities in different kinds of social environments were sampled. Three dimensions of cultural variation (individualism--collectivism, tightness-looseness, cultural complexity) are discussed in relation to the sampling of these three aspects of the self. The more complex the culture, the more frequent the sampling of the public and private self and the less frequent the sampling of the collective self. The more individualistic the culture, the more frequent the sampling of the private self and the less frequent the sampling of the collective self. Collectivism, external threat, competition with outgroups, and common fate increase the sampling of the collective self. Cultural homogeneity results in tightness and in the sampling of the collective self. The article outlines theoretical links among aspects of the environment, childrearing patterns, and cultural patterns, which are linked to differential sampling of aspects of the self. Such sampling has implications for social behavior. Empirical investigations of some of these links are reviewed.


title: Voice activity detection based on multiple statistical models

abstract: One of the key issues in practical speech processing is to achieve robust voice activity detection (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ the Gaussian assumption in the discrete Fourier transform (DFT) domain, which, however, deviates from the real observation. In this paper, we propose a class of VAD algorithms based on several statistical models. In addition to the Gaussian model, we also incorporate the complex Laplacian and Gamma probability density functions to our analysis of statistical properties. With a goodness-of-fit tests, we analyze the statistical properties of the DFT spectra of the noisy speech under various noise conditions. Based on the statistical analysis, the likelihood ratio test under the given statistical models is established for the purpose of VAD. Since the statistical characteristics of the speech signal are differently affected by the noise types and levels, to cope with the time-varying environments, our approach is aimed at finding adaptively an appropriate statistical model in an online fashion. The performance of the proposed VAD approaches in both the stationary and nonstationary noise environments is evaluated with the aid of an objective measure.


title: A template-based approach to automatic face enhancement

abstract: This paper presents Visual ENhancement of USers (VENUS), a system able to automatically enhance male and female frontal facial images exploiting a database of celebrities as reference patterns for attractiveness. Each face is represented by a set of landmark points that can be manually selected or automatically localized using active shape models. The faces can be compared remapping the landmarks by means of Catmull–Rom splines, a class of interpolating splines particularly useful to extract shape-based representations. Given the input image, its landmarks are compared against the known beauty templates and moved towards the K-nearest ones by 2D image warping. The VENUS performances have been evaluated by 20 volunteers on a set of images collected during the Festival of Creativity, held in Florence, Italy, on October 2007. The experiments show that the 73.9% of the beautified faces are more attractive than the original pictures.


title: The World-Wide Web: quagmire or gold mine?

abstract: An efficient continuous wave solid state laser is described. An array of semiconductor diode lasers is employed to generate a pump light source at an absorption wavelength for a solid state laser such as Nd:YAG laser. The array of diode lasers is pulsed at a high repetition rate to produce narrow light pulses which are directed upon an end surface of the solid state laser rod. The pump pulses rate is selected sufficiently high to establish CW operation of the solid state laser while the duty cycle for the pump-light source is sufficiently low to avoid excessive junction heating of the diode lasers, which would substantially reduce their output power.


title: Data augmentation for deep neural network acoustic modeling

abstract: This paper investigates data augmentation for deep neural network acoustic modeling based on label-preserving transformations to deal with data sparsity. Two data augmentation approaches, vocal tract length perturbation (VTLP) and stochastic feature mapping (SFM), are investigated for both deep neural networks (DNNs) and convolutional neural networks (CNNs). The approaches are focused on increasing speaker and speech variations of the limited training data such that the acoustic models trained with the augmented data are more robust to such variations. In addition, a two-stage data augmentation scheme based on a stacked architecture is proposed to combine VTLP and SFM as complementary approaches. Experiments are conducted on Assamese and Haitian Creole, two development languages of the IARPA Babel program, and improved performance on automatic speech recognition (ASR) and keyword search (KWS) is reported.


title: Investigating Spectrum Sharing between 5G Millimeter Wave Networks and Fixed Satellite Systems

abstract: In this paper we study coexistence of 5G small cells with fixed satellite systems (FSSs) in a scenario where both systems operate co-channel in the large spectrum bandwidth available around 28 GHz. Such studies are of great importance to inform the research community,industry and regulators which are currently investigating spectrum requirements and technology options for 5G systems. Focusing on the FSS uplink scenario, we use realistic FSS parameters and radiation pattern, combined with very recent channel from the literature, we analyzed the impact of interference resulting from FSS radiation on the achievable capacity and throughput of 5G small cells considering various multiple antenna configurations at the base stations (BSs) and different deployments of the mobile transmitters when no cooperation is allowed between the BSs. Starting from the lower bound, represented by an omnidirectional configuration of the transmitters, we extend our work to the analysis of large antenna arrays that will be used in the new generation of mobile cellular systems. Our results indicate that by exploiting a large number of antennas at the BSs and properly setting the protection distance between FSS and cellular BS, co-channel deployment of 5G small cells with FSS earth stations is possible, in the sense that adequate user data rates could be provided to the majority of mobile users.


