title: Prior-aware Dual Decomposition: Document-specific Topic Inference for Spectral Topic Models

abstract: Spectral topic modeling algorithms operate on matrices/tensors of word co-occurrence statistics to learn topic-specific word distributions. This approach removes the dependence on the original documents and produces substantial gains in efficiency and provable topic inference, but at a cost: the model can no longer provide information about the topic composition of individual documents. Recently Thresholded Linear Inverse (TLI) is proposed to map the observed words of each document back to its topic composition. However, its linear characteristics limit the inference quality without considering the important prior information over topics. In this paper, we evaluate Simple Probabilistic Inverse (SPI) method and novel Prior-aware Dual Decomposition (PADD) that is capable of learning document-specific topic compositions in parallel. Experiments show that PADD successfully leverages topic correlations as a prior, notably outperforming TLI and learning quality topic compositions comparable to Gibbs sampling on various data.


title: The GAN Landscape: Losses, Architectures, Regularization, and Normalization

abstract: Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of "tricks". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We reproduce the current state of the art and go beyond fairly exploring the GAN landscape. We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.


title: Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information

abstract: We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks. In the experiments, we show that GCNs can predict DDIs from the molecular structures of drugs in high accuracy and the molecular information can enhance text-based DDI extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set.


title: Deep neural heart rate variability analysis

abstract: Despite of the pain and limited accuracy of blood tests for early recognition of cardiovascular disease, they dominate risk screening and triage. On the other hand, heart rate variability is non-invasive and cheap, but not considered accurate enough for clinical practice. Here, we tackle heart beat interval based classification with deep learning. We introduce an end to end differentiable hybrid architecture, consisting of a layer of biological neuron models of cardiac dynamics (modified FitzHugh Nagumo neurons) and several layers of a standard feed-forward neural network. The proposed model is evaluated on ECGs from 474 stable at-risk (coronary artery disease) patients, and 1172 chest pain patients of an emergency department. We show that it can significantly outperform models based on traditional heart rate variability predictors, as well as approaching or in some cases outperforming clinical blood tests, based only on 60 seconds of inter-beat intervals.


title: A Haptic Knob for Rehabilitation of Hand Function

abstract: This paper describes a novel two-degree-of-freedom robotic interface to train opening/closing of the hand and knob manipulation. The mechanical design, based on two parallelogram structures holding an exchangeable button, offers the possibility to adapt the interface to various hand sizes and finger orientations, as well as to right-handed or left-handed subjects. The interaction with the subject is measured by means of position encoders and four force sensors located close to the output measuring grasping and insertion forces. Various knobs can be mounted on the interface, including a cone mechanism to train a complete opening movement from a strongly contracted and closed hand to a large opened position. We describe the design based on measured biomechanics, the redundant safety mechanisms as well as the actuation and control architecture. Preliminary experiments show the performance of this interface and some of the possibilities it offers for the rehabilitation of hand function.


title: Predictive optimal matrix converter control for a Dynamic Voltage Restorer with flywheel energy storage

abstract: This paper presents a predictive optimal matrix converter controller for a flywheel energy storage system used as Dynamic Voltage Restorer (DVR). The flywheel energy storage device is based on a steel seamless tube mounted as a vertical axis flywheel to store kinetic energy. The motor/generator is a Permanent Magnet Synchronous Machine driven by the AC-AC Matrix Converter. The matrix control method uses a discrete-time model of the converter system to predict the expected values of the input and output currents for all the 27 possible vectors generated by the matrix converter. An optimal controller minimizes control errors using a weighted cost functional. The flywheel and control process was tested as a DVR to mitigate voltage sags and swells. Simulation results show that the DVR is able to compensate the critical load voltage without delays, voltage undershoots or overshoots, overcoming the input/output coupling of matrix converters.


title: Millisecond Coupling of Local Field Potentials to Synaptic Currents in the Awake Visual Cortex

abstract: The cortical local field potential (LFP) is a common measure of population activity, but its relationship to synaptic activity in individual neurons is not fully established. This relationship has been typically studied during anesthesia and is obscured by shared slow fluctuations. Here, we used patch-clamp recordings in visual cortex of anesthetized and awake mice to measure intracellular activity; we then applied a simple method to reveal its coupling to the simultaneously recorded LFP. LFP predicted membrane potential as accurately as synaptic currents, indicating a major role for synaptic currents in the relationship between cortical LFP and intracellular activity. During anesthesia, cortical LFP predicted excitation far better than inhibition; during wakefulness, it predicted them equally well, and visual stimulation further enhanced predictions of inhibition. These findings reveal a central role for synaptic currents, and especially inhibition, in the relationship between the subthreshold activity of individual neurons and the cortical LFP during wakefulness.


title: Blockchain-Based Data Preservation System for Medical Data

abstract: Medical care has become an indispensable part of people's lives, with a dramatic increase in the volume of medical data (e.g., diagnosis certificates and medical records). Medical data, however, is easily stolen, tampered with, or even completely deleted. If the above occurs, medical data cannot be recorded or retrieved in a reliable manner, resulting in delay treatment progress, even endanger the patient's life. In this paper, we propose a novel blockchain-based data preservation system (DPS) for medical data. To provide a reliable storage solution to ensure the primitiveness and verifiability of stored data while preserving privacy for users, we leverage the blockchain framework. With the proposed DPS, users can preserve important data in perpetuity, and the originality of the data can be verified if tampering is suspected. In addition, we use prudent data storage strategies and a variety of cryptographic algorithms to guarantee user privacy; e.g., an adversary is unable to read the plain text even if the data are stolen. We implement a prototype of the DPS based on the real world blockchain-based platform Ethereum. Performance evaluation results demonstrate the effectiveness and efficiency of the proposed system.


title: Finding Anomalous and Suspicious Files from Directory Metadata on a Large Corpus

abstract: We describe a tool Dirim for automatically finding files on a drive that are anomalous or suspicious, and thus worthy of focus during digital-forensic investigation, based on solely their directory information. Anomalies are found both from comparing overall drive statistics and from comparing clusters of related files using a novel approach of "superclustering" of clusters. Suspicious file detection looks for a set of specific clues. We discuss results of experiments we conducted on a representative corpus on 1467 drive images where we did find interesting anomalies but not much deception (as expected given the corpus). Cluster comparison performed best at providing useful information for an investigator, but the other methods provided unique additional information albeit with a significant number of false alarms.


title: RPRep: A Robust and Privacy-Preserving Reputation Management Scheme for Pseudonym-Enabled VANETs

abstract: Establishing trust and reputation for evaluation of message reliability is key to the vehicular ad hoc networks (VANETs). Most of the previous reputation management systems focus on the effectiveness of the reputation management system in handling the liars who send false service messages. However, these reputation management systems have two drawbacks. One is that they are vulnerable to tactical attacks such as self-promoting attacks and bad-mouthing attacks. The other is that they may violate location privacy because they assume every vehicle communicates with a unique ID. Our research particularly investigates the robustness against these tactical attacks, as well as the preservation of privacy by integrating trust management with the pseudonym technique. To resist the tactical attacks in VANETs, we present a reputation model which builds both service reputation and feedback reputation. Moreover, we apply the information entropy and the majority rule to the reputation accumulation algorithms to counter false feedback. To defend the reputation link attack during pseudonym changes, we propose hidden-zone strategy and -anonymity strategy. The simulation results show that our scheme is robust to these tactical attacks and preserves privacy against the reputation link attack during the pseudonym changes.


title: Enhanced Photoluminescence and Solar Cell PerformanceviaLewis Base Passivation of Organic–Inorganic Lead Halide Perovskites

abstract: Organic-inorganic metal halide perovskites have recently emerged as a top contender to be used as an absorber material in highly efficient, low-cost photovoltaic devices. Solution-processed semiconductors tend to have a high density of defect states and exhibit a large degree of electronic disorder. Perovskites appear to go against this trend, and despite relatively little knowledge of the impact of electronic defects, certified solar-to-electrical power conversion efficiencies of up to 17.9% have been achieved. Here, through treatment of the crystal surfaces with the Lewis bases thiophene and pyridine, we demonstrate significantly reduced nonradiative electron-hole recombination within the CH(3)NH(3)PbI(3-x)Cl(x) perovskite, achieving photoluminescence lifetimes which are enhanced by nearly an order of magnitude, up to 2 μs. We propose that this is due to the electronic passivation of under-coordinated Pb atoms within the crystal. Through this method of Lewis base passivation, we achieve power conversion efficiencies for solution-processed planar heterojunction solar cells enhanced from 13% for the untreated solar cells to 15.3% and 16.5% for the thiophene and pyridine-treated solar cells, respectively.


title: Circulant Binary Embedding

abstract: Binary embedding of high-dimensional data requires long codes to preserve the discriminative power of the input space. Traditional binary coding methods often suffer from very high computation and storage costs in such a scenario. To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix. The circulant structure enables the use of Fast Fourier Transformation to speed up the computation. Compared to methods that use unstructured matrices, the proposed method improves the time complexity from O(d2) to O(d log d), and the space complexity from O(d2) to O(d) where d is the input dimensionality. We also propose a novel time-frequency alternating optimization to learn data-dependent circulant projections, which alternatively minimizes the objective in original and Fourier domains. We show by extensive experiments that the proposed approach gives much better performance than the state-of-the-art approaches for fixed time, and provides much faster computation with no performance degradation for fixed number of bits.


