title: Generalized solid-state marx modulator topology

abstract: A generalized circuit topology for bipolar or unipolar high voltage repetitive pulse power applications is proposed. This circuit merges the negative and positive solid state Marx modulator concepts, which take advantage of the intensive use of semiconductor devices to increase the performance of the original dissipative Marx modulators. The flexibility of the proposed modular circuit enables the operation with negative and/or positive pulses, selectable duty cycles, frequencies and relaxation times between the positive and negative pulse. Additionally, the switching topology enables the discharge of the parasitic capacitances after each pulse, allowing the use of capacitive loads, and the clamping of inductive loads, recovering the reset energy back to the main capacitors. Analysis of efficiency and power loss will be addressed, as well as experimental details for different conditions based on laboratory prototype, with 1200 volt Insulated Gate Bipolar Transistors (IGBT), diodes, and 4.5 muF capacitors.


title: The conundrum of IT management *

abstract: This paper presents a robust argument as to why it can be difficult for chief information officers (CIOs) to generate business value from investments that their organizations make in information technology (IT) with contemporary organizational structures, authority patterns, processes and mindsets. This argument is built on the subtle premise that organizations should not seek to merely manage IT but to manage the delivery of business value through IT. It takes the view that this latter quest is knowledge-based and that the knowledge resources to successfully deliver this value are distributed throughout the organization. Crucially, this knowledge is not located solely within the IT function, presenting a challenge for the CIO for its integration and coordination. With the CIO having little or no jurisdiction over all required knowledge, its deployment will therefore be fragmented. The conundrum of IT management is how to generate value through IT without having access and authority over necessary resources. Research and practitioner implications of this analysis are highlighted.


Ontology Design and Development : Some aspects : An overview


title: High performance SHA-2 core using the Round Pipelined Technique

abstract: Secured Hashing Algorithms are used to ensure the integrity and authenticity of data and data origin in order to achieve higher level of security. A proposed design for SHA-2 hashing functions (SHA-224, SHA-256, SHA-384, SHA-512) are implemented with fully iterative and pipelined architecture using Verilog HDL. For every hashing operation, throughput per slice plays a major role in optimized hardware design. In order to improve throughput per slice, the proposed design uses a Round Pipelined Technique (RPT) in hash functions. The SHA-2 algorithm outputs a message digest / hash of size 224, 256, 384 and 512-bits. Proposed research investigates optimization techniques in terms of area and frequency for SHA-2 hash functions on the FPGA and achieves higher throughput per slice. The proposed architecture is implemented on Xilinx Virtex-6 xc6vlx550t-2ff1759 FPGA device. Further, the fully iterative and Round Pipelined Technique based architectures are compared in terms of frequency and throughput per slice with other known published results. The proposed design for SHA-256 and SHA-512 with Round Pipelined Technique exhibits throughput per slice improvement of 57% and 17% respectively than other implementations.


title: Inside out : two jointly predictive models for word representations and phrase representations

abstract: Distributional hypothesis lies in the root of most existing word representation models by inferring word meaning from its external contexts. However, distributional models cannot handle rare and morphologically complex words very well and fail to identify some fine-grained linguistic regularity as they are ignoring the word forms. On the contrary, morphology points out that words are built from some basic units, i.e., morphemes. Therefore, the meaning and function of such rare words can be inferred from the words sharing the same morphemes, and many syntactic relations can be directly identified based on the word forms. However, the limitation of morphology is that it cannot infer the relationship between two words that do not share any morphemes. Considering the advantages and limitations of both approaches, we propose two novel models to build better word representations by modeling both external contexts and internal morphemes in a jointly predictive way, called BEING and SEING. These two models can also be extended to learn phrase representations according to the distributed morphology theory. We evaluate the proposed models on similarity tasks and analogy tasks. The results demonstrate that the proposed models can outperform state-of-the-art models significantly on both word and phrase representation learning.


title: Design and validation of slot spiral antenna for stepped frequency ground penetrating radar

abstract: A wide band dual arm slot spiral antenna operating over 600 MHz to 3000 MHz was designed and tested for a Stepped Frequency Ground Penetrating Radar (SFGPR) application. This antenna is low profile, planar, uses a simple current element as the balanced feed. The influence of the receiver Intermediate Frequency (IF) Band Width (BW) on the Signal to Noise Ratio (SNR) for antenna operation under SFGPR mode is investigated for a metal rod buried inside dry sand.


title: Article Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach

abstract: A computer-aided detection (CAD) system is helpful for radiologists to detect pulmonary nodules at an early stage. In this paper, we propose a novel pulmonary nodule detection method based on hierarchical block classification. The proposed CAD system consists of three steps. In the first step, input computed tomography images are split into three-dimensional block images, and we apply entropy analysis on the block images to select informative blocks. In the second step, the selected block images are segmented and adjusted for detecting nodule candidates. In the last step, we classify the nodule candidate images into nodules and non-nodules. We extract feature vectors of the objects in the selected blocks. Lastly, the support vector machine is applied to classify the extracted feature vectors. Performance of the proposed system is evaluated on the Lung Image Database Consortium database. The proposed method has reduced the false positives in the nodule candidates significantly. It achieved 95.28% sensitivity with only 2.27 false positives per scan.


Monitoring an Institution’s Research Activities


title: Detecting Faces Using Inside Cascaded Contextual CNN

abstract: Deep Convolutional Neural Networks (CNNs) achieve substantial improvements in face detection in the wild. Classical CNN-based face detection methods simply stack successive layers of filters where an input sample should pass through all layers before reaching a face/non-face decision. Inspired by the fact that for face detection, filters in deeper layers can discriminate between difficult face/non-face samples while those in shallower layers can efficiently reject simple non-face samples, we propose Inside Cascaded Structure that introduces face/non-face classifiers at different layers within the same CNN. In the training phase, we propose data routing mechanism which enables different layers to be trained by different types of samples, and thus deeper layers can focus on handling more difficult samples compared with traditional architecture. In addition, we introduce a two-stream contextual CNN architecture that leverages body part information adaptively to enhance face detection. Extensive experiments on the challenging FD-DB and WIDER FACE benchmarks demonstrate that our method achieves competitive accuracy to the state-of-the-art techniques while keeps real time performance.


title: An ethical adaptor: Behavioral modification derived from moral emotions

abstract: This paper presents the motivation, basis and a prototype implementation of an ethical adaptor capable of using a moral affective function, guilt, as a basis for altering a robot's ongoing behavior. While the research is illustrated in the context of the battlefield, the methods described are believed generalizable to other domains such as eldercare and are potentially extensible to a broader class of moral emotions, including compassion and empathy.


title: Fully Deep Blind Image Quality Predictor

abstract: In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not consider reference images; thus, its performance is inferior to that of FR-IQA. To alleviate this accuracy discrepancy between FR-IQA and NR-IQA methods, we propose a blind image evaluator based on a convolutional neural network (BIECON). To imitate FR-IQA behavior, we adopt the strong representation power of a deep convolutional neural network to generate a local quality map, similar to FR-IQA. To obtain the best results from the deep neural network, replacing hand-crafted features with automatically learned features is necessary. To apply the deep model to the NR-IQA framework, three critical problems must be resolved: 1) lack of training data; 2) absence of local ground truth targets; and 3) different purposes of feature learning. BIECON follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR-IQA prediction accuracy that is comparable with that of state-of-the-art FR-IQA methods.