title: Evolving Agents for the Hanabi 2018 CIG Competition

abstract: Hanabi is a cooperative card game with hidden information that has won important awards in the industry and received some recent academic attention. A two-track competition of agents for the game will take place in the 2018 CIG conference. In this paper, we develop a genetic algorithm that builds rule-based agents by determining the best sequence of rules from a fixed rule set to use as strategy. In three separate experiments, we remove human assumptions regarding the ordering of rules, add new, more expressive rules to the rule set and independently evolve agents specialized at specific game sizes. As result, we achieve scores superior to previously published research for the mirror and mixed evaluation of agents.


title: A history of Haskell: being lazy with class

abstract: This paper describes the history of Haskell, including its genesis and principles, technical contributions, implementations and tools, and applications and impact.


title: Curve Matching, Time Warping, and Light Fields: New Algorithms for Computing Similarity between Curves

abstract: The problem of curve matching appears in many application domains, like time series analysis, shape matching, speech recognition, and signature verification, among others. Curve matching has been studied extensively by computational geometers, and many measures of similarity have been examined, among them being the Frechet distance (sometimes referred in folklore as the "dog-man" distance). ::: ::: A measure that is very closely related to the Frechet distance but has never been studied in a geometric context is the Dynamic Time Warping measure (DTW), first used in the context of speech recognition. This measure is ubiquitous across different domains, a surprising fact because notions of similarity usually vary significantly depending on the application. However, this measure suffers from some drawbacks, most importantly the fact that it is defined between sequences of points rather than curves. Thus, the way in which a curve is sampled to yield such a sequence can dramatically affect the quality of the result. Some attempts have been made to generalize the DTW to continuous domains, but the resulting algorithms have exponential complexity. ::: ::: In this paper we propose similarity measures that attempt to capture the "spirit" of dynamic time warping while being defined over continuous domains, and present efficient algorithms for computing them. Our formulation leads to a very interesting connection with finding short paths in a combinatorial manifold defined on the input chains, and in a deeper sense relates to the way light travels in a medium of variable refractivity.


Application of CYCLONE in the Optimization of Top-Down Earthwork Construction Machine Arrangement


title: Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study

abstract: Background: Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking. Methods: Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. Results: Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16). Conclusion: Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.


title: A Practical and Provably Secure Coalition-Resistant Group Signature Scheme

abstract: A group signature scheme allows a group member to sign messages anonymously on behalf of the group. However, in the case of a dispute, the identity of a signature's originator can be revealed (only) by a designated entity. The interactive counterparts of group signatures are identity escrow schemes or group identification scheme with revocable anonymity. This work introduces a new provably secure group signature and a companion identity escrow scheme that are significantly more efficient than the state of the art. In its interactive, identity escrow form, our scheme is proven secure and coalition-resistant under the strong RSA and the decisional Diffie-Hellman assumptions. The security of the non-interactive variant, i.e., the group signature scheme, relies additionally on the Fiat-Shamir heuristic (also known as the random oracle model).


title: A Review of Smart Cities Based on the Internet of Things Concept

abstract: With the expansion of smart meters, like the Advanced Metering Infrastructure (AMI), and the Internet of Things (IoT), each smart city is equipped with various kinds of electronic devices. Therefore, equipment and technologies enable us to be smarter and make various aspects of smart cities more accessible and applicable. The goal of the current paper is to provide an inclusive review on the concept of the smart city besides their different applications, benefits, and advantages. In addition, most of the possible IoT technologies are introduced, and their capabilities to merge into and apply to the different parts of smart cities are discussed. The potential application of smart cities with respect to technology development in the future provides another valuable discussion in this paper. Meanwhile, some practical experiences all across the world and the key barriers to its implementation are thoroughly expressed.


Classifying Emotions in Customer Support Dialogues in Social Media


title: SAP HANA distributed in-memory database system: Transaction, session, and metadata management

abstract: One of the core principles of the SAP HANA database system is the comprehensive support of distributed query facility. Supporting scale-out scenarios was one of the major design principles of the system from the very beginning. Within this paper, we first give an overview of the overall functionality with respect to data allocation, metadata caching and query routing. We then dive into some level of detail for specific topics and explain features and methods not common in traditional disk-based database systems. In summary, the paper provides a comprehensive overview of distributed query processing in SAP HANA database to achieve scalability to handle large databases and heterogeneous types of workloads.


Multimedia mining


title: Machine Learning Approaches for Predicting High Utilizers in Health Care

abstract: We applied and developed several machine learning techniques, including linear model, tree-based model, and deep neural networks to forecast expenditures for high utilizers in a very large public health program. The results show promise for predicting health care expenditures for these high utilizers. To improve interpretability, we quantified the contributions of influential input variables to the prediction score. These results help to advance the field toward targeted preventive care to lower overall health care costs.


title: Wireless Sensor Network Based Remote Irrigation Control System and Automation Using DTMF Code

abstract: Agriculture is a source of livelihood of majority Indians and has great impact on the economy of the country. In dry areas or in case of inadequate rainfall, irrigation becomes difficult. So, it needs to be automated for proper yield and handled remotely for farmer safety. In this paper we suggest a Wireless sensor network and Embedded based technique of DTMF (Dual Tone Multiple Frequency) signaling to control water flow for sectored, sprinkler or drip section irrigation. This system will be very economical in terms of the hardware cost, power consumption and call charges. The proposed control system works on two principles one is wireless sensor network and another is of 'circuit switching' instead of 'packet switching' used by SMS controlled devices available currently in the market. In this paper, the complete installation process along with the circuit, designed and tested on a prototype environment, is explained.