title: Better Access, Quality, and Cost for Clinically Complex Veterans with Home‐Based Primary Care

abstract: In successfully reducing healthcare expenditures, patient goals must be met and savings differentiated from cost shifting. Although the Department of Veterans Affairs (VA) Home Based Primary Care (HBPC) program for chronically ill individuals has resulted in cost reduction for the VA, it is unknown whether cost reduction results from restricting services or shifting costs to Medicare and whether HBPC meets patient goals. Cost projection using a hierarchical condition category (HCC) model adapted to the VA was used to determine VA plus Medicare projected costs for 9,425 newly enrolled HBPC recipients. Projected annual costs were compared with observed annualized costs before and during HBPC. To assess patient perspectives of care, 31 veterans and caregivers were interviewed from three representative programs. During HBPC, Medicare costs were 10.8% lower than projected, VA plus Medicare costs were 11.7% lower than projected, and combined hospitalizations were 25.5% lower than during the period without HBPC. Patients reported high satisfaction with HBPC team access, education, and continuity of care, which they felt contributed to fewer exacerbations, emergency visits, and hospitalizations. HBPC improves access while reducing hospitalizations and total cost. Medicare is currently testing the HBPC approach through the Independence at Home demonstration.


title: Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation

abstract: Machine learning algorithms are typically run on large scale, distributed compute infrastructure that routinely face a number of unavailabilities such as failures and temporary slowdowns. Adding redundant computations using coding-theoretic tools called"codes"is an emerging technique to alleviate the adverse effects of such unavailabilities. A code consists of an encoding function that proactively introduces redundant computation and a decoding function that reconstructs unavailable outputs using the available ones. Past work focuses on using codes to provide resilience for linear computations and specific iterative optimization algorithms. However, computations performed for a variety of applications including inference on state-of-the-art machine learning algorithms, such as neural networks, typically fall outside this realm. In this paper, we propose taking a learning-based approach to designing codes that can handle non-linear computations. We present carefully designed neural network architectures and a training methodology for learning encoding and decoding functions that produce approximate reconstructions of unavailable computation results. We present extensive experimental results demonstrating the effectiveness of the proposed approach: we show that the our learned codes can accurately reconstruct $64 - 98\%$ of the unavailable predictions from neural-network based image classifiers on the MNIST, Fashion-MNIST, and CIFAR-10 datasets. To the best of our knowledge, this work proposes the first learning-based approach for designing codes, and also presents the first coding-theoretic solution that can provide resilience for any non-linear (differentiable) computation. Our results show that learning can be an effective technique for designing codes, and that learned codes are a highly promising approach for bringing the benefits of coding to non-linear computations.


Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms.


title: Bionic Design of Wind Turbine Blade Based on Long-Eared Owl's Airfoil

abstract: The main purpose of this paper is to demonstrate a bionic design for the airfoil of wind turbines inspired by the morphology of Long-eared Owl’s wings. Glauert Model was adopted to design the standard blade and the bionic blade, respectively. Numerical analysis method was utilized to study the aerodynamic characteristics of the airfoils as well as the blades. Results show that the bionic airfoil inspired by the airfoil at the 50% aspect ratio of the Long-eared Owl’s wing gives rise to a superior lift coefficient and stalling performance and thus can be beneficial to improving the performance of the wind turbine blade. Also, the efficiency of the bionic blade in wind turbine blades tests increases by 12% or above (up to 44%) compared to that of the standard blade. The reason lies in the bigger pressure difference between the upper and lower surface which can provide stronger lift.


title: DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks

abstract: Information extraction and user intention identification is a central topic in modern query understanding and recommendation systems. In this paper, we propose DeepProbe, a generic information-directed interaction framework which is built around an attention-based sequence to sequence (seq2seq) recurrent neural network. DeepProbe can rephrase, evaluate, and even actively ask questions, leveraging the generative ability and likelihood estimation made possible by seq2seq models. DeepProbe makes decisions based on a derived uncertainty (entropy) measure conditioned on user inputs, possibly with multiple rounds of interactions. Three applications, namely a rewritter, a relevance scorer and a chatbot for ad recommendation, were built around DeepProbe, with the first two serving as precursory building blocks for the third. We first use the seq2seq model in DeepProbe to rewrite a user query into one of standard query form, which is submitted to an ordinary recommendation system. Secondly, we evaluate DeepProbe's seq2seq model-based relevance scoring. Finally, we build a chatbot prototype capable of making active user interactions, which can ask questions that maximize information gain, allowing for a more efficient user intention idenfication process. We evaluate first two applications by 1) comparing with baselines by BLEU and AUC, and 2) human judge evaluation. Both demonstrate significant improvements compared with current state-of-the-art systems, proving their values as useful tools on their own, and at the same time laying a good foundation for the ongoing chatbot application.


title: The vsaUT-II: A novel rotational variable stiffness actuator

abstract: In this paper, the vsaUT-II, a novel rotational variable stiffness actuator, is presented. As the other designs in this class of actuation systems, the vsaUT-II is characterized by the property that the output stiffness can be changed independently of the output position. It consists of two internal elastic elements and two internal actuated degrees of freedom. The mechanical design of the vsaUT-II is such that the apparent output stiffness can be varied by changing the transmission ratio between the elastic elements and the output. This kinematic structure guarantees that the output stiffness can be changed without changing the potential energy stored internally in the elastic elements. This property is validated in simulations with the port-based model of the system and in experiments, through a proper control law design, on the prototype.


Bag of MFCC-based Words for Bird Identification


title: Dual system encryption: Realizing fully secure IBE and HIBE under simple assumptions

abstract: We present a new methodology for proving security of encryption systems using what we call Dual System Encryption. Our techniques result in fully secure Identity-Based Encryption (IBE) and Hierarchical Identity-Based Encryption (HIBE) systems under the simple and established decisional Bilinear Diffie-Hellman and decisional Linear assumptions. Our IBE system has ciphertexts, private keys, and public parameters each consisting of a constant number of group elements. These results are the first HIBE system and the first IBE system with short parameters under simple assumptions. ::: ::: In a Dual System Encryption system both ciphertexts and private keys can take on one of two indistinguishable forms. A private key or ciphertext will be normal if they are generated respectively from the system's key generation or encryption algorithm. These keys and ciphertexts will behave as one expects in an IBE system. In addition, we define semi-functional keys and ciphertexts. A semi-functional private key will be able to decrypt all normally generated ciphertexts; however, decryption will fail if one attempts to decrypt a semi-functional ciphertext with a semi-functional private key. Analogously, semi-functional ciphertexts will be decryptable only by normal private keys. ::: ::: Dual System Encryption opens up a new way to prove security of IBE and related encryption systems. We define a sequence of games where we change first the challenge ciphertext and then the private keys one by one to be semi-functional. We finally end up in a game where the challenge ciphertext and all private keys are semi-functional at which point proving security is straightforward.


title: Modeling Dynamic Hair as a Continuum

abstract: In this paper we address the difficult problem of hair dynamics, particularly hair-hair and hair-air interactions. To model these interactions, we propose to consider hair volume as a continuum. Subsequently, we treat the interaction dynamics to be fluid dynamics. This proves to be a strong as well as viable approach for an otherwise very complex phenomenon. However, we retain the individual character of hair, which is vital to visually realistic rendering of hair animation. For that, we develop an elaborate model for stiffness and inertial dynamics of individual hair strand. Being a reduced coordinate formulation, the stiffness dynamics is numerically stable and fast. We then unify the continuum interaction dynamics and the individual hair’s stiffness dynamics.


title: Active ROCOF Relay for Islanding Detection

abstract: Intentional frequency perturbation by recently researched active islanding detection techniques for inverter based distributed generation (DG) define new threshold settings for the frequency relays. This innovation has enabled the modern frequency relays to operate inside the non-detection zone (NDZ) of the conventional frequency relays. However, the effect of such perturbation on the performance of the rate of change of frequency (ROCOF) relays has not been researched so far. This paper evaluates the performance of ROCOF relays under such perturbations for an inverter interfaced DG and proposes an algorithm along with the new threshold settings to enable it work under the NDZ. The proposed algorithm is able to differentiate between an islanding and a non-islanding event. The operating principle of relay is based on low frequency current injection through grid side voltage source converter (VSC) control of doubly fed induction generator (DFIG) and therefore, the relay is defined as “active ROCOF relay”. Simulations are done in MATLAB.