title: Hybrid Cascode Compensation for Two-Stage CMOS Opamps

abstract: To seal against the surround of the door opening of a coke oven, the door has an elongate knife-edge element which engages the surround and is held against a sealing surface on the side of the door. The knife edge element is movable past the sealing surface on the door for adjustment. The knife-edge element is divided into a plurality of mutually abutting parts in the adjustment direction. In order to allow a greater amount of adjustment of the knife edge element and to make assembly of the seal easier, holding means e.g. a plurality of brackets, are provided to hold the parts of the knife edge together in the adjustment direction and to restrain at least the outermost part from lateral movement relative to the door. The holding means permits relative sliding of the parts in the longitudinal direction.


title: Romantic Relationship Development in the Age of Facebook: An Exploratory Study of Emerging Adults' Perceptions, Motives, and Behaviors

abstract: Abstract Social networking sites are becoming a prevalent form of communication in the escalation of romantic relationships. An online survey (n=403) addressed emerging adults' experiences with Facebook and romantic relationships, particularly a unique affordance of Facebook: the ability to declare oneself as “In a Relationship” and actively link one's profile to a romantic partner's, commonly known as going Facebook official. Results identified common social perceptions of the meaning of this status (regarding commitment, intensity, and social response) and both interpersonal and social motives for posting it on Facebook. Additionally, sex differences were identified in perceptions of meaning, wherein women felt this status conveyed commitment and intensity moreso than men did. Implications of this discrepancy on heterosexual relationship satisfaction and the prevailing role of technology in romantic relationships are discussed.


title: Raspberry Pi based complete embedded system for iris recognition

abstract: This paper presents a complete embedded system for iris recognition. The proposed system is based on the Raspberry Pi 3B single-board computer. The main emphasis has been placed on functionality of the whole recognition process, including hardware and software aspects. A carefully designed infra-red data acquisition enabled efficient iris segmentation and coding. The data processing software is based on the USIT project and uses the OpenCV library. Experimental research was carried out for a group of fourteen volunteers.


title: Visual outcome of cataract surgery with IOL implantation in traumatic cataract among 189 children: The Lahan experience

abstract: Trauma is an important cause of monocular blindness in the developing world. This study aims to determine the demographic profile and visual outcome of cataract surgery among the children with traumatic cataract. It was a prospective and longitudinal study of 189 consecutive children below 16 years who underwent cataract surgery with intra ocular lens (IOL) implantation for traumatic cataract at Sagarmatha Choudhary Eye Hospital, Lahan, Nepal from October 2012 to March 2014. Assessment included visual acuity measurement in the Snellen’s chart or the Cardiff card, anterior segment examination with slit lamp, dilated fundus examination with the help of +20D lens in indirect ophthalmoscope, B-scan ultrasonography of posterior segment and objective and subjective refraction. Follow up was scheduled at first post-operative day, at discharge, one month and three months. Cause and type of trauma, demographic factors, surgical intervention, complications, and visual acuity was recorded. Among 189, majority of them were males (73%) and the average age was 8.8±3.6 years. The time of presentation ranged from 3 days to 8 years (median age two months). Wooden stick was the most common cause of injury (34.4%). The average preoperative visual acuity in logMAR scale was 1.6. The average postoperative visual acuity in logMAR scale was 0.8. Eye injuries with traumatic cataract are associated with significant visual impairment. Cataract surgery with intraocular lens implantation restores vision significantly.


title: A Unity Power Factor Bridgeless Isolated Cuk Converter-Fed Brushless DC Motor Drive

abstract: This paper presents a power factor correction (PFC)-based bridgeless isolated Cuk converter-fed brushless dc (BLDC) motor drive. A variable dc link voltage of the voltage source inverter (VSI) feeding the BLDC motor is used for its speed control. This allows the operation of VSI in fundamental frequency switching to achieve an electronic commutation of the BLDC motor for reduced switching losses. A bridgeless configuration of an isolated Cuk converter is derived for the elimination of the front-end diode bridge rectifier to reduce conduction losses in it. The proposed PFC-based bridgeless isolated Cuk converter is designed to operate in discontinuous inductor current mode to achieve an inherent PFC at the ac mains. The proposed drive is controlled using a single voltage sensor to develop a cost-effective solution. The proposed drive is implemented to achieve a unity power factor at the ac mains for a wide range of speed control and supply voltages. An improved power quality is achieved at ac mains with power quality indices within the limits of the IEC 61000-3-2 standard.


title: A global optimisation method for robust affine registration of brain images

abstract: Registration is an important component of medical image analysis and for analysing large amounts of data it is desirable to have fully automatic registration methods. Many different automatic registration methods have been proposed to date, and almost all share a common mathematical framework — one of optimising a cost function. To date little attention has been focused on the optimisation method itself, even though the success of most registration methods hinges on the quality of this optimisation. This paper examines the assumptions underlying the problem of registration for brain images using inter-modal voxel similarity measures. It is demonstrated that the use of local optimisation methods together with the standard multi-resolution approach is not sufficient to reliably find the global minimum. To address this problem, a global optimisation method is proposed that is specifically tailored to this form of registration. A full discussion of all the necessary implementation details is included as this is an important part of any practical method. Furthermore, results are presented for inter-modal, inter-subject registration experiments that show that the proposed method is more reliable at finding the global minimum than several of the currently available registration packages in common usage.


title: Recognition and Segmentation of 3-D Human Action Using

abstract: Our goal is to automatically segment and recognize basic human actions, such as stand, walk and wave hands, from a sequence of joint positions or pose angles. Such recognition is difficult due to high dimensionality of the data and large spatial and temporal variations in the same action. We decompose the high dimensional 3-D joint space into a set of feature spaces where each feature corresponds to the motion of a single joint or combination of related multiple joints. For each feature, the dynamics of each action class is learned with one HMM. Given a sequence, the observation probability is computed in each HMM and a weak classifier for that feature is formed based on those probabilities. The weak classifiers with strong discriminative power are then combined by the Multi-Class AdaBoost (AdaBoost.M2) algorithm. A dynamic programming algorithm is applied to segment and recognize actions simultaneously. Results of recognizing 22 actions on a large number of motion capture sequences as well as several annotated and automatically tracked sequences show the effectiveness of the proposed algorithms.


title: A Regularization Post Layer: An Additional Way How to Make Deep Neural Networks Robust

abstract: Neural Networks (NNs) are prone to overfitting. Especially, the Deep Neural Networks in the cases where the training data are not abundant. There are several techniques which allow us to prevent the overfitting, e.g., L1/L2 regularization, unsupervised pre-training, early training stopping, dropout, bootstrapping or cross-validation models aggregation. In this paper, we proposed a regularization post-layer that may be combined with prior techniques, and it brings additional robustness to the NN. We trained the regularization post-layer in the cross-validation (CV) aggregation scenario: we used the CV held-out folds to train an additional neural network post-layer that boosts the network robustness. We have tested various post-layer topologies and compared results with other regularization techniques. As a benchmark task, we have selected the TIMIT phone recognition which is a well-known and still favorite task where the training data are limited, and the used regularization techniques play a key role. However, the regularization post-layer is a general method, and it may be employed in any classification task.


