title: Application of Functional Safety in Autonomous Vehicles Using ISO 26262 Standard: A Survey

abstract: Currently there is extensive research and investments in safety technologies, such as ADAS (Advanced Driver Assistance Systems) for enabling road vehicles to become intelligent and safer, thus making them detect and prevent possible accidents, assist the driver in changing lanes efficiently and making more accurate turns. Almost every automotive company is researching and developing autonomous vehicles. This huge amount of investment in terms of money and efforts might soon make self-driving vehicles a reality and we might start seeing autonomous and non-autonomous vehicles running together on the road. Along with the perks of these autonomous vehicles, some new risks are also introduced into the vehicle and road safety. The ISO 26262 is a standard that deals with the functional safety of the E/E (Electric and Electronic) components of a road vehicle. As of now, there is no such standard that directly applies to the functional safety of autonomous vehicles and hence, many researchers have tried to use this ISO 26262 standard as a guideline for developing software/hardware models for making autonomous vehicles compatible to the functional safety standards. This paper provides an overview of some of these methods or techniques by which functional safety can be introduced in autonomous and semi-autonomous vehicles.


title: Improving Vehicle Detection in Point Cloud Data with Novel Features

abstract: To understand one of the autonomous tasks for self-driving car is to classify the vehicles. This paper presents a pipeline of Lidar point data's clustering and classifying, basically conducted by combining the grid-occupy segmentation and Support Vector Machine learning method. To improve the classification, a novel feature related to vehicle's plate intensity characteristic is investigated. After point cloud clustering, different classifiers were utilized to evaluate the accuracy and our proposed method produced better performance.


The Oxford Handbook of Computational Linguistics


title: Nitrogen biogeochemical cycles in lakes with strong hydraulic power during summer stratification:A case study of Hongfeng Lake in Guizhou Province,Southwest China

abstract: Nitrogen stable isotopic technique,combined with the concentration ch anges of nitrate,chlorophyll a (chla)and dissolved oxygen(DO),is used to study nitrogen biogeochemistry cycles during summer stratif ication in Hongfeng Lake.In general,nitrate concentrations in lakes are lower i n summer than in spring because algae consume nitrate in summer.However,nitrate concentrations in South Lak e of Hongfeng Lake increase by 0.83mg /L.This indicates that there are o ther important sources of nitrate to the lake in summer.It is calculated t hat the net yield of chla is about 640kg,a nd 3.52×10 5 kg nitrates is assimilated.The increase of 0.83mg /L in the lake waters also needs about 1.66×10 5 kg nitrates.And about 10.1×10 5 kg nitrates are discharged into North Lake of Hongfeng Lake.Apart fr om the import of 4.42×10 5 kg nitrates from inflow steams,there is a margin of 10.86×10 5 kg nitrates.It is suggested that they come from the thermocline of the lak e due to mineralization(and nitrification)of organic matters.With the help of hydraulic power,waters with high DO f rom inflow steams and from the epilimnio n of the lake sink at the inlet of the la ke.Mineralization(and nitrification)is the result of the hydraulic pattern.The nitrogen biogeochemistry cycles in the lakes with strong hydrauli c power,such as the South Lake,is a special type,with the concurrence of mi neralization(and nitrification)of organic matters in thermocline and u ptake of nitrate by algae in epilimni on.


title: Internet addiction, usage, gratification, and pleasure experience: the Taiwan college students' case

abstract: Abstract This study explores Internet addiction among some of the Taiwan’s college students. Also covered are a discussion of the Internet as a form of addiction, and related literature on this issue. This study used the Uses and Gratifications theory and the Play theory in mass communication. Nine hundred and ten valid surveys were collected from 12 universities and colleges around Taiwan. The results indicated that Internet addiction does exist among some of Taiwan’s college students. In particular, 54 students were identified as Internet addicts. It was found that Internet addicts spent almost triple the number of hours connected to the Internet as compare to non-addicts, and spent significantly more time on BBSs, the WWW, e-mail and games than non-addicts. The addict group found the Internet entertaining, interesting, interactive, and satisfactory. The addict group rated Internet impacts on their studies and daily life routines significantly more negatively than the non-addict group. The study also found that the most powerful predictor of Internet addiction is the communication pleasure score, followed by BBS use hours, sex, satisfaction score, and e-mail-use hours.


title: Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space

abstract: We explore Deep Reinforcement Learning in a parameterized action space. Specifically, we investigate how to achieve sample-efficient end-to-end training in these tasks. We propose a new compact architecture for the tasks where the parameter policy is conditioned on the output of the discrete action policy. We also propose two new methods based on the state-of-the-art algorithms Trust Region Policy Optimization (TRPO) and Stochastic Value Gradient (SVG) to train such an architecture. We demonstrate that these methods outperform the state of the art method, Parameterized Action DDPG, on test domains.


3 D Objects Indexing and Retrieval Based On A New Efficient Optimal 2 D Views Selection Method


title: StickEar: making everyday objects respond to sound

abstract: This paper presents StickEar, a system consisting of a network of distributed 'Sticker-like' sound-based sensor nodes to propose a means of enabling sound-based interactions on everyday objects. StickEar encapsulates wireless sensor network technology into a form factor that is intuitive to reuse and redeploy. Each StickEar sensor node consists of a miniature sized microphone and speaker to provide sound-based input/output capabilities. We provide a discussion of interaction design space and hardware design space of StickEar that cuts across domains such as remote sound monitoring, remote triggering of sound, autonomous response to sound events, and controlling of digital devices using sound. We implemented three applications to demonstrate the unique interaction capabilities of StickEar.


title: For Video Games, Bad News Is Good News: News Reporting of Violent Video Game Studies

abstract: News coverage of video game violence studies has been critiqued for focusing mainly on studies supporting negative effects and failing to report studies that did not find evidence for such effects. These concerns were tested in a sample of 68 published studies using child and adolescent samples. Contrary to our hypotheses, study effect size was not a predictor of either newspaper coverage or publication in journals with a high-impact factor. However, a relationship between poorer study quality and newspaper coverage approached significance. High-impact journals were not found to publish studies with higher quality. Poorer quality studies, which tended to highlight negative findings, also received more citations in scholarly sources. Our findings suggest that negative effects of violent video games exposure in children and adolescents, rather than large effect size or high methodological quality, increase the likelihood of a study being cited in other academic publications and subsequently receiving news media coverage.


Development and practical application of a stairclimbing wheelchair in Nagasaki


title: Combining Multiple Coverage Criteria in Search-Based Unit Test Generation

abstract: Automated test generation techniques typically aim at maximising coverage of well-established structural criteria such as statement or branch coverage. In practice, generating tests only for one specific criterion may not be sufficient when testing object oriented classes, as standard structural coverage criteria do not fully capture the properties developers may desire of their unit test suites. For example, covering a large number of statements could be easily achieved by just calling the main method of a class; yet, a good unit test suite would consist of smaller unit tests invoking individual methods, and checking return values and states with test assertions. There are several different properties that test suites should exhibit, and a search-based test generator could easily be extended with additional fitness functions to capture these properties.


title: Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation

abstract: We introduce Fluid Annotation, an intuitive human-machine collaboration interface for annotating the class label and outline of every object and background region in an image. Fluid annotation is based on three principles: (I) Strong Machine-Learning aid. We start from the output of a strong neural network model, which the annotator can edit by correcting the labels of existing regions, adding new regions to cover missing objects, and removing incorrect regions. The edit operations are also assisted by the model. (II) Full image annotation in a single pass. As opposed to performing a series of small annotation tasks in isolation, we propose a unified interface for full image annotation in a single pass. (III) Empower the annotator. We empower the annotator to choose what to annotate and in which order. This enables concentrating on what the machine does not already know, i.e. putting human effort only on the errors it made. This helps using the annotation budget effectively. Through extensive experiments on the COCO+Stuff dataset, we demonstrate that Fluid Annotation leads to accurate annotations very efficiently, taking three times less annotation time than the popular LabelMe interface.


title: Cross-Domain Deep Face Matching for Real Banking Security Systems

abstract: Ensuring the security of transactions is currently one of the major challenges facing banking systems. The usage of face for biometric authentication of users is becoming adopted worldwide due its convenience and acceptability by people, and also given that, nowadays, almost all computers and mobile devices have built-in cameras. Such user authentication approach is attracting large investments from banking and financial institutions, especially in cross-domain scenarios, in which facial images from ID documents are compared with digital self-portraits (selfies) taken with the cameras of mobile devices, for the automated opening of new checking accounts or financial transactions authorization. In this work, besides of collecting a large cross-domain face database, with 27,002 real facial images of selfies and ID documents (13,501 subjects) captured from the systems of the major public Brazilian bank, we propose a novel approach for such cross-domain face matching based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN). Results obtained on the large dataset collected, which we called FaceBank, with accuracy rates higher than 93%, demonstrate the robustness of the proposed approach to the cross-domain problem (comparing faces in IDs and selfies) and its feasible application in real banking security systems.


