title: Normal / abnormal heart sound recordings classification using convolutional neural network

abstract: As part of the PhysioNet / Computing in Cardiology Challenge 2016, this work focuses on automatic classification of normal / abnormal phonocardiogram (PCG) recording, with the aim of quickly identifying subjects that need further expert diagnosis. To improve the robustness of the classifiers by increasing the number of training samples, the recordings were windowed into 5 second segments and our classifiers were trained to classify these segments. Overall recording classification was then generated using a voting scheme from classification results of its segments. Our features include spectrograms and Mel-frequency cepstrum coefficients. Our best submission result during the official phase (evaluated on a random 20% of the hidden test set) has a score of 0.813, with 0.735 sensitivity and 0.892 specificity. Two more submissions are still being evaluated.


title: $A^{4}NT$: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation

abstract: Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author. Such methods can compromise the privacy of an anonymous author even when the author tries to remove privacy sensitive content. In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ($A^4NT$), to combat such text-based adversaries. We combine sequence-to-sequence language models used in machine translation and generative adversarial networks to obfuscate author attributes. Unlike machine translation techniques which need paired data, our method can be trained on unpaired corpora of text containing different authors. Importantly, we propose and evaluate techniques to impose constraints on our $A^4NT$ to preserve the semantics of the input text. $A^4NT$ learns to make minimal changes to the input text to successfully fool author attribute classifiers, while aiming to maintain the meaning of the input. We show through experiments on two different datasets and three settings that our proposed method is effective in fooling the author attribute classifiers and thereby improving the anonymity of authors.


title: Bulletproofs: Short Proofs for Confidential Transactions and More

abstract: We propose Bulletproofs, a new non-interactive zero-knowledge proof protocol with very short proofs and without a trusted setup; the proof size is only logarithmic in the witness size. Bulletproofs are especially well suited for efficient range proofs on committed values: they enable proving that a committed value is in a range using only 2 log_2(n)+9 group and field elements, where n is the bit length of the range. Proof generation and verification times are linear in n. Bulletproofs greatly improve on the linear (in n) sized range proofs in existing proposals for confidential transactions in Bitcoin and other cryptocurrencies. Moreover, Bulletproofs supports aggregation of range proofs, so that a party can prove that m commitments lie in a given range by providing only an additive O(log(m)) group elements over the length of a single proof. To aggregate proofs from multiple parties, we enable the parties to generate a single proof without revealing their inputs to each other via a simple multi-party computation (MPC) protocol for constructing Bulletproofs. This MPC protocol uses either a constant number of rounds and linear communication, or a logarithmic number of rounds and logarithmic communication. We show that verification time, while asymptotically linear, is very efficient in practice. The marginal cost of batch verifying 32 aggregated range proofs is less than the cost of verifying 32 ECDSA signatures. Bulletproofs build on the techniques of Bootle et al. (EUROCRYPT 2016). Beyond range proofs, Bulletproofs provide short zero-knowledge proofs for general arithmetic circuits while only relying on the discrete logarithm assumption and without requiring a trusted setup. We discuss many applications that would benefit from Bulletproofs, primarily in the area of cryptocurrencies. The efficiency of Bulletproofs is particularly well suited for the distributed and trustless nature of blockchains. The full version of this article is available on ePrint.


title: A Review on Data Envelopment Analysis (DEA)

abstract: This paper is a review paper focusing on the methodological development of Data Envelopment Analysis (DEA), a multi-factor performance measurement and improvement tool. Since its introduction in 1978, vast studies have been done on DEA, causing significant growth in its methodology and applications in the real world. The purpose of this paper is to provide a general introduction to DEA. The basic DEA models and some important methodological extensions of DEA, such as multilevel DEA models, stochastic DEA models, and fuzzy DEA models, are discussed in the paper. In addition, some current and future research trends are highlighted.


title: Composite retrieval of heterogeneous web search

abstract: Traditional search systems generally present a ranked list of documents as answers to user queries. In aggregated search systems, results from different and increasingly diverse verticals (image, video, news, etc.) are returned to users. For instance, many such search engines return to users both images and web documents as answers to the query "flower". Aggregated search has become a very popular paradigm. In this paper, we go one step further and study a different search paradigm: composite retrieval. Rather than returning and merging results from different verticals, as is the case with aggregated search, we propose to return to users a set of "bundles", where a bundle is composed of "cohesive" results from several verticals. For example, for the query "London Olympic", one bundle per sport could be returned, each containing results extracted from news, videos, images, or Wikipedia. Composite retrieval can promote exploratory search in a way that helps users understand the diversity of results available for a specific query and decide what to explore in more detail. In this paper, we propose and evaluate a variety of approaches to construct bundles that are relevant, cohesive and diverse. Compared with three baselines (traditional "general web only" ranking, federated search ranking and aggregated search), our evaluation results demonstrate significant performance improvement for a highly heterogeneous web collection.


title: WNGrad: Learn the Learning Rate in Gradient Descent

abstract: Adjusting the learning rate schedule in stochastic gradient methods is an important unresolved problem which requires tuning in practice. If certain parameters of the loss function such as smoothness or strong convexity constants are known, theoretical learning rate schedules can be applied. However, in practice, such parameters are not known, and the loss function of interest is not convex in any case. The recently proposed batch normalization reparametrization is widely adopted in most neural network architectures today because, among other advantages, it is robust to the choice of Lipschitz constant of the gradient in loss function, allowing one to set a large learning rate without worry. Inspired by batch normalization, we propose a general nonlinear update rule for the learning rate in batch and stochastic gradient descent so that the learning rate can be initialized at a high value, and is subsequently decreased according to gradient observations along the way. The proposed method is shown to achieve robustness to the relationship between the learning rate and the Lipschitz constant, and near-optimal convergence rates in both the batch and stochastic settings ($O(1/T)$ for smooth loss in the batch setting, and $O(1/\sqrt{T})$ for convex loss in the stochastic setting). We also show through numerical evidence that such robustness of the proposed method extends to highly nonconvex and possibly non-smooth loss function in deep learning problems.Our analysis establishes some first theoretical understanding into the observed robustness for batch normalization and weight normalization.


title: Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees

abstract: Interactive Tree Of Life (http://itol.embl.de) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. It is freely available and open to everyone. The current version was completely redesigned and rewritten, utilizing current web technologies for speedy and streamlined processing. Numerous new features were introduced and several new data types are now supported. Trees with up to 100,000 leaves can now be efficiently displayed. Full interactive control over precise positioning of various annotation features and an unlimited number of datasets allow the easy creation of complex tree visualizations. iTOL 3 is the first tool which supports direct visualization of the recently proposed phylogenetic placements format. Finally, iTOL's account system has been redesigned to simplify the management of trees in user-defined workspaces and projects, as it is heavily used and currently handles already more than 500,000 trees from more than 10,000 individual users.


title: An incremental constraint-based framework for task and motion planning

abstract: We present a new constraint-based framework for task and motion planning TMP. Our approach is extensible, probabilistically complete, and offers improved performance and generality compared with a similar, state-of-the-art planner. The key idea is to leverage incremental constraint solving to efficiently incorporate geometric information at the task level. Using motion feasibility information to guide task planning improves scalability of the overall planner. Our key abstractions address the requirements of manipulation and object rearrangement. We validate our approach on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared with the benchmark planner and improved scalability from additional geometric guidance. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.


title: Social Engineering Attack Strategies and Defence Approaches

abstract: This paper examines the role and value of information security awareness efforts in defending against social engineering attacks. It categories the different social engineering threats and tactics used in targeting employees and the approaches to defend against such attacks. While we review these techniques, we attempt to develop a thorough understanding of human security threats, with a suitable balance between structured improvements to defend human weaknesses, and efficiently focused security training and awareness building. Finally, the paper shows that a multi-layered shield can mitigate various security risks and minimize the damage to systems and data.


