Query: Exploring romantic relationships on social networking sites using the self-expansion model

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The Benefits of Facebook "Friends: " Social Capital and College Students' Use of Online Social Network Sites <=SEP=> This study examines the relationship between use of Facebook, a popular online social network site, and the formation and maintenance of social capital. In addition to assessing bonding and bridging social capital, we explore a dimension of social capital that assesses one's ability to stay connected with members of a previously inhabited community, which we call maintained social capital. Regression analyses conducted on results from a survey of undergraduate students (N=286) suggest a strong association between use of Facebook and the three types of social capital, with the strongest relationship being to bridging social capital. In addition, Facebook usage was found to interact with measures of psychological well-being, suggesting that it might provide greater benefits for users experiencing low self-esteem and low life satisfaction.

A Review of Facebook Research in the Social Sciences. <=SEP=> With over 800 million active users, Facebook is changing the way hundreds of millions of people relate to one another and share information. A rapidly growing body of research has accompanied the meteoric rise of Facebook as social scientists assess the impact of Facebook on social life. In addition, researchers have recognized the utility of Facebook as a novel tool to observe behavior in a naturalistic setting, test hypotheses, and recruit participants. However, research on Facebook emanates from a wide variety of disciplines, with results being published in a broad range of journals and conference proceedings, making it difficult to keep track of various findings. And because Facebook is a relatively recent phenomenon, uncertainty still exists about the most effective ways to do Facebook research. To address these issues, the authors conducted a comprehensive literature search, identifying 412 relevant articles, which were sorted into 5 categories: descriptive analysis of users, motivations for using Facebook, identity presentation, the role of Facebook in social interactions, and privacy and information disclosure. The literature review serves as the foundation from which to assess current findings and offer recommendations to the field for future research on Facebook and online social networks more broadly.

Information Disclosure and Control on Facebook: Are They Two Sides of the Same Coin or Two Different Processes? <=SEP=> Facebook, the popular social network site, is changing the nature of privacy and the consequences of information disclosure. Despite recent media reports regarding the negative consequences of disclosing information on social network sites such as Facebook, students are generally thought to be unconcerned about the potential costs of this disclosure. The current study explored undergraduate students' information disclosure and information control on Facebook and the personality factors that influence levels of disclosure and control. Participants in this online survey were 343 undergraduate students who were current users of Facebook. Results indicated that participants perceived that they disclosed more information about themselves on Facebook than in general, but participants also reported that information control and privacy were important to them. Participants were very likely to have posted information such as their birthday and e-mail address, and almost all had joined an online network. They were also very likely to post pictures such as a profile picture, pictures with friends, and even pictures at parties and drinking with friends. Contrary to expectations, information disclosure and information control were not significantly negatively correlated, and multiple regression analyses revealed that while disclosure was significantly predicted by the need for popularity, levels of trust and self-esteem predicted information control. Therefore, disclosure and control on Facebook are not as closely related as expected but rather are different processes that are affected by different aspects of personality. Implications of these findings and suggestions for future research are discussed.

Beyond Bowling Together : SocioTechnical Capital <=SEP=> Social resources like trust and shared identity make it easier for people to work and play together. Such social resources are sometimes referred to as social capital. Thirty years ago, Americans built social capital as a side effect of participation in civic organizations and social activities, including bowling leagues. Today, they do so far less frequently (Putnam 2000). HCI researchers and practitioners need to find new ways for people to interact that will generate even more social capital than bowling together does. A new theoretical construct, SocioTechnical Capital, provides a framework for generating and evaluating technology-mediated social relations.