title: Integrated solar panel antennas

abstract: An antenna which combines solar cells and printed patches is presented. The antenna is designed so as to accommodate the solar cells that provide power to an MMIC amplifier. A 2×4 array which operates at the frequency of 3.76 GHz and has a bandwidth of 16% and gain of up to 30 dBi (active) is also presented.


title: Beyond the Stars: Improving Rating Predictions using Review Text Content

abstract: Online reviews are an important asset for users deciding to buy a product, see a movie, or go to a restaurant, as well as for businesses tracking user feedback. However, most reviews are written in a free-text format, and are therefore difficult for computer systems to understand, analyze, and aggregate. One consequence of this lack of structure is that searching text reviews is often frustrating for users. User experience would be greatly improved if the structure and sentiment conveyed in the content of the reviews were taken into account. Our work focuses on identifying this information from free-form text reviews, and using the knowledge to improve user experience in accessing reviews. Specifically, we focused on improving recommendation accuracy in a restaurant review scenario. In this paper, we report on our classification effort, and on the insight on user-reviewing behavior that we gained in the process. We propose new ad-hoc and regression-based recommendation measures, that both take into account the textual component of user reviews. Our results show that using textual information results in better general or personalized review score predictions than those derived from the numerical star ratings given by the users.


title: Analysing vulnerability reproducibility for Firefox browser

abstract: Fixing some security failures are difficult because they cannot be easily reproduced. To address Hardly Reproducible Vulnerabilities (HRVs), security experts spend a significant amount of time, effort, and budget. Sometimes they do not succeed in the reproduction step and ignore some security failures. The exploitation of a vulnerability due to its irreproducibility may cause severe consequences. An efficient solution is to explore the behaviour of both hardly and easily reproducible security issues at the code level. We use linear regression techniques to build models based on the classical software complexity metrics and a set of attributes related to the environment of the system. The results show that the considered metrics and the vulnerability types do not have significant linear correlations with each other. Also, predicting the HRV-prone parts of large systems is a great help for security experts to focus their effort on the top-ranked vulnerable files. After identifying the suitable indicators based on linear regression, different machine learning techniques such as Random Forest, Logistic Regression, C4.5 Decision Tree, and Naive Bayes are employed to build HRV prediction models. The Random Forest technique achieves the precision of 82% and recall of 84% to classify vulnerable files into HRV-prone or non HRV-prone files. We believe that the results encourage the use of software metrics for vulnerability prediction in some projects.


The Critical Period for Language Acquisition: Evidence from Second Language Learning


title: Semantic Web for the Legal Domain: The next step

abstract: Ontology-driven systems with reasoning capabilities in the legal field are now better understood. Legal concepts are not discrete, but make up a dynamic continuum between common sense terms, specific technical use, and professional knowledge, in an evolving institutional reality. Thus, the tension between a plural understanding of regulations and a more general understanding of law is bringing into view a new landscape in which general legal frameworks – grounded in well-known legal theories stemming from 20th-century c. legal positivism or sociological jurisprudence – are made compatible with specific forms of rights management on the Web. In this sense, Semantic Web tools are not only being designed for information retrieval, classification, clustering, and knowledge management. They can also be understood as regulatory tools, i.e. as components of the contemporary legal architecture, to be used by multiple stakeholders – front-line practitioners, policymakers, legal drafters, companies, market agents, and citizens. That is the issue broadly addressed in this Special Issue on the Semantic Web for the Legal Domain, overviewing the work carried out over the last fifteen years, and seeking to foster new research in this field, beyond the state of the art.


Internet of Things Controlled Home Objects for the Elderly


title: Auto-encoder Based Data Clustering

abstract: Linear or non-linear data transformations are widely used processing techniques in clustering. Usually, they are beneficial to enhancing data representation. However, if data have a complex structure, these techniques would be unsatisfying for clustering. In this paper, based on the auto-encoder network, which can learn a highly non-linear mapping function, we propose a new clustering method. Via simultaneously considering data reconstruction and compactness, our method can obtain stable and effective clustering. Experiments on three databases show that the proposed clustering model achieves excellent performance in terms of both accuracy and normalized mutual information.


title: Improving unsupervised query segmentation using parts-of-speech sequence information

abstract: We present a generic method for augmenting unsupervised query segmentation by incorporating Parts-of-Speech (POS) sequence information to detect meaningful but rare n-grams. Our initial experiments with an existing English POS tagger employing two different POS tagsets and an unsupervised POS induction technique specifically adapted for queries show that POS information can significantly improve query segmentation performance in all these cases.


Tilburg University Stimulation of the subthalamic region facilitates the selection and inhibition of motor responses in Parkinson ¿ s disease


title: Design of a low noise amplifier for LTE radio base station receivers

abstract: The objective of this work is to model a low noise amplifier and study the key properties of GaN HEMT LNAs in terms of gain and noise performance, for LTE radio base station receiver front-end design. A well-designed LNA is eventually simulated using Advanced Design System. The design uses microstrip open shunt matching networks, inductors and capacities for DC Biasing. The simulation results show at 2.6 GHz a gain of 18.22 dB, noise figure of 0.34 dB, while VSWRin= 1.613 and VSWRout=1.613 with biasing voltage at 4.35 V.


Virtual Reality for the Web : Oculus Rift


title: The effect of store image and service quality on brand image and purchase intention for private label brands

abstract: This study aims to investigate the direct effects of store image and service quality on brand image and purchase intention for a private label brand (PLB). This study also investigates the indirect effects mediated by perceived risk and price consciousness on these relationships. The sample in this study consisted of three hundred and sixty (360) customers of the Watsons and Cosmed chain of drugstores. The pre-test results identified ‘‘Watsons’’ and ‘‘My Beauty Diary’’ as the research brands of the PLB for the two stores, respectively. This study uses LISREL to examine the hypothesized relationships. This study reveals that (1) store image has a direct and positive effect on the purchase intention of the PLB; (2) service quality has a direct and positive effect on the PLB image; (3) the perceived risk of PLB products has a mediating effect on the relationship between the brand image and the consumers purchase intention of the PLB.


title: The effect of heparin on the activity of Trichosporon cutaneum casein kinase I.

abstract: Casein kinase I from Trichosporon cutaneum ribosome-free extracts was purified. Its molecular mass was calculated for 33 kDa. It was shown that casein, phosvitin and Trichosporon cutaneum ribosomal protein of 15 kDa were preferable substrates for the enzyme. It was found that heparin can stimulate or inhibit CKI activity depending on the substrate used. Stimulation of casein and inhibition of phosvitin phosphorylation was observed. In addition it was shown that ribosomal proteins of 19 kDa and 38 kDa were phosphorylated by CKI only in the presence of heparin.


title: MapReduce across Distributed Clusters for Data-intensive Applications

abstract: Recently, the computational requirements for large scale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data are processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In this paper, we present the design and implementation of GHadoop, a MapReduce framework that aims to enable large-scale distributed computing across multiple clusters. G-Hadoop uses the Gfarm file system as an underlying file system and executes MapReduce tasks across distributed clusters. Experiments of the G-Hadoop framework on distributed clusters show encouraging results.


title: Unified Perceptual Parsing for Scene Understanding

abstract: Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes. Models are available at \url{this https URL}.


title: The price of robustness

abstract: A robust approach to solving linear optimization problems with uncertain data was proposed in the early 1970s and has recently been extensively studied and extended. Under this approach, we are willing to accept a suboptimal solution for the nominal values of the data in order to ensure that the solution remains feasible and near optimal when the data changes. A concern with such an approach is that it might be too conservative. In this paper, we propose an approach that attempts to make this trade-off more attractive; that is, we investigate ways to decrease what we call the price of robustness. In particular, we flexibly adjust the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations. An attractive aspect of our method is that the new robust formulation is also a linear optimization problem. Thus we naturally extend our methods to discrete optimization problems in a tractable way. We report numerical results for a portfolio optimization problem, a knapsack problem, and a problem from the Net Lib library.