The use of aquatic insects and benthic macroinvertebrate communities to assess water quality upstream and downstream of the Village of Stamford wastewater treatment facility


Popularity-driven Caching Strategy for Dynamic Adaptive Streaming over Information-Centric Networks


title: Dose-related neurocognitive effects of marijuana use.

abstract: BACKGROUND ::: Although about 7 million people in the US population use marijuana at least weekly, there is a paucity of scientific data on persistent neurocognitive effects of marijuana use. ::: ::: ::: OBJECTIVE ::: To determine if neurocognitive deficits persist in 28-day abstinent heavy marijuana users and if these deficits are dose-related to the number of marijuana joints smoked per week. ::: ::: ::: METHODS ::: A battery of neurocognitive tests was given to 28-day abstinent heavy marijuana abusers. ::: ::: ::: RESULTS ::: As joints smoked per week increased, performance decreased on tests measuring memory, executive functioning, psychomotor speed, and manual dexterity. When dividing the group into light, middle, and heavy user groups, the heavy group performed significantly below the light group on 5 of 35 measures and the size of the effect ranged from 3.00 to 4.20 SD units. Duration of use had little effect on neurocognitive performance. ::: ::: ::: CONCLUSIONS ::: Very heavy use of marijuana is associated with persistent decrements in neurocognitive performance even after 28 days of abstinence. It is unclear if these decrements will resolve with continued abstinence or become progressively worse with continued heavy marijuana use.


title: Using genetic algorithms to create solutions for conflict resolution

abstract: The process of devising solutions for conflict resolution generally configures a challenging task. There exist different approaches to address the problem, namely the use of case-based models or even relying on the parties themselves to perform the task. From a computational point of view, these problems generally represent a NP-complete problem. In order to surpass this shortcoming, in this paper it is presented a biologically inspired method to deal with the problem in which genetic algorithms are used to create possible solutions for a given dispute. The approach presented is able to generate a broad number of diverse solutions that cover virtually the whole search space for a given problem. This approach provides better results than a case-based approach since: (1) it is independent of the legal domain and (2) it does not depend on the number and quality of cases present in a database. The results of this work are being applied in a negotiation tool that is part of the UMCourt conflict resolution platform.


title: A Scienceographic Comparison of Physics Papers from the arXiv and viXra Archives

abstract: arXiv is an e-print repository of papers in physics, computer science, and biology, amongst others. viXra is a newer repository of e-prints on similar topics. Scienceography is the study of the writing of science. In this work we perform a scienceographic comparison of a selection of papers from the physics section of each archive. We provide the first study of the viXra archive and describe key differences on how science is written by these communities.


title: Factors Affecting Consumer Adoption Decisions and Intents in Mobile Commerce: Empirical Insights

abstract: Mobile commerce (m-commerce) is marking the start of new era of innovation in business. M-commerce will continue to extend the way organizations conduct business – and change the relationships between companies, customers, suppliers and partners. Mobility means freedom – say Keen and Macintosh – and freedom creates choice and value, something much more than convenience as it may revolutionize the way companies work, buy, sell and collaborate. Although the TAM is widely accepted as a pertinent model – on a general level to explain the acceptance of information technology and information systems, it is questionable whether the model is applicable to consumers’ choice of commercial channels, which makes its use questionable to explain the adoption of mobile and ecommerce. This proposal was tested with the material derived from a 1000 consumer survey in Finland and we found other drivers/inhibitors than perceived usefulness, perceived ease of use and perceived enjoyment.


title: AprilCal: Assisted and repeatable camera calibration

abstract: Reliable and accurate camera calibration usually requires an expert intuition to reliably constrain all of the parameters in the camera model. Existing toolboxes ask users to capture images of a calibration target in positions of their choosing, after which the maximum-likelihood calibration is computed using all images in a batch optimization. We introduce a new interactive methodology that uses the current calibration state to suggest the position of the target in the next image and to verify that the final model parameters meet the accuracy requirements specified by the user. Suggesting target positions relies on the ability to score candidate suggestions and their effect on the calibration. We describe two methods for scoring target positions: one that computes the stability of the focal length estimates for initializing the calibration, and another that subsequently quantifies the model uncertainty in pixel space. We demonstrate that our resulting system, AprilCal, consistently yields more accurate camera calibrations than standard tools using results from a set of human trials. We also demonstrate that our approach is applicable for a variety of lenses.


title: The diverse CB1 and CB2 receptor pharmacology of three plant cannabinoids: delta9-tetrahydrocannabinol, cannabidiol and delta9-tetrahydrocannabivarin.

abstract: Cannabis sativa is the source of a unique set of compounds known collectively as plant cannabinoids or phytocannabinoids. This review focuses on the manner with which three of these compounds, (-)-trans-delta9-tetrahydrocannabinol (delta9-THC), (-)-cannabidiol (CBD) and (-)-trans-delta9-tetrahydrocannabivarin (delta9-THCV), interact with cannabinoid CB1 and CB2 receptors. Delta9-THC, the main psychotropic constituent of cannabis, is a CB1 and CB2 receptor partial agonist and in line with classical pharmacology, the responses it elicits appear to be strongly influenced both by the expression level and signalling efficiency of cannabinoid receptors and by ongoing endogenous cannabinoid release. CBD displays unexpectedly high potency as an antagonist of CB1/CB2 receptor agonists in CB1- and CB2-expressing cells or tissues, the manner with which it interacts with CB2 receptors providing a possible explanation for its ability to inhibit evoked immune cell migration. Delta9-THCV behaves as a potent CB2 receptor partial agonist in vitro. In contrast, it antagonizes cannabinoid receptor agonists in CB1-expressing tissues. This it does with relatively high potency and in a manner that is both tissue and ligand dependent. Delta9-THCV also interacts with CB1 receptors when administered in vivo, behaving either as a CB1 antagonist or, at higher doses, as a CB1 receptor agonist. Brief mention is also made in this review, first of the production by delta9-THC of pharmacodynamic tolerance, second of current knowledge about the extent to which delta9-THC, CBD and delta9-THCV interact with pharmacological targets other than CB1 or CB2 receptors, and third of actual and potential therapeutic applications for each of these cannabinoids.


title: Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization

abstract: In recent years, a number of artificial intelligent services have been developed such as defect detection system or diagnosis system for customer services. Unfortunately, the core in these services is a black-box in which human cannot understand the underlying decision making logic, even though the inspection of the logic is crucial before launching a commercial service. Our goal in this paper is to propose an analytic method of a model explanation that is applicable to general classification models. To this end, we introduce the concept of a contribution matrix and an explanation embedding in a constraint space by using a matrix factorization. We extract a rule-like model explanation from the contribution matrix with the help of the nonnegative matrix factorization. To validate our method, the experiment results provide with open datasets as well as an industry dataset of a LTE network diagnosis and the results show our method extracts reasonable explanations.


Automatic License Plate Detection Using Deep Learning Techniques


title: Climate Change, Responsibility, and Justice

abstract: In this paper I make the following claims. In order to see anthropogenic climate change as clearly involving moral wrongs and global injustices, we will have to revise some central concepts in these domains. Moreover, climate change threatens another value (“respect for nature”) that cannot easily be taken up by concerns of global justice or moral responsibility.


title: Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms

abstract: Abstract Over the last decade, we have observed a revolution in brain structural and functional Connectomics. On one hand, we have an ever-more detailed characterization of the brain's white matter structural connectome. On the other, we have a repertoire of consistent functional networks that form and dissipate over time during rest. Despite the evident spatial similarities between structural and functional connectivity, understanding how different time-evolving functional networks spontaneously emerge from a single structural network requires analyzing the problem from the perspective of complex network dynamics and dynamical system's theory. In that direction, bottom-up computational models are useful tools to test theoretical scenarios and depict the mechanisms at the genesis of resting-state activity. Here, we provide an overview of the different mechanistic scenarios proposed over the last decade via computational models. Importantly, we highlight the need of incorporating additional model constraints considering the properties observed at finer temporal scales with MEG and the dynamical properties of FC in order to refresh the list of candidate scenarios.


AUTOMATIC LANE DETECTION IN IMAGE SEQUENCES FOR VISION-BASED NAVIGATION PURPOSES


title: UPPER BOUND ON SNR GAIN IN WAVELET TRANSFORM PREDICTIVE-ENTROPY IMAGE CODING

abstract: Using the rate-distortion theory approach and assuming the Laplacian probability density function of the quantizer input signal, we propose a function which gives the signal-to-quantizing-noise ratio (SNR) gain in discrete wavelet transform predictive-entropy coding over fullband predictive image coding. The upper bound on SNR gain is determined as a function of the subband number only. The practical SNR performances of realizable wavelet still image coders are compared with their theoretical bounds. The computer simulation results of wavelet based predictive coded test images show that the SNR gain grows faster with the subband number increment than its theoretical upper bounds do.


title: Design of upper limb by adhesion of muscles and bones — Detail human mimetic musculoskeletal humanoid kenshiro

abstract: This paper presents a design methodology for humanoid upper limb based on human anatomy. Kenshiro is a full body tendon driven humanoid robot and is designed from the data of average 14 year old Japanese boy. The design of his upper limb is realizing detail features of muscles, bones and the adhesive relation of the two. Human mimetic design is realized by focusing on the fact that joints are being stabled by muscles winding around the bones, and by accurately mimicking the bone shape this was enabled. In this paper we also introduce details of mechanical specifications of the upper limb. By having muscles, bones, and joint structures based on human anatomy, Kenshiro can move flexibly. The use as human body simulator can be expected by measuring sensor data which can correspond to biological data.