title: RANGE - robust autonomous navigation in GPS-denied environments

abstract: This video highlights our system that enables a Micro Aerial Vehicle (MAV) to autonomously explore and map unstructured and unknown GPS-denied environments. While mapping and exploration solutions are now well-established for ground vehicles, air vehicles face unique challenges which have hindered the development of similar capabilities. Although there has been recent progress toward sensing, control, and navigation techniques for GPS-denied flight, there have been few demonstrations of stable, goal-directed flight in real-world environments. Our system leverages a multi-level sensing and control hierarchy that matches the computational complexity of the component algorithms with the real-time needs of a MAV to achieve autonomy in unconstrained environments.


title: Acquired methemoglobinemia–A sporadic Holi disaster

abstract: Objective ::: To study clinical profile and outcome in patients with methemoglobinemia following exposure to toxic colors during Holi festival.


title: Correction of penile curvature using the 16-dot plication technique: a review of 132 patients.

abstract: PURPOSE ::: We review the results of 132 cases of congenital and acquired penile curvature corrected with our 16 or 24-dot, minimal tension technique using multiple parallel plications performed under papaverine induced erection. ::: ::: ::: MATERIALS AND METHODS ::: Chart and telephone interviews were conducted on 132 consecutive patients 16 to 79 years old who underwent penile plication between December 1995 and November 2000. Patient data as well as outcomes were analyzed. ::: ::: ::: RESULTS ::: We were unable to contact 8 patients. Of the patients 16 had congenital penile curvature, including 4 in whom the Nesbit procedure performed elsewhere had failed, and 116 had Peyronie's disease, including 8 in whom a previous Nesbit procedure had failed. Preoperative complaints included persistent penile pain with erection for more than 1 year in 15 of 132 cases, difficult intercourse or partner discomfort in 106 and poor self-image in 11. Curvature ranged from 30 to 120 degrees. Erections were evaluated preoperatively with duplex ultrasound after intracavernous injection and self-stimulation. Of the patients 63% had good erections, 25% moderate erections requiring sildenafil and 12% poor erections requiring injection therapy. Foreskin edema necessitating subsequent circumcision and an organized hematoma requiring evacuation occurred in 1 case each. At 6 months 93% of patients reported straight erections and 7% reported almost straight but acceptable erections. Recurrence of curvature was reported by 15% of patients at a mean of 2.6 years of followup. Four patients reported worsening of erectile function after the procedure. ::: ::: ::: CONCLUSIONS ::: Penile plication is a simple, safe method to correct congenital and acquired penile curvature. Using a minimal tension parallel plication technique, excellent durable results can be attained. This simplified repair avoids the neurovascular bundles and has a minimal to no detrimental affect on erectile function. Preoperative counseling must be given regarding penile shortening and the palpable small bumps from the nonabsorbable sutures.


title: From social media to public health surveillance: Word embedding based clustering method for twitter classification

abstract: Social media provide a low-cost alternative source for public health surveillance and health-related classification plays an important role to identify useful information. In this paper, we summarized the recent classification methods using social media in public health. These methods rely on bag-of-words (BOW) model and have difficulty grasping the semantic meaning of texts. Unlike these methods, we present a word embedding based clustering method. Word embedding is one of the strongest trends in Natural Language Processing (NLP) at this moment. It learns the optimal vectors from surrounding words and the vectors can represent the semantic information of words. A tweet can be represented as a few vectors and divided into clusters of similar words. According to similarity measures of all the clusters, the tweet can then be classified as related or unrelated to a topic (e.g., influenza). Our simulations show a good performance and the best accuracy achieved was 87.1%. Moreover, the proposed method is unsupervised. It does not require labor to label training data and can be readily extended to other classification problems or other diseases.


Digital Transformation in Latecomer Industries: CIO and CEO Leadership Lessons from Encana Oil & Gas (USA) Inc


An intelligent Analysis of a City Crime Data Using Data Mining


A Novel Fast Framework for Topic Labeling Based on Similarity-preserved Hashing


title: Are We Having Fun Yet? Misapplying Motivation to Gamification

abstract: The practice of gamification has seen a popularity surge in recent years. Despite this, the theoretical underpinnings of gamification remain poorly understood, which has led to numerous failures and criticisms of the process. Self-Determination Theory (SDT) has emerged as the most popular approach for explaining the motivational aspects of gamification, yet many applications use incorrect or specious interpretations of the theory. This may lead to incorrect results about the psychology underlying gamification, or worse still, negatively affect the mental wellbeing of the end-users of gamified systems. This paper summarises the main findings of SDT as they apply to gamification, highlights previous misapplications of SDT in the field, and presents directions for future research. The purpose of this paper is to highlight flaws in current gamification practice, so that they may be rectified and thereby create a more sound foundation for gamification research.