title: Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences

abstract: Fully articulated hand tracking promises to enable fundamentally new interactions with virtual and augmented worlds, but the limited accuracy and efficiency of current systems has prevented widespread adoption. Today's dominant paradigm uses machine learning for initialization and recovery followed by iterative model-fitting optimization to achieve a detailed pose fit. We follow this paradigm, but make several changes to the model-fitting, namely using: (1) a more discriminative objective function; (2) a smooth-surface model that provides gradients for non-linear optimization; and (3) joint optimization over both the model pose and the correspondences between observed data points and the model surface. While each of these changes may actually increase the cost per fitting iteration, we find a compensating decrease in the number of iterations. Further, the wide basin of convergence means that fewer starting points are needed for successful model fitting. Our system runs in real-time on CPU only, which frees up the commonly over-burdened GPU for experience designers. The hand tracker is efficient enough to run on low-power devices such as tablets. We can track up to several meters from the camera to provide a large working volume for interaction, even using the noisy data from current-generation depth cameras. Quantitative assessments on standard datasets show that the new approach exceeds the state of the art in accuracy. Qualitative results take the form of live recordings of a range of interactive experiences enabled by this new approach.


title: Ellipse Detection with Hough Transform in One Dimensional Parametric Space

abstract: The main advantage of using the Hough Transform to detect ellipses is its robustness against missing data points. However, the storage and computational requirements of the Hough Transform preclude practical applications. Although there are many modifications to the Hough Transform, these modifications still demand significant storage requirement. In this paper, we present a novel ellipse detection algorithm which retains the original advantages of the Hough Transform while minimizing the storage and computation complexity. More specifically, we use an accumulator that is only one dimensional. As such, our algorithm is more effective in terms of storage requirement. In addition, our algorithm can be easily parallelized to achieve good execution time. Experimental results on both synthetic and real images demonstrate the robustness and effectiveness of our algorithm in which both complete and incomplete ellipses can be extracted.


title: Simulation and Analysis of Random Decision Errors in Clocked Comparators

abstract: Clocked comparators have found widespread use in noise sensitive applications including analog-to-digital converters, wireline receivers, and memory bit-line detectors. However, their nonlinear, time-varying dynamics resulting in discrete output levels have discouraged the use of traditional linear time-invariant (LTI) small-signal analysis and noise simulation techniques. This paper describes a linear, time-varying (LTV) model of clock comparators that can accurately predict the decision error probability without resorting to more general stochastic system models. The LTV analysis framework in conjunction with the linear, periodically time-varying (LPTV) simulation algorithms available from RF circuit simulators can provide insights into the intrinsic sampling and decision operations of clock comparators and the major contribution sources to random decision errors. Two comparators are simulated and compared with laboratory measurements. A 90-nm CMOS comparator is measured to have an equivalent input-referred random noise of 0.73 mVrms for dc inputs, matching simulation results with a short channel excess noise factor ? = 2.


title: Spectral Regression: A Unified Approach for Sparse Subspace Learning

abstract: Recently the problem of dimensionality reduction (or, subspace learning) has received a lot of interests in many fields of information processing, including data mining, information retrieval, and pattern recognition. Some popular methods include principal component analysis (PCA), linear discriminant analysis (LDA) and locality preserving projection (LPP). However, a disadvantage of all these approaches is that the learned projective functions are linear combinations of all the original features, thus it is often difficult to interpret the results. In this paper, we propose a novel dimensionality reduction framework, called Unified Sparse Subspace Learning (USSL), for learning sparse projections. USSL casts the problem of learning the projective functions into a regression framework, which facilitates the use of different kinds of regularizes. By using a L1-norm regularizer (lasso), the sparse projections can be efficiently computed. Experimental results on real world classification and clustering problems demonstrate the effectiveness of our method.


title: Regulators, mount up! analysis of privacy policies for mobile money services

abstract: Emerging digital financial services use mobile phones to provide access to populations traditionally excluded from the global economy. These "mobile money" services have proven extremely successful in their first ten years of deployment, and provide a powerful means of raising people out of poverty. Such services have access to a wealth of customer information, potentially including entire purchase histories, geolocation, and social network information. In this paper, we perform the first study of privacy policies in mobile money services, evaluating policies from 54 services and comparing them to 50 policies from traditional financial institutions. Because mobile money services are developed under a wide range of regulatory environments, we compare policies to the industry standard (the GSMA's Mobile Privacy Principles) and to a traditional national standard (the FDIC's Privacy Rule Handbook). Our analysis shows that almost half (44%) of these mobile money services do not have any privacy policy whatsoever. Of the services that do have privacy policies, roughly one-third (33%) fail to provide them in either of the two most common languages of their market. Furthermore, 50% of these policies do not ever identify to the user what data is actually being collected and stored. Finally, we find that where policies do exist, they are often incomplete and difficult to read by their target customers. These findings show that more work is needed to protect consumer privacy within these mobile money services.


title: Channelrhodopsin-2, a directly light-gated cation-selective membrane channel

abstract: Microbial-type rhodopsins are found in archaea, prokaryotes, and eukaryotes. Some of them represent membrane ion transport proteins such as bacteriorhodopsin, a light-driven proton pump, or channelrhodopsin-1 (ChR1), a recently identified light-gated proton channel from the green alga Chlamydomonas reinhardtii. ChR1 and ChR2, a related microbial-type rhodopsin from C. reinhardtii, were shown to be involved in generation of photocurrents of this green alga. We demonstrate by functional expression, both in oocytes of Xenopus laevis and mammalian cells, that ChR2 is a directly light-switched cation-selective ion channel. This channel opens rapidly after absorption of a photon to generate a large permeability for monovalent and divalent cations. ChR2 desensitizes in continuous light to a smaller steady-state conductance. Recovery from desensitization is accelerated by extracellular H+ and negative membrane potential, whereas closing of the ChR2 ion channel is decelerated by intracellular H+. ChR2 is expressed mainly in C. reinhardtii under low-light conditions, suggesting involvement in photoreception in dark-adapted cells. The predicted seven-transmembrane alpha helices of ChR2 are characteristic for G protein-coupled receptors but reflect a different motif for a cation-selective ion channel. Finally, we demonstrate that ChR2 may be used to depolarize small or large cells, simply by illumination.


Terrain Models for Mass Movement Erosion


title: An Analysis on a Dynamic Amplifier and Calibration Methods for a Pseudo-Differential Dynamic Comparator

abstract: This paper analyzes a pseudo-differential dynamic comparator with a dynamic pre-amplifier. The transient gain of a dynamic pre-amplifier is derived and applied to equations of the thermal noise and the regeneration time of a comparator. This analysis enhances understanding of the roles of transistor's parameters in pre-amplifier's gain. Based on the calculated gain, two calibration methods are also analyzed. One is calibration of a load capacitance and the other is calibration of a bypass current. The analysis helps designers' estimation for the accuracy of calibration, dead-zone of a comparator with a calibration circuit, and the influence of PVT variation. The analyzed comparator uses 90-nm CMOS technology as an example and each estimation is compared with simulation results.


title: Focused Crawler Optimization Using Genetic Algorithm

abstract: As the size of the Web continues to grow, searching it for useful information has become more difficult. Focused crawler intends to explore the Web conform to a specific topic. This paper discusses the problems caused by local searching algorithms. Crawler can be trapped within a limited Web community and overlook suitable Web pages outside its track. A genetic algorithm as a global searching algorithm is modified to address the problems. The genetic algorithm is used to optimize Web crawling and to select more suitable Web pages to be fetched by the crawler. Several evaluation experiments are conducted to examine the effectiveness of the approach. The crawler delivers collections consist of 3396 Web pages from 5390 links which had been visited, or filtering rate of Roulette-Wheel selection at 63% and precision level at 93% in 5 different categories. The result showed that the utilization of genetic algorithm had empowered focused crawler to traverse the Web comprehensively, despite it relatively small collections. Furthermore, it brought up a great potential for building an exemplary collections compared to traditional focused crawling methods.


title: The GABAA Receptor as a Therapeutic Target for Neurodevelopmental Disorders

abstract: Intellectual disability, autism spectrum disorder, and epilepsy are prime examples of neurodevelopmental disorders that collectively affect a significant percentage of the world population. Recent technological breakthroughs allowed the elucidation of the genetic causes of many of these disorders. As neurodevelopmental disorders are genetically heterogeneous, the development of rational therapy is extremely challenging. Fortunately, many causative genes are interconnected and cluster in specific cellular pathways. Targeting a common node in such a network would allow us to interfere with a series of related neurodevelopmental disorders at once. Here, we argue that the GABAergic system is disturbed in many neurodevelopmental disorders, including fragile X syndrome, Rett syndrome, and Dravet syndrome, and is a key candidate target for therapeutic intervention. Many drugs that modulate the GABAergic system have already been tested in animal models with encouraging outcomes and are readily available for clinical trials.


title: Videolization: knowledge graph based automated video generation from web content

abstract: Web content nowadays can also be accessed through new generation of Internet connected TVs. However, these products failed to change users’ behavior when consuming online content. Users still prefer personal computers to access Web content. Certainly, most of the online content is still designed to be accessed by personal computers or mobile devices. In order to overcome the usability problem of Web content consumption on TVs, this paper presents a knowledge graph based video generation system that automatically converts textual Web content into videos using semantic Web and computer graphics based technologies. As a use case, Wikipedia articles are automatically converted into videos. The effectiveness of the proposed system is validated empirically via opinion surveys. Fifty percent of survey users indicated that they found generated videos enjoyable and 42 % of them indicated that they would like to use our system to consume Web content on their TVs.