The application of multi-agent technology in multi-motor control system


title: Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

abstract: In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.


title: CORECLUSTER: a degeneracy based graph clustering framework

abstract: Graph clustering or community detection constitutes an important task for investigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such as spectral methods, typically suffer from high time and space complexity. In this article, we present CORECLUSTER, an efficient graph clustering framework based on the concept of graph degeneracy, that can be used along with any known graph clustering algorithm. Our approach capitalizes on processing the graph in a hierarchical manner provided by its core expansion sequence, an ordered partition of the graph into different levels according to the k-core decomposition. Such a partition provides a way to process the graph in an incremental manner that preserves its clustering structure, while making the execution of the chosen clustering algorithm much faster due to the smaller size of the graph's partitions onto which the algorithm operates.


title: Competence similarities between native and near-native speakers: An investigation of the Preterite/Imperfect contrast in Spanish. Studies in Second Language Acquisition 25

abstract: It has been suggested that tense and aspect distributions are very difficult to learn in a second language (L2), they are prone to fossilize universally, and their interpretive properties are subject to a critical period (Coppieters, 1987). This study focuses on the acquisition of the semantic implications of the preterite-imperfect contrast in Spanish by English-speaking individuals of very advanced proficiency in Spanish who were not living in a Spanish-speaking country. By assuming that aspect is encoded in a functional category where the features [+/-perfective] are checked, depending on the language (Giorgi & Pianesi, 1997), this study asks whether ultimate attainment in the aspectual domain is possible and whether features of functional categories not selected in early childhood are subject to a critical period, as Hawkins and Chan's (1997) Failed Formal Features Hypothesis states. Experimental evidence from two tasks probing the interpretations of perfective and imperfective aspectual forms in Spanish suggests that many learners (almost 30%) in our total subject pool (including advanced to near-native speakers) and 70% of the near-native group performed like native speakers on all sentence types in all tasks. Although aspect is certainly a difficult area to master, particularly because the meanings of the imperfect are acquired quite late, L2 learners are clearly able to overcome the parametric options of their native language. At least for this domain, it is suggested that access to Universal Grammar does not necessarily decay with age in L2 acquisition.


title: On the design of individual and group recommender systems for tourism

abstract: Research highlights? Recommender system able to provide recommendations for groups of tourists. ? Group members must be equally satisfied with the recommendation. ? Two techniques are explored: intersection and aggregation. ? Intersection obtains better results, it brings together the preferences of all group members. This paper presents a recommender system for tourism based on the tastes of the users, their demographic classification and the places they have visited in former trips. The system is able to offer recommendations for a single user or a group of users. The group recommendation is elicited out of the individual personal recommendations through the application of mechanisms such as aggregation and intersection. The elicitation mechanism is implemented as an extension of e-Tourism, a user-adapted tourism and leisure application whose main component is the Generalist Recommender System Kernel (GRSK), a domain-independent taxonomy-driven recommender system.


title: Temperature Dependence of the Raman Spectra of Graphene and Graphene Multilayers

abstract: We investigated the temperature dependence of the frequency of G peak in the Raman spectra of graphene on Si/SiO2 substrates. The micro-Raman spectroscopy was carried out under the 488 nm laser excitation over the temperature range from −190 to +100 °C. The extracted value of the temperature coefficient of G mode of graphene is χ = −0.016 cm-1/°C for the single layer and χ = −0.015 cm-1/°C for the bilayer. The obtained results shed light on the anharmonic properties of graphene.


title: Speckle processing method for synthetic-aperture-radar phase correction

abstract: Uncompensated phase errors present in synthetic-aperture-radar data can have a disastrous effect on reconstructed image quality. We present a new iterative algorithm that holds promise of being a robust estimator and corrector for arbitrary phase errors. Our algorithm is similar in many respects to speckle processing methods currently used in optical astronomy. We demonstrate its ability to focus scenes containing large amounts of phase error regardless of the phase-error structure or its source. The algorithm works extremely well in both high and low signal-to-clutter conditions without human intervention.


Chapter 8 Beauty , Body Image , and the Media


"All I know about politics is what I read in Twitter": Weakly Supervised Models for Extracting Politicians' Stances From Twitter


title: Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation

abstract: The red edge position (REP) in the vegetation spectral reflectance is a surrogate measure of vegetation chlorophyll content, and hence can be used to monitor the health and function of vegetation. The Multi-Spectral Instrument (MSI) aboard the future ESA Sentinel-2 (S-2) satellite will provide the opportunity for estimation of the REP at much higher spatial resolution (20 m) than has been previously possible with spaceborne sensors such as Medium Resolution Imaging Spectrometer (MERIS) aboard ENVISAT. This study aims to evaluate the potential of S-2 MSI sensor for estimation of canopy chlorophyll content, leaf area index (LAI) and leaf chlorophyll concentration (LCC) using data from multiple field campaigns. Included in the assessed field campaigns are results from SEN3Exp in Barrax, Spain composed of 35 elementary sampling units (ESUs) of LCC and LAI which have been assessed for correlation with simulated MSI data using a CASI airborne imaging spectrometer. Analysis also presents results from SicilyS2EVAL, a campaign consisting of 25 ESUs in Sicily, Italy supported by a simultaneous Specim Aisa-Eagle data acquisition. In addition, these results were compared to outputs from the PROSAIL model for similar values of biophysical variables in the ESUs. The paper in turn assessed the scope of S-2 for retrieval of biophysical variables using these combined datasets through investigating the performance of the relevant Vegetation Indices (VIs) as well as presenting the novel Inverted Red-Edge Chlorophyll Index (IRECI) and Sentinel-2 Red-Edge Position (S2REP). Results indicated significant relationships between both canopy chlorophyll content and LAI for simulated MSI data using IRECI or the Normalised Difference Vegetation Index (NDVI) while S2REP and the MERIS Terrestrial Chlorophyll Index (MTCI) were found to have the strongest correlation for retrieval of LCC.


title: Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription

abstract: We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of polyphonic transcription.


title: Effective Quadtree Plus Binary Tree Block Partition Decision for Future Video Coding

abstract: Block partition structure has been recognized as a crucial module in video coding scheme. Recently, a quadtree plus binary tree (QTBT) block partition structure has been proposed in the Joint Video Exploration Team (JVET) development. Compared to the quadtree structure in HEVC, QTBT can achieve better coding performance with hugely increased encoding complexity. Here, we propose an effective QTBT partition decision algorithm to achieve a good trade-off between computational complexity and coding performance. In particular, at the Coding Tree Unit level, the partition parameters of QTBT are dynamically derived to adapt to the local characteristics without transmitting any overhead. Subsequently, at the Coding Unit level, a joint-classifier decision tree structure is designed to eliminate unnecessary iterations and meanwhile control the risk of false prediction. Experimental results show that the proposed algorithm can achieve 64% encoding time reduction on average with only 1.26% increase in terms of bit rate. This greatly benefits the practical implementations of QTBT in real application scenarios.


title: Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features

abstract: Recurrent neural network (RNN) and long short-term memory (LSTM) have achieved great success in processing sequential multimedia data and yielded the state-of-the-art results in speech recognition, digital signal processing, video processing, and text data analysis. In this paper, we propose a novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network. First, deep features are extracted from every sixth frame of the videos, which helps reduce the redundancy and complexity. Next, the sequential information among frame features is learnt using DB-LSTM network, where multiple layers are stacked together in both forward pass and backward pass of DB-LSTM to increase its depth. The proposed method is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval. Experimental results show significant improvements in action recognition using the proposed method on three benchmark data sets including UCF-101, YouTube 11 Actions, and HMDB51 compared with the state-of-the-art action recognition methods.


title: Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

abstract: PurposeWe propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI).MethodsThe method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour.ResultsThe proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively.ConclusionsThis provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.


title: Learning to Search Better Than Your Teacher

abstract: Methods for learning to search for structured prediction typically imitate a reference policy, with existing theoretical guarantees demonstrating low regret compared to that reference. This is unsatisfactory in many applications where the reference policy is suboptimal and the goal of learning is to improve upon it. Can learning to search work even when the reference is poor? ::: ::: We provide a new learning to search algorithm, LOLS, which does well relative to the reference policy, but additionally guarantees low regret compared to deviations from the learned policy: a local-optimality guarantee. Consequently, LOLS can improve upon the reference policy, unlike previous algorithms. This enables us to develop structured contextual bandits, a partial information structured prediction setting with many potential applications.


title: Mathematical and Computational Modeling in Complex Biological Systems

abstract: The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.