Suhrawardi on Innateness: A Reply to John Walbridge


title: Review of charge equalization schemes for Li-ion battery and super-capacitor energy storage systems

abstract: Charge equalization of series connected energy storage elements (batteries and super-capacitors) has significant ramifications on their life and also reduces their operational hazards. This paper reviews the current status and art of power electronics converter topologies employed for charge equalization of Li-ion battery and super-capacitors based energy storage systems. Charge equalization schemes are broadly categorized into passive and active cell balancing. Operating principles of various schemes are discussed and compared.


title: A Novel Discriminative Framework for Sentence-Level Discourse Analysis

abstract: We propose a complete probabilistic discriminative framework for performing sentence-level discourse analysis. Our framework comprises a discourse segmenter, based on a binary classifier, and a discourse parser, which applies an optimal CKY-like parsing algorithm to probabilities inferred from a Dynamic Conditional Random Field. We show on two corpora that our approach outperforms the state-of-the-art, often by a wide margin.


Telemarketing Success: Evaluation of Supervised Classifiers


title: Making Time-series Classification More Accurate Using Learned Constraints

abstract: It has long been known that Dynamic Time Warping (DTW) is superior to Euclidean distance for classification and clustering of time series. However, until lately, most research has utilized Euclidean distance because it is more efficiently calculated. A recently introduced technique that greatly mitigates DTWs demanding CPU time has sparked a flurry of research activity. However, the technique and its many extensions still only allow DTW to be applied to moderately large datasets. In addition, almost all of the research on DTW has focused exclusively on speeding up its calculation; there has been little work done on improving its accuracy. In this work, we target the accuracy aspect of DTW performance and introduce a new framework that learns arbitrary constraints on the warping path of the DTW calculation. Apart from improving the accuracy of classification, our technique as a side effect speeds up DTW by a wide margin as well. We show the utility of our approach on datasets from diverse domains and demonstrate significant gains in accuracy and efficiency. E u clid ean D istan c e D yn am ic T im e W arp in g D is tan ce Figure 1: Note that while the two time series have an overall similar shape, they are not aligned in the time axis. Euclidean distance, which assumes the i point in one sequence is aligned with the i point in the other, will produce a pessimistic dissimilarity measure. The non-linear Dynamic Time Warped alignment allows a more intuitive distance measure to be calculated.


title: Extreme-angle broadband metamaterial lens

abstract: One of the more promising uses of metamaterials is in imaging, where the capability to control the propagation of light could lead to new applications. In particular, the realization of a broadband metamaterial lens that has an almost complete hemispherical field of view that is focused on a flat plane represents a significant step towards such new uses.


title: Analysis of Axial-Flux Halbach Permanent-Magnet Machine

abstract: This paper presents a 3-D analytical magnetic field analysis of an air-cored axial-flux Halbach permanent-magnet machine (AFPMM with Halbach). The 3-D analytical magnetic distribution is established using the magnetic scalar potential and modified BESSEL functions in the cylindrical coordinate system. The analytical magnetic field distributions and the electromagnetic force are derived and verified by the computation results using the finite-element method.


title: Collective Action, Institutions and Development

abstract: Publisher Summary This chapter reviews the diverse principles relevant to the analysis of collective actions and examines their usefulness in the theory and policy of economic development. The importance of the theory of collective action lies in the fact that collective action is generally required for the production and maintenance of public goods. The theory of public goods and the theory of collective action address the same issues relating to the provision of public goods, in particular the free-rider problem. The chapter presents the definition of public goods and the identification of the free-rider problem that plagues attempts at collective action. It also explains the numerous factors hypothesized to affect the likelihood of success in collective action in general. These include the type of public goods, group characteristics, environmental considerations, expectations, and other dynamic factors. The relationships between collective action and the state on the one hand and collective action and development on the other are examined in the chapter.


title: Integrated Decision Support by Combining Textual Information Extraction, Facetted Search and Information Visualisation

abstract: This work focusses on our integration steps of complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated facetted search tool, followed by information visualisation based on automatic information extraction results from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals. Our exemplary use case is nephrology, where we try to answer questions about the temporal characteristics of sequences and gain significant insight from the data for cohort selection. We report on this case study, illustrating how the application can be used by a clinician and which questions can be answered.


title: Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks

abstract: While there have been significant advances in detecting emotions from speech and image recognition, emotion detection on text is still under-explored and remained as an active research field. This paper introduces a corpus for text-based emotion detection on multiparty dialogue as well as deep neural models that outperform the existing approaches for document classification. We first present a new corpus that provides annotation of seven emotions on consecutive utterances in dialogues extracted from the show, Friends. We then suggest four types of sequence-based convolutional neural network models with attention that leverage the sequence information encapsulated in dialogue. Our best model shows the accuracies of 37.9% and 54% for fine- and coarse-grained emotions, respectively. Given the difficulty of this task, this is promising.


title: It's More than Just Sharing Game Play Videos! Understanding User Motives in Mobile Game Social Media

abstract: As mobile gaming has become increasingly popular in recent years, new forms of mobile game social media such as GameDuck that share mobile gameplay videos have emerged. In this work, we set out to understand the user motives of GameDuck by leveraging the Uses and Gratification Theory. We first explore the major motive themes from users' responses (n=138) and generate motivation survey items. We then identify the key motivators by conducting exploratory factor analysis of the survey results (n=354). Finally, we discuss how this new social media relates to existing systems such as Twitch.


title: Building organizational commitment: A multifirm study

abstract: Although much research has been conducted in the area of organizational commitment, few studies have explicitly examined how organizations facilitate commitment among members. Using a sample of 291 respondents from 45 firms, the results of this study show that rigorous recruitment and selection procedures and a strong, clear organizational value system are associated with higher levels of employee commitment based on internalization and identification. Strong organizational career and reward systems are related to higher levels of instrumental or compliance-based commitment.