title: Graph embedding as a new approach for unknown malware detection

abstract: Malware is any type of computer program which is developed to harm computers, networks, and information. Noticeable growth of malware development has made computer and network security a significant and challenging area in recent years. There is an intensive competition between malwares and antiviruses. Malware authors make every effort to develop new harmful codes using various programming tricks and exploits which are unseen for detection techniques. On the other hand, antivirus developers upgrade their methods and algorithms to recognize unknown malware. Therefore, an accurate and rapid detection method is an irrefutable demand in computer security area. This paper proposes a new malware detection method based on the OpCodes within an executable file. Proposed method generates a graph of operational codes (OpCode) within an executable file and then embeds this graph into eigenspace using “Power Iteration” method. This will help us represent an executable file as a linear combination of eigenvectors proportionate to their eigenvalues, which is beneficial to train machine learning classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The main advantages of our proposed method are high detection rate despite utilizing simple classifiers like KNN, acceptable computational complexity even in large scale datasets against rival methods, and low false positive rate.


title: Multicultural minds: A dynamic constructivist approach to culture and cognition

abstract: The authors present a new approach to culture and cognition, which focuses on the dynamics through which specific pieces of cultural knowledge (implicit theories) become operative in guiding the construction of meaning from a stimulus. Whether a construct comes to the fore in a perceiver's mind depends on the extent to which the construct is highly accessible (because of recent exposure). In a series of cognitive priming experiments, the authors simulated the experience of bicultural individuals (people who have internalized two cultures) of switching between different cultural frames in response to culturally laden symbols. The authors' discuss how this dynamic, constructivist approach illuminates (a) when cultural constructs are potent drivers of behavior and (b) how bicultural individuals may control the cognitive effects of culture. A lthough the multiplicity of cultural identities and influences is hardly a new phenomenon, it is one increasingly discussed. In contemporary popular discourse, it is becoming increasingly rare to hear the word cultural without the prefix multi-. Multicultural experience, however, has been underinvestigated in psychological research on culture, particularly within the most prominent research paradigm of cross-cultural psychology (see Segall, Lonner, & Berry, 1998). There are several reasons for this. First, somewhat obviously, methodological orientations influence a researcher' s choice of topics, and culture has been assessed primarily as an individual difference, with the methods for its evaluation developed by clinical and personality researchers to distinguish types of persons. Insofar as the cross-cultural method relies on uncovering differences across cultural groups (usually indexed by nationality), the influence of multiple cultures on an individual merely creates error variance. Second, on a more subtle level, the theoretical assumptions predominant in crosscultural scholarship have impeded an analysis of the dynamics of multiple cultures in the same mind. The effort to identify the knowledge that varies between but not within large cultural groups has led to the conceptualization of cultural knowledge in terms of very general constructs, such as individualistic as opposed to collectivist value orientations, which apply to all aspects of life (Segall et al., 1998). With the emphasis on domain-general constructs has come the assumption that the influence of culture on cognition is continual and constant. Cultural knowledge is conceptualized to be like a contact lens that affects the individual's perceptions of visual stimuli all of the time. This conception unfortunately leaves little room for a second internalized culture within an individual's psychology. In sum, the methods and assumptions of cross-cultural psychology have not fostered the analysis of how individuals incorporate more than one culture. Our introduction of an alternative approach to culture


Chapter 31 Multilevel Power Converters 31.1 Introduction


title: Eastern meditative techniques and hypnosis: a new synthesis.

abstract: Abstract In this article major ancient Buddhist meditation techniques, samatha, vipassana, Zen, and ton-len, will be described in reference to contemporary clinical hypnosis. In so doing, the Eastern healing framework out of which these techniques emerged is examined in comparison with and in contrast to its Western counterpart. A growing body of empirical literature shows that meditation and hypnosis have many resemblances despite the distinct differences in underlying philosophy and technical methodologies. Although not all meditation techniques “fit” the Western culture, each has much to offer to clinicians who are familiar with hypnosis.


title: Does attendance matter? An examination of student attitudes, participation, performance and attendance

abstract: Non attendance of lectures and tutorials appears to be a growing trend. The literature suggests many possible reasons including students’ changing lifestyle, attitudes, teaching and technology. This paper looks at the reasons for non attendance of students in the Faculty of Commerce at the University of Wollongong and identifies relationships between attendance, participation and performance. The results indicate that there are valid reasons for non attendance that are both in the control of learners and teachers. There are also clear benefits for students to be gained in attendance; however, changes in the way we learn, teach, assess and use technology are recommended if we wish to reverse the trend. Acknowledgement: The authors wish to thank Kieren Diment for his assistance in the article’s statistical analysis. D o es A t t en d a n c e M a t t er ? A n E xa m in a t i o n o f S tu d e n t A t t i t u d es , Pa r t i c ip a t io n , P er f o r m a n c e a n d A t t en d a n c e? P e t er Ma s s in g h a m a n d T o n y H er r in g to n Journal of University Teaching and Learning Practice – Vol 3/2, 2006 83 Pre-amble


title: CMATERdb1: a database of unconstrained handwritten Bangla and Bangla–English mixed script document image

abstract: In this paper, we have described the preparation of a benchmark database for research on off-line Optical Character Recognition (OCR) of document images of handwritten Bangla text and Bangla text mixed with English words. This is the first handwritten database in this area, as mentioned above, available as an open source document. As India is a multi-lingual country and has a colonial past, so multi-script document pages are very much common. The database contains 150 handwritten document pages, among which 100 pages are written purely in Bangla script and rests of the 50 pages are written in Bangla text mixed with English words. This database for off-line-handwritten scripts is collected from different data sources. After collecting the document pages, all the documents have been preprocessed and distributed into two groups, i.e., CMATERdb1.1.1, containing document pages written in Bangla script only, and CMATERdb1.2.1, containing document pages written in Bangla text mixed with English words. Finally, we have also provided the useful ground truth images for the line segmentation purpose. To generate the ground truth images, we have first labeled each line in a document page automatically by applying one of our previously developed line extraction techniques [Khandelwal et al., PReMI 2009, pp. 369–374] and then corrected any possible error by using our developed tool GT Gen 1.1. Line extraction accuracies of 90.6 and 92.38% are achieved on the two databases, respectively, using our algorithm. Both the databases along with the ground truth annotations and the ground truth generating tool are available freely at http://code.google.com/p/cmaterdb.


title: Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction

abstract: Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learning based method named Improved GoogLeNet is proposed to remove streak artifacts due to projection missing in sparse-view CT reconstruction. Residual learning is used in GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.


title: Are valence and arousal separable in emotional experience

abstract: The bipolar valence-arousal model of conscious experience of emotions is prominent in emotion research. In this work, we examine the validity of this model in the context of feelings elicited by visual stimuli. In particular, we examine whether arousal has a unique contribution over bivariate valence (separate measures for pleasure and displeasure) in explaining physiological arousal (electrodermal activity, EDA) and self-reported feelings at the level of item-specific responses across and within individuals. Our results suggest that self-reports of arousal have neither an advantage in predicting EDA nor make a unique contribution when valence is present in the model. Acceptance of the null hypothesis was confirmed with the use of the Bayesian information criterion. Arousal also showed no advantage over valence in predicting global feelings, but demonstrated a small unique component (1.5% to 4% of variance explained). These results have practical implications for both experimental design in the study of emotions and the underlying bases of their conscious experience. The bipolar valence-arousal model is a prevalent model of conscious experience of emotions in the scientific literature. Feel- ings in this model are estimated by using two questions. The first question asks about the degree of feelings of arousal (e.g., feelings of stimulation, excitation, arousal). The second question asks about the degree of pleasure versus displeasure. The bipolar valence- arousal model posits that when participants introspect and report on different aspects of their emotional experience, arousal, and valence are two distinct types of feeling qualia. Much as how one can separately attend to color and motion as distinct dimensions of perceptual experience that rely on distinct appraisals of external events, when attention is turned inward, emotional experience can be decomposed into qualitatively distinct perceptions of valence and arousal. In the present work, we examined the validity of the dissociation of arousal and valence in the context of internal feelings elicited by external visual stimuli. In much of emotion research, the rule or a criterion that is used to assign stimuli to the experimental conditions is based on emo- tional experience. To support this claim, we note that the three largest emotion stimuli pools of pictures (IAPS: the International Affective Picture System; Lang, Bradley, & Cuthbert, 1997), audio recording (IADS: The International Affective Digitized Sounds; Bradley & Lang, 2007), and English words (ANEW: Affective Norms for English Words; Bradley & Lang, 1999) are mapped on self-reports of feelings. Each stimulus in the IAPS, IADS, and ANEW (henceforth referred as IIA) has a value that is based on self-reports of emotional experience averaged across many partic- ipants. Therefore, each time researchers use stimuli from the IIA and assign them to the experimental conditions according to their associated norms, they essentially use self-reports of feelings as criteria to assign stimuli to the experimental conditions. When using stimuli and norms from the IIA (or any other stimuli cate- gorized by self-reports of feelings), the implicit model of emotions is a model of conscious experience of emotions. Therefore, at least when the norms are in use, models of conscious experience of emotions influence the experimental design, data, results, conclu- sions, and terms by which knowledge about emotions is accumu- lated, regardless of whether one examines brain activity, peripheral physiological activity, or any other measure that may not incor- porate self-reports of feelings. As long as one uses self-reports of feelings to assign stimuli to the experimental conditions, parsing the emotional space is done in terms of self-reports of conscious experience of emotions. Whether intended or not, this is why models of conscious experience of emotions are fundamental to all emotion research.