On the evolution of user interaction in Facebook <=SEP=> Online social networks have become extremely popular; numerous sites allow users to interact and share content using social links. Users of these networks often establish hundreds to even thousands of social links with other users. Recently, researchers have suggested examining the activity network - a network that is based on the actual interaction between users, rather than mere friendship - to distinguish between strong and weak links. While initial studies have led to insights on how an activity network is structurally different from the social network itself, a natural and important aspect of the activity network has been disregarded: the fact that over time social links can grow stronger or weaker. In this paper, we study the evolution of activity between users in the Facebook social network to capture this notion. We find that links in the activity network tend to come and go rapidly over time, and the strength of ties exhibits a general decreasing trend of activity as the social network link ages. For example, only 30% of Facebook user pairs interact consistently from one month to the next. Interestingly, we also find that even though the links of the activity network change rapidly over time, many graph-theoretic properties of the activity network remain unchanged.

Spectral collaborative filtering <=SEP=> Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the spectral domain, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the spectral domains of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly out-performs state-of-the-art models. Code and data are available at https://github.com/lzheng21/SpectralCF.

Eco-Routing Navigation System Based on Multisource Historical and Real-Time Traffic Information <=SEP=> Due to increased public awareness on global climate change and other energy and environmental problems, a variety of strategies are being developed and used to reduce the energy consumption and environmental impact of roadway travel. In advanced traveler information systems, recent efforts have been made in developing a new navigation concept called “eco-routing,” which finds a route that requires the least amount of fuel and/or produces the least amount of emissions. This paper presents an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination. It consists of the following four components: 1) a Dynamic Roadway Network database, which is a digital map of a roadway network that integrates historical and real-time traffic information from multiple data sources through an embedded data fusion algorithm; 2) an energy/emissions operational parameter set, which is a compilation of energy/emission factors for a variety of vehicle types under various roadway characteristics and traffic conditions; 3) a routing engine, which contains shortest path algorithms used for optimal route calculation; and 4) user interfaces that receive origin-destination inputs from users and display route maps to the users. Each of the system components and the system architecture are described. Example results are also presented to prove the validity of the eco-routing concept and to demonstrate the operability of the developed eco-routing navigation system. In addition, current limitations of the system and areas for future improvements are discussed.

Popularity prediction of images and videos on Instagram <=SEP=> We live in a world surrounded by numerous social media platforms, applications and websites which produce various texts, images and videos (posts) daily. People share their moments with their friends and families via these tools to keep in touch. This extensiveness of social media has led to an expansion of information in various forms. It is difficult to imagine someone totally unfamiliar with these concepts and not having posted any content on a platform. All users, ranging from individuals to large companies, want to get the most of their audiences' attention. Nevertheless, the problem is that not all these posts are admired and noticed by their audience. Therefore, it would be important to know what characteristics a post should have to become the most popular. Studying this enormous data will develop a knowledge from which we can understand the best way to publish our posts. To this end, we gathered images and videos from Instagram accounts and we used some image/video context features to predict the number of likes a post obtains as a meaning of popularity through some regression and classification methods. By the experiments with 10-fold cross-validation, we get the results of Popularity Score prediction with 0.002 in RMSE and Popularity Class prediction with 90.77% accuracy. As we know, this study is the first exploring of Iranian Instagram users for popularity prediction.

Optimization Using Simulation of Traffic Light Signal Timings <=SEP=> Traffic congestion has become a great challenge and a large burden on both the governments and the citizens in vastly populated cities. The main problem, originally initiated by several factors, continues to threaten the stability of modern cities and the livelihood of its habitants. Hence, improving the control strategies that govern the traffic operations is a powerful solution that can solve the congestion problem. These improvements can be achieved by enhancing the traffic control performance through adjusting the traffic signal timings. This paper focuses on finding various solutions for the addressed problem through the optimization using simulation of traffic signal timings under oversaturated conditions. The system under study is an actual road network in Alexandria, Egypt; where, numerous data have been collected in different time intervals. A series of computer simulation models to represent the actual system as well as proposed solutions have been developed using the ExtendSim simulation environment. Furthermore, an evolutionary optimizer is utilized to attain a set of optimum/near-optimum signal timings to minimize the total time in system of the vehicles, resulting in an improved performance of the road network. Analysis of experimentation results shows that the adopted methodology optimizes the vehicular flow in a multiple-junction urban traffic network.

