Query: Reduction of THD in Diode Clamped Multilevel Inverter employing SPWM technique

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Novel Multilevel Inverter Carrier-Based PWM Method <=SEP=> The advent of the transformerless multilevel inverter topology has brought forth various pulsewidth modulation (PWM) schemes as a means to control the switching of the active devices in each of the multiple voltage levels in the inverter. An analysis of how existing multilevel carrier-based PWM affects switch utilization for the different levels of a diode-clamped inverter is conducted. Two novel carrier-based multilevel PWM schemes are presented which help to optimize or balance the switch utilization in multilevel inverters. A 10-kW prototype sixlevel diode-clamped inverter has been built and controlled with the novel PWM strategies proposed in this paper to act as a voltage-source inverter for a motor drive.

A New Neutral-Point-Clamped PWM Inverter <=SEP=> A new neutral-point-clamped pulsewidth modulation (PWM) inverter composed of main switching devices which operate as switches for PWM and auxiliary switching devices to clamp the output terminal potential to the neutral point potential has been developed. This inverter output contains less harmonic content as compared with that of a conventional type. Two inverters are compared analytically and experimentally. In addition, a new PWM technique suitable for an ac drive system is applied to this inverter. The neutral-point-clamped PWM inverter adopting the new PWM technique shows an excellent drive system efficiency, including motor efficiency, and is appropriate for a wide-range variable-speed drive system.

Multilevel inverters: a survey of topologies, controls, and applications <=SEP=> Multilevel inverter technology has emerged recently as a very important alternative in the area of high-power medium-voltage energy control. This paper presents the most important topologies like diode-clamped inverter (neutral-point clamped), capacitor-clamped (flying capacitor), and cascaded multicell with separate dc sources. Emerging topologies like asymmetric hybrid cells and soft-switched multilevel inverters are also discussed. This paper also presents the most relevant control and modulation methods developed for this family of converters: multilevel sinusoidal pulsewidth modulation, multilevel selective harmonic elimination, and space-vector modulation. Special attention is dedicated to the latest and more relevant applications of these converters such as laminators, conveyor belts, and unified power-flow controllers. The need of an active front end at the input side for those inverters supplying regenerative loads is also discussed, and the circuit topology options are also presented. Finally, the peripherally developing areas such as high-voltage high-power devices and optical sensors and other opportunities for future development are addressed.

Multilevel PWM Methods at Low Modulation Indices <=SEP=> When utilized at low amplitude modulation indices, existing multilevel carrier-based PWM strategies have no special provisions for this operating region, and several levels of the inverter go unused. This paper proposes some novel multilevel PWM strategies to take advantage of the multiple levels in both a diodeclamped inverter and a cascaded H-bridges inverter by utilizing all of the levels in the inverter even at low modulation indices. Simulation results show what effects the different strategies have on the active device utilization. A prototype 6-level diode-clamped inverter and an 11-level cascaded H-bridges inverter have been built and controlled with the novel PWM strategies proposed in this paper.

Multilevel Converters for Large Electric Drives <=SEP=> Traditional two-level high-frequency pulse width modulation (PWM) inverters for motor drives have several problems associated with their high frequency switching which produces common-mode voltage and high voltage change (dV/dt) rates to the motor windings. Multilevel inverters solve these problems because their devices can switch at a much lower frequency. Two different multilevel topologies are identified for use as a converter for electric drives, a cascade inverter with separate dc sources and a back-to-back diode clamped converter. The cascade inverter is a natural fit for large automotive allelectric drives because of the high VA ratings possible and because it uses several levels of dc voltage sources which would be available from batteries or fuel cells. The back-to-back diode clamped converter is ideal where a source of ac voltage is available such as a hybrid electric vehicle. Simulation and experimental results show the superiority of these two converters over PWM based drives.

Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition <=SEP=> Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods for image recognition. In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition. To this end, we make three main contributions from the following aspects. First, we propose a patch-level and end-to-end architecture to model the appearance of local patches, called PatchNet. PatchNet is essentially a customized network trained in a weakly supervised manner, which uses the image-level supervision to guide the patch-level feature extraction. Second, we present a hybrid visual representation, called VSAD, by utilizing the robust feature representations of PatchNet to describe local patches and exploiting the semantic probabilities of PatchNet to aggregate these local patches into a global representation. Third, based on the proposed VSAD representation, we propose a new state-of-the-art scene recognition approach, which achieves an excellent performance on two standard benchmarks: MIT Indoor67 (86.2%) and SUN397 (73.0%).

Autophagy as a regulated pathway of cellular degradation. <=SEP=> Macroautophagy is a dynamic process involving the rearrangement of subcellular membranes to sequester cytoplasm and organelles for delivery to the lysosome or vacuole where the sequestered cargo is degraded and recycled. This process takes place in all eukaryotic cells. It is highly regulated through the action of various kinases, phosphatases, and guanosine triphosphatases (GTPases). The core protein machinery that is necessary to drive formation and consumption of intermediates in the macroautophagy pathway includes a ubiquitin-like protein conjugation system and a protein complex that directs membrane docking and fusion at the lysosome or vacuole. Macroautophagy plays an important role in developmental processes, human disease, and cellular response to nutrient deprivation.

Machine learning aided cognitive RAT selection for 5G heterogeneous networks <=SEP=> The starring role of the Heterogeneous Networks (HetNet) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current user association (cell selection) mechanisms used in cellular networks. The max-SINR algorithm, although historically effective for performing this function, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs that optimize the association and routing decisions in the context of single-RAT and multi-RAT connections, respectively. This paper proposes a framework under these guidelines that relies on Machine Learning techniques at the terminal device level for Cognitive RAT Selection and presents simulation results to suppport it.

An Anonymous Electronic Voting Protocol for Voting Over The Internet <=SEP=> In this work we propose a secure electronic voting protocol that is suitable for large scale voting over the Internet. The protocol allows a voter to cast his or her ballot anonymously, by exchanging untraceable yet authentic messages. The protocol ensures that (i) only eligible voters are able to cast votes, (ii) a voter is able to cast only one vote, (iii) a voter is able to verify that his or her vote is counted in the final tally, (iv) nobody, other than the voter, is able to link a cast vote with a voter, and (v) if a voter decides not to cast a vote, nobody is able to cast a fraudulent vote in place of the voter. The protocol does not require the cooperation of all registered voters. Neither does it require the use of complex cryptographic techniques like threshold cryptosystems or anonymous channels for casting votes. This is in contrast to other voting protocols that have been proposed in the literature. The protocol uses three agents, other than the voters, for successful operation. However, we do not require any of these agents to be trusted. That is, the agents may be physically co-located or may collude with one another to try to commit a fraud. If a fraud is committed, it can be easily detected and proven, so that the vote can be declared null and void. Although we propose the protocol with electronic voting in mind, the protocol can be used in other applications that involve exchanging an untraceable yet authentic message. Examples of such applications are answering confidential questionnaire anonymously or anonymous financial transactions.

The Love/Hate Relationship with the C Preprocessor: An Interview Study (Artifact) <=SEP=> The C preprocessor has received strong criticism in academia, among others regarding separation of concerns, error proneness, and code obfuscation, but is widely used in practice. Many (mostly academic) alternatives to the preprocessor exist, but have not been adopted in practice. Since developers continue to use the preprocessor despite all criticism and research, we ask how practitioners perceive the C preprocessor. We performed interviews with 40 developers, used grounded theory to analyze the data, and cross-validated the results with data from a survey among 202 developers, repository mining, and results from previous studies. In particular, we investigated four research questions related to why the preprocessor is still widely used in practice, common problems, alternatives, and the impact of undisciplined annotations. Our study shows that developers are aware of the criticism the C preprocessor receives, but use it nonetheless, mainly for portability and variability. Many developers indicate that they regularly face preprocessorrelated problems and preprocessor-related bugs. The majority of our interviewees do not see any current C-native technologies that can entirely replace the C preprocessor. However, developers tend to mitigate problems with guidelines, even though those guidelines are not enforced consistently. We report the key insights gained from our study and discuss implications for practitioners and researchers on how to better use the C preprocessor to minimize its negative impact. 1998 ACM Subject Classification D.3.4 Processors

Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments <=SEP=> Active and passive sensing technologies are providing powerful mechanisms to track, model, and understand a range of health behaviors and well-being states. Despite yielding rich, dense and high fidelity data, current sensing technologies often require highly engineered study designs and persistent participant compliance, making them difficult to scale to large populations and to data acquisition tasks spanning extended time periods. This paper situates social media as a new passive, unobtrusive sensing technology. We propose a semi-supervised machine learning framework to combine small samples of data gathered through active sensing, with large-scale social media data to infer mood instability (MI) in individuals. Starting from a theoretically-grounded measure of MI obtained from mobile ecological momentary assessments (EMAs), we show that our model is able to infer MI in a large population of Twitter users with 96% accuracy and F-1 score. Additionally, we show that, our model predicts self-identifying Twitter users with bipolar and borderline personality disorder to exhibit twice the likelihood of high MI, compared to that in a suitable control. We discuss the implications and the potential for integrating complementary sensing capabilities to address complex research challenges in precision medicine.

The Therapeutic Potentials of Ayahuasca: Possible Effects against Various Diseases of Civilization <=SEP=> Ayahuasca is an Amazonian psychoactive brew of two main components. Its active agents are β-carboline and tryptamine derivatives. As a sacrament, ayahuasca is still a central element of many healing ceremonies in the Amazon Basin and its ritual consumption has become common among the mestizo populations of South America. Ayahuasca use amongst the indigenous people of the Amazon is a form of traditional medicine and cultural psychiatry. During the last two decades, the substance has become increasingly known among both scientists and laymen, and currently its use is spreading all over in the Western world. In the present paper we describe the chief characteristics of ayahuasca, discuss important questions raised about its use, and provide an overview of the scientific research supporting its potential therapeutic benefits. A growing number of studies indicate that the psychotherapeutic potential of ayahuasca is based mostly on the strong serotonergic effects, whereas the sigma-1 receptor (Sig-1R) agonist effect of its active ingredient dimethyltryptamine raises the possibility that the ethnomedical observations on the diversity of treated conditions can be scientifically verified. Moreover, in the right therapeutic or ritual setting with proper preparation and mindset of the user, followed by subsequent integration of the experience, ayahuasca has proven effective in the treatment of substance dependence. This article has two important take-home messages: (1) the therapeutic effects of ayahuasca are best understood from a bio-psycho-socio-spiritual model, and (2) on the biological level ayahuasca may act against chronic low grade inflammation and oxidative stress via the Sig-1R which can explain its widespread therapeutic indications.

Epigenetics and the embodiment of race: developmental origins of US racial disparities in cardiovascular health. <=SEP=> The relative contribution of genetic and environmental influences to the US black-white disparity in cardiovascular disease (CVD) is hotly debated within the public health, anthropology, and medical communities. In this article, we review evidence for developmental and epigenetic pathways linking early life environments with CVD, and critically evaluate their possible role in the origins of these racial health disparities. African Americans not only suffer from a disproportionate burden of CVD relative to whites, but also have higher rates of the perinatal health disparities now known to be the antecedents of these conditions. There is extensive evidence for a social origin to prematurity and low birth weight in African Americans, reflecting pathways such as the effects of discrimination on maternal stress physiology. In light of the inverse relationship between birth weight and adult CVD, there is now a strong rationale to consider developmental and epigenetic mechanisms as links between early life environmental factors like maternal stress during pregnancy and adult race-based health disparities in diseases like hypertension, diabetes, stroke, and coronary heart disease. The model outlined here builds upon social constructivist perspectives to highlight an important set of mechanisms by which social influences can become embodied, having durable and even transgenerational influences on the most pressing US health disparities. We conclude that environmentally responsive phenotypic plasticity, in combination with the better-studied acute and chronic effects of social-environmental exposures, provides a more parsimonious explanation than genetics for the persistence of CVD disparities between members of socially imposed racial categories.