title: Implementation of a Covert Channel in the 802.11 Header

abstract: Covert channels are an immense cause of security concern because they can be used to pass malicious messages. The messages could be in form of computer virus, spy programs, terrorist messages, etc. Most available techniques proposed covert channels that use the upper layers of the OSI model. In this paper, we discuss a novel covert channel in the data link layer dedicated to wireless local area networks. Depending on the configuration of the network, the covert channel uses either sequence control or initial vector fields, or both of them. We present also some measurements to protect the proposed covert channel against steganalysis processes and sniffing attack. Finally, the performance of proposed covert channel is compared with the available common TCP/IP covert channels regarding the offered bandwidth and the imperceptibly.


title: Deep Learning Based Semantic Video Indexing and Retrieval

abstract: We share the implementation details and testing results for video retrieval system based exclusively on features extracted by convolutional neural networks. We show that deep learned features might serve as universal signature for semantic content of video useful in many search and retrieval tasks. We further show that graph-based storage structure for video index allows to efficiently retrieving the content with complicated spatial and temporal search queries.


title: Design space exploration of FPGA accelerators for convolutional neural networks

abstract: The increasing use of machine learning algorithms, such as Convolutional Neural Networks (CNNs), makes the hardware accelerator approach very compelling. However the question of how to best design an accelerator for a given CNN has not been answered yet, even on a very fundamental level. This paper addresses that challenge, by providing a novel framework that can universally and accurately evaluate and explore various architectural choices for CNN accelerators on FPGAs. Our exploration framework is more extensive than that of any previous work in terms of the design space, and takes into account various FPGA resources to maximize performance including DSP resources, on-chip memory, and off-chip memory bandwidth. Our experimental results using some of the largest CNN models including one that has 16 convolutional layers demonstrate the efficacy of our framework, as well as the need for such a high-level architecture exploration approach to find the best architecture for a CNN model.


title: Simulating crowd movement in agent-based model of large-scale flood

abstract: Crowd movement during natural disasters and major accidents can affect the success of evacuation procedure. Thus to develop an effective evacuation plan, a wide-range of scenarios causing different routes and patterns of crowd movement should be considered. Recent researches in agent-based simulation have achieved techniques to simulate crowd movement in emergency scenarios at city scale. However simulating crowd movement at a macroscopic level for disasters which might affect several cities, like large-scale flood, is still a challenge. This paper addresses this issue and makes three contributions. First, the development of an agent-based layered model to simulate large-scale flood using GIS is demonstrated. Second, the simulation of crowd agents' movement on available roads is presented. Third, the preliminary experiments running on private Cloud server is reported. The experiments cover case studies illustrated the movement of crowd agents around Thailand while several parts of the country were inundated. The 2D animation depict the movement of crowd; and the simulation results show the status of the agents and the amount of individuals which required shelters. To simulate one day events, the simulator took 4–9 hours execution time depending on the severity of floods and available facilities.


title: Mobility and Handoff Management in Wireless Networks

abstract: With the increasing demands for new data and real-time services, wireless networks should support calls with different traffic characteristics and different Quality of Service (QoS)guarantees. In addition, various wireless technologies and networks exist currently that can satisfy different needs and requirements of mobile users. Since these different wireless networks act as complementary to each other in terms of their capabilities and suitability for different applications, integration of these networks will enable the mobile users to be always connected to the best available access network depending on their requirements. This integration of heterogeneous networks will, however, lead to heterogeneities in access technologies and network protocols. To meet the requirements of mobile users under this heterogeneous environment, a common infrastructure to interconnect multiple access networks will be needed. In this chapter, the design issues of a number of mobility management schemes have been presented. Each of these schemes utilizes IP-based technologies to enable efficient roaming in heterogeneous network. Efficient handoff mechanisms are essential for ensuring seamless connectivity and uninterrupted service delivery. A number of handoff schemes in a heterogeneous networking environment are also presented in this chapter.


Attention Assisted Discovery of Sub-Utterance Structure in Speech Emotion Recognition


title: Social Media Analytics: Tracking, Modeling and Predicting the Flow of Information through Networks Jure Leskovec

abstract: Online social media represent a fundamental shift of how information is being produced, transferred and consumed. User generated content in the form of blog posts, comments, and tweets establishes a connection between the producers and the consumers of information. Tracking the pulse of the social media outlets, enables companies to gain feedback and insight in how to improve and market products better. For consumers, the abundance of information and opinions from diverse sources helps them tap into the wisdom of crowds, to aid in making more informed decisions. The present tutorial investigates techniques for social media modeling, analytics and optimization. First we present methods for collecting large scale social media data and then discuss techniques for coping with and correcting for the effects arising from missing and incomplete data. We proceed by discussing methods for extracting and tracking information as it spreads among the users. Then we examine methods for extracting temporal patterns by which information popularity grows and fades over time. We show how to quantify and maximize the influence of media outlets on the popularity and attention given to particular piece of content, and how to build predictive models of information diffusion and adoption. As the information often spreads through implicit social and information networks we present methods for inferring networks of influence and diffusion. Last, we discuss methods for tracking the flow of sentiment through networks and emergence of polarization.


title: Multi-Label Classification of Music into Emotions

abstract: In this paper, the automated detection of emotion in music is modeled as a multilabel classification task, where a piece of music may belong to more than one class. Four algorithms are evaluated and compared in this task. Furthermore, the predictive power of several audio features is evaluated using a new multilabel feature selection method. Experiments are conducted on a set of 593 songs with 6 clusters of music emotions based on the Tellegen-Watson-Clark model. Results provide interesting insights into the quality of the discussed algorithms and features.


title: Analysis of Proximal and Distal Muscle Activity During Handwriting Tasks

abstract: OBJECTIVE ::: In this study we sought to describe upper-extremity proximal and distal muscle activity in typically developing children during a handwriting task and to explore the relationship between muscle activity and speed and quality of writing. ::: ::: ::: METHOD ::: We evaluated 35 third- and fourth-grade Israeli children using the Alef-Alef Ktav Yad Hebrew Handwriting Test. Simultaneously, we recorded the participants' upper trapezius and thumb muscle activity by surface electromyography. Using the coefficient of variation (standard deviation divided by mean amplitude) as a measure of variability within each muscle, we analyzed differences in muscle activity variability within and between muscles. ::: ::: ::: RESULTS ::: The proximal muscle displayed significantly less variability than the distal muscles. Decreased variability in proximal muscle activity was associated with decreased variability in distal muscle activity, and decreased variability in the distal muscles was significantly associated with faster speed of writing. ::: ::: ::: CONCLUSION ::: The lower amount of variability exhibited in the proximal muscle compared with the distal muscles seems to indicate that the proximal muscle functions as a stabilizer during a handwriting task. In addition, decreased variability in both proximal and distal muscle activity appears to be more economical and is related to faster writing speed. Knowledge of the type of proximal and distal muscle activity used during handwriting can help occupational therapists plan treatment for children with handwriting disabilities.


title: Electro-thermal-mechanical modeling of wire bonding failures in IGBT

abstract: In power electronic module (PEM), IGBT (Isolated Gate Bipolar Transistor) is a high electrical power device and control wide power range from micro-voltage-ampere to mega-voltage-ampere. In addition, IGBT device usually operates in high-temperature environment, such as engine of automobile. As a result, the thermal problem is one of main concern regardless of the influence from dissipated power or environmental temperature. This paper discusses temperature distribution and mechanical behavior of wire bonding in IGBT based on finite element method. The purpose of this research would focus on the interface of wire bonding and connection substrate. Because the material of wire and substrate are different, it would result in CTE mismatch. Due to thermal damage, it could cause failure in the interface, e.g. wire bond lift off and heel crack. Among these, wire bond lift off is a critical failure and even lead to the degradation of IGBT. In order to simulate electro-thermal-mechanical behavior, two analytical methods are adopted in this research, one is electro-thermal analysis; the other is thermal-mechanical analysis. In this paper, we would use ANSYS software to analyze electro-thermal-mechanical behavior of wire bonding structure, furthermore, the wire bonding structure we build up is symmetrical, so only half of model is analyzed to save more time and reduce computational complexity. In electro-thermal analysis, we would show the temperature distribution in wire bonding structure when electric current is applied to wire. Furthermore, the natural convection and forced convection are taken into consideration in order to correspond to the realistic operational situation. The temperature change is dominated by Joule heating caused by the magnitude of electric current. For the purpose of obtaining the relation between temperature and electric current, nonlinear transient finite element simulation is proposed in this research. The analytical results show that the temperature would change along electric current and there is nearly temperature distribution in wire bonding structure after stopping applying electric current for a period of time. In thermal-mechanical analysis, we would adopt indirect coupling method, that is, use the results obtained from electro-thermal analysis as thermal load to thermal-mechanical analysis. According to analysis, the maximum von-Mises stress occurs in the heel of wire bonding due to CTE mismatch and high Joule heating. Besides, the displacement of wire bonding structure has upward direction, namely, the warpage phenomenon would occur due to temperature change. Under continuing of temperature change, it would bring fatigue failure in the interface of wire bonding and connection substrate. The objective in this research is to improve and increase the mechanical strength of wire bonding by discussing the relation of electro-thermal-mechanical behavior.