Emergence of neural encoding of auditory objects while listening to competing speakers. <=SEP=> A visual scene is perceived in terms of visual objects. Similar ideas have been proposed for the analogous case of auditory scene analysis, although their hypothesized neural underpinnings have not yet been established. Here, we address this question by recording from subjects selectively listening to one of two competing speakers, either of different or the same sex, using magnetoencephalography. Individual neural representations are seen for the speech of the two speakers, with each being selectively phase locked to the rhythm of the corresponding speech stream and from which can be exclusively reconstructed the temporal envelope of that speech stream. The neural representation of the attended speech dominates responses (with latency near 100 ms) in posterior auditory cortex. Furthermore, when the intensity of the attended and background speakers is separately varied over an 8-dB range, the neural representation of the attended speech adapts only to the intensity of that speaker but not to the intensity of the background speaker, suggesting an object-level intensity gain control. In summary, these results indicate that concurrent auditory objects, even if spectrotemporally overlapping and not resolvable at the auditory periphery, are neurally encoded individually in auditory cortex and emerge as fundamental representational units for top-down attentional modulation and bottom-up neural adaptation.

The NASD Securities Observation , News Analysis & Regulation System ( SONAR ) <=SEP=> The Securities Observation, News Analysis, and Regulation (SONAR) system was developed by NASD to monitor the Nasdaq, Over the Counter (OTC), and Nasdaq-Liffe (futures) stock markets for potential insider trading and fraud through misrepresentation. SONAR has been in operational use at NASD since December 2001, processing approximately 10,000 news wires stories and SEC filings, evaluating price/volume models for 25,000 securities, and generating 50-60 alerts (or “breaks”) per day for review by several groups of regulatory analysts and investigators. In addition, SONAR has greatly expanded surveillance coverage to new areas of the market and increased accuracy significantly over an earlier break detection system. SONAR makes use of several AI and statistical techniques, including NLP text mining, statistical regression, rule-based inference, uncertainty, and fuzzy matching. Sonar combines these enabling technologies in a system designed to deliver a steady stream of high-quality breaks to the analysts for further investigation. Additional components including visualization, text search agents, and flexible displays add to the system’s utility. Finally, SONAR is designed as a knowledge-based system. Domain knowledge is maintained by knowledge engineers working closely with the regulatory analysts. In this way, SONAR is adaptable to new market conditions, data sources, and regulatory concerns.

Mood Effects on Cognition : Affective Influences on the Content and Process of Information Processing and Behavior <=SEP=> 

In Defense of Single-column Networks for Crowd Counting <=SEP=> Crowd counting usually addressed by density estimation becomes an increasingly important topic in computer vision due to its widespread applications in video surveillance, urban planning, and intelligence gathering. However, it is essentially a challenging task because of the greatly varied sizes of objects, coupled with severe occlusions and vague appearance of extremely small individuals. Existing methods heavily rely on multi-column learning architectures to extract multi-scale features, which however suffer from heavy computational cost, especially undesired for crowd counting. In this paper, we propose the single-column counting network (SCNet) for efficient crowd counting without relying on multi-column networks. SCNet consists of residual fusion modules (RFMs) for multi-scale feature extraction, a pyramid pooling module (PPM) for information fusion, and a sub-pixel convolutional module (SPCM) followed by a bilinear upsampling layer for resolution recovery. Those proposed modules enable our SCNet to fully capture multi-scale features in a compact single-column architecture and estimate high-resolution density map in an efficient way. In addition, we provide a principled paradigm for density map generation and data augmentation for training, which shows further improved performance. Extensive experiments on three benchmark datasets show that our SCNet delivers new state-of-the-art performance and surpasses previous methods by large margins, which demonstrates the great effectiveness of SCNet as a singlecolumn network for crowd counting. c © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. *Equal contribution 2 WANG et al.: IN DEFENSE OF SINGLE-COLUMN NETWORKS FOR CROWD COUNTING