Face spoofing detection from single images using texture and local shape analysis <=SEP=> Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterisation of printing artefacts and differences in light reflection, the authors propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture and local shape features. Hence, the authors present a novel approach based on analysing facial image for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyses the texture and gradient structures of the facial images using a set of low-level feature descriptors, fast linear classification scheme and score level fusion. Compared to many previous works, the authors proposed approach is robust and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on three publicly available databases showed excellent results compared to existing works.

Subjective Well ‐ Being and Income : Is There Any Evidence of Satiation ? * <=SEP=> Subjective Well‐Being and Income: Is There Any Evidence of Satiation? Many scholars have argued that once “basic needs” have been met, higher income is no longer associated with higher in subjective well-being. We assess the validity of this claim in comparisons of both rich and poor countries, and also of rich and poor people within a country. Analyzing multiple datasets, multiple definitions of “basic needs” and multiple questions about well-being, we find no support for this claim. The relationship between wellbeing and income is roughly linear-log and does not diminish as incomes rise. If there is a satiation point, we are yet to reach it. JEL Classification: D6, I3, N3, O1, O4

TangiPaint: A Tangible Digital Painting System <=SEP=> TangiPaint is a digital painting application that provides the experience of working with real materials such as canvas and oil paint. Using fingers on the touchscreen of an iPad or iPhone, users can lay down strokes of thick, three-dimensional paint on a simulated canvas. Then using the Tangible Display technology introduced by Darling and Ferwerda [1], users can tilt the display screen to see the gloss and relief or "impasto" of the simulated surface, and modify it until they get the appearance they desire. Scene lighting can also be controlled through direct gesture-based interaction. A variety of "paints" with different color and gloss properties and substrates with different textures are available and new ones can be created or imported. The tangiPaint system represents a first step toward developing digital art media that look and behave like real materials. Introduction The development of computer-based digital art tools has had a huge impact on a wide range of creative fields. In commercial art, advertisements incorporating images, text, and graphic elements can be laid out and easily modified using digital illustration applications. In cinema, background matte elements can be digitally drawn, painted, and seamlessly integrated with live footage. In fine art, painters, printers, and engravers have also been embracing the new creative possibilities of computer-based art tools. The recent introduction of mobile, tablet-based computers with high-resolution displays, graphics processing units (GPUs) and multi-touch capabilities is also creating new possibilities for direct interaction in digital painting. However a significant limitation of most digital painting tools is that the final product is just a digital image (typically an array of RGB color values). All the colors, textures and lighting effects that we see when we look at the digital painting are “baked in” to the image by the painter. In contrast, when a painter works with real tools and media, the color, gloss, and textural properties of the work are a natural byproduct of the creative process, and lighting effects such as highlights and shadows are produced directly through interactions of the surface with light in the environment. In this paper we introduce tangiPaint, a new tablet-based digital painting system that attempts to bridge the gap between the real and digital worlds. tangiPaint is a tangible painting application that allows artists to work with digital media that look and behave like real materials. Figure 1 shows screenshots from the tangiPaint application implemented on an Apple iPad2. In Figure 1a an artist has painted a number of brushstrokes on a blank canvas. Note that in addition to color, the strokes vary in gloss, thickness, and texture, and run out just as if they were real paint laid down with a real brush. The paints also layer and mix realistically as they would on a real canvas. Figure 1. Screenshots of paintings created using the tangiPaint system. Note the gloss and relief of the brushstrokes and the texture of the underlying canvas. The system allows direct interaction with the “painted” surface both in terms of paint application and manipulation of surface orientation and

Dynamic mixed membership blockmodel for evolving networks <=SEP=> In a dynamic social or biological environment, interactions between the underlying actors can undergo large and systematic changes. Each actor can assume multiple roles and their degrees of affiliation to these roles can also exhibit rich temporal phenomena. We propose a state space mixed membership stochastic blockmodel which can track across time the evolving roles of the actors. We also derive an efficient variational inference procedure for our model, and apply it to the Enron email networks, and rewiring gene regulatory networks of yeast. In both cases, our model reveals interesting dynamical roles of the actors.