title: Single & multi beam laser grooving process parameter development and die strength characterization for 40nm node low-K/ULK wafer

abstract: This paper describes the development work on single and multi beam laser grooving technology for 40nm node low-k/ULK semiconductor device. A Nd:YAG ultraviolet (UV) laser diode operating at a wavelength of 355 nm was used in this study. The effects of single and multi beam laser micromachining parameters, i.e. laser power, laser frequency, feed speed, and defocus amount were investigated. The laser processed die samples were thoroughly inspected and characterized. This includes the die edge and die sidewall grooving quality, the grooving shape/profile and the laser grooving depth analysis. Die strength is important and critical. Die damage from thermal and ablation caused by the laser around the die peripheral weakens the mechanical strength within the die, causing a reduction in die strength. The strength of a laser grooved die was improved by optimizing the laser process parameter. High power optical microscopy, Scanning Electron Microscopy (SEM), and focused ion beam (FIB) were the inspection tools/methods used in this study. Package reliability and stressing were carried out to confirm the robustness of the multi beam laser grooving process parameter and condition in a mass production environment. The dicing defects caused by the laser were validated by failure analysis. The advantages and limitations of conventional single beam compared to multi beam laser grooving process were also discussed. It was concluded that, multi beam laser grooving is possibly one of the best solutions to consider for dicing quality and throughput improvements for low-k/ULK wafer dicing. The multi beam laser process is a feasible, efficient, and cost effective process compared to the conventional single beam laser ablation process.


title: A visual attention operator for playing Pac-Man

abstract: The paper explores the use of reinforcement learning in tasks that involve image-like (but not necessarily raw-pixel) state representations. It proposes a visual attention operator, related to the concept of visual attention presented in previous works. The operator is tested on the game of Pac-Man, where it allows the use of the same agent with various game layouts, no matter what their dimensions (and thus the size of the image-like representation) are. It is shown that the approach is able to summarize information present in the original representation into a fixed-size glimpse and that the approach is able to outperform several more direct approaches.


title: Adversarial Reprogramming of Neural Networks

abstract: Deep neural networks are susceptible to \emph{adversarial} attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker. We introduce attacks that instead {\em reprogram} the target model to perform a task chosen by the attacker---without the attacker needing to specify or compute the desired output for each test-time input. This attack finds a single adversarial perturbation, that can be added to all test-time inputs to a machine learning model in order to cause the model to perform a task chosen by the adversary---even if the model was not trained to do this task. These perturbations can thus be considered a program for the new task. We demonstrate adversarial reprogramming on six ImageNet classification models, repurposing these models to perform a counting task, as well as classification tasks: classification of MNIST and CIFAR-10 examples presented as inputs to the ImageNet model.


title: Automated indoor Aquaponic cultivation technique

abstract: Aquaponic has become an interesting model for private sector, aquaculture and environmental scientist because of many advantages that can be obtained. The word `Aquaponic' refer to the integration of `hydroponic' (growing plant/vegetable production without soil) with aquaculture (fish farming). The growth performance of comet goldfish (aquatic) against a hydroponic plant of Ipomoea aquatic (water spinach), Spinacia oleracea (spinach) type of leafy vegetable and water plant were evaluated in recirculation of this Aquaponic system towards temperature, light and fish waste effectiveness. The fish were feed with commercial pelleted feeds containing 30% crude protein which can provide almost all nutrients required for the plant growth. Auto feeder place in this system used to maintain the growth and survival rates. Filter systems used to remove the amount of waste materials and breakdown products from the water. In this project, a set point is the desired value that needed by the user. The set point will be the desired water level, the monitored temperature in fish tank, the monitored temperature at plant area and the desired amount of food. While Arduino (Mega) function as a brain that used to receive the information from the sensor and come out with an instruction in term of response (action) as the feedback. Then the action will be based on the actuator that was reacted towards the act received. Therefore, this closed system was completed the project development system.


title: Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor

abstract: With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods.


title: Technology of Creating DMD-based Dynamic Infrared Image

abstract: In this paper,the technology of creating dynamic infrared image based on DMD is presented.By this technology,the infrared image with vary objects and backgrounds in different conditions,can be easily and quickly obtained,and the infrared radiation at the pupil of the guide system can be realistic.Then all around performance criterions and improvement information will be got by using the infrared image to evaluate the guide system.


title: Joint-Sparse Decentralized Heterogeneous Data Fusion for Target Estimation

abstract: In our recent work, we developed a new joint-sparse data-level fusion (JSDLF) approach to integrate heterogeneous sensor data for target discovery. In the approach, the target state space is discretized and data fusion is formulated as a joint sparse signal reconstruction problem, which is solved by using simultaneous orthogonal matching pursuit (SOMP). In our previous work, the joint sparse signal recovery approach has been implemented in a centralized manner. Namely, all the raw sensor data are transmitted to a fusion center, where they are fused to detect and estimate the targets. The drawback of the centralized network is its high communication cost and its lack of robustness, since the global information is stored and processed at a single point, the fusion center. In this paper, several decentralized JSDLF approaches have been developed, that provide exactly the same estimation result at each sensor node as the centralized algorithm does. Further, two distributed database query algorithms, Threshold Algorithm (TA) and Three-Phase Uniform Threshold (TPUT) have been combined with the SOMP algorithm to reduce communication costs. Numerical examples are provided to demonstrate that the proposed decentralized JSDLF approaches obtain excellent performance with accurate target position and velocity estimates to support situational awareness, while at the same time achieving dramatic communication savings.