Open production: scientific foundation for co-creative product realization <=SEP=> Globalization and the use of technology call for an adaptation of value creation strategies. As the potential for rationalization and achieving flexibility within companies is to the greatest possible extent exhausted, approaches to the corporate reorganization of value creation are becoming increasingly important. In this process, the spread and further development of information and communication technology often provide the basis for a reorganization of cross-company value nets and lead to a redistribution of roles and tasks between the actors involved in value creation. While cooperative, decentralized and self-organizing value creation processes are in fact being promoted, the associated potential for development and production engineering is being underestimated and hence not implemented sufficiently. This contribution will introduce a value creation taxonomy and then, using its notion and structure, describe the emerging transformations in value creation on the basis of case studies. Finally an adequate framework for analysing and configuring value creation will be presented.

What Users Ask a Search Engine: Analyzing One Billion Russian Question Queries <=SEP=> We analyze the question queries submitted to a large commercial web search engine to get insights about what people ask, and to better tailor the search results to the users' needs. Based on a dataset of about one billion question queries submitted during the year 2012, we investigate askers' querying behavior with the support of automatic query categorization. While the importance of question queries is likely to increase, at present they only make up 3-4% of the total search traffic.
 Since questions are such a small part of the query stream, and are more likely to be unique than shorter queries, click-through information is typically rather sparse. Thus, query categorization methods based on the categories of clicked web documents do not work well for questions. As an alternative, we propose a robust question query classification method that uses the labeled questions from a large community question answering platform (CQA) as a training set. The resulting classifier is then transferred to the web search questions. Even though questions on CQA platforms tend to be different to web search questions, our categorization method proves competitive with strong baselines with respect to classification accuracy.
 To show the scalability of our proposed method we apply the classifiers to about one billion question queries and discuss the trade-offs between performance and accuracy that different classification models offer.

Cache-Conscious Data Placement <=SEP=> As the gap between memory and processor speeds continues to widen, cache eficiency is an increasingly important component of processor performance. Compiler techniques have been used to improve instruction cache pet$ormance by mapping code with temporal locality to different cache blocks in the virtual address space eliminating cache conflicts. These code placement techniques can be applied directly to the problem of placing data for improved data cache pedormance.In this paper we present a general framework for Cache Conscious Data Placement. This is a compiler directed approach that creates an address placement for the stack (local variables), global variables, heap objects, and constants in order to reduce data cache misses. The placement of data objects is guided by a temporal relationship graph between objects generated via profiling. Our results show that profile driven data placement significantly reduces the data miss rate by 24% on average.

Approaches to Interpreter Composition <=SEP=> In this paper, we compose six different Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs.

A framework for efficient network anomaly intrusion detection with features selection <=SEP=> An intrusion Detection System (IDS) provides alarts against intrusion attacks where a traditional firewall fails. Machine learning algorithms aim to detect anomalies using supervised and unsupervised approaches. Features selection techniques identify important features and remove irrelevant and redundant attributes to reduce the dimensionality of feature space. This paper presents a features selection framework for efficient network anomaly detection using different machine learning classifiers. The framework applies different strategies by using filter and wrapper features selection methodologies. The aim of this framework is to select the minimum number of features that achieve the highest accuracy. UNSW-NB15 dataset is used in the experimental results to evaluate the proposed framework. The results show that by using 18 features from one of the filter ranking methods and applying J48 as a classifier, an accuracy of 88% is achieved.

Application-specific resource provisioning for wide-area distributed computing <=SEP=> Some modern distributed applications require cooperation among multiple geographically separated computing facilities to perform intensive computing at the end sites and large-scale data transfers in the wide area network. It has been widely recognized that WDM networks are cost-effective means to support data transfers in this type of data-intensive applications. However, neither the traditional approaches to establishing lightpaths between given source destination pairs nor the existing application-level approaches that only consider computing resources but take the underlying connectivity for granted are sufficient. In this article we identify key limitations and issues in existing systems, and focus on joint resource allocation of both computing resources and network resources in federated computing and network systems. A variety of resource allocation schemes that provide modern distributed computing applications with performance and reliability guarantees are presented.