Regression approaches to voice quality controll based on one-to-many eigenvoice conversion <=SEP=> This paper proposes techniques for flexibly controlling voice quality of converted speech from a particular source speaker based on one-to-many eigenvoice conversion (EVC). EVC realizes a voice quality control based on the manipulation of a small number of parameters, i.e., weights for eigenvectors, of an eigenvoice Gaussian mixture model (EV-GMM), which is trained with multiple parallel data sets consisting of a single source speaker and many pre-stored target speakers. However, it is difficult to control intuitively the desired voice quality with those parametersbecause each eigenvector doesn’t usually represent a specific physical meaning. In order to cope with this problem, we propose regression approaches to the EVC-based voice quality controller. The tractable voice quality control of the converted speech is achieved with a low-dimensionalvoice quality control vector capturing specific voice characteristics. We conducted experimental verifications of each of the proposed approaches.

A brief survey of blackhole detection and avoidance for ZRP protocol in MANETs <=SEP=> Within Mobile Ad-Hoc Network(MANETs) unusual categories of routing protocols have been determined. It is a group of wireless system. MANET is other susceptible to a variety of attack than wired system. Black hole attack is further meticulous intimidation to MANETs. The routing protocols of MANET is less protected and therefore consequenced the system with malicious node. There are various routing protocols being used for MANETs. All routing protocols have been briefly discussed, however, Zone Routing Protocol (ZRP) is discussed in detail. Black hole is a major security threat for MANETs. Hence, in this paper, the various techniques used for detection and avoidance of Black hole attack in MANETs using ZRP routing protocol have been discussed.

A Practical Attack to De-anonymize Social Network Users <=SEP=> Social networking sites such as Facebook, LinkedIn, and Xing have been reporting exponential growth rates and have millions of registered users. In this paper, we introduce a novel de-anonymization attack that exploits group membership information that is available on social networking sites. More precisely, we show that information about the group memberships of a user (i.e., the groups of a social network to which a user belongs) is sufficient to uniquely identify this person, or, at least, to significantly reduce the set of possible candidates. That is, rather than tracking a user's browser as with cookies, it is possible to track a person. To determine the group membership of a user, we leverage well-known web browser history stealing attacks. Thus, whenever a social network user visits a malicious website, this website can launch our de-anonymization attack and learn the identity of its visitors. The implications of our attack are manifold, since it requires a low effort and has the potential to affect millions of social networking users. We perform both a theoretical analysis and empirical measurements to demonstrate the feasibility of our attack against Xing, a medium-sized social network with more than eight million members that is mainly used for business relationships. Furthermore, we explored other, larger social networks and performed experiments that suggest that users of Facebook and LinkedIn are equally vulnerable.

Cryptography on FPGAs: State of the Art Implementations and Attacks <=SEP=> In the last decade, it has become aparent that embedded systems are integral parts of our every day lives. The wireless nature of many embedded applications as well as their omnipresence has made the need for security and privacy preserving mechanisms particularly important. Thus, as FPGAs become integral parts of embedded systems, it is imperative to consider their security as a whole. This contribution provides a state-of-the-art description of security issues on FPGAs, both from the system and implementation perspectives. We discuss the advantages of reconfigurable hardware for cryptographic applications, show potential security problems of FPGAs, and provide a list of open research problems. Moreover, we summarize both public and symmetric-key algorithm implementations on FPGAs.