title: A Logic Programming Approach to Aspect Extraction in Opinion Mining

abstract: Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach performs well. In this paper, we show that Logic Programming, particularly Answer Set Programming (ASP), can be used to elegantly and efficiently implement the key components of syntax based aspect extraction. Specifically, the well known double propagation (DP) method is implemented using 8 ASP rules that naturally model all key ideas in the DP method. Our experiment on a widely used data set also shows that the ASP implementation is much faster than a Java-based implementation. Syntactical approach has its limitation too. To further improve the performance of syntactical approach, we identify a set of general words from Word Net that have little chance to be an aspect and prune them when extracting aspects. The concept of general words and their pruning are concisely captured by 10 new ASP rules, and a natural extension of the 8 rules for the original DP method. Experimental results show a major improvement in precision with almost no drop in recall compared with those reported in the existing work on a typical benchmark data set. Logic Programming provides a convenient and effective tool to encode and thus test knowledge needed to improve the aspect extraction methods so that the researchers can focus on the identification and discovery of new knowledge to improve aspect extraction.


title: Using Artificial Intelligence to Support Compliance with the General Data Protection Regulation

abstract: The General Data Protection Regulation (GDPR) is a European Union regulation that will replace the existing Data Protection Directive on 25 May 2018. The most significant change is a huge increase in the maximum fine that can be levied for breaches of the regulation. Yet fewer than half of UK companies are fully aware of GDPR--and a number of those who were preparing for it stopped doing so when the Brexit vote was announced. A last-minute rush to become compliant is therefore expected, and numerous companies are starting to offer advice, checklists and consultancy on how to comply with GDPR. In such an environment, artificial intelligence technologies ought to be able to assist by providing best advice; asking all and only the relevant questions; monitoring activities; and carrying out assessments. The paper considers four areas of GDPR compliance where rule based technologies and/or machine learning techniques may be relevant: Following compliance checklists and codes of conduct; Supporting risk assessments; Complying with the new regulations regarding technologies that perform automatic profiling; Complying with the new regulations concerning recognising and reporting breaches of security. It concludes that AI technology can support each of these four areas. The requirements that GDPR (or organisations that need to comply with GDPR) state for explanation and justification of reasoning imply that rule-based approaches are likely to be more helpful than machine learning approaches. However, there may be good business reasons to take a different approach in some circumstances.


Stock price prediction using genetic algorithms and evolution strategies


title: Traveling time prediction in scheduled transportation with journey segments

abstract: Urban mobility impacts urban life to a great extent. To enhance urban mobility, much research was invested in traveling time prediction: given an origin and destination, provide a passenger with an accurate estimation of how long a journey lasts. In this work, we investigate a novel combination of methods from Queueing Theory and Machine Learning in the prediction process. We propose a prediction engine that, given a scheduled bus journey (route) and a 'source/destination' pair, provides an estimate for the traveling time, while considering both historical data and real-time streams of information that are transmitted by buses. We propose a model that uses natural segmentation of the data according to bus stops and a set of predictors, some use learning while others are learning-free, to compute traveling time. Our empirical evaluation, using bus data that comes from the bus network in the city of Dublin, demonstrates that the snapshot principle, taken from Queueing Theory, works well yet suffers from outliers. To overcome the outliers problem, we use Machine Learning techniques as a regulator that assists in identifying outliers and propose prediction based on historical data.


title: A novel PSIM and Matlab co-simulation approach to particle swarm optimization tuning of PID controllers

abstract: The classical PID algorithm has proven to be a consistently popular solution across a range of different control applications. This popularity can be attributed to the algorithm offering both flexibility and robustness whilst being relatively simple to implement. The PID algorithm is given as: 1( 1) One important consideration for the optimal use of the PID controller is the selection of the parameters Kp, Tr and Td in Eq 1. Although a number of methods have been proposed for the selection of these parameters, such as the Ziegler-Nichols approach, this tuning operation continues to be a research theme. One modern approach which has been shown to give good results is the use of evolutionary algorithms such as in (1) and (2). The Particle Swarm Optimization (PSO) algorithm has proven to be particularly popular thanks to its simplicity, suitability as a function approximator and the favorable results it produces (3). This paper presents a new modeling technique as shown in Fig 2, featuring Co-Simulation between Matlab and PSIM, a specialist control and power electronics simulation environment. The tuned PID is applied to a buck converter, driven by a PV cell as shown in Fig 1. The novel solution is compared with and evaluated against a system which is tuned using a traditional method. The simulation results indicate that the controller developed using the PSO algorithm achieves a faster response time without adversely affecting peak errors. There are added benefits of this new approach, in terms of a shorter time scale for conceptual model development.


title: Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning

abstract: Computational models in cognitive neuroscience should ideally use biological properties and powerful computational principles to produce behavior consistent with psychological findings. Error-driven backpropagation is computationally powerful and has proven useful for modeling a range of psychological data but is not biologically plausible. Several approaches to implementing backpropagation in a biologically plausible fashion converge on the idea of using bidirectional activation propagation in interactive networks to convey error signals. This article demonstrates two main points about these error-driven interactive networks: (1) they generalize poorly due to attractor dynamics that interfere with the network's ability to produce novel combinatorial representations systematically in response to novel inputs, and (2) this generalization problem can be remedied by adding two widely used mechanistic principles, inhibitory competition and Hebbian learning, that can be independently motivated for a variety of biological, psychological, and computational reasons. Simulations using the Leabra algorithm, which combines the generalized recirculation (GeneRec), biologically plausible, error-driven learning algorithm with inhibitory competition and Hebbian learning, show that these mechanisms can result in good generalization in interactive networks. These results support the general conclusion that cognitive neuroscience models that incorporate the core mechanistic principles of interactivity, inhibitory competition, and error-driven and Hebbian learning satisfy a wider range of biological, psychological, and computational constraints than models employing a subset of these principles.


title: Location Extraction from Social Media: Geoparsing, Location Disambiguation, and Geotagging

abstract: Location extraction, also called “toponym extraction,” is a field covering geoparsing, extracting spatial representations from location mentions in text, and geotagging, assigning spatial coordinates to content items. This article evaluates five “best-of-class” location extraction algorithms. We develop a geoparsing algorithm using an OpenStreetMap database, and a geotagging algorithm using a language model constructed from social media tags and multiple gazetteers. Third-party work evaluated includes a DBpedia-based entity recognition and disambiguation approach, a named entity recognition and Geonames gazetteer approach, and a Google Geocoder API approach. We perform two quantitative benchmark evaluations, one geoparsing tweets and one geotagging Flickr posts, to compare all approaches. We also perform a qualitative evaluation recalling top N location mentions from tweets during major news events. The OpenStreetMap approach was best (F1 0.90+) for geoparsing English, and the language model approach was best (F1 0.66) for Turkish. The language model was best (F1@1km 0.49) for the geotagging evaluation. The map database was best (R@20 0.60+) in the qualitative evaluation. We report on strengths, weaknesses, and a detailed failure analysis for the approaches and suggest concrete areas for further research.


title: The Use of 2D and 3D Displays for Shape-Understanding versus Relative-Position Tasks

abstract: Research on when and how to use three-dimensional (3D) perspective views on flat screens for operational tasks such as air traffic control is complex. We propose a functional distinction between tasks: those that require shape understanding versus those that require precise judgments of relative position. The distortions inherent in 3D displays hamper judging relative positions, whereas the integration of dimensions in 3D displays facilitates shape understanding. We confirmed these hypotheses with two initial experiments involving simple block shapes. The shape-understanding tasks were identification or mental rotation. The relative-position tasks were locating shadows and determining directions and distances between objects. We then extended the results to four experiments involving complex natural terrain. We compare our distinction with the integral/separable task distinction of Haskel and Wickens (1993). Applications for this research include displays for air traffic control, geoplots for military command and control, and potentially, any display of 3D information.


title: CLUSTERGEN: a statistical parametric synthesizer using trajectory modeling.

abstract: Unit selection synthesis has shown itself to be capable of producing high quality natural sounding synthetic speech when constructed from large databases of well-recorded, well-labeled speech. However, the cost in time and expertise of building such voices is still too expensive and specialized to be able to build individual voices for everyone. The quality in unit selection synthesis is directly related to the quality and size of the database used. As we require our speech synthesizers to have more variation, style and emotion, for unit selection synthesis, much larger databases will be required. As an alternative, more recently we have started looking for parametric models for speech synthesis, that are still trained from databases of natural speech but are more robust to errors and allow for better modeling of variation. This paper presents the CLUSTERGEN synthesizer which is implemented within the Festival/FestVox voice building environment. As well as the basic technique, three methods of modeling dynamics in the signal are presented and compared: a simple point model, a basic trajectory model and a trajectory model with overlap and add. Index Terms: speech synthesis, statistical parametric synthesis, trajectory HMMs.