title: Erect Penile Dimensions in a Cohort of 778 Middle Eastern Men: Establishment of a Nomogram

abstract: Abstract Introduction Accurate data regarding the size of the erect penis are of great importance to several disciplines working with male patients, but little high-quality research exists on the subject, particularly in different ethnic groups and for erect penis size. Aim The aim of this study was to create a nomogram of erect penile dimensions in a large sample of Middle Eastern men. Methods A retrospective cohort study of 778 men (mean age 43.7; range 20–82) attending urological outpatient clinics in Saudi Arabia was conducted. Exclusion criteria were age under 18 years, a presenting complaint of small or short penis, Peyronie's disease or complaint of congenital curvature, clinical hypogonadism, and previous penile surgery or trauma. Main Outcome Measures Three erect penile dimensions following induction of erection using intracavernosal injection of Quadrimix. Results Mean patient body mass index (BMI) was 29.09 (standard deviation [SD] 5.76). The mean suprapubic skin-to-penile tip erect length was 12.53 cm (SD 1.93); the mean erect length from the symphysis pubis to the penile tip was 14.34 cm (SD 1.86); and the mean erect shaft circumference was 11.50 cm (SD 1.74). A nomogram was constructed and statistical analysis performed, demonstrating a weak negative correlation between BMI and erect penile length measured from the suprapubic skin (r = −0.283, P P P = 0.006). Conclusion A nomogram for Middle Eastern men can be used as a standard when advising men with small penis anxiety. The importance of measuring erect size and allowing for infra-pubic fat interference in measurement is emphasized. We envisage that this tool can be used to educate and reassure concerned men about the size of their penises. Habous M, Tealab A, Williamson B, Binsaleh S, El Dawy S, Mahmoud S, Abdelwahab O, Nassar M, Mulhall JP, Veale D, and Muir G. Erect penile dimensions in a cohort of 778 Middle Eastern men: Establishment of a nomogram. J Sex Med 2015;12:1402–1406.


title: E: Cannabis and cannabis extracts: greater than the sum of their parts

abstract: SUMMARY. A central tenet underlying the use of botanical remedies is that herbs contain many active ingredients. Primary active ingredients may be enhanced by secondary compounds, which act in beneficial synergy. Other herbal constituents may mitigate the side effects of dominant active ingredients. We reviewed the literature concerning medical cannabis and its primary active ingredient, ∆ 9 -tetrahydrocannabinol (THC). Good evidence shows that secondary compounds in cannabis may enhance the beneficial effects of THC. Other cannabinoid and non-cannabinoid compounds in herbal cannabis or its extracts may reduce THC-induced anxiety, cholinergic deficits, and immunosuppression. Cannabis terpenoids and flavonoids may also increase cerebral blood flow, enhance cortical activity, kill respiratory pathogens, and provide anti-inflammatory activity. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: Website: 2001 by The Haworth Press, Inc. All rights reserved.]


title: Smooth Rotation Enhanced As-Rigid-As-Possible Mesh Animation

abstract: In recent years, the As-Rigid-As-Possible (ARAP) shape deformation and shape interpolation techniques gained popularity, and the ARAP energy was successfully used in other applications as well. We improve the ARAP animation technique in two aspects. First, we introduce a new ARAP-type energy, named SR-ARAP, which has a consistent discretization for surfaces (triangle meshes). The quality of our new surface deformation scheme competes with the quality of the volumetric ARAP deformation (for tetrahedral meshes). Second, we propose a new ARAP shape interpolation method that is superior to prior art also based on the ARAP energy. This method is compatible with our new SR-ARAP energy, as well as with the ARAP volume energy.


title: A study of the wide-band L-probe fed planar patch antenna mounted on a cylindrical or conical surface

abstract: The characteristics of an L-probe fed planar patch antenna mounted on a cylindrical or conical surface are investigated in this paper. It is found that the wide-band characteristic of the L-probe patch antenna can be maintained with this structure and further enhanced by using a curved ground. An antenna mounted on a cylinder is built and measured to confirm the simulated results. A 10 dB impedance bandwidth of 45.2%, which is about 50% larger than that of the patch mounted on a flat ground plane, and a 3 dB beamwidth of 89/spl deg/ are achieved. This antenna, with a peak gain of 8.6 dBi, provides stable radiation patterns across the entire passband. Then an antenna with the same structure on a metallic conical ground is also evaluated. It is found that the bandwidth can be further improved when the antenna is mounted on a conical structure. The result is useful for the design of wide-band cylindrical antenna arrays with down tilted patterns.


title: Overcoming the memory wall in packet processing: hammers or ladders?

abstract: Overhead of memory accesses limits the performance of packet processing applications. To overcome this bottleneck, today's network processors can utilize a wide-range of mechanisms-such as multi-level memory hierarchy, wide-word accesses, special-purpose result-caches, asynchronous memory, and hardware multi-threading. However, supporting all of these mechanisms complicates programmability and hardware design, and wastes systemresources. In this paper, we address the following fundamental question: what minimal set of hardware mechanisms must a network processor support to achieve the twin goals of simplified programmability and high packet throughput? We show that no single mechanism sufficies; the minimal set must include data-caches and multi-threading. Data-caches and multi-threading are complementary; whereas data-caches exploit locality to reduce the number of context-switches and the off-chip memory bandwidth requirement, multi-threading exploits parallelism to hide long cache-miss latencies.


title: Efficient Preparation of Liposomes Encapsulating Food Materials Using Lecithins by a Mechanochemical Method

abstract: In order to evaluate to the feasibility of using lecithins for nanocapsules including functional food materials, liposomes were prepared from different commercially available lecithins (SLP-WHITE, SLP-PC70 and PL30S) by the Bangham method, and their physicochemical properties were examined by using a confocal laser scanning microscopy (CLSM) and the measurements of trapping efficiency. There was little difference in the trapping efficiency among the three types of liposomes. In all cases, the trapping efficiency clearly increased with an increase of the lecithin concentration up to 10 wt % , and the maximum efficiency reached at approximately 15%. CLSM observation showed the particle size of liposomes prepared from SLP-WHITE is significantly smaller than that prepared from other lecithins. In addition, liposomal solution prepared from SLP-WHITE remained well dispersed for at least 30 days, while two other liposomal solutions showed a phase separation due to aggregation and/or fusion of liposomes. These results indicated that SLP-WHITE is the most appropriate for the preparation of stable liposomes with well dispersed among the lecithins tested. SLP-WHITE liposomes were then prepared by the mechanochemical method using a homogenizer and microfluidizer, aiming at improving the preparation efficiency and liposome stability. The particle size of the prepared SLP-WHITE liposomes decreased with increasing inlet pressure and the number of processed cycles, and reached between 73 and 123 nm based on the measurement using dynamic light scattering. Moreover, freeze-fracture transmission electron microscopy revealed that the prepared liposomes are small unilamellar vesicles (SUV) with a diameter of approximately 100 nm. The extract of Curcuma longa Linn. (Ukon), which contains curcumins as a functional food material, was then subjected to the mechanochemical method with SLP-WHITE to give liposomes including the functional materials. Interestingly, the trapping efficiency of the liposomes for curcumins was found to reach over 85%. From these results, the present mechanochemical method is very likely to allow us to efficiently prepare stable and functional liposomes from the low-cost lecithin. The method may thus have a potential for manufacturing practical nanocapsules, which serves as a novel carrier of functional food materials.


Geometric Adaptive Tracking Control of a Quadrotor UAV on SE ( 3 ) for Agile Maneuvers


title: Weakly Supervised Coupled Networks for Visual Sentiment Analysis

abstract: Automatic assessment of sentiment from visual content has gained considerable attention with the increasing tendency of expressing opinions on-line. In this paper, we solve the problem of visual sentiment analysis using the high-level abstraction in the recognition process. Existing methods based on convolutional neural networks learn sentiment representations from the holistic image appearance. However, different image regions can have a different influence on the intended expression. This paper presents a weakly supervised coupled convolutional network with two branches to leverage the localized information. The first branch detects a sentiment specific soft map by training a fully convolutional network with the cross spatial pooling strategy, which only requires image-level labels, thereby significantly reducing the annotation burden. The second branch utilizes both the holistic and localized information by coupling the sentiment map with deep features for robust classification. We integrate the sentiment detection and classification branches into a unified deep framework and optimize the network in an end-to-end manner. Extensive experiments on six benchmark datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods for visual sentiment analysis.


title: Multi-class semantic segmentation of faces

abstract: In this paper the problem of multi-class face segmentation is introduced. Differently from previous works which only consider few classes - typically skin and hair - the label set is extended here to six categories: skin, hair, eyes, nose, mouth and background. A dataset with 70 images taken from MIT-CBCL and FEI face databases is manually annotated and made publicly available1. Three kind of local features - accounting for color, shape and location - are extracted from uniformly sampled square patches. A discriminative model is built with random decision forests and used for classification. Many different combinations of features and parameters are explored to find the best possible model configuration. Our analysis shows that very good performance (~ 93% in accuracy) can be achieved with a fairly simple model.


title: Learning with augmented class by exploiting unlabeled data

abstract: In many real-world applications of learning, the environment is open and changes gradually, which requires the learning system to have the ability of detecting and adapting to the changes. Class-incremental learning (CIL) is an important and practical problem where data from unseen augmented classes are fed, but has not been studied well in the past. In C-IL, the system should beware of predicting instances from augmented classes as a seen class, and thus faces the challenge that no such instances were observed during training stage. In this paper, we tackle the challenge by using unlabeled data, which can be cheaply collected in many real-world applications. We propose the LACU framework as well as the LACU-SVM approach to learn the concept of seen classes while incorporating the structure presented in the unlabeled data, so that the misclassification risks among the seen classes as well as between the augmented and the seen classes are minimized simultaneously. Experiments on diverse datasets show the effectiveness of the proposed approach.