title: Targeting DNA double-strand breaks with TAL effector nucleases.

abstract: Engineered nucleases that cleave specific DNA sequences in vivo are valuable reagents for targeted mutagenesis. Here we report a new class of sequence-specific nucleases created by fusing transcription activator-like effectors (TALEs) to the catalytic domain of the FokI endonuclease. Both native and custom TALE-nuclease fusions direct DNA double-strand breaks to specific, targeted sites.


title: Comparative study on connected component labeling algorithms for embedded video processing systems.

abstract: The objective of this paper is to carry out a detailed analysis of the most popular connected components labeling (CCL) algorithms for binary images. This study investigates their usability for processing streaming data and suitability for implementation using Field-Programmable Gate Array (FPGA) devices. The first part of this paper presents the state of the art on CCL algorithms. Both capability for real-time video processing as well as memory requirements are taken into consideration. The second part of the paper describes an efficient implementation of the single pass labeling algorithm using a Virtex-II Pro FPGA. It is verified on the development board with an infrared camera module as a real-time video source. The system is capable of processing video stream with 640 x 480 pixels per frame at a speed of 30 fps limited by the bandwidth of the video source.


title: Learning to Trade via Direct Reinforcement

abstract: We present methods for optimizing portfolios, asset allocations, and trading systems based on direct reinforcement (DR). In this approach, investment decision-making is viewed as a stochastic control problem, and strategies are discovered directly. We present an adaptive algorithm called recurrent reinforcement learning (RRL) for discovering investment policies. The need to build forecasting models is eliminated, and better trading performance is obtained. The direct reinforcement approach differs from dynamic programming and reinforcement algorithms such as TD-learning and Q-learning, which attempt to estimate a value function for the control problem. We find that the RRL direct reinforcement framework enables a simpler problem representation, avoids Bellman's curse of dimensionality and offers compelling advantages in efficiency. We demonstrate how direct reinforcement can be used to optimize risk-adjusted investment returns (including the differential Sharpe ratio), while accounting for the effects of transaction costs. In extensive simulation work using real financial data, we find that our approach based on RRL produces better trading strategies than systems utilizing Q-learning (a value function method). Real-world applications include an intra-daily currency trader and a monthly asset allocation system for the S&P 500 Stock Index and T-Bills.


title: Improving recommender systems with an intention-based algorithm switching strategy

abstract: Modern e-commerce websites are equipped with hybrid recommendation systems aiming at bringing novelty and diversity to consumers. However, mobilizing several recommendation algorithms simultaneously not only incurs unnecessary computation costs, but also jeopardizes consumers' shopping experience due to excessive information load. Hence, recommending less but better (more relevant) items is critical, especially when consumers depend more and more on mobile devices, whose screen is much smaller. In this paper, we present a switching hybrid strategy capable of selecting recommendation algorithms according to consumers' instantaneous intention. Compared with a benchmark system which simultaneously uses all algorithms, our system achieved higher performance in terms of item view and consumption while sending less items, though both systems are empowered by the same recommendation algorithms. Meanwhile, the interface of our system is more concise and user-friendly. The result indicates that the intention as an important context factor can be used to enhance the performance and consumer experience of e-commerce recommender systems.


title: Predicting the Price of Bitcoin Using Machine Learning

abstract: The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index. The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. The LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7%.


title: Brain-Computer Interface Control in a Virtual Reality Environment and Applications for the Internet of Things

abstract: Brain-computer interfaces (BCIs) have enabled individuals to control devices such as spellers, robotic arms, drones, and wheelchairs, but often these BCI applications are restricted to research laboratories. With the advent of virtual reality (VR) systems and the internet of things (IoT) we can couple these technologies to offer real-time control of a user's virtual and physical environment. Likewise, BCI applications are often single-use with user's having no control outside of the restrictions placed upon the applications at the time of creation. Therefore, there is a need to create a tool that allows users the flexibility to create and modularize aspects of BCI applications for control of IoT devices and VR environments. Using a popular video game engine, Unity, and coupling it with BCI2000, we can create diverse applications that give the end-user additional autonomy during the task at hand. We demonstrate the validity of controlling a Unity-based VR environment and several commercial IoT devices via direct neural interfacing processed through BCI2000.


title: Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI

abstract: Automatic computer processing of large multidimensional images such as those produced by magnetic resonance imaging (MRI) is greatly aided by deformable models, which are used to extract, identify, and quantify specific neuroanatomic structures. A general method of deforming polyhedra is presented here, with two novel features. First, explicit prevention of self-intersecting surface geometries is provided, unlike conventional deformable models, which use regularization constraints to discourage but not necessarily prevent such behavior. Second, deformation of multiple surfaces with intersurface proximity constraints allows each surface to help guide other surfaces into place using model-based constraints such as expected thickness of an anatomic surface. These two features are used advantageously to identify automatically the total surface of the outer and inner boundaries of cerebral cortical gray matter from normal human MR images, accurately locating the depths of the sulci, even where noise and partial volume artifacts in the image obscure the visibility of sulci. The extracted surfaces are enforced to be simple two-dimensional manifolds (having the topology of a sphere), even though the data may have topological holes. This automatic 3-D cortex segmentation technique has been applied to 150 normal subjects, simultaneously extracting both the gray/white and gray/cerebrospinal fluid interface from each individual. The collection of surfaces has been used to create a spatial map of the mean and standard deviation for the location and the thickness of cortical gray matter. Three alternative criteria for defining cortical thickness at each cortical location were developed and compared. These results are shown to corroborate published postmortem and in vivo measurements of cortical thickness.


title: a Tool for Automatic Speech Recognition Quality Estimation

abstract: We present TranscRater, an open-source tool for automatic speech recognition (ASR) quality estimation (QE). The tool allows users to perform ASR evaluation bypassing the need of reference transcripts and confidence information, which is common to current assessment protocols. TranscRater includes: i) methods to extract a variety of quality indicators from (signal, transcription) pairs and ii) machine learning algorithms which make possible to build ASR QE models exploiting the extracted features. Confirming the positive results of previous evaluations, new experiments with TranscRater indicate its effectiveness both in WER prediction and transcription ranking tasks.


title: Consumer Load Prediction Based on NARX for Electricity Theft Detection

abstract: A range of load prediction techniques has largely been used for energy management at various levels. However, the data used for the prediction are cumulative energy data, which reveal the activities of consumers and not individual consumer, on the distribution power network. Individual consumer data is essential for real time prediction, monitoring and detect of electricity theft. A new approach of monitoring individual consumer based on consumer load prediction using nonlinear autoregressive with eXogenous input (NARX) network is considered in this study. One month average energy consumption data acquired from consumer load prototype developed was used. Consequently, 5-minute step ahead load prediction was achieved. The NARX architecture was based on nine hidden neurons and two tapped delay and the network trained using Bayesian regulation backpropagation technique. The data set contains a total of 8928 data points representing energy consumed at five minute interval for one month. The data was divided into two sets at ratio 70:30 for training and validation, respectively. The training data equals 6206 while the validation data is 2722. MATLAB environment was used for the processing of the data. The training and validation MSE is 0.0225 and 0.0533 respectively, while the total time for the training is 0.016s.