title: ACT-R: a theory of higher level cognition and its relation to visual attention

abstract: The ACT-R system is a general system for modeling a wide range of higher level cognitive processes. Recently, it has been embellished with a theory of how its higher level processes interact with a visual interface. This includes a theory of how visual attention can move across the screen, encoding information into a form that can be processed by ACT-R. This system is applied to modeling several classic phenomena in the literature that depend on the speed and selectivity with which visual attention can move across a visual display. ACT-R is capable of interacting with the same computer screens that subjects do and, as such, is well suited to provide a model for tasks involving human-computer interaction. In this article, we discuss a demonstration of ACT-R's application to menu selection and show that the ACT-R theory makes unique predictions, without estimating any parameters, about the time to search a menu. These predictions are confirmed.


title: On the efficient representation and execution of deep acoustic models

abstract: In this paper we present a simple and computationally efficient quantization scheme that enables us to reduce the resolution of the parameters of a neural network from 32-bit floating point values to 8-bit integer values. The proposed quantization scheme leads to significant memory savings and enables the use of optimized hardware instructions for integer arithmetic, thus significantly reducing the cost of inference. Finally, we propose a "quantization aware" training process that applies the proposed scheme during network training and find that it allows us to recover most of the loss in accuracy introduced by quantization. We validate the proposed techniques by applying them to a long short-term memory-based acoustic model on an open-ended large vocabulary speech recognition task.


title: Impulsive and self-control choices in opioid-dependent patients and non-drug-using control patients: Drug and monetary rewards.

abstract: Delay discounting was investigated in opioid-dependent and non-drug-using control participants. The latter participants were matched to the former on age, gender, education, and IQ. Participants in both groups chose between hypothetical monetary rewards available either immediately or after a delay. Delayed rewards were $1,000, and the immediate-reward amount was adjusted until choices reflected indifference. This procedure was repeated at each of 7 delays (1 week to 25 years). Opioid-dependent participants were given a second series of choices between immediate and delayed heroin, using the same procedures (i.e., the amount of delayed heroin was that which could be purchased with $1,000). Opioid-dependent participants discounted delayed monetary rewards significantly more than did non-drug-using participants. Furthermore opioid-dependent participants discounted delayed heroin significantly more than delayed money.


title: High power DC/DC converter using extreme close-coupled inductors aimed for electric vehicles

abstract: The DC-DC converter using coupled inductors is known as one of the topologies that achieve higher power density. This paper focused on this topology especially using extreme close-coupled inductors (ECCI). Following the elucidation of design guidelines for the coupled inductor theoretically, the superiority of the interleaved converter using ECCI in terms of reducing the size and losses in the magnetic components is demonstrated clearly through comparison with an interleaved converter using a set of looser-coupled inductors and a conventional single inductor converter. The effect of increasing a magnetic coupling factor of a set of coupled inductors from 0.980 to 0.999 is investigated by comparing it with other inductors of interleaved converters. The high power prototype converter with the coupling factor of 0.999 provides excellent performance with a time rating power density of 25.6 kW/liter.


Sensable and Desirable: a Framework for Designing Physical Interfaces


title: Comparison of CoAP and MQTT Performance Over Capillary Radios

abstract: The IoT protocols used in the application layer, namely the Constraint Application Protocol (CoAP) and Message Queue Telemetry Transport (MQTT) have dependencies to the transport layer. The choice of transport, Transmission Control Protocol (TCP) or the User Datagram Protocol(UDP), on the other hand, has an impact on the Internet of Things (IoT) application level performance, especially over a wireless medium. The motivation of this work is to look at the impact of the protocol stack on performance over two different wireless medium realizations, namely Bluetooth Low Energy and Wi-Fi. The use case studied is infrequent small reports sent from the sensor device to a central cloud storage over a last mile radio access link. We find that while CoAP/UDP based transport performs consistently better both in terms of latency and power consumption over both links, MQTT/TCP may also work when the use requirements allow for longerlatency providing better reliability. All in all, the full connectivity stack needs to be considered when designing an IoT deployment.


title: Multi-dimensional scanning multi-beam array antenna fed by integrated waveguide Butler matrix

abstract: A one-port two-dimensional (2-D) radiating antenna element is proposed and then used to form a substrate integrated waveguide (SIW) multi-beam antenna array. Leaky- wave antenna (LWA) consists of periodic arrangement of slot pairs designed on the top ground plane of the waveguide. Two-port LWA has frequency dependent radiation characteristics from −24° to +15° including the broadside direction. The output port of the LWA is loaded by antipodal linearly tapered slot antenna (ALTSA) to radiate all the remaining amount of power in the orthogonal direction. 4×4 Butler matrix based beam forming network (BFN) is proposed for the phase scanning of a 1×4 one-port LWA array and forms four independent simultaneous beams in the second dimension. The antenna and BFN are fabricated by using the SIW technology to implement highly efficient, compact, multi-dimensional scanning multibeam antenna array.


title: Real-time simulation and hardware-in-the-loop tests of a battery system

abstract: In this paper, a real time model of the microgrid with an energy storage system has been implemented in RT-Lab. Coordinated control of the battery storage system with a PV system is simulated. To be more realistic, hardware-in-the-loop (HIL) testbed has been implemented with a real battery cell (3.2-V, 40-Ahr) connected to the real-time simulation model of the microgrid. A programmable current source (Magna) and programmable load (Bk 8500) have been used to create charging and discharging paths between the energy storage unit and the RT-LAB.


title: Non-Parametric Transformation Networks

abstract: ConvNets have been very effective in many applications where it is required to learn invariances to within-class nuisance transformations. However, through their architecture, ConvNets only enforce invariance to translation. In this paper, we introduce a new class of convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn general invariances and symmetries directly from data. NPTNs are a direct and natural generalization of ConvNets and can be optimized directly using gradient descent. They make no assumption regarding structure of the invariances present in the data and in that aspect are very flexible and powerful. We also model ConvNets and NPTNs under a unified framework called Transformation Networks which establishes the natural connection between the two. We demonstrate the efficacy of NPTNs on natural data such as MNIST and CIFAR 10 where it outperforms ConvNet baselines with the same number of parameters. We show it is effective in learning invariances unknown apriori directly from data from scratch. Finally, we apply NPTNs to Capsule Networks and show that they enable them to perform even better.


Indexing Based on Scale Invariant Interest Points


title: Using sets of feature vectors for similarity search on voxelized CAD objects

abstract: In modern application domains such as multimedia, molecular biology and medical imaging, similarity search in database systems is becoming an increasingly important task. Especially for CAD applications, suitable similarity models can help to reduce the cost of developing and producing new parts by maximizing the reuse of existing parts. Most of the existing similarity models are based on feature vectors. In this paper, we shortly review three models which pursue this paradigm. Based on the most promising of these three models, we explain how sets of feature vectors can be used for more effective and still efficient similarity search. We first introduce an intuitive distance measure on sets of feature vectors together with an algorithm for its efficient computation. Furthermore, we present a method for accelerating the processing of similarity queries on vector set data. The experimental evaluation is based on two real world test data sets and points out that our new similarity approach yields more meaningful results in comparatively short time.


Factors Affecting Computer Crime Protection Behavior


title: Road scene analysis for determination of road traffic density

abstract: Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.


title: The impact of environmental temperature on the diagnosis of gestational diabetes mellitus

abstract: OBJECTIVE ::: To investigate a probable impact of seasons on the diagnosis of GDM, as well as the specific effect of the environmental temperature on the diagnosis of this clinical entity. ::: ::: ::: PATIENTS AND METHODS ::: Two observational studies, one retrospective and one prospective, were conducted in a referral center. Study A included retrospectively 7618 pregnant women who underwent a 3-h 100 g OGTT during the 3rd trimester of gestation. Study B prospectively included 768 pregnant women tested in the 3rd trimester of gestation with a 75 g OGTT. Temperature was recorded every day at 09:00 h. ::: ::: ::: RESULTS ::: Retrospective Study A: GDM prevalence differed significantly by season: winter = 28.1%, summer = 39.2%, spring = 32.4% and autumn = 32.4% (P < 0.0001). The odds ratio for being diagnosed with GDM was much higher during summer 1.65 (95% CI: 1.43-1.90), with spring and autumn following with 1.23 (95% CI: 1.08-1.39) compared to winter. Glucose levels during OGTT were measured: significantly increased blood glucose values were observed at 60, 120 and 180 min in summer, which remained significant after adjustment for age, gestational age, BMI, weight gain during pregnancy and blood pressure. Prospective Study B: At temperatures above 25°C, the average glucose 60-min and 120-min levels were increased. The relative risk for abnormal glucose values at 60 min, when the environmental temperature increased over 25°C, was 2.2 (1.5-3.3). ::: ::: ::: CONCLUSIONS ::: GDM prevalence in Greece presents seasonal variation, with higher risk during summer due to post glucose load level variations. These variations could be attributed to differences in environmental temperature.