title: Direct type-specific conic fitting and eigenvalue bias correction

abstract: A new method to fit specific types of conics to scattered data points is introduced. Direct, specific fitting of ellipses and hyperbolae is achieved by imposing a quadratic constraint on the conic coefficients, whereby an improved partitioning of the design matrix is devised so as to improve computational efficiency and numerical stability by eliminating redundant aspects of the fitting procedure. Fitting of parabolas is achieved by determining an orthogonal basis vector set in the Grassmannian space of the quadratic terms' coefficients. The linear combination of the basis vectors that fulfills the parabolic condition and has a minimum residual norm is determined using Lagrange multipliers. This is the first known direct solution for parabola specific fitting. Furthermore, the inherent bias of a linear conic fit is addressed. We propose a linear method of correcting this bias, producing better geometric fits which are still constrained to specific conic type.


title: Face recognition using scattering convolutional network

abstract: Face recognition has been an active research area in the past few decades. In general, face recognition can be very challenging due to variations in viewpoint, illumination, facial expression, etc. Therefore it is essential to extract features which are invariant to some or all of these variations. Here a new image representation, called scattering transform/network, has been used to extract features from faces. The scattering transform is a kind of convolutional network which provides a powerful multi-layer representation for signals. After extraction of scattering features, PCA is applied to reduce the dimensionality of the data and then a multi-class support vector machine is used to perform recognition. The proposed algorithm has been tested on three face datasets and achieved a very high recognition rate.


title: Effort estimation using analogy

abstract: The staff resources or effort required for a software project are notoriously difficult to estimate in advance. To date most work has focused upon algorithmic cost models such as COCOMO and Function Points. These can suffer from the disadvantage of the need to calibrate the model to each individual measurement environment coupled with very variable accuracy levels even after calibration. An alternative approach is to use analogy for estimation. We demonstrate that this method has considerable promise in that we show it to out perform traditional algorithmic methods for six different datasets. A disadvantage of estimation by analogy is that it requires a considerable amount of computation. The paper describes an automated environment known as ANGEL that supports the collection, storage and identification of the most analogous projects in order to estimate the effort for a new project. ANGEL is based upon the minimisation of Euclidean distance in n-dimensional space. The software is flexible and can deal with differing datasets both in terms of the number of observations (projects) and in the variables collected. Our analogy approach is evaluated with six distinct datasets drawn from a range of different environments and is found to outperform other methods. It is widely accepted that effective software effort estimation demands more than one technique. We have shown that estimating by analogy is a candidate technique and that with the aid of an automated environment is an eminently practical technique.


title: Unclonable Functions and Applications : A Tutorial This paper is a tutorial on ongoing work in physical-disorder-based security , security analysis , and implementation choices . By

abstract: This paper describes the use of physical unclonable functions (PUFs) in low-cost authentication and key generation applications. First, it motivates the use of PUFs versus conventional secure nonvolatile memories and defines the two primary PUF types: “strong PUFs” and “weak PUFs.” It describes strong PUF implementations and their use for low-cost authentication. After this description, the paper covers both attacks and protocols to address errors. Next, the paper covers weak PUF implementations and their use in key generation applications. It covers error-correction schemes such as pattern matching and index-based coding. Finally, this paper reviews several emerging concepts in PUF technologies such as public model PUFs and new PUF implementation technologies.


title: Regulation of Gene Expression Related to Allelopathic Potential

abstract: An improvement on allelopathic potential in crop varieties will have a great impact on weed management systems. Biotechnology is revolutionizing pesticide industry in countries that have accepted transgenic crops. A transgenic herbicide resistant crops mostly using glyphosate have not led to significant reduction in the amount of herbicide used. Glyphosate is a very safe herbicide but it is not low use-rate herbicide. An improvement on crop allelopathy by transgenic technology can be an alternative to reduce herbicide use-rate. For this purpose, the target chemical and gene should above all be identified. The promising allelochemicals reported thus far are sorgoleone, artemisinin, momilactone B produced from terpenoid pathway and some phenolics from phenylpropanoid pathway. Sorgoleone produced from sorghum is the most effective and promising model compound due to (1) no autotoxicity, (2) produced and secreted only by root, (3) a compound with more than one molecular target sites, (4) proper physicochemical properties to move a little bit in soil but not leach rapidly etc. An insertion of a specific promoter to the target gene can help regulate gene expression. This has been implemented in CA4H gene in rice inserted with a CASC (Capsicum annum sesqiterpene cyclase) promoter, enhancing 3～5 times higher activity of the CA4H


title: Choice Poetics by Example

abstract: Choice poetics is a formalist framework that seeks to concretely describe the impacts choices have on player experiences within narrative games. Developed in part to support algorithmic generation of narrative choices, the theory includes a detailed analytical framework for understanding the impressions choice structures make by analyzing the relationships among options, outcomes, and player goals. The theory also emphasizes the need to account for players’ various modes of engagement, which vary both during play and between players. In this work, we illustrate the non-computational application of choice poetics to the analysis of two different games to further develop the theory and make it more accessible to others. We focus first on using choice poetics to examine the central repeated choice in “Undertale,” and show how it can be used to contrast two different player types that will approach a choice differently. Finally, we give an example of fine-grained analysis using a choice from the game “Papers, Please,” which breaks down options and their outcomes to illustrate exactly how the choice pushes players towards complicity via the introduction of uncertainty. Through all of these examples, we hope to show the usefulness of choice poetics as a framework for understanding narrative choices, and to demonstrate concretely how one could productively apply it to choices “in the wild.”