Image Similarity Measure using Color Histogram , Color Coherence Vector , and Sobel Method


title: An adaptive fusion scheme of color and edge features for background subtraction

abstract: Recent background subtraction methods use fusion of multiple features to achieve consistent performance in non-stationary environment. During the segmentation, most of these methods apply logical operators (AND, or OR) to the results of each observed feature. Using logical operators has two critical problems, e.g. i) AND operator may reduce true-positives, and ii) OR operator may increase false-positive. In this paper, we address these issues by proposing a new way to fuse multiple features in such a way that each feature is treated adaptively based on its influence to decision making. We conduct quantitative experiments on CD-net 2012 dataset, and observe that proposed feature fusion strategy outperforms the existing logical operator based features fusion approach. We use edge and color features in the feature fusion part, where Local Direction Number Pattern (LDN) is used as the edge feature on each RGB color channel. Our comparative analysis show that proposed fusion strategy with color (RGB) and edge features (LDN) outperforms existing state-of-the-art methods.


title: Hardware Implementation of the Time-Triggered Ethernet Controller

abstract: Time-triggered (TT) Ethernet is a novel communication system that integrates real-time and non-real-time traffic into a single communication architecture. A TT Ethernet system consists od a set of nodes interconnected by a specific switch called TT Ethernet switch. A node consist of a TT Ethernet communication controller that executes the TT Ethernet protocol and a host computer that executes the user application. The protocol distinguishes between event-triggered (ET) and time-triggered (TT) Ethernet traffic. Time-triggered traffic is scheduled and transmitted with a predictable transmission delay, whereas event-triggered traffic is transmitted on a best-effort basis. The event-triggered traffic in TT Ethernet is handled in conformance with the existing Ethernet standards of the IEEE. This paper presents the design of the TT Ethernet communication controller optimized to be implemented in hardware. The paper describes a prototypical implementation using a custom built hardware platform and presents the results of evaluation experiments.


title: SIFT Meets CNN: A Decade Survey of Instance Retrieval

abstract: In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors ( de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.


title: Fast corner detection

abstract: Abstract This paper describes a new corner detection algorithm, based on the property of corners that the change of image intensity should be high in all directions. Consequently, the corner response function (CRF) is computed as a minimum change of intensity over all possible directions. To compute the intensity change in an arbitrary direction an interpixel approximation is used. A multigrid approach is employed to reduce the computational complexity and to improve the quality of the detected corners. This algorithm, and other popular corner detectors, were evaluated and compared on the basis of their consistency, accuracy and speed using a range of images and video sequences. It was found that our algorithm performs well compared to the other algorithms, but it is significantly faster to compute.


title: Factor structure for Young's Internet Addiction Test: A confirmatory study

abstract: A number of diagnostic scales have been developed in recent years to assess Internet addiction. To better understand the structure, validity, and reliability of such assessment instruments, Young's Internet Addiction Test (IAT) was evaluated using a confirmatory approach. Data collected through a survey of 410 Hong Kong university undergraduates was subjected to exploratory factor analysis and data from a hold-out sample was analyzed using confirmatory factor analysis in order to assess the psychometric properties and factor structure of the IAT scale. Three dimensions, namely, ''Withdrawal and Social Problems'', ''Time Management and Performance'', and ''Reality Substitute'' were extracted. These dimensions were then correlated with a number of criterion variables, including academic performance, online activities, gender, and Internet usage. The results show that academic performance was negatively correlated with the Internet addiction scores. The degree of Internet addiction was also found to vary across different types of online activity, with people engaged in cyberrelationships and online gambling having higher Internet addiction scores.


title: Pelvic Girdle Pain and Low Back Pain in Pregnancy: A Review

abstract: Pregnancy-related pelvic girdle pain (PGP) and pregnancy-related low back pain (PLBP) are common prob- lems with significant physical, psychological, and socioeco- nomic implications. There are several management options that are underutilized because of lack of comprehensive knowledge by health-care professionals and fear of harmful effects of treatment on the developing fetus. Interventions such as patient education, the use of pelvic belts, acupunc- ture, and aquatic and tailored postpartum exercises can be of some benefit to these patients. This article will focus on the diagnosis and management of PGP and PLBP, with discussion of terminology, epidemiology, risk factors, pathophysiology, and prognosis.


title: Optical interconnection networks in data centers: recent trends and future challenges

abstract: Warehouse-scale data center operators need much-higher-bandwidth intra-data center networks (DCNs) to sustain the increase of network traffic due to cloud computing and other emerging web applications. Current DCNs based on commodity switches require excessive amounts of power to face this traffic increase. Optical intra-DCN interconnection networks have recently emerged as a promising solution that can provide higher throughput while consuming less power. This article provides an update on recent developments in the field of ultra-highcapacity optical interconnects for intra-DCN communication. Several recently proposed architectures and technologies are examined and compared, while future trends and research challenges are outlined.


title: Evolution to the Edge of Chaos in Imitation Game

abstract: Motivated by the evolution of complex bird songs, an abstract imitation game is proposed to study the increase of dynamical complexity: Artificial"birds"display a"song"time series to each other, and those that imitate the other's song better win the game. With the introduction of population dynamics according to the score of the game and the mutation of parameters for the song dynamics, the dynamics is found to evolve towards the borderline between chaos and a periodic window, after punctuated equilibria. The importance of edge of chaos with topological chaos for complexity is stressed.


title: A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms

abstract: Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to traditional decision-tree algorithms such as C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.


title: Framewise speech-nonspeech classification by neural networks for voice activity detection with statistical noise suppression

abstract: A new voice activity detection (VAD) algorithm is proposed. The proposed algorithm is the combination of augmented statistical noise suppression (ASNS) and convolutional neural network (CNN). Since the combination of ASNS and simple power thresholding was known to be very powerful for VAD under noisy conditions, even more accurate VAD is expected by replacing the power thresholding with the more elaborate classifier. Among various model-based classifiers, CNN with noise adaptive training presented the highest accuracy, and the improvement was confirmed by the experiments using CENSREC-1-C public database.


title: fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs

abstract: Convolutional Neural Networks (ConvNets) are a powerful Deep Learning model, providing state-of-the-art accuracy to many emerging classification problems. However, ConvNet classification is a computationally heavy task, suffering from rapid complexity scaling. This paper presents fpgaConvNet, a novel domain-specific modelling framework together with an automated design methodology for the mapping of ConvNets onto reconfigurable FPGA-based platforms. By interpreting ConvNet classification as a streaming application, the proposed framework employs the Synchronous Dataflow (SDF) model of computation as its basis and proposes a set of transformations on the SDF graph that explore the performance-resource design space, while taking into account platform-specific resource constraints. A comparison with existing ConvNet FPGA works shows that the proposed fully-automated methodology yields hardware designs that improve the performance density by up to 1.62x and reach up to 90.75% of the raw performance of architectures that are hand-tuned for particular ConvNets.


title: DynaNewton - Accelerating Newton's Method for Machine Learning

abstract: Newton's method is a fundamental technique in optimization with quadratic convergence within a neighborhood around the optimum. However reaching this neighborhood is often slow and dominates the computational costs. We exploit two properties specific to empirical risk minimization problems to accelerate Newton's method, namely, subsampling training data and increasing strong convexity through regularization. We propose a novel continuation method, where we define a family of objectives over increasing sample sizes and with decreasing regularization strength. Solutions on this path are tracked such that the minimizer of the previous objective is guaranteed to be within the quadratic convergence region of the next objective to be optimized. Thereby every Newton iteration is guaranteed to achieve super-linear contractions with regard to the chosen objective, which becomes a moving target. We provide a theoretical analysis that motivates our algorithm, called DynaNewton, and characterizes its speed of convergence. Experiments on a wide range of data sets and problems consistently confirm the predicted computational savings.


title: LITE Kernel RDMA Support for Datacenter Applications

abstract: Recently, there is an increasing interest in building data-center applications with RDMA because of its low-latency, high-throughput, and low-CPU-utilization benefits. However, RDMA is not readily suitable for datacenter applications. It lacks a flexible, high-level abstraction; its performance does not scale; and it does not provide resource sharing or flexible protection. Because of these issues, it is difficult to build RDMA-based applications and to exploit RDMA's performance benefits. To solve these issues, we built LITE, a Local Indirection TiEr for RDMA in the Linux kernel that virtualizes native RDMA into a flexible, high-level, easy-to-use abstraction and allows applications to safely share resources. Despite the widely-held belief that kernel bypassing is essential to RDMA's low-latency performance, we show that using a kernel-level indirection can achieve both flexibility and low-latency, scalable performance at the same time. To demonstrate the benefits of LITE, we developed several popular datacenter applications on LITE, including a graph engine, a MapReduce system, a Distributed Shared Memory system, and a distributed atomic logging system. These systems are easy to build and deliver good performance. For example, our implementation of PowerGraph uses only 20 lines of LITE code, while outperforming PowerGraph by 3.5x to 5.6x.