title: Formal verification of ethical choices in autonomous systems

abstract: Abstract Autonomous systems such as unmanned vehicles are beginning to operate within society. All participants in society are required to follow specific regulations and laws. An autonomous system cannot be an exception. Inevitably an autonomous system will find itself in a situation in which it needs to not only choose to obey a rule or not, but also make a complex ethical decision. However, there exists no obvious way to implement the human understanding of ethical behaviour in computers. Even if we enable autonomous systems to distinguish between more and less ethical alternatives, how can we be sure that they would choose right? We consider autonomous systems with a hybrid architecture in which the highest level of reasoning is executed by a rational (BDI) agent. For such a system, formal verification has been used successfully to prove that specific rules of behaviour are observed when making decisions. We propose a theoretical framework for ethical plan selection that can be formally verified. We implement a rational agent that incorporates a given ethical policy in its plan selection and show that we can formally verify that the agent chooses to execute, to the best of its beliefs, the most ethical available plan.


title: Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network

abstract: Detection of nuclei is an important step in phenotypic profiling of 1) histology sections imaged in bright field; and 2) colony formation of the 3-D cell culture models that are imaged using confocal microscopy. It is shown that feature-based representation of the original image improves color decomposition (CD) and subsequent nuclear detection using convolutional neural networks independent of the imaging modality. The feature-based representation utilizes the Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Moreover, in the case of samples imaged in bright field, the LoG response also provides the necessary initial statistics for CD using nonnegative matrix factorization. Several permutations of input data representations and network architectures are evaluated to show that by coupling improved CD and the LoG response of this representation, detection of nuclei is advanced. In particular, the frequencies of detection of nuclei with the vesicular or necrotic phenotypes, or poor staining, are improved. The overall system has been evaluated against manually annotated images, and the F-scores for alternative representations and architectures are reported.


title: Patent Foramen Ovale Closure or Anticoagulation vs. Antiplatelets after Stroke

abstract: BACKGROUND ::: Trials of patent foramen ovale (PFO) closure to prevent recurrent stroke have been inconclusive. We investigated whether patients with cryptogenic stroke and echocardiographic features representing risk of stroke would benefit from PFO closure or anticoagulation, as compared with antiplatelet therapy. ::: ::: ::: METHODS ::: In a multicenter, randomized, open-label trial, we assigned, in a 1:1:1 ratio, patients 16 to 60 years of age who had had a recent stroke attributed to PFO, with an associated atrial septal aneurysm or large interatrial shunt, to transcatheter PFO closure plus long-term antiplatelet therapy (PFO closure group), antiplatelet therapy alone (antiplatelet-only group), or oral anticoagulation (anticoagulation group) (randomization group 1). Patients with contraindications to anticoagulants or to PFO closure were randomly assigned to the alternative noncontraindicated treatment or to antiplatelet therapy (randomization groups 2 and 3). The primary outcome was occurrence of stroke. The comparison of PFO closure plus antiplatelet therapy with antiplatelet therapy alone was performed with combined data from randomization groups 1 and 2, and the comparison of oral anticoagulation with antiplatelet therapy alone was performed with combined data from randomization groups 1 and 3. ::: ::: ::: RESULTS ::: A total of 663 patients underwent randomization and were followed for a mean (±SD) of 5.3±2.0 years. In the analysis of randomization groups 1 and 2, no stroke occurred among the 238 patients in the PFO closure group, whereas stroke occurred in 14 of the 235 patients in the antiplatelet-only group (hazard ratio, 0.03; 95% confidence interval, 0 to 0.26; P<0.001). Procedural complications from PFO closure occurred in 14 patients (5.9%). The rate of atrial fibrillation was higher in the PFO closure group than in the antiplatelet-only group (4.6% vs. 0.9%, P=0.02). The number of serious adverse events did not differ significantly between the treatment groups (P=0.56). In the analysis of randomization groups 1 and 3, stroke occurred in 3 of 187 patients assigned to oral anticoagulants and in 7 of 174 patients assigned to antiplatelet therapy alone. ::: ::: ::: CONCLUSIONS ::: Among patients who had had a recent cryptogenic stroke attributed to PFO with an associated atrial septal aneurysm or large interatrial shunt, the rate of stroke recurrence was lower among those assigned to PFO closure combined with antiplatelet therapy than among those assigned to antiplatelet therapy alone. PFO closure was associated with an increased risk of atrial fibrillation. (Funded by the French Ministry of Health; CLOSE ClinicalTrials.gov number, NCT00562289 .).


title: Factors Affecting Customer Satisfaction of Mobile Services in Yemen

abstract: The aim of this study is to find out the customer's satisfaction with Yemen i Mobile service providers. Th is study examined the relationship between perceived quality, perceived value, customer expectation, and corporate image with customer satisfaction. The result of this study is based on data gathered online fro m 118 academic staff in public universit ies in Yemen. The study found that the relationship between perceived value, perceived quality and corporate image have a significant positive influence on customer satisfaction, whereas customer expectation has positive but without statistical significance.


title: Rat bone marrow stromal cells-seeded porous gelatin/tricalcium phosphate/oligomeric proanthocyanidins composite scaffold for bone repair.

abstract: Repair of bone defects remains a major challenge in orthopaedic surgery. Bone tissue engineering is an attractive approach for treating bone loss in various shapes and amounts. The aim of this study was to prepare and evaluate the feasibility of a porous scaffold, which was composed of oligomeric proanthocyanidin crosslinked gelatin mixed with β-tricalcium phosphate (GTP) and was seeded with bone marrow stromal cells (BMSCs) as a bone substitute. GTP scaffolds were made porous using a salt-leaching method. The physicochemical properties of the scaffold were evaluated to determine the optimal salt:composite weight ratio. The results indicated that the GTP scaffold had a favourable macroporous structure and higher porosity when the salt:composite weight ratio was 4:1. Cytotoxic tests demonstrated that extracts from the GTP scaffolds promoted the proliferation of BMSCs. Rat BMSCs were seeded on a GTP scaffold and cultured in a spinner flask. After 2 weeks of culture, scanning electron microscopy observation showed that the cells adhered well to the surfaces of the pores in the scaffold. Moreover, this study explored the biological response of rat calvarial bone to the scaffold to evaluate its potential in bone tissue engineering. Bone defects were filled with BMSC-seeded GTP scaffold and acellular GTP scaffold. After 8 weeks, the scaffold induced new bone formation at a bone defect, as was confirmed by X-ray microradiography and histology. The BMSC-seeded scaffold induced more new bone formation than did an acellular scaffold. These observations suggest that the BMSCs-seeded GTP scaffold can promote the regeneration of defective bone tissue.


title: GPU‐Based Spherical Light Field Rendering with Per‐Fragment Depth Correction

abstract: Image-based rendering techniques are a powerful alternative to traditional polygon-based computer graphics. This paper presents a novel light field rendering technique which performs per-pixel depth correction of rays for high-quality reconstruction. Our technique stores combined RGB and depth values in a parabolic 2D texture for every light field sample acquired at discrete positions on a uniform spherical setup. Image synthesis is implemented on the GPU as a fragment program which extracts the correct image information from adjacent cameras for each fragment by applying per-pixel depth correction of rays. We show that the presented image-based rendering technique provides a significant improvement compared to previous approaches. We explain two different rendering implementations which make use of a uniform parametrisation to minimise disparity problems and ensure full six degrees of freedom for virtual view synthesis. While one rendering algorithm implements an iterative refinement approach for rendering light fields with per pixel depth correction, the other approach employs a raycaster, which provides superior rendering quality at moderate frame rates.


title: Non–Adherence to Antiretroviral Treatment by People Living with HIV/AIDS in Black Communities in South Africa: Socio-Cultural Challenges

abstract: A disproportionately high number of poor black population living in both rural and urban areas in South Africa, experience barriers to accessing ARVs, despite the efforts made by the government. Various scholars cite that cultural beliefs, poverty, witchcraft, lack of family support, gender-based violence and substance abuse derail the progress of HIV treatment programmes. This paper, therefore discusses the challenges faced by PLWHA with the aim to provide an integrated approach to strengthen the support base required for adherence to ARV treatment. The findings show that due to discrimination PLWHA experience the side-effects, depression and other opportunistic infections such Kaposi’s sarcoma, even death, leaving children as orphans. DOI: 10.5901/mjss.2014.v5n14p450


title: On the electrical characteristics of complementary metamaterial resonators

abstract: In this letter, a method to obtain the electrical characteristics of complementary split ring resonators (CSRRs) coupled to planar transmission lines is presented. CSRRs have been recently proposed by some of the authors as new constitutive elements for the synthesis of metamaterials with negative effective permittivity, and they have been applied to the fabrication of metamaterial-based circuits in planar technology. The method provides the electrical characteristics of CSRRs (including the intrinsic resonant frequency and the unloaded Q-factor), as well as the coupling capacitance between line and CSRRs, and the parameters of the host line. Parameter extraction from the proposed method is applied to two different structures corresponding to the basic cells of left handed (LH) and negative permittivity lines. The method is of actual interest for the design of microwave circuits and metamaterials based on these complementary resonant particles


title: Today's DBMSs: how autonomic are they

abstract: The increasing complexity of database management systems (DBMSs) and the dearth of their experienced administrators make an urgent call for an autonomic DBMS that is capable of managing and maintaining itself. In this paper, we examine the characteristics that a DBMS should have in order to be considered autonomic and assess the position of today's commercial DBMSs such as DB2, SQL Server, and Oracle.