title: Bicycle-Sharing System Analysis and Trip Prediction

abstract: Bicycle-sharing systems, which can provide shared bike usage services for the public, have been launched in many big cities. In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently. Therefore, bicycle-sharing systems are normally used as a short-distance trip supplement for private vehicles as well as regular public transportation. Meanwhile, for stations located at different places in the service region, the bike usages can be quite skewed and imbalanced. Some stations have too many incoming bikes and get jammed without enough docks for upcoming bikes, while some other stations get empty quickly and lack enough bikes for people to check out. Therefore, inferring the potential destinations and arriving time of each individual trip beforehand can effectively help the service providers schedule manual bike re-dispatch in advance. In this paper, we will study the individual trip prediction problem for bicycle-sharing systems. To address the problem, we study a real-world bicycle-sharing system and analyze individuals' bike usage behaviors first. Based on the analysis results, a new trip destination prediction and trip duration inference model will be introduced. Experiments conducted on a real-world bicycle-sharing system demonstrate the effectiveness of the proposed model.


title: Intelligent scheduling at NASA: Application to ground operations at Kennedy Space Center

abstract: NASA's Kennedy Space Center (KSC) is working with Stottler Henke to improve KSC's Ground Processing (GP) scheduling. The solution called, Aurora-KSC, has been designed, developed and deployed at KSC to automate a large amount of Kennedy Space Center's planning, scheduling, and execution decision-making. Aurora-KSC leverages Aurora, the world's most intelligent scheduling framework; Aurora itself originated in part from many earlier NASA-funded efforts and has been utilized by NASA for some of its most complex scheduling challenges, including the scheduling of the maintenance, repair & overhaul (MRO) of the Space Shuttle during its tenure. Aurora-KSC expands on this foundation, enhancing and extending Aurora for improved generalization and adaptability. This includes an interface with Primavera P6 and Microsoft Project so project information already available in these formats can benefit from Aurora-KSC. This allows KSC to realize significant efficiency improvements in several different areas. Aurora-KSC also includes adaptive execution, that is, executing the scheduled activities/procedures intelligently as actual execution actually deviates from the original schedule. Aurora-KSC is available to the wider NASA community to provide its many benefits to the rest of NASA. Aurora-KSC is being applied to several problems at KSC relating to Ground Processing (GP). The delivered application improves the scheduling of Space Launch System (SLS) Processing and verification & validation (V&V) activity, including reduced turnaround time in response to changes and what-ifs, and more optimal schedules. This paper reviews the goals that lead to the development of Aurora-KSC, including background on the pre-existing proven technologies that were leveraged. The application to NASA challenges will be discussed with benefits to date. Finally, the benefits of leveraging Aurora-KSC for other NASA applications will be discussed.


title: An Experimental Comparison of Several Clustering and Initialization Methods

abstract: We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation-Maximization (EM) algorithm, a "winner take all" version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden root node, using high-dimensional discrete-variable data sets (both real and synthetic). We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization schemes on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of hierarchical agglomerative clustering. Although the methods are substantially different, they lead to learned models that are strikingly similar in quality.


title: Joint Action Unit localisation and intensity estimation through heatmap regression

abstract: This paper proposes a supervised learning approach to jointly perform facial Action Unit (AU) localisation and intensity estimation. Contrary to previous works that try to learn an unsupervised representation of the Action Unit regions, we propose to directly and jointly estimate all AU intensities through heatmap regression, along with the location in the face where they cause visible changes. Our approach aims to learn a pixel-wise regression function returning a score per AU, which indicates an AU intensity at a given spatial location. Heatmap regression then generates an image, or channel, per AU, in which each pixel indicates the corresponding AU intensity. To generate the ground-truth heatmaps for a target AU, the facial landmarks are first estimated, and a 2D Gaussian is drawn around the points where the AU is known to cause changes. The amplitude and size of the Gaussian is determined by the intensity of the AU. We show that using a single Hourglass network suffices to attain new state of the art results, demonstrating the effectiveness of such a simple approach. The use of heatmap regression allows learning of a shared representation between AUs without the need to rely on latent representations, as these are implicitly learned from the data. We validate the proposed approach on the BP4D dataset, showing a modest improvement on recent, complex, techniques, as well as robustness against misalignment errors. Code for testing and models will be available to download from https://github.com/ESanchezLozano/Action-Units-Heatmaps.