title: LLC resonant DC transformer (DCX) with parallel PWM output tight regulation

abstract: This paper proposes a parallel output regulation method for isolated resonant DC transformer (DCX). With an auxiliary transformer in series with the main transformer at primary side and independent at secondary side, the DCX creates an additional output which feeds the cascaded PWM DC-DC converter. Because the output of the DC-DC converter is parallel with main output, the output voltage is regulated by the duty cycle of DC-DC stage. With the proposed topology, the major part power is delivered to load through the main transformer of DCX, and only small portion of power is converted by two-stage, DCX with cascaded DC-DC. Therefore, the output voltage is tightly regulated by PWM DC-DC stage and the conversion efficiency is almost as high as conventional LLC resonant DCX. The steady state operating principle of the proposed converter is analyzed, and a prototype with an LLC-DCX and an auxiliary Buck DC-DC stage is built-up based on the theoretical analysis.


title: Optimal combination of document binarization techniques using a self-organizing map neural network

abstract: This paper proposes an integrated system for the binarization of normal and degraded printed documents for the purpose of visualization and recognition of text characters. In degraded documents, where considerable background noise or variation in contrast and illumination exists, there are many pixels that cannot be easily classified as foreground or background pixels. For this reason, it is necessary to perform document binarization by combining and taking into account the results of a set of binarization techniques, especially for document pixels that have high vagueness. The proposed binarization technique takes advantage of the benefits of a set of selected binarization algorithms by combining their results using a Kohonen self-organizing map neural network. Specifically, in the first stage the best parameter values for each independent binarization technique are estimated. In the second stage and in order to take advantage of the binarization information given by the independent techniques, the neural network is fed by the binarization results obtained by those techniques using their estimated best parameter values. This procedure is adaptive because the estimation of the best parameter values depends on the content of images. The proposed binarization technique is extensively tested with a variety of degraded document images. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented.


title: Changes in the Gonial Angle Following Bilateral Sagittal Split Osteotomy and Vertical Ramus Osteotomy for Mandibular Excess

abstract: AIM ::: The gonial angle plays an important role in ensuring a harmonious facial profile. Changes in this angle after surgery may be an esthetic concern for both the patient and the surgeon. The aim of the present study was to evaluate gonial angle changes after mandibular setback by the bilateral sagittal split osteotomy (BSSO) and vertical ramus osteotomy (VRO) techniques. ::: ::: ::: METHODS ::: Fifty-eight male patients with mandibular prognathism only were treated from 2004 to 2006 (deformities such as discrepancy of jaws, mandibular setback of more than 10 mm, asymmetry, and vertical discrepancy were excluded). Patients were randomly divided into 2 groups. In the first group, mandibular setback was performed using the Obwegeser technique and wire osteosynthesis with 4 weeks' fixation (IMF), and in the second group, mandibular setback via VRO without wire osteosynthesis and 4 weeks' IMF was carried out. Lateral cephalograms were obtained for all the patients before surgery (T(0)) and 1 year after surgery (T(1)). Gonial angle and occlusal plane-SN in T(0) and T(1) were evaluated. ::: ::: ::: RESULTS ::: After surgery, the gonial angle had decreased in all patients. Decrease in the gonial angle in the VRO group was greater than the BSSO group. The average decrease in the gonial angle was significantly more (P < 0.05) in the VRO group (7 degrees) than in the BSSO group (2 degrees). ::: ::: ::: CONCLUSION ::: Gonial angle decrease was observed in the present study following mandibular setback by the VRO and BSSO techniques. This decrease in the VRO group was significantly greater.


title: SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory

abstract: Imbalanced data is a common problem in classification. This phenomenon is growing in importance since it appears in most real domains. It has special relevance to highly imbalanced data-sets (when the ratio between classes is high). Many techniques have been developed to tackle the problem of imbalanced training sets in supervised learning. Such techniques have been divided into two large groups: those at the algorithm level and those at the data level. Data level groups that have been emphasized are those that try to balance the training sets by reducing the larger class through the elimination of samples or increasing the smaller one by constructing new samples, known as undersampling and oversampling, respectively. This paper proposes a new hybrid method for preprocessing imbalanced data-sets through the construction of new samples, using the Synthetic Minority Oversampling Technique together with the application of an editing technique based on the Rough Set Theory and the lower approximation of a subset. The proposed method has been validated by an experimental study showing good results using C4.5 as the learning algorithm.


title: Real-Time Hand Pose Recognition Based on a Neural Network Using Microsoft Kinect

abstract: The Microsoft Kinect sensor is largely used to detect and recognize body gestures and layout with enough reliability, accuracy and precision in a quite simple way. However, the pretty low resolution of the optical sensors does not allow the device to detect gestures of body parts, such as the fingers of a hand, with the same straightforwardness. Given the clear application of this technology to the field of the user interaction within immersive multimedia environments, there is the actual need to have a reliable and effective method to detect the pose of some body parts. In this paper we propose a method based on a neural network to detect in real time the hand pose, to recognize whether it is closed or not. The neural network is used to process information of color, depth and skeleton coming from the Kinect device. This information is preprocessed to extract some significant feature. The output of the neural network is then filtered with a time average, to reduce the noise due to the fluctuation of the input data. We analyze and discuss three possible implementations of the proposed method, obtaining an accuracy of 90% under good conditions of lighting and background, and even reaching the 95% in best cases, in real time.


title: Computing and Information LATINIZED, IMPROVED LHS, AND CVT POINT SETS IN HYPERCUBES

abstract: (Communicated by Steve Hou) Abstract. It is shown how an arbitrary set of points in the hypercube can be Latinized, i.e., can be transformed into a point set that has the Latin hy- percube property. The effect of Latinization on the star discrepancy and other uniformity measures of a point set is analyzed. For a few selected but represen- tative point sampling methods, evidence is provided to show that Latinization lowers the star discrepancy measure. A novel point sampling method is pre- sented based on centroidal Voronoi tessellations of the hypercube. These point sets have excellent volumetric distributions, but have poor star discrepancies. Evidence is given that the Latinization of CVT points sets greatly lowers their star discrepancy measure but still preserves superior volumetric uniformity. As a result, means for determining improved Latin hypercube point samples are given.


title: Improvement of the stability of water-in-oil-in-water multiple emulsions by the addition of surfactants in the internal aqueous phase of the emulsions

abstract: Abstract In order to improve the stability of w/o/w emulsions, an attempt was made to elucidate the influence of addition of hydrophilic surfactants into the internal aqueous compartment of the emulsion, on the breakdown of the suspending vesicular globules of w/o/w emulsion, the changes in diameter of the internal aqueous compartments and of the suspending vesicular globules of w/o/w emulsion, as compared with emulsions without additives and with addition of sodium chloride. It was also attempted to measure the effect of addition of hydrophilic surfactant to the three different phases on the stability of w/o/w emulsions. The stability of diluted w/o/w emulsions with a large difference in osmolarity between the internal aqueous compartments and the external aqueous phase was also tested, w/o/w emulsions were prepared by the two-step emulsification procedure. New coccine was employed as a marker to check the yield of formation and the breakdown of the w/o/w emulsions. Sodium octanesulfonate was used as an additive in the internal aqueous compartment of the w/o/w emulsions as well as sodium chloride. Polyoxyethylene (20) sorbitan monooleate was used as an additive to the three different phases of the emulsions. The stability of w/o/w emulsions, as determined by coalescence of internal aqueous compartments and their increase in size by the influx of water to the internal aqueous compartment from the external aqueous phase, was improved by the addition of hydrophilic surfactant in the internal aqueous compartments of the emulsions. This was observed even under the condition of a large difference in osmolarity between the internal aqueous compartments and the external aqueous phase, and was affected by the concentration of hydrophilic surfactant. The addition of sodium chloride to the internal aqueous compartments and of hydrophilic surfactant to the external aqueous phase did not improve the stability of w/o/w emulsions. Only the addition of hydrophilic surfactant into the internal aqueous compartments was demonstrated to inhibit both coalescence of internal aqueous compartments and influx of water to the internal aqueous compartment from the external aqueous phase. On the other hand, the addition of hydrophilic surfactant into the oil phase was observed not to inhibit coalescence but water influx.