Compliance with a structured bedside handover protocol : An observational , multicentred study ☆ <=SEP=> Background: Bedside handover is the delivery of the nurse-to-nurse shift handover at the patient’s bedside. The method is increasingly used in nursing, but the evidence concerning the implementation process and compliance to the method is limited. Objectives: To determine the compliance with a structured bedside handover protocol following ISBARR and if there were differences in compliance between wards. Design: A multicentred observational study with unannounced and non-participatory observations (n=638) one month after the implementation of a structured bedside handover protocol. Settings and participants: Observations of individual patient handovers between nurses from the morning shift and the afternoon shift in 12 nursing wards in seven hospitals in Flanders, Belgium. Methods: A tailored and structured bedside handover protocol following ISBARR was developed, and nurses were trained accordingly. One month after implementation, a minimum of 50 observations were performed with a checklist, in each participating ward. To enhance reliability, 20% of the observations were conducted by two researchers, and inter-rater agreement was calculated. Data were analysed using descriptive statistics, one-way ANOVAs and multilevel analysis. Results: Average compliance rates to the structured content protocol during bedside handovers were high (83.63%; SD 11.44%), and length of stay, the type of ward and the nursing care model were influencing contextual factors. Items that were most often omitted included identification of the patient (46.27%), the introduction of nurses (36.51%), hand hygiene (35.89%), actively involving the patient (34.44%), and using the call light (21.37%). Items concerning the exchange of clinical information (e.g., test results, reason for admittance, diagnoses) were omitted less (8.09%–1.45%). Absence of the patients (27.29%) and staffing issues (26.70%) accounted for more than half of the non-executed bedside handovers. On average, a bedside handover took 146 s per patient. Conclusions: When the bedside handover was delivered, compliance to the structured content was high, indicating that the execution of a bedside handover is a feasible step for nurses. The compliance rate was influenced by the patient’s length of stay, the nursing care model and the type of ward, but their influence was limited. Future implementation projects on bedside handover should focus sufficiently on standard hospital procedures and patient involvement. According to the nurses, there was however a high number of situations where bedside handovers could not be delivered, perhaps indicating a reluctance in practice to use bedside

A Novel Feature Selection Approach Based on FODPSO and SVM <=SEP=> A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle swarm optimization (FODPSO). The overall accuracy (OA) of a support vector machine (SVM) classifier on validation samples is used as fitness values in order to evaluate the informativity of different groups of bands. In order to show the capability of the proposed method, two different applications are considered. In the first application, the proposed feature selection approach is directly carried out on the input hyperspectral data. The most informative bands selected from this step are classified by the SVM. In the second application, the main shortcoming of using attribute profiles (APs) for spectral-spatial classification is addressed. In this case, a stacked vector of the input data and an AP with all widely used attributes are created. Then, the proposed feature selection approach automatically chooses the most informative features from the stacked vector. Experimental results successfully confirm that the proposed feature selection technique works better in terms of classification accuracies and CPU processing time than other studied methods without requiring the number of desired features to be set a priori by users.

CMiner: Opinion Extraction and Summarization for Chinese Microblogs <=SEP=> Sentiment analysis of microblog texts has drawn lots of attention in both the academic and industrial fields. However, most of the current work only focuses on polarity classification. In this paper, we present an opinion mining system for Chinese microblogs called CMiner. Instead of polarity classification, CMiner focuses on more complicated opinion mining tasks - opinion target extraction and opinion summarization. Novel algorithms are developed for the two tasks and integrated into the end-to-end system. CMiner can help to effectively understand the users' opinion towards different opinion targets in a microblog topic. Specially, we develop an unsupervised label propagation algorithm for opinion target extraction. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. In addition, we build an aspect-based opinion summarization framework for microblog topics. After getting the opinion targets of all the microblog messages in a topic, we cluster the opinion targets into several groups and extract representative targets and summaries for each group. A co-ranking algorithm is proposed to rank both the opinion targets and microblog sentences simultaneously. Experimental results on a benchmark dataset show the effectiveness of our system and the algorithms.