title: Decoding the perception of sincerity in written dialogues

abstract: Understanding how people perceive sincerity is important in identifying malevolent deceptive behavior in social media. This is a difficult problem because the only signal in most such cases is written text, and the larger proportion of human communication is non-verbal. Furthermore, perception is very subjective, and people adapt to each other's perceptions. We define the problem of decoding sincerity perception for two domains, dating and online games. We introduce and evaluate new psycholinguistic features of perceived sincerity. Our results shed light into the connection between language, deception, and perception, and underline the challenges and difficulty of assessing perceptions from written text.


title: A Short-term Intervention for Long-term Fairness in the Labor Market

abstract: The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisions are bound by an input-output notion of “”individual fairness.”” Instead, we construct a dynamic reputational model of the labor market that illustrates the reinforcing nature of asymmetric outcomes resulting from groups’ divergent access to resources and as a result, investment choices. To address these disparities, we adopt a dual labor market composed of a Temporary Labor Market (TLM), in which firms’ hiring strategies are constrained to ensure statistical parity of workers granted entry into the pipeline, and a Permanent Labor Market (PLM), in which firms hire top performers as desired. Individual worker reputations produce externalities for their group; the corresponding feedback loop raises the collective reputation of the initially disadvantaged group via a TLM fairness intervention that need not be permanent. We show that such a restriction on hiring practices induces an equilibrium that, under particular market conditions, Pareto-dominates those arising from strategies that employ statistical discrimination or a “”group-blind”” criterion. The enduring nature of equilibria that are both inequitable and Pareto suboptimal suggests that fairness interventions beyond procedural checks of hiring decisions will be of critical importance in a world where machines play a greater role in the employment process.


title: EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras

abstract: Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, developing algorithms for event measurements requires a new class of hand crafted algorithms. Deep learning has shown great success in providing model free solutions to many problems in the vision community, but existing networks have been developed with frame based images in mind, and there does not exist the wealth of labeled data for events as there does for images for supervised training. To these points, we present EV-FlowNet, a novel self-supervised deep learning pipeline for optical flow estimation for event based cameras. In particular, we introduce an image based representation of a given event stream, which is fed into a self-supervised neural network as the sole input. The corresponding grayscale images captured from the same camera at the same time as the events are then used as a supervisory signal to provide a loss function at training time, given the estimated flow from the network. We show that the resulting network is able to accurately predict optical flow from events only in a variety of different scenes, with performance competitive to image based networks. This method not only allows for accurate estimation of dense optical flow, but also provides a framework for the transfer of other self-supervised methods to the event-based domain.


title: Design, implementation and characterization of an RF translator for TVWS communication

abstract: This study describes a radio frequency (RF) frontend that enables direct RF translation of an IEEE 802.11 signal at 2.4GHz ISM band to UHF TV frequencies. Specifically, the intention is to allow 2.4GHz IEEE 802.11 or WiFi-compliant radios to be used for opportunistic communication at so-called TV white spaces (TVWS). In order to faithfully translate WiFi signals to TVWS frequencies, while preventing undue interference to the primary users in the TV band, the spurious response of the RF translator should be closely examined as part of the design considerations. We present the results of single tone spurious response and spectral mask tests in order to characterize the nonlinear performance of the translator. The tests are conducted for different 2.4GHz WiFi channels at the input, and for different target TV channel frequencies at the output of the translator. The performance of the translator was found to vary significantly across the 2.4GHz input frequencies with WiFi channel 11 (2.462GHz) providing the best results. This suggests that the choice of the input WiFi channel to the translator should be fixed to that which provides the best spurious response at the output. In addition, we evaluate system functionality by measuring TCP throughput with and without the translator, and compare the results with the spurious response tests. The results show that the signal distortion caused by the spurious response contributes to degrading both the measured RSSI and throughput levels. In particular, the lower RSSI and peak throughput values achieved at certain output TV channel frequencies is consistent with the high levels of spurious response that were measured in those cases. Furthermore, the variation in throughput performance across different cases is more apparent in the peak, rather than the average, throughput levels over time.


title: Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model

abstract: Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.


title: Comparison of Several Cloud Computing Platforms

abstract: Cloud computing is the development of parallel computing, distributed computing and grid computing. It has been one of the most hot research topics. Now many corporations have involved in the cloud computing related techniques and many cloud computing platforms have been put forward. This is a favorable situation to study and application of cloud computing related techniques. Though interesting, there are also some problems for so many flatforms. For to a novice or user with little knowledge about cloud computing, it is still very hard to make a reasonable choice. What differences are there for different cloud computing platforms and what characteristics and advantages each has? To answer these problems, the characteristics, architectures and applications of several popular cloud computing platforms are analyzed and discussed in detail. From the comparison of these platforms, users can better understand the different cloud platforms and more reasonablely choose what they want.


title: Proportional EMG control of ankle plantar flexion in a powered transtibial prosthesis

abstract: The human calf muscle generates 80% of the mechanical work to walk throughout stance-phase, powered plantar flexion. Powered plantar flexion is not only important for walking energetics, but also to minimize the impact on the leading leg at heel-strike. For unilateral transtibial amputees, it has recently been shown that knee load on the leading, intact limb decreases as powered plantar flexion in the trailing prosthetic ankle increases. Not surprisingly, excessive loads on the leading, intact knee are believed to be causative of knee osteoarthritis, a leading secondary impairment in lower-extremity amputees. In this study, we hypothesize that a transtibial amputee can learn how to control a powered ankle-foot prosthesis using a volitional electromyographic (EMG) control to directly modulate ankle powered plantar flexion. We here present preliminary data, and find that an amputee participant is able to modulate toe-off angle, net ankle work and peak power across a broad range of walking speeds by volitionally modulating calf EMG activity. The modulation of these key gait parameters is shown to be comparable to the dynamical response of the same powered prosthesis controlled intrinsically (No EMG), suggesting that transtibial amputees can achieve an adequate level of powered plantar flexion controllability using direct volitional EMG control.


title: Using Retweet Information as a Feature to Classify Messages Contents

abstract: We investigate the use of machine learning algorithms to classify content published in Online Social Networks using as input solely user interaction data, instead of the actual message content. During a period of six months, we monitored and gathered data from users interacting with news messages on Twitter, creating thousands of information diffusion processes. The data set presented regular patterns on how messages were spread over the network by users, depending on its content, so we could build classifiers to predict the topic of a message using as input only information of how it was diffused. Thus, we demonstrate the explanatory power of user behavior data on identifying content present in Social Networks, proposing techniques for topic classification that can be used to assist traditional content identification strategies (such as natural language or image processing) in challenging contexts, or be applied in scenarios with limited information access.