title: Pulse-Modulation Imaging—Review and Performance Analysis

abstract: In time-domain or pulse-modulation (PM) imaging, the incident light intensity is not encoded in amounts of charge, voltage, or current as it is in conventional image sensors. Instead, the image data are represented by the timing of pulses or pulse edges. This method of visual information encoding optimizes the phototransduction individually for each pixel by abstaining from imposing a fixed integration time for the entire array. Exceptionally high dynamic range (DR) and improved signal-to-noise ratio (SNR) are immediate benefits of this approach. In particular, DR is no longer limited by the power-supply rails as in conventional complementary metal-oxide semiconductor (CMOS) complementary metal-oxide semiconductor active pixel sensors, thus providing relative immunity to the supply-voltage scaling of modern CMOS technologies. In addition, PM imaging naturally supports pixel-parallel analog-to-digital conversion, thereby enabling high temporal resolution/frame rates or an asynchronous event-based array readout. The applications of PM imaging in emerging areas, such as sensor network, wireless endoscopy, retinal prosthesis, polarization imaging, and energy harvesting are surveyed to demonstrate the effectiveness of PM imaging in low-power, high-performance machine vision, and biomedical applications of the future. The evolving design innovations made in PM imaging, such as high-speed arbitration circuits and ultra-compact processing elements, are expected to have even wider impacts in disciplines beyond CMOS image sensors. This paper thoroughly reviews and classifies all common PM image sensor architectures. Analytical models and a universal figure of merit - image quality and dynamic range to energy complexity factor are proposed to quantitatively assess different PM imagers across the entire spectrum of PM architectures.


title: Efficient probabilistic Range-Only SLAM

abstract: This work addresses range-only SLAM (RO-SLAM) as the Bayesian inference problem of sequentially tracking a vehicle while estimating the location of a set of beacons without any prior information. The only assumptions are the availability of odometry and a range sensor able of identifying the different beacons. We propose exploiting the conditional independence between the position distributions of each beacon within a Rao-Blackwellized Particle Filter (RBPF) for maintaining independent Sum of Gaussians (SOGs) for each beacon. Unlike other approaches, it is shown then that a proper probabilistic observation model can be derived for online operation with no need for delayed initializations. We provide a rigorous statistical comparison of this proposal with previous work of the authors where a Monte-Carlo approximation was employed instead for the conditional densities. As verified experimentally, this new proposal represents a significant improvement in accuracy, computation time, and robustness against outliers.


GuarDroid: A Trusted Path for Password Entry


title: FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos

abstract: We propose an end-to-end learning framework for segmenting generic objects in videos. Our method learns to combine appearance and motion information to produce pixel level segmentation masks for all prominent objects in videos. We formulate this task as a structured prediction problem and design a two-stream fully convolutional neural network which fuses together motion and appearance in a unified framework. Since large-scale video datasets with pixel level segmentations are problematic, we show how to bootstrap weakly annotated videos together with existing image recognition datasets for training. Through experiments on three challenging video segmentation benchmarks, our method substantially improves the state-of-the-art for segmenting generic (unseen) objects. Code and pre-trained models are available on the project website.


title: From linear story generation to branching story graphs

abstract: Narrative is an important part of the way we interact with and make sense of the world. Interactive narrative systems tell stories in a virtual world in which the user is an interactive participant. Since the behaviors the user performs in the virtual world can affect the way in which a storyline unfolds, interactive narrative systems often use a branching story structure where non-interactive story presentations are interleaved with user decision points. An alternative approach – narrative mediation – represents story as a linear progression of events with anticipated user actions and system-controlled agent actions together in a partially-ordered plan. For every possible way the user can violate the story plan, an alternative story plan is generated. If narrative mediation is powerful enough to express the same interactive stories as systems that use branching story structures, then linear narrative generation techniques can be applied to interactive narrative generation with the use of narrative mediation. This paper sketches out a proof that narrative mediation is at least as powerful as acyclic branching story structures.


title: Log-aesthetic space curve segments

abstract: For designing aesthetic surfaces, such as the car bodies, it is very important to use aesthetic curves as characteristic lines. In such curves, the curvature should be monotonically varying, since it dominates the distortion of reflected images on curved surfaces. In this paper, we present an interactive control method of log-aesthetic space curves. We define log-aesthetic space curves to be curves whose logarithmic curvature and torsion graphs are both linear. The linearity of these graphs constrains that the curvature and torsion are monotonically varying. We clarify the characteristics of log-aesthetic space curves and identify their family. Moreover, we present a novel method for drawing a log-aesthetic space curve segment by specifying two endpoints, their tangents, the slopes, α and β, of straight lines of the logarithmic curvature and torsion graphs, and the torsion parameter Ω. Our implementation shows that log-aesthetic curve segments can be controlled fully interactively.


title: Cotton Pests and Diseases Detection Based on Image Processing

abstract: Extract the damaged image form the cotton image in order to measure the damage ratio of the cotton leaf which caused by the diseases or pests. Several algorithms like image enhancement, image filtering which suit for cotton leaf processing were explored in this paper. Three different color models for extracting the damaged image from cotton leaf images were implemented, namely RGB color model, HSI color model, and YCbCr color model. The ratio of damage (γ) was chosen as feature to measure the degree of damage which caused by diseases or pests. This paper also shows the comparison of the results obtained by the implementing in different color models, the comparison of results shows good accuracy in both color models and YCbCr color space is considered as the best color model for extracting the damaged image. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2721 Full Text: PDF


title: Visualizing and Exploring Dynamic High-Dimensional Datasets with LION-tSNE

abstract: T-distributed stochastic neighbor embedding (tSNE) is a popular and prize-winning approach for dimensionality reduction and visualizing high-dimensional data. However, tSNE is non-parametric: once visualization is built, tSNE is not designed to incorporate additional data into existing representation. It highly limits the applicability of tSNE to the scenarios where data are added or updated over time (like dashboards or series of data snapshots). ::: In this paper we propose, analyze and evaluate LION-tSNE (Local Interpolation with Outlier coNtrol) - a novel approach for incorporating new data into tSNE representation. LION-tSNE is based on local interpolation in the vicinity of training data, outlier detection and a special outlier mapping algorithm. We show that LION-tSNE method is robust both to outliers and to new samples from existing clusters. We also discuss multiple possible improvements for special cases. ::: We compare LION-tSNE to a comprehensive list of possible benchmark approaches that include multiple interpolation techniques, gradient descent for new data, and neural network approximation.


title: Nasal bone length: prenasal thickness ratio: a strong 2D ultrasound marker for Down syndrome

abstract: Objectives To evaluate the feasibility of incorporating two-dimensional ultrasound measurements of nasal bone length (NBL) and prenasal thickness (PT) into the second-trimester anomaly scan and to determine whether the NBL: PT ratio could help in differentiating euploid and Down syndrome fetuses. Method Two-dimensional measurements of NBL and PT were obtained from the midsagittal plane of the fetal head at 14–28weeks of gestation in a Caucasian population at risk for aneuploidy. The screening performances of NBL, PT, and the ratios NBL:PT and PT:NBL were analyzed in euploid (n=1330) and Down syndrome (n=33) fetuses. Results Nasal bone length and PT alone showed strong correlations with Down syndrome (sensitivity: 76% at 1.88% and 2.35% false positive rate, respectively). However, the NBL:PT ratio showed an even stronger correlation with Down syndrome (false positive rate: 0.9%, sensitivity: 97%). The mean NBL:PT ratio showed a gradual increase from 1.48 to 1.79 (a 21.2% increase) between 14 and 28weeks of gestation. Conclusion Two-dimensional ultrasound measurements of NBL and PT, particularly the NBL:PT ratio, are highly sensitive markers for Down syndrome fetuses. © 2014 John Wiley & Sons, Ltd. Funding sources: None Conflicts of interest: None declared


title: Neural Networks Approach to the Random Walk Dilemma of Financial Time Series

abstract: Predictions of financial time series often show a characteristic one step shift relative to the original data as in a random walk. This has been the cause for opposing views whether such time series do contain information that can be extracted for predictions, or are simply random walks. In this case study, we show that NNs that are capable of extracting weak low frequency periodic signals buried in a strong high frequency signal, consistently predict the next value in the series to be the current value, as in a random walk, when used for one-step-ahead predictions of the detrended S&P 500 time series. In particular for the Time Delay Feed Forward Networks and Elman Networks of various configurations, our study supports the view of the detrended S&P 500 being a random walk series. This is consistent with the long standing hypothesis that some financial time series are random walk series.