Applying an optimized switching strategy to a high gain boost converter for input current ripple cancellation <=SEP=> This paper discusses applying various switching strategies such as conventional complementary and proportional methods on a specific converter. This converter was recently presented and it can provide high voltage gain and has current ripple cancelation ability at a preselected duty-cycle. The input current ripple is zero when the converter works at a special duty-cycle. But load disturbance leads to output voltage changes that maybe cause the duty-cycle to deviate from its preselected value. In this situation, the input current ripple cannot be completely canceled. The proposed proportional strategy is an optimized method for minimizing input current ripple at other operating duty-cycles and also it provides a voltage gain lower than conventional complementary strategy. Here, the converter's performance is analyzed under two switching strategies and the effect of various strategies has been investigated on converter parameters including output voltage and input current ripple. These considerations are verified by implementing a 100-W prototype of the proposed converter in laboratory.

The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data Ronen Feldman and James Sanger (Bar-Ilan University and ABS Ventures) Cambridge, England: Cambridge University Press, 2007, xii+410 pp; hardbound, ISBN 0-521-83657-3, $70.00 <=SEP=> Text mining is a new and exciting area of computer science that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. The Text Mining Handbook presents a comprehensive discussion of the latest techniques in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world applications.

A fractional open circuit voltage based maximum power point tracker for photovoltaic arrays <=SEP=> In this paper a fractional open circuit voltage based maximum power point tracker (MPPT) for photovoltaic (PV) arrays is proposed. The fractional open circuit voltage based MPPT utilizes the fact that the PV array voltage corresponding to the maximum power exhibits a linear dependence with respect to array open circuit voltage for different irradiation and temperature levels. This method is the simplest of all the MPPT methods described in the literature. The main disadvantage of this method is that the PV array is disconnected from the load after regular intervals for the sampling of the array voltage. This results in power loss. Another disadvantage is that if the duration between two successive samplings of the array voltage, called the sampling period, is too long, there is a considerable loss. This is because the output voltage of the PV array follows the unchanged reference during one sampling period. Once a maximum power point (MPP) is tracked and a change in irradiation occurs between two successive samplings, then the new MPP is not tracked until the next sampling of the PV array voltage. This paper proposes an MPPT circuit in which the sampling interval of the PV array voltage, and the sampling period have been shortened. The sample and hold circuit, which samples and holds the MPP voltage, has also been simplified. The proposed circuit does not utilize expensive microcontroller or a digital signal processor and is thus suitable for low cost photovoltaic applications.

HCI Education: Innovation, Creativity and Design Thinking <=SEP=> Human-Computer Interaction (HCI) education needs re-thinking. In this paper, we explore how and what creativity and design thinking could contribute with, if included as a part of the HCI curriculum. The findings from courses where design thinking was included, indicate that design thinking contributed to increased focus on innovation and creativity, as well as prevented too early fixation on a single solution in the initial phases of HCI design processes, fostering increased flexibility and adaptability in learning processes. The creativity and adaptability may be the best long-term foci that HCI education can add to its curriculums and offer to students when preparing them for future work

Compressed matching for feature vectors <=SEP=> The problem of compressing a large collection of feature vectors is investigated, so that object identification can be processed on the compressed form of the features. The idea is to perform matching of a query image against an image database, using directly the compressed form of the descriptor vectors, without decompression. Specifically, we concentrate on the Scale Invariant Feature Transform (SIFT), a known object detection method, as well as on Dense SIFT and PHOW features, that contain, for each image, about 300 times as many vectors as the original SIFT. Given two feature vectors, we suggest achieving our goal by compressing them using a lossless encoding by means of a Fibonacci code, for which the pairwise matching can be done directly on the compressed files. In our experiments, this approach improves the processing time and incurs only a small loss in compression efficiency relative to standard compressors requiring a decoding phase.

Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries <=SEP=> How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the PHOENIX-R model which captures the popularity dynamics of individual objects. PHOENIX-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.