The Multidimensional Work Motivation Scale : Validation evidence in seven languages and nine countries <=SEP=> The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries Marylène Gagné, Jacques Forest, Maarten Vansteenkiste, Laurence Crevier-Braud, Anja Van den Broeck, Ann Kristin Aspeli, Jenny Bellerose, Charles Benabou, Emanuela Chemolli, Stefan Tomas Güntert, Hallgeir Halvari, Devani Laksmi Indiyastuti, Peter A. Johnson, Marianne Hauan Molstad, Mathias Naudin, Assane Ndao, Anja Hagen Olafsen, Patrice Roussel, Zheni Wang & Cathrine Westbye a School of Psychology, University of Western Australia, Perth, Australia b Department of Organization and Human Resource Management, UQAM School of Management Sciences, Université du Québec à Montréal, Montréal, Canada c Department of Developmental, Personality and Social Psychology, University of Gent, Gent, Belgium d Department of Psychology, Université du Québec à Montréal, Montréal, Canada e Department of Economy and Management, Hogeschool Universiteit Brussel, Brussels, and KU Leuven, Leuven, Belgium f Faculty of Economics and Social Science, Høgskolen i Buskerud og Vestfold, Kongsberg, Norway g Department of Psychology, Université de Montréal, Montréal, Canada h Psychology of Work in Organization and Society Research Group, Swiss Federal Institute of Technology, Zurich, Switzerland i Faculty of Economics and Business, Universitas Gadjah Mada, Yogyakarta, Indonesia j Institute of Work Psychology, University of Sheffield, Sheffield, United Kingdom k International Institute of Commerce and Development, Université Panthéon Assas — Paris II, Paris, France l Faculty of Economics and Management Sciences, Université Cheikh Anta DIOP, Dakkar, Sénégal m Department of Management and Human Resources, Université Toulouse 1, Toulouse, France Published online: 05 Feb 2014.

Beyond budgeting and Agile Software Development: a Conceptual Framework for the Performance Management of Agile Software Development Teams <=SEP=> Around the same time as the emergence of agile methods as a formalized concept, the management accounting literature introduced the concept of Beyond Budgeting as a performance management model for changing business environments. Both concepts share many similarities with both having a distinctly agile or adaptive perspective. The Beyond Budgeting model promises to enable companies to keep pace with changing business environments, quickly create and adapt strategy and empower people throughout the organization to make effective changes. This research in progress paper attempts to develop the Beyond Budgeting model within the context of agile software development teams. The twelve Beyond Budgeting principles are discussed and a research framework is presented. This framework is being used in two case studies to investigate the organizational issues and challenges that affect the performance of agile software

Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding <=SEP=> Semantic slot filling is one of the most challenging problems in spoken language understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for this task, and present several novel architectures designed to efficiently model past and future temporal dependencies. Specifically, we implemented and compared several important RNN architectures, including Elman, Jordan, and hybrid variants. To facilitate reproducibility, we implemented these networks with the publicly available Theano neural network toolkit and completed experiments on the well-known airline travel information system (ATIS) benchmark. In addition, we compared the approaches on two custom SLU data sets from the entertainment and movies domains. Our results show that the RNN-based models outperform the conditional random field (CRF) baseline by 2% in absolute error reduction on the ATIS benchmark. We improve the state-of-the-art by 0.5% in the Entertainment domain, and 6.7% for the movies domain.

MPNET: An End-to-End Deep Neural Network for Object Detection in Surveillance Video <=SEP=> Object detection is one of the most important topics in computer vision task and has obtained impressive performance thanks to the use of deep convolutional neural network. For object detection, especially in still image, it has achieved excellent performance during past two years, such as the series of R-CNN which plays a vital role in improving performance. However, with the number of surveillance videos increasing, the current methods may not meet the growing demand. In this paper, we propose a new framework named moving-object proposals generation and prediction framework (MPGP) to reduce the searching space and generate some accurate proposals which can reduce computational cost. In addition, we explore the relation of moving regions in feature map of different layers and predict candidates according to the results of previous frames. Last but not least, we utilize spatial-temporal information to strengthen the detection score and further adjust the location of the bounding boxes. Our MPGP framework can be applied to different region-based networks. Experiments on CUHK data set, XJTU data set, and AVSS data set, show that our approach outperforms the state-of-the-art approaches.