abstract: This paper presents the design and fabrication of Sierpinski gasket fractal antenna with defected ground structure (DGS) with center frequency at 5.8 GHz. A slot was used as a DGS. The antenna was designed and simulated using Computer Simulation Technology (CST) software and fabricated on FR-4 board with a substrate thickness of 1.6 mm and dielectric constant, e r of 5.0 and dielectric loss tangent 0.025. Measurement of the parameters of the antenna was carried by using a Vector Network Analyzer. The result shows a good agreement between simulation and measurement and a compact size antenna was realized.


title: Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System

abstract: systems or recommendation systems are a subset of information filtering system that used to anticipate the 'evaluation' or 'preference' that user would feed to an item. In recent years E-commerce applications are widely using Recommender system. Generally the most popular E- commerce sites are probably music, news, books, research articles, and products. Recommender systems are also available for business experts, jokes, restaurants, financial services, life insurance and twitter followers. Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. Currently, these systems are incorporating social information for enhancing a quality of recommendation process. For betterment of recommendation process in the future, Recommender systems will use personal, implicit and local information from the Internet. This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.


title: A Novel Tunable Multi-Frequency Hybrid Vibration Energy Harvester Using Piezoelectric and Electromagnetic Conversion Mechanisms

abstract: This paper presents a novel tunable multi-frequency hybrid energy harvester (HEH). It consists of a piezoelectric energy harvester (PEH) and an electromagnetic energy harvester (EMEH), which are coupled with magnetic interaction. An electromechanical coupling model was developed and numerically simulated. The effects of magnetic force, mass ratio, stiffness ratio, and mechanical damping ratios on the output power were investigated. A prototype was fabricated and characterized by experiments. The measured first peak power increases by 16.7% and 833.3% compared with that of the multi-frequency EMEH and the multi-frequency PEH, respectively. It is 2.36 times more than the combined output power of the linear PEH and linear EMEH at 22.6 Hz. The half-power bandwidth for the first peak power is also broadened. Numerical results agree well with the experimental data. It is indicated that magnetic interaction can tune the resonant frequencies. Both magnetic coupling configuration and hybrid conversion mechanism contribute to enhancing the output power and widening the operation bandwidth. The magnitude and direction of magnetic force have significant effects on the performance of the HEH. This proposed HEH is an effective approach to improve the generating performance of the micro-scale energy harvesting devices in low-frequency range.


title: Disjoint out-of-order execution processor

abstract: High-performance superscalar architectures used to exploit instruction level parallelism in single-thread applications have become too complex and power hungry for the multicore processors era. We propose a new architecture that uses multiple small latency-tolerant out-of-order cores to improve single-thread performance. Improving single-thread performance with multiple small out-of-order cores allows designers to place more of these cores on the same die. Consequently, emerging highly parallel applications can take full advantage of the multicore parallel hardware without sacrificing performance of inherently serial and hard to parallelize applications. Our architecture combines speculative multithreading (SpMT) with checkpoint recovery and continual flow pipeline architectures. It splits single-thread program execution into disjoint control and data threads that execute concurrently on multiple cooperating small and latency-tolerant out-of-order cores. Hence we call this style of execution Disjoint Out-of-Order Execution (DOE). DOE uses latency tolerance to overcome performance issues of SpMT caused by interthread data dependences. To evaluate this architecture, we have developed a microarchitecture performance model of DOE based on PTLSim, a simulation infrastructure of the x86 instruction set architecture. We evaluate the potential performance of DOE processor architecture using a simple heuristic to fork control independent threads in hardware at the target addresses of future procedure return instructions. Using applications from SpecInt 2000, we study DOE under ideal as well as realistic architectural constraints. We discuss the performance impact of key DOE architecture and application variables such as number of cores, interthread data dependences, intercore data communication delay, buffers capacity, and branch mispredictions. Without any DOE specific compiler optimizations, our results show that DOE outperforms conventional SpMT architectures by 15p, on average. We also show that DOE with four small cores can perform on average equally well to a large superscalar core, consuming about the same power. Most importantly, DOE improves throughput performance by a significant amount over a large superscalar core, up to 2.5 times, when running multitasking applications.


title: Variant step size RRT: An efficient path planner for UAV in complex environments

abstract: Rapidly Exploring Random Tree (RRT) is a popular way for motion planning especially in high-dimensional environments. Efficient path planner is prerequisite for high-speed Unmanned Aerial Vehicles (UAV). In this paper, we proposed a path planning algorithm for UAV in complex environments based on RRT. Our contributions are mainly on three aspects. Firstly we proposed a novel RRT algorithm which can explore the complex environment more rapidly. It is achieved by adaptively changes the step size of the tree according to the location of obstacles. The proposed method also takes random points failed to pass the collision check process as indicators to speed up exploring. We proposed an optimizaion algorithm which can get the optimal path without time-consuming sampling. The simulation result demonstrates that our proposed can work efficiently.


title: Hieroglyph: Locally-Sufficient Graph Processing via Compute-Sync-Merge

abstract: Despite their widespread adoption, large-scale graph processing systems do not fully decouple computation and communication, often yielding suboptimal performance. Locally-sufficient computation-computation that relies only on the graph state local to a computing host-can mitigate the effects of this coupling. In this paper, we present Compute-Sync-Merge (CSM), a new programming abstraction that achieves efficient locally-sufficient computation. CSM enforces local sufficiency at the programming abstraction level and enables the activation of vertex-centric computation on all vertex replicas, thus supporting vertex-cut partitioning. We demonstrate the simplicity of expressing several fundamental graph algorithms in CSM. Hieroglyph-our implementation of a graph processing system with CSM support-outperforms state of the art by up to 53x, with a median speedup of 3.5x and an average speedup of 6x across a wide range of datasets.


title: An attention model based on spatial transformers for scene recognition

abstract: Scene recognition is an important and challenging task in computer vision. We propose an end-to-end pipeline by combing convolutional neural networks (CNNs) with explicit attention model to determine several meaningful regions of original images for scene recognition. In the proposed pipeline, the spatial transformer network is leveraged as the attention module, which can automatically learn the scales and movements of centers of attention windows. As for feature extraction, the basic CNN architecture is utilized. Furthermore, the stronger descriptors of scenes are constructed by feature fusion. The highlight of our proposed network is that it is capable to localize discriminative regions from an image in a data-driven manner without any additional supervision. We conduct experiments on a subset of the Places205 database to evaluate the performance of the proposed basic network and the involved parameters. Our model achieves state-of-the-art top-1 accuracy 82.10% on the evaluation dataset comparing with fine-tuned PlacesCNN (80.98%). We find that our model is able to learn informative attention regions for discriminating scene categories.


title: Analysis and Design of Multiphase Receiver With Reduction of Output Fluctuation for EV Dynamic Wireless Charging System

abstract: The multiphase receiver for more steady output is proposed in this paper. The conventional power supply rail with multipole such as I-type, S-type, and n-type for dynamic wireless charging (DWC) system has a main drawback: the induced voltage of the double-D coil of receiver has a sinusoidal fluctuation along the driving direction and the fluctuation factor is 1.0. Therefore, the multiphase receivers including two-phase, three-phase, and four-phase receiver are proposed to solve the problem. The fluctuation factor, effective value (rms) of induced voltage and mutual inductance, cost, and loss of different numbers of phase in different connection modes are analyzed and compared. The applicable conditions of different types are given. The structure size of single-phase coil is optimized to achieve larger coupling coefficient. The influence of distance between phases on fluctuation factor and induced voltage (rms) are calculated and the design principle of the distance between phases is determined. Based on above research, design guideline for the multiphase receiver is proposed including the constraints of installation environment and the distribution of different phases. The structure size of single-phase coil and the distance between phases are optimized to improve the coupling coefficient and reduce cost, loss, and voltage level of resonator. Considering the practical application environment, a four-phase receiver of 10-kW DWC system is designed. A prototype system is also established and the analysis and simulation are verified. In the dynamic 10-kW experiment, the fluctuation factor is reduced to 0.146 from 0.3 of two-phase receiver and 1.0 of single-phase receiver.


title: Pydoop: a Python MapReduce and HDFS API for Hadoop

abstract: MapReduce has become increasingly popular as a simple and efficient paradigm for large-scale data processing. One of the main reasons for its popularity is the availability of a production-level open source implementation, Hadoop, written in Java. There is considerable interest, however, in tools that enable Python programmers to access the framework, due to the language's high popularity. Here we present a Python package that provides an API for both the MapReduce and the distributed file system sections of Hadoop, and show its advantages with respect to the other available solutions for Hadoop Python programming, Jython and Hadoop Streaming.


title: Precise indoor localization using smart phones

abstract: We present an indoor localization application leveraging the sensing capabilities of current state of the art smart phones. To the best of our knowledge, our application is the first one to be implemented in smart phones and integrating both offline and online phases of fingerprinting, delivering an accuracy of up to 1.5 meters. In particular, we have studied the possibilities offered by WiFi radio, cellular communications radio, accelerometer and magnetometer, already embedded in smart phones, with the intention to build a multimodal solution for localization. We have also implemented a new approach for the statistical processing of radio signal strengths, showing that it can outperform existing deterministic techniques.