title: Causal inference via sparse additive models with application to online advertising

abstract: Advertising effectiveness measurement is a fundamental problem in online advertising. Various causal inference methods have been employed to measure the causal effects of ad treatments. However, existing methods mainly focus on linear logistic regression for uni-variate and binary treatments and are not well suited for complex ad treatments of multi-dimensions, where each dimension could be discrete or continuous. In this paper we propose a novel two-stage causal inference framework for assessing the impact of complex ad treatments. In the first stage, we estimate the propensity parameter via a sparse additive model; in the second stage, a propensity-adjusted regression model is applied for measuring the treatment effect. Our approach is shown to provide an unbiased estimation of the ad effectiveness under regularity conditions. To demonstrate the efficacy of our approach, we apply it to a real online advertising campaign to evaluate the impact of three ad treatments: ad frequency, ad channel, and ad size. We show that the ad frequency usually has a treatment effect cap when ads are showing on mobile device. In addition, the strategies for choosing best ad size are completely different for mobile ads and online ads.


title: Image-based vehicle analysis using deep neural network: A systematic study

abstract: We address the vehicle detection and classification problems using Deep Neural Networks (DNNs) approaches. Here we answer to questions that are specific to our application including how to utilize DNN for vehicle detection, what features are useful for vehicle classification, and how to extend a model trained on a limited size dataset, to the cases of extreme lighting condition. Answering these questions we propose our approach that outperforms state-of-the-art methods, and achieves promising results on image with extreme lighting conditions.


title: SiCloPe: Silhouette-Based Clothed People

abstract: We introduce a new silhouette-based representation for modeling clothed human bodies using deep generative models. Our method can reconstruct a complete and textured 3D model of a person wearing clothes from a single input picture. Inspired by the visual hull algorithm, our implicit representation uses 2D silhouettes and 3D joints of a body pose to describe the immense shape complexity and variations of clothed people. Given a segmented 2D silhouette of a person and its inferred 3D joints from the input picture, we first synthesize consistent silhouettes from novel view points around the subject. The synthesized silhouettes which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction. We then infer the texture of the subject's back view using the frontal image and segmentation mask as input to a conditional generative adversarial network. Our experiments demonstrate that our silhouette-based model is an effective representation and the appearance of the back view can be predicted reliably using an image-to-image translation network. While classic methods based on parametric models often fail for single-view images of subjects with challenging clothing, our approach can still produce successful results, which are comparable to those obtained from multi-view input.


A Decision Support System for Material and Manufacturing Process Selection


OntoMetrics: Application of On-line Ontology Metric Calculation


title: A data locality methodology for matrix–matrix multiplication algorithm

abstract: Matrix-Matrix Multiplication (MMM) is a highly important kernel in linear algebra algorithms and the performance of its implementations depends on the memory utilization and data locality. There are MMM algorithms, such as standard, Strassen---Winograd variant, and many recursive array layouts, such as Z-Morton or U-Morton. However, their data locality is lower than that of the proposed methodology. Moreover, several SOA (state of the art) self-tuning libraries exist, such as ATLAS for MMM algorithm, which tests many MMM implementations. During the installation of ATLAS, on the one hand an extremely complex empirical tuning step is required, and on the other hand a large number of compiler options are used, both of which are not included in the scope of this paper. In this paper, a new methodology using the standard MMM algorithm is presented, achieving improved performance by focusing on data locality (both temporal and spatial). This methodology finds the scheduling which conforms with the optimum memory management. Compared with (Chatterjee et al. in IEEE Trans. Parallel Distrib. Syst. 13:1105, 2002; Li and Garzaran in Proc. of Lang. Compil. Parallel Comput., 2005; Bilmes et al. in Proc. of the 11th ACM Int. Conf. Super-comput., 1997; Aberdeen and Baxter in Concurr. Comput. Pract. Exp. 13:103, 2001), the proposed methodology has two major advantages. Firstly, the scheduling used for the tile level is different from the element level's one, having better data locality, suited to the sizes of memory hierarchy. Secondly, its exploration time is short, because it searches only for the number of the level of tiling used, and between (1, 2) (Sect. 4) for finding the best tile size for each cache level. A software tool (C-code) implementing the above methodology was developed, having the hardware model and the matrix sizes as input. This methodology has better performance against others at a wide range of architectures. Compared with the best existing related work, which we implemented, better performance up to 55% than the Standard MMM algorithm and up to 35% than Strassen's is observed, both under recursive data array layouts.


title: A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication

abstract: Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous approaches including face recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the face recognition problem focus mainly on the static analysis to determine the face recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the face recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a face recognition process under the situation of vague facial features using deep reinforcement learning (DRL) approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of face recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of face recognition (100% at gamma correction γlocated in [0.5, 1.6]) compared with the average precision for face image (approximately 99.5% at normal lighting γ=1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.


title: A Low-Power Variable-Gain Amplifier With Improved Linearity: Analysis and Design

abstract: A low-power variable-gain amplifier (VGA) based on transconductance (gm)-ratioed amplification is analyzed and designed with improved linearity. The VGA has the merits of continuous gain tuning, low power consumption and small chip area. However, the linearity performance of the gm-ratioed amplifier is usually poor. We analyze distortion in gm-ratioed amplifiers and propose to improve the output linearity by applying load degeneration technique. It is found that theoretically the output linearity can be improved by 8.5 dB at the same power consumption. We also analyze gain, bandwidth and noise performance of the gm-ratioed amplifiers. Two VGAs based on gm-ratioed amplification are designed and fabricated in a 0.18-μm CMOS process-one with load degeneration only and the other with both input and load degeneration. The VGA with load degeneration only achieves gain of -20 to 41 dB, bandwidth of 121 to 211 MHz, and input and output P1dB up to - 17 dBm and 0.65 dBm, respectively. The VGA with both input and load degeneration achieves gain of -37 to 28 dB, bandwidth of 76 to 809 MHz, and input and output P1dB up to - 2.63 dBm and 2.29 dBm, respectively. The two VGAs consume a similar amount of power, which is about 3 to 5 mW from a 1.8-V supply. For the same bias condition, the proposed load degeneration improves the output linearity by more than 15 dB.


Why it matters. A paired editorial for "Hypoglycemia after Roux-en-Y gastric bypass: detection rates of continuous glucose monitoring (CGM) versus mixed meal test".


title: Arduino Debugger

abstract: This letter describes our source-level debugger for Arduino which can be used to debug the code in Arduino using GNU debugger. The presented solution uses Eclipse as the visual front-end. It does not require any modification of the Arduino board or additional hardware; debug functionality is achieved by adding a program library to the user application. Standard functionality expected from a debugger is available, such as setting breakpoints while debugging, stepping through the code and viewing variables. This allows developing projects with Arduino more efficiently and also using the Arduino for teaching embedded programming at a more advanced level, including debugging the program.


title: A Model of Information and Communication Technology Acceptance and Utilisation by Occupational Therapists

abstract: There is evidence to suggest that health professionals are reluctant to accept and utilise information and communication technologies (ICT) and concern is growing within health informatics research that this is contributing to the lag in adoption and utilisation of ICT across the health sector. Technology acceptance research within the field of information systems has been limited in its application to health and there is a concurrent need to develop and gain empirical support for models of technology acceptance within health and to examine acceptance and utilisation issues amongst health professionals to improve the success of information system implementation in this arena. This paper outlines a project that examines ICT acceptance and utilisation by Australian occupational therapists. It describes the theoretical basis behind the development of a research model and the methodology being employed to empirically validate the model using substantial quantitative, qualitative and longitudinal data. Preliminary results from Phase II of the study are presented. The theoretical significance of this work is that it uses a thoroughly constructed research model, with potentially the largest sample size ever tested, to extend technology acceptance research into the health sector.


title: Autonomy in Space: Current Capabilities and Future Challenge

abstract: This article provides an overview of the nature and role of autonomy for space exploration, with a bias in focus towards describing the relevance of AI technologies. It explores the range of autonomous behavior that is relevant and useful in space exploration and illustrates the range of possible behaviors by presenting four case studies in space-exploration systems, each differing from the others in the degree of autonomy exemplified. Three core requirements are defined for autonomous space systems, and the architectures for integrating capabilities into an autonomous system are described. The article concludes with a discussion of the challenges that are faced currently in developing and deploying autonomy technologies for space.


title: Group Signatures with Time-bound Keys Revisited: A New Model and an Efficient Construction

abstract: Chu et al. (ASIACCS 2012) proposed group signature with time-bound keys (GS-TBK) where each signing key is associated to an expiry time τ. In addition to prove the membership of the group, a signer needs to prove that the expiry time has not passed, i.e., t


title: Design and Development of Low-Cost Low Sidelobe Level Slotted Waveguide Antenna Array in X-Band

abstract: This paper presents a low-cost low-profile compact waveguide-fed slotted antenna array with very low average sidelobe level (ASLL) in X-band. The desired current distribution on the antenna array’s aperture is synthesized using Schelkunoff’s unit circle technique combined with genetic algorithm technique. The antenna array design based on the synthesized current distribution is carried out using finite integration technique -based commercial EM simulator. The designed antenna is fabricated using a low-cost fabrication process. The measured results obtained are in good agreement with the simulated results. This work is extremely useful for the radar applications.