title: CLive: Cloud-assisted P2P live streaming

abstract: Peer-to-peer (P2P) video streaming is an emerging technology that reduces the barrier to stream live events over the Internet. Unfortunately, satisfying soft real-time constraints on the delay between the generation of the stream and its actual delivery to users is still a challenging problem. Bottlenecks in the available upload bandwidth, both at the media source and inside the overlay network, may limit the quality of service (QoS) experienced by users. A potential solution for this problem is assisting the P2P streaming network by a cloud computing infrastructure to guarantee a minimum level of QoS. In such approach, rented cloud resources (helpers) are added on demand to the overlay, to increase the amount of total available bandwidth and the probability of receiving the video on time. Hence, the problem to be solved becomes minimizing the economical cost, provided that a set of constraints on QoS is satisfied. The main contribution of this paper is CLIVE, a cloud-assisted P2P live streaming system that demonstrates the feasibility of these ideas. CLIVE estimates the available capacity in the system through a gossip-based aggregation protocol and provisions the required resources from the cloud to guarantee a given level of QoS at low cost. We perform extensive simulations and evaluate CLIVE using large-scale experiments under dynamic realistic settings.


title: Dynamic Probabilistic Drivability Maps for Lane Change and Merge Driver Assistance

abstract: In this paper, we present a novel probabilistic compact representation of the on-road environment, i.e., the dynamic probabilistic drivability map (DPDM), and demonstrate its utility for predictive lane change and merge (LCM) driver assistance during highway and urban driving. The DPDM is a flexible representation and readily accepts data from a variety of sensor modalities to represent the on-road environment as a spatially coded data structure, encapsulating spatial, dynamic, and legal information. Using the DPDM, we develop a general predictive system for LCMs. We formulate the LCM assistance system to solve for the minimum-cost solution to merge or change lanes, which is solved efficiently using dynamic programming over the DPDM. Based on the DPDM, the LCM system recommends the required acceleration and timing to safely merge or change lanes with minimum cost. System performance has been extensively validated using real-world on-road data, including urban driving, on-ramp merges, and both dense and free-flow highway conditions.


Bug Prediction with Neural Nets Using regression-and classification-based approaches Bachelor Thesis


title: Associations between attitudes toward cosmetic surgery, celebrity worship, and body image among South Korean and US female college students

abstract: Very little information is known regarding the level of psychological attachment to celebrities needed to influence one’s desire to engage in cosmetic surgery in consumers. More importantly, no previous research examined this in relation to attitudes toward cosmetic surgery and level of body satisfaction in cross-cultural contexts. The present study aimed to examine associations between attitudes toward cosmetic surgery, celebrity worship, and body image among South Korean and US female college students. A total of 370 female undergraduates were recruited for survey from a central university in Seoul, South Korea (n = 196) and a mid-Atlantic university in the US (n = 174). The South Korean participants showed greater acceptance toward cosmetic surgery than did the US participants, whereas the US participants showed more positive attitudes toward their favorite celebrities than did the South Korean participants. For the US participants, the level of body dissatisfaction was negatively associated with acceptance for cosmetic surgery while attitudes toward favorite celebrities was positively associated with acceptance for cosmetic surgery. Findings suggest that cultural contexts affect factors influencing the likelihood of having cosmetic surgery.


Quantitative extraction of the emission times of high-order harmonics via the determination of instantaneous frequencies


title: Robust volume minimization-based matrix factorization via alternating optimization

abstract: This paper focuses on volume minimization (VolMin)-based structured matrix factorization (SMF), which factors a data matrix into a full-column rank basis and a coefficient matrix whose columns reside in the unit simplex. The VolMin criterion achieves this goal via finding a minimum-volume enclosing convex hull of the data. Recent works showed that VolMin guarantees the identifiability of the factor matrices under mild and realistic conditions, which suit many applications in signal processing and machine learning. However, the existing VolMin algorithms are sensitive to outliers or lack efficiency in dealing with volume-associated cost functions. In this work, we propose a new VolMin-based matrix factorization criterion and algorithm that take outliers into consideration. The proposed algorithm detects outliers and suppress them automatically, and it does so in an algorithmically very simple way. Simulations are used to showcase the effectiveness of the proposed algorithm.


title: Multicultural experience enhances creativity: The when and how.

abstract: Many practices aimed at cultivating multicultural competence in educational and organizational settings (e.g., exchange programs, diversity education in college, diversity management at work) assume that multicultural experience fosters creativity. In line with this assumption, the research reported in this article is the first to empirically demonstrate that exposure to multiple cultures in and of itself can enhance creativity. Overall, the authors found that extensiveness of multicultural experiences was positively related to both creative performance (insight learning, remote association, and idea generation) and creativity-supporting cognitive processes (retrieval of unconventional knowledge, recruitment of ideas from unfamiliar cultures for creative idea expansion). Furthermore, their studies showed that the serendipitous creative benefits resulting from multicultural experiences may depend on the extent to which individuals open themselves to foreign cultures, and that creativity is facilitated in contexts that deemphasize the need for firm answers or existential concerns. The authors discuss the implications oftheir findings for promoting creativity in increasingly global learning and work environments.


title: Research on Lightweight MVC Framework Based on Spring MVC and Mybatis

abstract: For the development and operating efficiency of Web applications based on the Model-View-Controller (MVC) framework, and, according to the actual business environment and needs in the project practice, the framework of Web application system is studied in this paper. Through the research of Spring MVC framework and Mybatis framework as well as some related core techniques, combined with JSP and JSTL technology, this paper realizes the design of a lightweight Web application framework based on Spring MVC and Mybatis.


title: MARTE: Technology Development and Lessons Learned from a Mars Drilling Mission Simulation 1

abstract: 29 pages, 21 figures, 2 tables.-- ISI Article Identifier: 000250768000006.-- Special issue: Mining Robotics.


title: Dietary phytochemicals alter epigenetic events and signaling pathways for inhibition of metastasis cascade

abstract: Cancer metastasis is a multistep process in which a cancer cell spreads from the site of the primary lesion, passes through the circulatory system, and establishes a secondary tumor at a new nonadjacent organ or part. Inhibition of cancer progression by dietary phytochemicals (DPs) offers significant promise for reducing the incidence and mortality of cancer. Consumption of DPs in the diet has been linked to a decrease in the rate of metastatic cancer in a number of preclinical animal models and human epidemiological studies. DPs have been reported to modulate the numerous biological events including epigenetic events (noncoding micro-RNAs, histone modification, and DNA methylation) and multiple signaling transduction pathways (Wnt/β-catenin, Notch, Sonic hedgehog, COX-2, EGFR, MAPK-ERK, JAK-STAT, Akt/PI3K/mTOR, NF-κB, AP-1, etc.), which can play a key role in regulation of metastasis cascade. Extensive studies have also been performed to determine the molecular mechanisms underlying antimetastatic activity of DPs, with results indicating that these DPs have significant inhibitory activity at nearly every step of the metastatic cascade. DPs have anticancer effects by inducing apoptosis and by inhibiting cell growth, migration, invasion, and angiogenesis. Growing evidence has also shown that these natural agents potentiate the efficacy of chemotherapy and radiotherapy through the regulation of multiple signaling pathways. In this review, we discuss the variety of molecular mechanisms by which DPs regulate metastatic cascade and highlight the potentials of these DPs as promising therapeutic inhibitors of cancer.