title: A word at a time: Computing word relatedness using temporal semantic analysis

abstract: Computing the degree of semantic relatedness of words is a key functionality of many language applications such as search, clustering, and disambiguation. Previous approaches to computing semantic relatedness mostly used static language resources, while essentially ignoring their temporal aspects. We believe that a considerable amount of relatedness information can also be found in studying patterns of word usage over time. Consider, for instance, a newspaper archive spanning many years. Two words such as "war" and "peace" might rarely co-occur in the same articles, yet their patterns of use over time might be similar. In this paper, we propose a new semantic relatedness model, Temporal Semantic Analysis (TSA), which captures this temporal information. The previous state of the art method, Explicit Semantic Analysis (ESA), represented word semantics as a vector of concepts. TSA uses a more refined representation, where each concept is no longer scalar, but is instead represented as time series over a corpus of temporally-ordered documents. To the best of our knowledge, this is the first attempt to incorporate temporal evidence into models of semantic relatedness. Empirical evaluation shows that TSA provides consistent improvements over the state of the art ESA results on multiple benchmarks.


title: Terahertz Imaging Systems With Aperture Synthesis Techniques

abstract: This paper presents the research and development of two terahertz imaging systems based on photonic and electronic principles, respectively. As part of this study, a survey of ongoing research in the field of terahertz imaging is provided focusing on security applications. Existing terahertz imaging systems are reviewed in terms of the employed architecture and data processing strategies. Active multichannel measurement method is found to be promising for real-time applications among the various terahertz imaging techniques and is chosen as a basis for the imaging instruments presented in this paper. An active system operation allows for a wide dynamic range, which is important for image quality. The described instruments employ a multichannel high-sensitivity heterodyne architecture and aperture filling techniques, with close to real-time image acquisition time. In the case of the photonic imaging system, mechanical scanning is completely obsolete. We show 2-D images of simulated 3-D image data for both systems. The reconstruction algorithms are suitable for 3-D real-time operation, only limited by mechanical scanning.


title: See what you want to see: visual user-driven approach for hybrid recommendation

abstract: Research in recommender systems has traditionally focused on improving the predictive accuracy of recommendations by developing new algorithms or by incorporating new sources of data. However, several studies have shown that accuracy does not always correlate with a better user experience, leading to recent research that puts emphasis on Human-Computer Interaction in order to investigate aspects of the interface and user characteristics that influence the user experience on recommender systems. Following this new research this paper presents SetFusion, a visual user-controllable interface for hybrid recommender system. Our approach enables users to manually fuse and control the importance of recommender strategies and to inspect the fusion results using an interactive Venn diagram visualization. We analyze the results of two field studies in the context of a conference talk recommendation system, performed to investigate the effect of user controllability in a hybrid recommender. Behavioral analysis and subjective evaluation indicate that the proposed controllable interface had a positive effect on the user experience.


title: SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture

abstract: Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%.


title: Achieving higher magnification in context

abstract: The difficulty of accessing information details while preserving context has generated many different focus-in-context techniques. A common limitation of focus-in-context techniques is their ability to work well at high magnification. We present a set of improvements that will make high magnification in context more feasible. We demonstrate new distortion functions that effectively integrate high magnification within its context. Finally, we show how lenses can be used on top of other lenses, effectively multiplying their magnification power in the same manner that a magnifying glass applied on top of another causes multiplicative magnification. The combined effect is to change feasible detail-in-context magnification factors from less than 8 to more than 40.


title: Simple vs. compound mark hierarchical marking menus

abstract: We present a variant of hierarchical marking menus where items are selected using a series of inflection-free simple marks, rather than the single "zig-zag" compound mark used in the traditional design. Theoretical analysis indicates that this simple mark approach has the potential to significantly increase the number of items in a marking menu that can be selected efficiently and accurately. A user experiment is presented that compares the simple and compound mark techniques. Results show that the simple mark technique allows for significantly more accurate and faster menu selections overall, but most importantly also in menus with a large number of items where performance of the compound mark technique is particularly poor. The simple mark technique also requires significantly less physical input space to perform the selections, making it particularly suitable for small footprint pen-based input devices. Visual design alternatives are also discussed.


title: Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

abstract: Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other. In this paper, we propose a new occlusion-aware R-CNN (OR-CNN) to improve the detection accuracy in the crowd. Specifically, we design a new aggregation loss to enforce proposals to be close and locate compactly to the corresponding objects. Meanwhile, we use a new part occlusion-aware region of interest (PORoI) pooling unit to replace the RoI pooling layer in order to integrate the prior structure information of human body with visibility prediction into the network to handle occlusion. Our detector is trained in an end-to-end fashion, which achieves state-of-the-art results on three pedestrian detection datasets, i.e., CityPersons, ETH, and INRIA, and performs on-pair with the state-of-the-arts on Caltech.


title: Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks

abstract: We propose an off-line approach to explicitly encode temporal patterns spatially as different types of images, namely, Gramian Angular Fields and Markov Transition Fields. This enables the use of techniques from computer vision for feature learning and classification. We used Tiled Convolutional Neural Networks to learn high-level features from individual GAF, MTF, and GAF-MTF images on 12 benchmark time series datasets and two real spatial-temporal trajectory datasets. The classification results of our approach are competitive with state-of-the-art approaches on both types of data. An analysis of the features and weights learned by the CNNs explains why the approach works.


title: Matching dependence-related queries in the system dependence graph

abstract: In software maintenance and evolution, it is common that developers want to apply a change to a number of similar places. Due to the size and complexity of the code base, it is challenging for developers to locate all the places that need the change. A main challenge in locating the places that need the change is that, these places share certain common dependence conditions but existing code searching techniques can hardly handle dependence relations satisfactorily. In this paper, we propose a technique that enables developers to make queries involving dependence conditions and textual conditions on the system dependence graph of the program. We carried out an empirical evaluation on four searching tasks taken from the development history of two real-world projects. The results of our evaluation indicate that, compared with code-clone detection, our technique is able to locate many required code elements that code-clone detection cannot locate, and compared with text search, our technique is able to effectively reduce false positives without losing any required code elements.


title: The main external alternative NAD(P)H dehydrogenase of Neurospora crassa mitochondria.

abstract: A DNA sequence homologous to non-proton-pumping NADH dehydrogenase genes was found in the genome of Neurospora crassa encoding a polypeptide of 577 amino acid residues, molecular mass of 64,656 Da, with a putative transmembrane domain. Analysis of fungal mitochondria fractionated with digitonin indicates that the protein is located at the outer face of the inner membrane of the organelle (external enzyme). The corresponding gene was inactivated by the generation of repeat-induced point mutations. Mitochondria from the resulting null-mutant nde2 are highly deficient in the oxidation of cytosolic NADH and NADPH. A triple mutant nde1/nde2/ndi1, lacking mitochondrial alternative NAD(P)H dehydrogenases, was obtained, indicating that these proteins are not essential in N. crassa. However, crosses between the nde2 mutant strain and complex I-deficient mutants yielded no viable double mutants. Transcription of the nde-2 gene, as well as of ndi-1 (internal enzyme), is repressed in the late exponential phase of fungal growth.


25 Upper-Limb Robotic Rehabilitation Exoskeleton : Tremor Suppression


title: ICABiDAS: Intuition Centred Architecture for Big Data Analysis and Synthesis

abstract: Abstract Humans are expert in the amount of sensory data they deal with each moment. Human brain not only "analyses" these data but also starts "synthesizing" new information from the existing data. The current age Big-data systems are needed not just to analyze data but also to come up new interpretation. We believe that the pivotal ability in human brain which enables us to do this is what is known as "intuition". Here, we present an intuition based architecture for big data analysis and synthesis.


title: Principles and Applications of Dual-Polarization Weather Radar. Part I: Description of the Polarimetric Radar Variables

abstract: The United States Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network has been upgraded to dual-polarization capabilities, providing operational and research meteorologists with a wealth of new information regarding the types and distributions of hydrometeors within precipitating storms, as well as a means for improved radar data quality. In addition to the conventional moments of reflectivity factor at horizontal polarization (ZH), Doppler velocity (Vr), and Doppler spectrum width (W), the new variables available from upgraded radars are the differential reflectivity (ZDR), differential propagation phase shift (ΦDP), specific differential phase (KDP), and the co-polar correlation coefficient (ρhv or CC). In the first part of this review series, a description of the polarimetric radar variables available from the newly polarimetric WSR-88D radars is provided. An emphasis is made on their physical meaning and interpretation in the context of operational meteorology.