title: Human adipose derived stem cells are superior to human osteoblasts (HOB) in bone tissue engineering on a collagen-fibroin-ELR blend

abstract: Abstract The ultrastructure of the bone provides a unique mechanical strength against compressive, torsional and tensional stresses. An elastin-like recombinamer (ELR) with a nucleation sequence for hydroxyapatite was incorporated into films prepared from a collagen – silk fibroin blend carrying microchannel patterns to stimulate anisotropic osteogenesis. SEM and fluorescence microscopy showed the alignment of adipose-derived stem cells (ADSCs) and the human osteoblasts (HOBs) on the ridges and in the grooves of microchannel patterned collagen-fibroin-ELR blend films. The Young's modulus and the ultimate tensile strength (UTS) of untreated films were 0.58 ± 0.13 MPa and 0.18 ± 0.05 MPa, respectively. After 28 days of cell culture, ADSC seeded film had a Young's modulus of 1.21 ± 0.42 MPa and UTS of 0.32 ± 0.15 MPa which were about 3 fold higher than HOB seeded films. The difference in Young's modulus was statistically significant (p: 0.02). ADSCs attached, proliferated and mineralized better than the HOBs. In the light of these results, ADSCs served as a better cell source than HOBs for bone tissue engineering of collagen-fibroin-ELR based constructs used in this study. We have thus shown the enhancement in the tensile mechanical properties of the bone tissue engineered scaffolds by using ADSCs.


title: Situational data integration with data services and nested table

abstract: Situational data integration is often ad hoc, involves active participation of business users, and requires just-in-time treatment. Agility and end-user programming are of importance. The paper presents a spreadsheet-like programming environment called Mashroom, which offers required agility and expressive power to support situational data integration by non-professional users. In Mashroom, various data sources are encapsulated as data services with nested tables as their unified data model both for internal processing and for external uses. Users can operate on the nested tables interactively. Mashroom also supports the basic control flow patterns. The expressive power of Mashroom is analyzed and proved to be richer than N1NF relational algebra. All the XQuery expressions can be mapped to Mashroom operations and formulas. Experiments have revealed the potentials of Mashroom in situational data integration.


title: Participatory rural appraisal (PRA): Challenges, potentials and paradigm

abstract: Abstract Much of the spread of participatory rural appraisal (PRA) as an emerging family of approaches and methods has been lateral, South–South, through experiential learning and changes in behavior, with different local applications. Rapid spread has made quality assurance a concern, with dangers from “instant fashion”, rushing, formalism and ruts. Promising potentials include farmers' own farming systems research, alternatives to questionaire surveys, monitoring, evaluation and lateral spread by local people, empowerment of the poorer and weaker, and policy review. Changes in personal behavior and attitudes, and in organizational cultures, are implied. PRA parallels and resonates with paradigm shifts in the social and natural sciences, business management, and development thinking, supporting decentralization, local diversity, and personal responsibility.


Intrusion Detection Using Data Mining Techniques Intrusion Detection Using Data Mining Techniques


title: Human auditory steady state responses to binaural and monaural beats

abstract: Abstract Objective Binaural beat sensations depend upon a central combination of two different temporally encoded tones, separately presented to the two ears. We tested the feasibility to record an auditory steady state evoked response (ASSR) at the binaural beat frequency in order to find a measure for temporal coding of sound in the human EEG. Methods We stimulated each ear with a distinct tone, both differing in frequency by 40 Hz, to record a binaural beat ASSR. As control, we evoked a beat ASSR in response to both tones in the same ear. We band-pass filtered the EEG at 40 Hz, averaged with respect to stimulus onset and compared ASSR amplitudes and phases, extracted from a sinusoidal non-linear regression fit to a 40 Hz period average. Results A 40 Hz binaural beat ASSR was evoked at a low mean stimulus frequency (400 Hz) but became undetectable beyond 3 kHz. Its amplitude was smaller than that of the acoustic beat ASSR , which was evoked at low and high frequencies. Both ASSR types had maxima at fronto-central leads and displayed a fronto-occipital phase delay of several ms. Conclusions The dependence of the 40 Hz binaural beat ASSR on stimuli at low, temporally coded tone frequencies suggests that it may objectively assess temporal sound coding ability. The phase shift across the electrode array is evidence for more than one origin of the 40 Hz oscillations. Significance The binaural beat ASSR is an evoked response, with novel diagnostic potential, to a signal that is not present in the stimulus, but generated within the brain.


title: Simultaneous Wireless Power/Data Transfer for Electric Vehicle Charging

abstract: The major drawback of the battery charging in traditional electric vehicles is the use of plug-in charging devices. The aim of this paper is to propose a wireless battery charging method, in addition to power transfer, data related to battery status, vehicle ID code, or emergency messages can be simultaneously transferred between the grid and vehicle. This work applies inductive power transfer to complete the charging system. The proposed control system can monitor the operating status on the secondary (vehicle) side in real time and adjust charging current depending on the battery status. Furthermore, the proposed mechanism is able to make an immediate stop, if there is any contingency, such as overcharging voltage or current. This will be beneficial to efficient and safety concern during the charging process.


title: Broadband suspended plate antenna with concaved center portion

abstract: A new suspended plate antenna is presented for the enhancement of impedance bandwidth. The probe-fed plate antenna is suspended above a ground plane and its center portion is concaved to form a "V" shape. The experiment and simulation show that without increase in size the proposed antenna is capable of providing an impedance bandwidth of up to 60% for |S/sub 11/|<-10 dB with an acceptable gain of 8 dBi.