title: Fourier-Based Imaging for Multistatic Radar Systems

abstract: Fourier-based methods for monostatic and bistatic setups have been widely used for high-accuracy radar imaging. However, the multistatic configuration has several characteristics that make Fourier processing more challenging: 1) a nonuniform grid in k-space, which requires multidimensional interpolation methods, and 2) image distortion when the incident spherical wave is approximated by a plane wave. This contribution presents a Fourier-based imaging method for multistatic systems, solving the aforementioned limitations: the first, by using k-space partitioning and applying interpolation in each domain; the second, by approximating the spherical wave with multiple plane waves. Both solutions are fully parallelizable, thus allowing calculation time savings. Validation and benchmarking with a synthetic aperture radar backpropagation algorithm have been performed through 2-D and 3-D simulation-based examples. Imaging results from radar measurements have been assessed.


title: Computational complexity of image processing algorithms for an intelligent mobile enabled tongue diagnosis scheme

abstract: Tongue diagnosis is an auxiliary, effective and non-invasive technique to evaluate the condition of a patient's internal organ in traditional East Asian medicine. The diagnosis process relies on expert's opinion based on visual inspection of colour, substance, coating, form and motion of the tongue. This work explores the computational complexity of image processing techniques to analyse chromatic properties and textural features for tongue image segmentation. The dynamic and novel approach of this work involves consideration of skin colour covering various range of contrast diversity while image segmentation, making it distinct from existing works. The aim of this research is to seek for an algorithm with reduced computational complexity suitable to be implemented in an enhanced mobile enable solution. The algorithm for tongue image processing needs to be fast and less complex making the system apt for mobile devices executing automatic tongue diagnosis entailing clinical decision support system. Analysing the performance of different colour models, RGB was unveiled to have a better enactment than others. The performance of edge detection techniques were evaluated on images with close contrast difference based on segmentation result and processing time. The morphological processing showed better result to separate the tongue from its background which can be further employed for geometric shape based disease diagnosis.


title: Resource Management for Device-to-Device Underlay Communication

abstract: Device-to-Device (D2D) communication will become a key feature supported by next generation cellular networks, a topic of enormous importance to modern communication. Currently, D2D serves as an underlay to the cellular network as a means to increase spectral efficiency. Although D2D communication brings large benefits in terms of system capacity, it also causes interference as well as increased computation complexity to cellular networks as a result of spectrum sharing. Thus, efficient resource management must be performed to guarantee a target performance level of cellular communication. This brief presents the state-of-the-art research on resource management for D2D communication underlaying cellular networks. Those who work with D2D communication will use this books information to help ensure their work is as efficient as possible. Along with the survey of existing work, this book also includes the fundamental theories, key techniques, and applications.


title: Depth-aware salient object detection using anisotropic center-surround difference

abstract: Most previous works on salient object detection concentrate on 2D images. In this paper, we propose to explore the power of depth cue for predicting salient regions. Our basic assumption is that a salient object tends to stand out from its surroundings in 3D space. To measure the object-to-surrounding contrast, we propose a novel depth feature which works on a single depth map. Besides, we integrate the 3D spatial prior into our method for saliency refinement. By sparse sampling and representing the image using superpixels, our method works very fast, whose complexity is linear to the image resolution. To segment the salient object, we also develop a saliency based method using adaptive thresholding and GrabCut. The proposed method is evaluated on two large datasets designed for depth-aware salient object detection. The results compared with several state-of-the-art 2D and depth-aware methods show that our method has the most satisfactory overall performance. HighlightsWe proposed a new depth feature for salient region detection.Spatial prior is integrated for saliency refinement.A saliency-based object segmentation method is presented.We built the largest dataset for depth-aware salient object detection evaluation.


title: An Ontology for Video Event Representation

abstract: Representation and recognition of events in a video is important for a number of tasks such as video surveillance, video browsing and content based video indexing. This paper describes the results of a "Challenge Project on Video Event Taxonomy" sponsored by the Advanced Research and Development Activity (ARDA) of the U.S. Government in the summer and fall of 2003. The project brought together more than 30 researchers in computer vision and knowledge representation and representatives of the user community. It resulted in the development of a formal language for describing an ontology of events, which we call VERL (Video Event Representation Language) and a companion language called VEML (Video Event Markup Language) to annotate instances of the events described in VERL. This paper provides a summary of VERL and VEML as well as the considerations associated with the specific design choices.


title: CacheBrowser: Bypassing Chinese Censorship without Proxies Using Cached Content

abstract: The cached Internet content served by content delivery networks (CDN) comprises a large fraction of today's Internet traffic, yet, there is little study on how real-world censors deal with blocking forbidden CDN-hosted Internet content. We investigate the techniques used by the Great Firewall of China to block CDN-hosted content, and demonstrate that blocking CDN content poses unique technical and non-technical challenges to the censors. We therefore design a client-side circumvention system, CacheBrowser, that leverages the censors' difficulties in blocking CDN content. We implement CacheBrowser and use it to unblock CDN-hosted content in China with a download latency significantly smaller than traditional proxy-based circumvention systems like Tor. CacheBrowser's superior quality-of-service is thanks to its publisher-centric approach, which retrieves blocked content directly from content publishers with no use of third-party proxies.


Scalable TCP Session Monitoring with Symmetric Receive-side Scaling


title: Survey of Various Homomorphic Encryption algorithms and Schemes

abstract: Homomorphic encryption is the encryption scheme which means the operations on the encrypted data. Homomorphic encryption can be applied in any system by using various public key algorithms. When the data is transferred to the public area, there are many encryption algorithms to secure the operations and the storage of the data. But to process data located on remote server and to preserve privacy, homomorphic encryption is useful that allows the operations on the cipher text, which can provide the same results after calculations as the working directly on the raw data. In this paper, the main focus is on public key cryptographic algorithms based on homomorphic encryption scheme for preserving security. The case study on various principles and properties of homomorphic encryption is given and then various homomorphic algorithms using asymmetric key systems such as RSA, ElGamal, Paillier algorithms as well as various homomorphic encryption schemes such as BrakerskiGentry-Vaikuntanathan (BGV), Enhanced homomorphic Cryptosystem (EHC), Algebra homomorphic encryption scheme based on updated ElGamal (AHEE), Non-interactive exponential homomorphic encryption scheme (NEHE) are investigated.


title: Mining the Dynamics of Music Preferences from a Social Networking Site

abstract: In this paper we present an application of our incremental graph clustering algorithm (DENGRAPH) on a data set obtained from the music community site Last.fm. The aim of our study is to determine the music preferences of people and to observe how the taste in music changes over time. Over a period of 130 weeks, we extract for each interval user profiles of 1,800 users that represent their music listening behavior. By building and incrementally clustering a graph of similar users, we obtain groups of people with similar music preferences. We label these clusters with genres according to the user profiles of the cluster members. Due to the incremental nature of DENGRAPH we show how clusters evolve over time. Besides the growth and decrease of clusters we observe how new clusters emerge and old clusters die. Furthermore, we show the merge and split of clusters. The results of our experiments indicate that DENGRAPH is particularly useful to efficiently detect groups of similar users and to track them over time.