title: Cross-layer design: a survey and the road ahead

abstract: Of late, there has been an avalanche of cross-layer design proposals for wireless networks. A number of researchers have looked at specific aspects of network performance and, approaching cross-layer design via their interpretation of what it implies, have presented several cross-layer design proposals. These proposals involve different layers of the protocol stack, and address both cellular and ad hoc networks. There has also been work relating to the implementation of cross-layer interactions. It is high time that these various individual efforts be put into perspective and a more holistic view be taken. In this article, we take a step in that direction by presenting a survey of the literature in the area of cross-layer design, and by taking stock of the ongoing work. We suggest a definition for cross-layer design, discuss the basic types of cross-layer design with examples drawn from the literature, and categorize the initial proposals on how cross-layer interactions may be implemented. We then highlight some open challenges and new opportunities for cross-layer design. Designers presenting cross-layer design proposals can start addressing these as they move ahead.


title: Brain activation during vaginocervical self-stimulation and orgasm in women with complete spinal cord injury: fMRI evidence of mediation by the Vagus nerves

abstract: Women diagnosed with complete spinal cord injury (SCI) at T10 or above report vaginal-cervical perceptual awareness. To test whether the Vagus nerves, which bypass the spinal cord, provide the afferent pathway for this response, we hypothesized that the Nucleus Tractus Solitarii (NTS) region of the medulla oblongata, to which the Vagus nerves project, is activated by vaginal-cervical self-stimulation (CSS) in such women, as visualized by functional magnetic resonance imaging (fMRI). Regional blood oxygen level-dependent (BOLD) signal intensity was imaged during CSS and other motor and sensory procedures, using statistical parametric mapping (SPM) analysis with head motion artifact correction. Physiatric examination and MRI established the location and extent of spinal cord injury. In order to demarcate the NTS, a gustatory stimulus and hand movement were used to activate the superior region of the NTS and the Nucleus Cuneatus adjacent to the inferior region of the NTS, respectively. Each of four women with interruption, or "complete" injury, of the spinal cord (ASIA criteria), and one woman with significant, but "incomplete" SCI, all at or above T10, showed activation of the inferior region of the NTS during CSS. Each woman showed analgesia, measured at the fingers, during CSS, confirming previous findings. Three women experienced orgasm during the CSS. The brain regions that showed activation during the orgasms included hypothalamic paraventricular nucleus, medial amygdala, anterior cingulate, frontal, parietal, and insular cortices, and cerebellum. We conclude that the Vagus nerves provide a spinal cord-bypass pathway for vaginal-cervical sensibility in women with complete spinal cord injury above the level of entry into spinal cord of the known genitospinal nerves.


title: How Does Foreign Direct Investment Affect Economic Growth

abstract: We test the effect of foreign direct investment (FDI) on economic growth in a cross-country regression framework, utilizing data on FDI flows from industrial countries to 69 developing countries over the last two decades. Our results suggest that FDI is an important vehicle for the transfer of technology, contributing relatively more to growth than domestic investment. However, the higher productivity of FDI holds only when the host country has a minimum threshold stock of human capital. In addition, FDI has the effect of increasing total investment in the economy more than one for one, which suggests the predominance of complementarity effects with domestic firms.


title: Academic Search in Response to Major Scientific Events

abstract: In this paper, we look at the search behavior of users of an academic search engine and in particular, their query patterns following the occurrence of major scientific events. We select Nobel Prize announcements as major scientific events and observe how academic searchers behave in response to their occurrences. We have found unique trends for the academic searchers, which are different from users of a web search engine. Moreover, academic searchers have similar query trends even for different topics, showing a commonality in their query behavior. The insights gained in this paper highlight the unique behavior of academic searchers and the differences from users of general web search engines. The findings may have implications for our understanding of the search behavior of academic searchers as well as for search engine design, for instance, concerning approaches to address information needs around major scientific events.


title: Tensor products of matrix algebras over the Grassmann algebra

abstract: Abstract We develop elementary tools for studying P. I. equivalences between algebras which are tensor products of matrix subalgebras. We then deduce Kemer's results about the P. I. equivalence of tensor products of matrix subalgebras over the Grassmann algebra.


title: Effect of antenatal expression of breast milk at term to improve lactational performance: a prospective study

abstract: Objectives: To study the effect of antenatal expression of breast milk at term in improving lactational performance as compared to conventional method of initiation of breast feeding. Methods: A prospective study was carried out in 100 booked cases at term. Prior examination was done to exclude any inverted or cracked nipples and appropriate treatment instituted. Daily expression of the breast discharge once at least (any time of the day) preferably during bathing time after 37 weeks of pregnancy was introduced in randomly selected 50 pregnant women (study group). The remaining 50 women formed the control group. Results: The study group did not find it difficult to initiate breast feeding after vaginal or cesarean delivery and sufficient milk started flowing within half an hour of initiation of breast feeding in 48 (96%) women. In the control group, it happened in 36 (72%) women only. Statistical analysis using the Fisher exact test showed significant difference in the results of both the groups. No increase in any delivery complication was found associated with the study group. There was one partial breast feeding failure in the control group but none in the study group. Conclusion: Daily antenatal breast milk expression after 37 completed weeks of pregnancy significantly reduced the time for establishing full breast feeding and reduced breast feeding failures.


title: National digital forensics framework for Bangladesh

abstract: At recent time, cybercrime has increased significantly in the world and also in Bangladesh due to consequence of massive digitalization efforts in every sector. These types of offense require systematic probing in order to be revealed properly at the court of law. This research work proposed a comprehensive digital forensics framework to facilitate forensically ready common standard digital forensics to the investigators. The framework recommended proactive and reactive forensics to enable live and post forensics issues. The purpose of the proposed framework is to allow systematic postmortem of digital forensics. Moreover, it allows further investigations suggested during examination or analysis phase. An expert system is introduced into the framework to improve efficiency through inference engine. This assistive system included digital forensics case database as a history keeper to store all documentations and results of the accomplished forensics examination. The inference engine is expected to generate a priority list from the historical data by a proposed algorithm. In addition, the expert system is expected to guide examiners during forensics. Furthermore, dissemination of forensics outcomes anticipated automatically to the authorized investigators by this system. However, the proposed framework is based on theoretical approach which is expected to be evaluated and implemented using assistive expert system in the future.


title: An eye tracking study of how pictures influence online reading

abstract: We present an eye tracking study to measure if and how including pictures - relevant or irrelevant to the text - affects online reading. In a between-subjects design, 82 subjects read a story on a computer screen. The text was accompanied by either: (a) pictures related to the text, (b) pictures unrelated to the text (advertisements), or (c) no pictures. Reading statistics such as reading speed and regressions were computed, as well as measures of picture gazes. When pictures related to the text were replaced with advertisements, we observed a number of significant differences, including speed, regressions, and re-reading.


title: Managing and Mining Sensor Data

abstract: Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and have even been integrated into day-to-day devices of use, such as mobile phones.This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data hascreated tremendous opportunities in integrating social aspects of sensor data collection into the mining process. Managing and Mining Sensor Data is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful.


title: Cluster validation techniques for genome expression data

abstract: Several clustering algorithms have been suggested to analyse genome expression data, but fewer solutions have been implemented to guide the design of clustering-based experiments and assess the quality of their outcomes. A cluster validity framework provides insights into the problem of predicting the correct the number of clusters. This paper presents several validation techniques for gene expression data analysis. Normalisation and validity aggregation strategies are proposed to improve the prediction about the number of relevant clusters. The results obtained indicate that this systematic evaluation approach may significantly support genome expression analyses for knowledge discovery applications.


title: Interpersonal influences on adolescent materialism: A new look at the role of parents and peers

abstract: Abstract What causes adolescents to be materialistic? Prior research shows parents and peers are an important influence. Researchers have viewed parents and peers as socialization agents that transmit consumption attitudes, goals, and motives to adolescents. We take a different approach, viewing parents and peers as important sources of emotional support and psychological well-being, which increase self-esteem in adolescents. Supportive parents and peers boost adolescents' self-esteem, which decreases their need to turn to material goods to develop positive self-perceptions. In a study with 12–18 year-olds, we find support for our view that self-esteem mediates the relationship between parent/peer influence and adolescent materialism.


title: Radar high resolution range & micro-Doppler analysis of human motions

abstract: In radar imaging it is well known that relative motion or deformation of parts of illuminated objects induce additional features in the Doppler frequency spectra. These features are called micro-Doppler effect and appear as sidebands around the central Doppler frequency. They can provide valuable information about the structure of the moving parts and may be used for identification purposes [1].