title: Automatic Transcription of Melody, Bass Line, and Chords in Polyphonic Music

abstract: This article proposes a method for the automatic transcription of the melody, bass line, and chords in polyphonic pop music. The method uses a frame-wise pitch-salience estimator as a feature extraction front-end. For the melody and bass-line transcription, this is followed by acoustic modeling of note events and musicological modeling of note transitions. The acoustic models include a model for the target notes (i.e., melody or bass notes) and a background model. The musicological model involves key estimation and note bigrams that determine probabilities for transitions between target notes. A transcription of the melody or the bass line is obtained using Viterbi search via the target and the background note models. The performance of the melody and the bass-line transcription is evaluated using approximately 8.5 hours of realistic polyphonic music. The chord transcription maps the pitch salience estimates to a pitch-class representation and uses trained chord models and chord-transition probabilities to produce a transcription consisting of major and minor triads. For chords, the evaluation material consists of the first eight Beatles albums. The method is computationally efficient and allows causal implementation, so it can process streaming audio. Transcription of music refers to the analysis of an acoustic music signal for producing a parametric representation of the signal. The representation may be a music score with a meticulous arrangement for each instrument or an approximate description of melody and chords in the piece, for example. The latter type of transcription is commonly used in commercial songbooks of pop music and is usually sufficient for musicians or music hobbyists to play the piece. On the other hand, more detailed transcriptions are often employed in classical music to preserve the exact arrangement of the composer. � 2008 Massachusetts Institute of Technology. We propose a method for the automatic transcription of the melody, bass line, and chords in pop-music recordings. Conventionally, these tasks have been carried out by trained musicians who listen to a piece of music and write down notes or chords by hand, which is time-consuming and requires musical training. A machine transcriber enables several applications. First, it provides an easy way of obtaining a description of a music recording, allowing musicians to play it. Second, the produced transcriptions may be used in music analysis, music information retrieval (MIR) from large music databases, content-based audio processing, and interactive music systems, for example.


title: Cyber Physical Systems: Design Challenges

abstract: Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. The economic and societal potential of such systems is vastly greater than what has been realized, and major investments are being made worldwide to develop the technology. There are considerable challenges, particularly because the physical components of such systems introduce safety and reliability requirements qualitatively different from those in general- purpose computing. Moreover, physical components are qualitatively different from object-oriented software components. Standard abstractions based on method calls and threads do not work. This paper examines the challenges in designing such systems, and in particular raises the question of whether today's computing and networking technologies provide an adequate foundation for CPS. It concludes that it will not be sufficient to improve design processes, raise the level of abstraction, or verify (formally or otherwise) designs that are built on today's abstractions. To realize the full potential of CPS, we will have to rebuild computing and networking abstractions. These abstractions will have to embrace physical dynamics and computation in a unified way.


title: Liquid: A Scalable Deduplication File System for Virtual Machine Images

abstract: A virtual machine (VM) has been serving as a crucial component in cloud computing with its rich set of convenient features. The high overhead of a VM has been well addressed by hardware support such as Intel virtualization technology (VT), and by improvement in recent hypervisor implementation such as Xen, KVM, etc. However, the high demand on VM image storage remains a challenging problem. Existing systems have made efforts to reduce VM image storage consumption by means of deduplication within a storage area network (SAN) cluster. Nevertheless, an SAN cannot satisfy the increasing demand of large-scale VM hosting for cloud computing because of its cost limitation. In this paper, we propose Liquid, a scalable deduplication file system that has been particularly designed for large-scale VM deployment. Its design provides fast VM deployment with peer-to-peer (P2P) data transfer and low storage consumption by means of deduplication on VM images. It also provides a comprehensive set of storage features including instant cloning for VM images, on-demand fetching through a network, and caching with local disks by copy-on-read techniques. Experiments show that Liquid's features perform well and introduce minor performance overhead.


title: Integrated SDN/NFV orchestration for the dynamic deployment of mobile virtual backhaul networks over a multilayer (packet/optical) aggregation infrastructure

abstract: Future 5G networks will bring important challenges to network operators, such as the traffic load increase mainly due to the proliferation of mobile broadband communications. This will force mobile network operators (MNOs) redesigning and investing in their infrastructures [e.g., new equipment for radio access network (RAN), backhaul] to cope with such data growth. Aiming at lowering both capital expenditures and operational expenditures, current networking trends on network virtualization, software-defined networking (SDN), and network function virtualization (NFV) provide an appealing scenario to flexibly deal with the increase in traffic for MNOs without overdimensioning the deployed network resources. To this end, we rely on an implemented SDN/NFV orchestrator that automatically serves MNO capacity requests by computing and allocating virtual backhaul tenants. Such backhaul tenants are built over a common physical aggregation network, formed by heterogeneous technologies (e.g., packet and optical) that may be owned by different infrastructure providers. MNO RAN traffic is transported toward a mobile core network [i.e., evolved packet core (EPC)], where required backhaul resources are tailored to the capacity needs. The EPC functions are virtualized within the cloud (vEPC), leveraging the NFV advantages. This increases MNO flexibility where cloud resources are instantiated according to EPC needs. The goal of the SDN/NFV orchestrator is to jointly allocate both network and cloud resources, deploying virtual backhaul tenants and vEPC instances for a number of MNOs with different service and capacity requirements. Each MNO's backhaul is isolated and controlled independently via a virtualized SDN (vSDN) controller deployed in the cloud. The SDN/NFV orchestrator architecture is detailed and experimentally validated in a setup provided by the Centre Tecnologic de Telecomunicacions de Catalunya and ADVA Optical Networking. Specifically, upon an MNO request, the orchestrator instantiates the vEPC and vSDN functions in the cloud and then composes the MNO's backhaul tenant over a multilayer (packet and optical) aggregation network.


title: Detecting credit card fraud by Modified Fisher Discriminant Analysis

abstract: We introduce Fisher Linear Discriminant Analysis (FLDA).We modify it to be sensitive toward profitable instances.We applied them together in credit card fraud detection problem.The results are compared in terms of total obtained profit with three well-known models.Modified fisher outperforms the other models in attaining high profit. In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which somehow over-fit the dataset they are built on, one can expect simpler algorithms may show a more robust performance on a range of datasets. Although, linear discriminant functions are less complex classifiers and can work on high-dimensional problems like credit card fraud detection, they did not receive considerable attention so far. This study investigates a linear discriminant, called Fisher Discriminant Function for the first time in credit card fraud detection problem. On the other hand, in this and some other domains, cost of false negatives is very higher than false positives and is different for each transaction. Thus, it is necessary to develop classification methods which are biased toward the most important instances. To cope for this, a Modified Fisher Discriminant Function is proposed in this study which makes the traditional function more sensitive to the important instances. This way, the profit that can be obtained from a fraud/legitimate classifier is maximized. Experimental results confirm that Modified Fisher Discriminant could eventuate more profit.


title: Kinematics modeling and analyses of articulated rovers

abstract: This paper describes a general approach to the kinematics modeling and analyses of articulated rovers traversing uneven terrain. The model is derived for full 6DOF (6-degree-of-freedom) motion, enabling movements in the x,y, and z directions, as well as pitch, roll, and yaw rotations. Differential kinematics is derived for the individual wheel motions in contact with the terrain. The resulting equations of the individual wheel motions are then combined to form the composite equation for the rover motion. Three types of kinematics, i.e., navigation, actuation, and slip kinematics are identified, and the equations and application of each are discussed. The derivations are specialized to Rocky 7, a highly articulated prototype Mars rover, to illustrate the developed methods. Simulation results are provided for the motion of the Rocky 7 over several terrains, and various motion profiles are provided to explain the behavior of the rover.


title: Millimeter wave printed antenna array with high side lobe suppression

abstract: In this paper we introduce new millimeter wave antenna array with relatively high gain of 20.8 dBi and high side lobe suppression. The antenna consists of axial array placed between corner reflector plates. The antenna is designed and realized in the frequency range about 26 GHz which is popular for microwave communication networks. Side lobe suppression in both H- and E-plane is better than 32 dB. The bandwidth of the antenna is wider than 15% and losses are about 1 dB. The antenna is suitable for integration with other microwave circuits. Agreement between simulated and measured results is very good. The presented antenna is low cost and very simple for realization.


title: Fusion of stereo camera and MIMO-FMCW radar for pedestrian tracking in indoor environments

abstract: In this paper, we present a fusion of stereo camera and radar targets to significantly improve the tracking of pedestrians indoors especially suited for surveillance and security applications. Indoor environments pose bigger challenges for pedestrian tracking compared to outdoor environments. This makes pedestrian tracking with only a camera or radar difficult. In this work, we demonstrate that in some preliminary experiments our fusion system increases the multiple object tracking accuracy (MOTA) from −16.7% and 38.0% for camera or radar only tracking to 90.9% using a fusion of both. Furthermore, the proposed fusion system achieves a multiple object tracking precision (MOTP3D) of 90.3% compared to 83.4% and 81.8% obtained via camera and radar only tracking, respectively.