title: Hybrid hepatitis B virus core antigen as a vaccine carrier moiety: I. presentation of foreign epitopes.

abstract: Abstract Hepatitis B virus (HBV) core antigen (HBcAg) is a highly immunogenic subviral particle. Here, we review recent progress in the use of HBcAg as a carrier moiety for heterologous epitopes. To define surface exposed and immunogenic insertion sites for foreign epitopes in HBcAg, peptidic epitopes representing binding sites for virus neutralizing antibodies on the HBV surface antigens were inserted at different positions within HBcAg using genetic engineering in an Escherichia coli expression system ( Schodel et al. (1992) J. Virol. 66, 106–114) . While fusion to the N-terminus required a linker to become surface accessible, both fusion to the N-terminus and to the C-terminus was compatible with particle assembly and preserved the native antigenicity and immunogenicity of HBcAg. Fusion to an immunodominant internal site of HBcAg reduced the HBcAg immunogenicity and antigenicity and most drastically enhanced the immunogenicity of the inserted foreign epitope. This internal site of HBcAg was used to express circumsporozoite antigen (CS) repeat epitopes of two rodent malaria parasites and of Plasmodium falciparum (Schodel et al. (1994b) J. Exp. Med. 180, 1037-1046 and Schodel et al. (1995a) 95th ASM General Meeting, Washington DC, Abstr. E61) . When purified from recombinant Salmonella typhimurium, the hybrid HBcAg-CS proteins were particulate and displayed CS antigenicity as well as reduced HBc antigenicity, as compared to native HBcAg. Immunization of several mouse strains with HBcAg-CS hybrid particles resulted in high titered serum anti-CS antibodies representing all murine IgG isotypes. Immunization of mice with HBcAg or HBcAg-CS particles formulated on alum, complete Freunds or incomplete Freunds adjuvant resulted in equivalent anti-CS and anti-HBc serum antibody titres. The possible influence of carrier-specific immunosuppression was examined and pre-existing immunity to HBcAg did not significantly alter the immunogenicity of hybrid HBcAg particles suggesting that they would be useful carrier moieties for repeated immunizations against multiple haptens or in immune subjects after HBV infection. Examination of T cell recognition of HBcAg-CS particles revealed that HBcAg-specific T cells were universally primed and CS-specific T cells were primed if the insert contained a CS-specific T cell recognition site. This indicates that the internal amino acid position in HBcAg is permissive for the inclusion of heterologous functional T helper as well as B cell epitopes. BALB/c mice immunized with HBcAg-CSl were protected against P. berghei challenge to 90% and 100%, respectively, in two independent experiments.


title: Power of Global Transnational Networks: Civilizing of World Order

abstract: Can be a world order shaped by equivalents in the framework of the supranational model of Europe with the same legitimacy and with the same effectiveness? In this study was argued that Civilizing World Order (CWO) by Transnational Norm-Building Networks (TNNs) should have the legitimacy and effectiveness of the European Union supranational order. In this context, the concept of decentration (supra: centralization and infra: decentralization) which includes the nexus of voice (democratic participation) and entitlement (legal-social rights and duties) was examined. In this study, as methodology, published secondary data and online resources were used in order to reinforce the hypothesis.


title: Driving Fitness Detection : A Holistic Approach For Prevention of Drowsy and Drunk Driving using Computer Vision Techniques

abstract: This paper presents a holistic, non intrusive approach for driving fitness detection by checking drowsiness of the driver and loss of vehicle control due to potential influence of alcohol, using computer vision techniques of facial landmark detection and motion detection. A smart phone camera is used to capture a real-time video feed of the driver in the vehicle. The proposed system continuously analyzes this feed to detect drowsiness by checking for persistent eye blinks using a single scalar quantity, Eye Aspect Ratio (EAR) which characterizes eye opening. The system simultaneously checks for the orientation of the head and body, with respect to the steering wheel, to flag potential drowsiness or drunkenness or both, leading to loss of control of the vehicle. This is done by detecting motion using the Differential Images technique, which operate in real-time. Both of these analyses cumulatively yield a severity score which indicates whether the person is fit to drive or not. In case of positive results, an alarm is sounded and for suitable severity, the geographical location of the smart phone is sent to the respective kin and concerned authorities.


title: Automatic Audio Chord Recognition With MIDI-Trained Deep Feature and BLSTM-CRF Sequence Decoding Model

abstract: With the advances of machine learning technologies, data-driven feature extraction and sequence modeling approaches are being widely explored for automatic chord recognition tasks. Currently, there is a bottleneck in the amount of enough annotated data for training robust acoustic models, as hand-annotating time-synchronized chord labels requires professional musical skills and considerable labor. To cope with this limitation, in this paper, we propose a convolutional neural network CNN based deep feature extractor, which is trained on a large set of time, synchronized musical instrument digital interface audio data pairs and can robustly estimate pitch class activations of real-world music audio recordings. The CNN feature extractor plus a bidirectional long short-term memory conditional random field decoding model forms the proposed hybrid system for automatic chord recognition. Experiments show that the proposed model is compatible for both regular major/minor triad chord classification and larger vocabulary chord recognition, and outperforms other state-of-the-art chord recognition systems.


title: Vision-based control of a quadrotor for perching on lines

abstract: We formulate the position-based visual servoing problem for a quadrotor equipped with a monocular camera and an IMU relying only on features on planes and lines in order to fly above and perch on arbitrarily oriented lines. We show that we are able to compute the orientation of an arbitrarily oriented line, the speed of the robot and its position with respect to the target line using two points at a known distance on the line. The direction of the velocity is derived from optical flow induced by features on a plane in the background Finally, we demonstrate fully autonomous flight and perching using a small 230 gram quadrotor with all the computations running on the robot.


title: Color map optimization for 3D reconstruction with consumer depth cameras

abstract: We present a global optimization approach for mapping color images onto geometric reconstructions. Range and color videos produced by consumer-grade RGB-D cameras suffer from noise and optical distortions, which impede accurate mapping of the acquired color data to the reconstructed geometry. Our approach addresses these sources of error by optimizing camera poses in tandem with non-rigid correction functions for all images. All parameters are optimized jointly to maximize the photometric consistency of the reconstructed mapping. We show that this optimization can be performed efficiently by an alternating optimization algorithm that interleaves analytical updates of the color map with decoupled parameter updates for all images. Experimental results demonstrate that our approach substantially improves color mapping fidelity.


title: Combinatorial aspects of covering arrays

abstract: Covering arrays generalize orthogonal arrays by requiring that t -tuples be covered, but not requiring that the appearance of t -tuples be balanced. Their uses in screening experiments has found application in software testing, hardware testing, and a variety of fields in which interactions among factors are to be identified. Here a combinatorial view of covering arrays is adopted, encompassing basic bounds, direct constructions, recursive constructions, algorithmic methods, and applications.