Lifetime Portfolio Selection By Dynamic Stochastic Programming <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Advocating an ethical memory model for artificial companions from a human-centred perspective <=SEP=> This paper considers the ethical implications of applying three major ethical theories to the memory structure of an artificial companion that might have different embodiments such as a physical robot or a graphical character on a hand-held device. We start by proposing an ethical memory model and then make use of an action-centric framework to evaluate its ethical implications. The case that we discuss is that of digital artefacts that autonomously record and store user data, where this data are used as a resource for future interaction with users.

Efficiently exploring architectural design spaces via predictive modeling <=SEP=> Architects use cycle-by-cycle simulation to evaluate design choices and understand tradeoffs and interactions among design parameters. Efficiently exploring exponential-size design spaces with many interacting parameters remains an open problem: the sheer number of experiments renders detailed simulation intractable. We attack this problem via an automated approach that builds accurate, confident predictive design-space models. We simulate sampled points, using the results to teach our models the function describing relationships among design parameters. The models produce highly accurate performance estimates for other points in the space, can be queried to predict performance impacts of architectural changes, and are very fast compared to simulation, enabling efficient discovery of tradeoffs among parameters in different regions. We validate our approach via sensitivity studies on memory hierarchy and CPU design spaces: our models generally predict IPC with only 1-2% error and reduce required simulation by two orders of magnitude. We also show the efficacy of our technique for exploring chip multiprocessor (CMP) design spaces: when trained on a 1% sample drawn from a CMP design space with 250K points and up to 55x performance swings among different system configurations, our models predict performance with only 4-5% error on average. Our approach combines with techniques to reduce time per simulation, achieving net time savings of three-four orders of magnitude.

3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder <=SEP=> We present an algorithm for registration between a large-scale point cloud and a close-proximity scanned point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems. The algorithm, denoted LORAX, selects super-points&#x2013;local subsets of points&#x2013;and describes the geometric structure of each with a low-dimensional descriptor. These descriptors are then used to infer potential matching regions for an efficient coarse registration process, followed by a fine-tuning stage. The set of super-points is selected by covering the point clouds with overlapping spheres, and then filtering out those of low-quality or nonsalient regions. The descriptors are computed using state-of-the-art unsupervised machine learning, utilizing the technology of deep neural network based auto-encoders. Abstract This novel framework provides a strong alternative to the common practice of using manually designed key-point descriptors for coarse point cloud registration. Utilizing super-points instead of key-points allows the available geometrical data to be better exploited to find the correct transformation. Encoding local 3D geometric structures using a deep neural network auto-encoder instead of traditional descriptors continues the trend seen in other computer vision applications and indeed leads to superior results. The algorithm is tested on challenging point cloud registration datasets, and its advantages over previous approaches as well as its robustness to density changes, noise, and missing data are shown.

Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor <=SEP=> In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the brushless DC motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions. & 2014 Elsevier B.V. All rights reserved.

High Performance Rogowski Current Transducers <=SEP=> A Rogowski current transducer is an invaluable tool for semiconductor and power electronic circuit development since it is non-intrusive and does not saturate at high currents. This paper reviews the operating principles, performance limitations and development of this measurement technology and outlines improvements to the integrator design that enables bandwidths of 10MHz to be achieved.

A preamplifier-latch comparator for high accuracy high speed switched-capacitors pipelined ADC <=SEP=> In this paper, a gain-boosting preamplifier-latch comparator with minimum input referred offset voltage is proposed. The preamplifier utilizes telescope structure with cross-coupled active loading, which enhances the gain of preamplifier. In order to minimize the delay time of comparator, the input DC range and the ratio of PMOS/NMOS are selected carefully. The circuit is fabricated in TSMC 0.18um with 1.8V power supply.