title: BlueBoX: A policy-driven, host-based intrusion detection system

abstract: Detecting attacks against systems has, in practice, largely been delegated to sensors, such as network intrustion detection systems. However, due to the inherent limitations of these systems and the increasing use of encryption in communication, intrusion detection and prevention have once again moved back to the host systems themselves. In this paper, we describe our experiences with building BlueBox, a host-based intrusion detection system. Our approach, based on the technique of system call introspection, can be viewed as creating an infrastructure for defining and enforcing very fine-grained process capabilities in the kernel. These capabilities are specified as a set of rules (policies) for regulating access to system resources on a per executable basis. The language for expressing the rules is intuitive and sufficiently expressive to effectively capture security boundaries.We have prototyped our approach on Linux operating system kernel and have built rule templates for popular daemons such as Apache and wu-ftpd. Our design has been validated by testing against a comprehensive database of known attacks. Our system has been designed to minimize the kernel changes and performance impact and thus can be ported easily to new kernels. We describe the motivation and rationale behind BlueBox, its design, implementation on Linux, and how it relates to prior work on detecting and preventing intrusions on host systems.


title: Learning from Scarce Experience

abstract: Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems. Typically, with each change of the target policy, its value is estimated from the results of following that very policy. This requires a large number of interactions with the environment as different polices are considered. We present a family of algorithms based on likelihood ratio estimation that use data gathered when executing one policy (or collection of policies) to estimate the value of a different policy. The algorithms combine estimation and optimization stages. The former utilizes experience to build a non-parametric representation of an optimized function. The latter performs optimization on this estimate. We show positive empirical results and provide the sample complexity bound.


title: Paying for Likes?: Understanding Facebook Like Fraud Using Honeypots

abstract: Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods. This paper presents a comparative measurement study of page likes garnered via Facebook ads and by a few like farms. We deploy a set of honeypot pages, promote them using both methods, and analyze garnered likes based on likers' demographic, temporal, and social characteristics. We highlight a few interesting findings, including that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior.


title: Traffic Demand Forecasting for Port Services

abstract: Successful management of any sea port depends primarily on the harmonisation of transport supply and demand, whereas their incompatibility leads to a number of problems. The port, i.e. its management, through its operation and part of port policy may affect the planning of the construction or modernization of its port facilities. In doing so, the specified planning requires forecasting and quantification of the needs for infrastructural services of specified port, i.e. assessment of traffic demand. Accordingly, the basic problem of research in this paper is forecasting of traffic demand for the port services by applying the appropriate forecasting methods. In order to find ways of solving concrete problems in the port operations the methods for traffic demand forecasting are set by applying the methodology and the presentation of the application of economic forecasting methods. The selected methods of demand forecasting for port services in demand are illustrated and tested on the example of the Croatian largest cargo port, the Port of Rijeka. ::: KEY WORDS: planning, forecasting, traffic demand, commodity flows, port capacity


title: Solution to a door crossing problem for an autonomous wheelchair

abstract: This paper proposes a solution to a door crossing problem in unknown environments for an autonomous wheelchair. The problem is solved by a dynamic path planning algorithm implementation based on successive frontier points determination. An adaptive trajectory tracking control based on the dynamic model is implemented on the vehicle to direct the wheelchair motion along the path in a smooth movement. An EKF feature-based SLAM is also implemented on the vehicle which gives an estimate of the wheelchair pose inside the environment. The SLAM allows the map reconstruction of the environment for future safe navigation purposes. The entire system is evaluated in a real time simulator of a robotic wheelchair.


title: Low-Profile Frequency-Scanned Antenna Based on Substrate Integrated Waveguide

abstract: A beam scanning flat antenna array with the frequency that covers about the whole X-/Ku-band is proposed in this communication. The radiation element is the continuous transverse stub (CTS) constituted by the substrate integrated waveguide (SIW). The feed structure is an SIW parabolic reflector fed by a linear source with a metallic post, which exhibits a good impedance matching characteristics within a broad bandwidth. The beam steering direction is tunable with the frequency increasing within the operation band. The design principles of the SIW-based CTS array and the feed structure are explained in detail. One 16-element array is simulated, designed, and fabricated. The measurement results show that the antenna has a scanning angle range from 52.2° to −16.8° with the 3-dB gain from 8.5 to 14.1 GHz. At the center frequency of 12 GHz, it achieves a maximum gain of 18.1 dBi with an array size of $11.7\lambda _{0}\times 1.28\lambda _{0}$ , while the first sidelobe level is −11.4 dB and the 3-dB beamwidth is 6.5°.


title: Design consideration of MHz active clamp flyback converter with GaN devices for low power adapter application

abstract: With ever increasing demands of smaller size, lighter weight for all forms of consumer electronics, efficient power conversion with higher operating frequency has always being pursued rigorously. This paper demonstrates high frequency, high efficiency and high power density design of active clamp flyback converter for adapter application. Both the primary and secondary switches are gallium nitride (GaN) devices which can significantly reduce the device related conduction and switching loss. The design procedures, including the selection of active clamping capacitor, optimization of flyback transformer, and EMI filter design, are presented in detail. A 65W (19.5V/3.3A) prototype of active clamp flyback front end converter is developed to verify the feasibility of the system design. The prototype efficiency is 1~2% higher than the state of art product and the power density (exclude case) is more than 40W/in 3 .


title: Lessons learned from complex hands-on defence exercises in a cyber range

abstract: We need more skilled cybersecurity professionals because the number of cyber threats and ingenuity of attackers is ever growing. Knowledge and skills required for cyber defence can be developed and exercised by lectures and lab sessions, or by active learning, which is seen as a promising and attractive alternative. In this paper, we present experience gained from the preparation and execution of cyber defence exercises involving various participants in a cyber range. The exercises follow a Red vs. Blue team format, in which the Red team conducts malicious activities against emulated networks and systems that have to be defended by Blue teams of learners. Although this exercise format is popular and used worldwide by numerous organizers in practice, it has been sparsely researched. We contribute to the topic by describing the general exercise life cycle, covering the exercise's development, dry run, execution, evaluation, and repetition. Each phase brings several challenges that exercise organizers have to deal with. We present lessons learned that can help organizers to prepare, run and repeat successful events systematically, with lower effort and costs, and avoid a trial-and-error approach that is often used.


title: A shared-subspace learning framework for multi-label classification

abstract: Multi-label problems arise in various domains such as multi-topic document categorization, protein function prediction, and automatic image annotation. One natural way to deal with such problems is to construct a binary classifier for each label, resulting in a set of independent binary classification problems. Since multiple labels share the same input space, and the semantics conveyed by different labels are usually correlated, it is essential to exploit the correlation information contained in different labels. In this paper, we consider a general framework for extracting shared structures in multi-label classification. In this framework, a common subspace is assumed to be shared among multiple labels. We show that the optimal solution to the proposed formulation can be obtained by solving a generalized eigenvalue problem, though the problem is nonconvex. For high-dimensional problems, direct computation of the solution is expensive, and we develop an efficient algorithm for this case. One appealing feature of the proposed framework is that it includes several well-known algorithms as special cases, thus elucidating their intrinsic relationships. We further show that the proposed framework can be extended to the kernel-induced feature space. We have conducted extensive experiments on multi-topic web page categorization and automatic gene expression pattern image annotation tasks, and results demonstrate the effectiveness of the proposed formulation in comparison with several representative algorithms.


title: Prosocial Behavior: Multilevel Perspectives

abstract: Current research on prosocial behavior covers a broad and diverse range of phenomena. We argue that this large research literature can be best organized and understood from a multilevel perspective. We identify three levels of analysis of prosocial behavior: (a) the “meso” level—the study of helper-recipient dyads in the context of a specific situation; (b) the micro level—the study of the origins of prosocial tendencies and the sources of variation in these tendencies; and (c) the macro level—the study of prosocial actions that occur within the context of groups and large organizations. We present research at each level and discuss similarities and differences across levels. Finally, we consider ways in which theory and research at these three levels of analysis might be combined in future intra- and interdisciplinary research on prosocial behavior.


title: A ZigBee -wireless wearable remote physiological monitoring system

abstract: Wearable health monitoring systems use integrated sensors to monitor vital physiological parameters of the wearer. The paper discusses the preliminary results of a prototype wearable physiological monitoring system to monitor physiological parameters such as Electrocardiogram (ECG), Heart Rate (HR), Electroencephalogram (EEG) and Body Temperature. The ECG and EEG signals are acquired using a textile electrodes integrated into the fabric of the wearer. The acquired ECG and EEG signals are processed to remove noises and the parameters are extracted and trend analysis is carried out. The physiological parameters are monitored at the remote monitoring station using a ZigBee wireless communication.


Beating the Bookies : Predicting the Outcome of Soccer Games


title: Smart antennas for wireless systems

abstract: In this article we discuss current and future antenna technology for wireless systems and the improvement that smart and adaptive antenna arrays can provide. We describe standard cellular antennas, smart antennas using fixed beams, and adaptive antennas for base stations, as well as antenna technologies for handsets. We show the potential improvement that these antennas can provide, including range extension, multipath diversity, interference suppression, capacity increase, and data rate increase. The issues involved in incorporating these antennas into wireless systems using CDMA, GSM, and IS-136 in different environments, such as rural, suburban, and urban areas, as well as indoors, are described. Theoretical, computer simulation, experimental, and field trial results are also discussed that demonstrate the potential of this technology.


title: Multi-Label Classification by Semi-Supervised Singular Value Decomposition

abstract: Multi-label problems arise in various domains, including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labelled data or even missing labelled data. In this paper, we proposed to use a semi-supervised singular value decomposition (SVD) to handle these two challenges. The proposed model takes advantage of the nuclear norm regularization on the SVD to effectively capture the label correlations. Meanwhile, it introduces manifold regularization on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labelled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve the proposed model based on the alternating direction method of multipliers, and thus, it can efficiently deal with large-scale data sets. Experimental results for synthetic and real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than the state-of-the-art methods.


title: Real-time human activity recognition in smart home with binary tree SVM

abstract: In this paper, binary tree support vector machines (BT-SVM) and a sliding sensor window have been applied to recognize the human activities in real time. To construct the optimal binary tree, genetic algorithm (GA) has been employed. The real data collected from participants performing activities was used to train the SVM models and evaluate the accuracy of the recognition algorithms. Compared with one-against-one SVM and BT-SVM with human knowledge, the experiment results show that the proposed BT-SVM with GA has better activity recognition performances in smart home.


title: Edible berries: Bioactive components and their effect on human health

abstract: Abstract The importance of food consumption in relation to human health has increased consumer attention in nutraceutical components and foods, especially fruits and vegetables. Berries are a rich source of a wide variety of non-nutritive, nutritive, and bioactive compounds such as flavonoids, phenolics, anthocyanins, phenolic acids, stilbenes, and tannins, as well as nutritive compounds such as sugars, essential oils, carotenoids, vitamins, and minerals. Bioactive compounds from berries have potent antioxidant, anticancer, antimutagenic, antimicrobial, anti-inflammatory, and antineurodegenerative properties, both in vitro and in vivo. The following is a comprehensive and critical review on nutritional and non-nutritional bioactive compounds of berries including their absorption, metabolism, and biological activity in relation to their potential effect on human health.


title: What Women and Men Should Be, Shouldn’t Be, Are Allowed to Be, and Don’t Have to Be: The Contents of Prescriptive Gender Stereotypes

abstract: This article presents a four-category framework to characterize the contents of prescriptive gender stereotypes. The framework distinguishes between prescriptions and proscriptions that are intensified by virtue of one's gender, and those that are relaxed by virtue of one's gender. Two studies examined the utility of this framework for characterizing prescriptive gender stereotypes in American society (Study 1) and in the highly masculine context of Princeton University (Study 2). The results demonstrated the persistence of traditional gender prescriptions in both contexts, but also revealed distinct areas of societal vigilance and leeway for each gender. In addition, they showed that women are seen more positively, relative to societal standards, than are men. We consider the implications of this framework for research on reactions to gender stereotype deviants and sex discrimination.


title: Increasing anthropomorphism and trust in automated driving functions by adding speech output

abstract: Conditionally Automated Driving (CAD) functions need to be carefully examined regarding related driver attitudes such as trust and usability to increase their acceptance among future system users. By adding speech output to an existing audio-visual Human-Machine Interface (HMI), the level of trust in automation was suspected to be increased due to semantic information and the application of anthropomorphic features such as voice and gender. To test this assumption, N = 17 drivers completed two simulator drives, once with ('Speech + generic') and once without additional speech output (‘Generic’). Having interacted with the automated system in different scenarios (i.e., letting the system execute a maneuver vs. taking over control from the vehicle), drivers completed comparative questionnaires on trust, anthropomorphism and usability. The applied questionnaire on trust in automation was structured after theoretical implications and derived from previous research on trust in automation. Results showed that the ‘Speech + generic’ system was rated as superior for all three attitude measures compared to the ‘Generic’ system. Furthermore, the present study describes a first approach on comprehensively examining trust in vehicle automation. We brought forth evidence that speech output is highly relevant in order to improve driver attitudes that affect acceptance of automated systems.


title: Understanding Gating Operations in Recurrent Neural Networks through Opinion Expression Extraction

abstract: Extracting opinion expressions from text is an essential task of sentiment analysis, which is usually treated as one of the word-level sequence labeling problems. In such problems, compositional models with multiplicative gating operations provide efficient ways to encode the contexts, as well as to choose critical information. Thus, in this paper, we adopt Long Short-Term Memory (LSTM) recurrent neural networks to address the task of opinion expression extraction and explore the internal mechanisms of the model. The proposed approach is evaluated on the Multi-Perspective Question Answering (MPQA) opinion corpus. The experimental results demonstrate improvement over previous approaches, including the state-of-the-art method based on simple recurrent neural networks. We also provide a novel micro perspective to analyze the run-time processes and gain new insights into the advantages of LSTM selecting the source of information with its flexible connections and multiplicative gating operations.


title: Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

abstract: Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.


title: A wavelet-based ECG delineator: evaluation on standard databases

abstract: In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.


title: Assessing the potential effectiveness of food and beverage taxes and subsidies for improving public health: a systematic review of prices, demand and body weight outcomes.

abstract: Taxes and subsidies are increasingly being considered as potential policy instruments to incentivize consumers to improve their food and beverage consumption patterns and related health outcomes. This study provided a systematic review of recent U.S. studies on the price elasticity of demand for sugar-sweetened beverages (SSBs), fast food, and fruits and vegetables, as well as the direct associations of prices/taxes with body weight outcomes. Based on the recent literature, the price elasticity of demand for SSBs, fast food, fruits and vegetables was estimated to be -1.21, -0.52, -0.49 and -0.48, respectively. The studies that linked soda taxes to weight outcomes showed minimal impacts on weight; however, they were based on existing state-level sales taxes that were relatively low. Higher fast-food prices were associated with lower weight outcomes particularly among adolescents, suggesting that raising prices would potentially impact weight outcomes. Lower fruit and vegetable prices were generally found to be associated with lower body weight outcomes among both low-income children and adults, suggesting that subsidies that would reduce the cost of fruits and vegetables for lower-socioeconomic populations may be effective in reducing obesity. Pricing instruments should continue to be considered and evaluated as potential policy instruments to address public health risks.


title: Efficient GPU path rendering using scanline rasterization

abstract: We introduce a novel GPU path rendering method based on scan-line rasterization, which is highly work-efficient but traditionally considered as GPU hostile. Our method is parallelized over boundary fragments, i.e., pixels directly intersecting the path boundary. Non-boundary pixels are processed in bulk as horizontal spans like in CPU scanline rasterizers, which saves a significant amount of winding number computation workload. The distinction also allows the majority of our algorithmic steps to focus on boundary fragments only, which leads to highly balanced workload among the GPU threads. In addition, we develop a ray shooting pattern that minimizes the global data dependency when computing winding numbers at anti-aliasing samples. This allows us to shift the majority of winding-number-related workload to the same kernel that consumes its result, which saves a significant amount of GPU memory bandwidth. Experiments show that our method gives a consistent 2.5X speedup over state-of-the-art alternatives for high-quality rendering at Ultra HD resolution, which can increase to more than 30X in extreme cases. We can also get a consistent 10X speedup on animated input.


title: Dual-frequency annular-ring slot antennas fed by CPW feed and microstrip line feed

abstract: The dual-frequency properties of a dual annular-ring slot antenna fed by coplanar waveguide (CPW) and microstrip feedline are presented and experimentally studied. The proposed antenna is constructed by dual concentric annular-ring slots fabricated on FR4 substrate with single feed. The proposed slot antennas have good impedance matching for the two operating frequencies with a frequency ratio ranging about 1.34/spl sim/3.11 and 1.4/spl sim/1.74 can be achieved for CPW-fed and microstrip-fed.