title: Video stabilization using maximally stable extremal region features

abstract: Video stabilization is an important technique in present day digital cameras as most of the cameras are hand-held, mounted on moving platforms or subjected to atmospheric vibrations. In this paper we propose a novel video stabilization scheme based on estimating the camera motion using maximally stable extremal region features. These features traditionally used in wide baseline stereo problems were never explored for video stabilization purposes. Through our extensive experiments show we how some properties of these region features are suitable for the stabilization task. After estimating the global camera motion parameters using these region features, we smooth the motion parameters using a gaussian filter to retain the desired motion. Finally, motion compensation is carried out to obtain a stabilized video sequence. A number of examples on real and synthetic videos demonstrate the effectiveness of our proposed approach. We compare our results to existing techniques and show how our proposed approach compares favorably to them. Interframe Transformation Fidelity is used for objective evaluation of our proposed approach.


title: Participatory design in community informatics

abstract: Participatory design – the direct involvement of end-users and other stakeholders in design – has become a standard design paradigm in informatics, that is, in developing information systems, applications, infrastructures, and associated work practices. Community informatics, which addresses the impacts and utilisation of information technology to facilitate community life, is a challenging, but important domain for further developing participatory design.


title: In vivo noninvasive blood glucose detection using near-infrared spectrum based on the PSO-2ANN model

abstract: BACKGROUND ::: To improving the nursing level of diabetics, it is necessary to develop noninvasive blood glucose method. ::: ::: ::: OBJECTIVE ::: In order to reduce the number of the near-infrared signal, consider the nonlinear relationship between the blood glucose concentration and near-infrared signal, and correct the individual difference and physiological glucose dynamic, 2 artificial neural networks (2ANN) combined with particle swarm optimization (PSO), named as PSO-2ANN, is proposed. ::: ::: ::: METHOD ::: Two artificial neural networks (ANNs) are employed as the basic structure of the PSO-ANN model, and the weight coefficients of the two ANNs which represent the difference of individual and daily physiological rule are optimized by particle swarm optimization (PSO). ::: ::: ::: RESULTS ::: Clarke error grid shows the blood glucose predictions are distributed in regions A and B, Bland-Altman analysis show that the predictions and measurements are in good agreement. ::: ::: ::: CONCLUSIONS ::: The PSO-2ANN model is a nonlinear calibration strategy with accuracy and robustness using 1550-nm spectroscopy, which can correct the individual difference and physiological glucose dynamics.


title: 3D Massive MIMO Systems: Modeling and Performance Analysis

abstract: Multiple-input-multiple-output (MIMO) systems of current LTE releases are capable of adaptation in the azimuth only. Recently, the trend is to enhance system performance by exploiting the channel's degrees of freedom in the elevation, which necessitates the characterization of 3D channels. We present an information-theoretic channel model for MIMO systems that supports the elevation dimension. The model is based on the principle of maximum entropy, which enables us to determine the distribution of the channel matrix consistent with the prior information on the angles. Based on this model, we provide analytical expression for the cumulative density function (CDF) of the mutual information (MI) for systems with a single receive and finite number of transmit antennas in the general signal-to-interference-plus-noise-ratio (SINR) regime. The result is extended to systems with finite receive antennas in the low SINR regime. A Gaussian approximation to the asymptotic behavior of MI distribution is derived for the large number of transmit antennas and paths regime. We corroborate our analysis with simulations that study the performance gains realizable through meticulous selection of the transmit antenna downtilt angles, confirming the potential of elevation beamforming to enhance system performance. The results are directly applicable to the analysis of 5G 3D-Massive MIMO-systems.


title: Real-Time Trading Models and the Statistical Properties

abstract: The contributions of this article are twofold. First, the performance of a widely used commercial real-time trading model is compared with the performance of systematic currency traders. Second, the real-time trading model is used to evaluate the statistical properties of foreign exchange rates. The out-of-sample test period is seven years of high-frequency data for four major rates. The trading model yields positive annualized returns (net of transaction costs) in all cases. The null hypothesis of whether the real-time performances of the foreign exchange series are consistent with traditional statistical processes is tested under the probability distributions of the performance measures.


title: On Efficiency and Scalability of Software-Defined Infrastructure for Adaptive Applications

abstract: This paper proposes and evaluates a novel analytical performance model to study the efficiency and scalability of software-defined infrastructure (SDI) to host adaptive applications. The SDI allows applications to communicate their adaptation requirements at run-time. Adaptation scenarios require computing and networking resources to be provided to applications in a timely manner to facilitate seamless service delivery. Our analytical model yields the response time of realizing adaptations on the SDI and reveals the scalability limitations. We conduct extensive testbed experiments on a cloud environment to verify the accuracy and fidelity of the model. Cloud service providers can leverage the proposed model to perform capacity planning and bottleneck analysis when they accommodate adaptive applications.


title: Mean value coordinates in 3d

abstract: Barycentric coordinates can be used both to express a point inside a tetrahedron as a convex combination of the four vertices and to linearly interpolate data given at the vertices. In this paper we generalize these coordinates to convex polyhedra and the kernels of star-shaped polyhedra. These coordinates generalize in a natural way a recently constructed set of coordinates for planar polygons, called mean value coordinates.


title: Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming

abstract: Text simplification aims to rewrite text into simpler versions, and thus make information accessible to a broader audience. Most previous work simplifies sentences using handcrafted rules aimed at splitting long sentences, or substitutes difficult words using a predefined dictionary. This paper presents a data-driven model based on quasi-synchronous grammar, a formalism that can naturally capture structural mismatches and complex rewrite operations. We describe how such a grammar can be induced from Wikipedia and propose an integer linear programming model for selecting the most appropriate simplification from the space of possible rewrites generated by the grammar. We show experimentally that our method creates simplifications that significantly reduce the reading difficulty of the input, while maintaining grammaticality and preserving its meaning.


title: Broadband eight-way coaxial waveguide high power combiner/divider

abstract: This article proposes two broadband eight-way coaxial combiner/dividerat 4–14 GHz frequency range. Two structures are introduced and designed based on physical limitation of structure and the best reflection coefficient through multi section and Klopfenstein taper matching. In multi section matching, a central coaxial line to peripheral coaxial line transition is employed to realize the circuit model elements values and, thus, achieve an excellent matching over frequency range. In second design, a combination of central coaxial line to peripheral coaxial line transition and Klopfenstein taper is employed to achieve an excellent wide band matching. Simulated structures have more than three octave bandwidth from 4 to 14 GHz for a return loss greater than 10 dB. The field strength simulation of the structure at 9 GHz with 20-KW output power shows that the maximum electric field that occurs on the sharp edges is ten times lower than the air-dielectric breakdown voltage, which is the typical maximum peak power handling of 7/16 connectors.


Postface to “Model Predictive Control: Theory and Design”


Data Mining for Fraud Detection


title: High gain broadband monopole antenna for wireless communications

abstract: In this paper, a wideband monopole antenna with high gain characteristics has been proposed. Number of slits was introduced at the far radiating edge to transform it to multiple monopole radiators. Partial ground plane has been used to widen the bandwidth while by inserting suitable slits at the radiating edges return loss and bandwidth has been improved. The proposed antenna provides high gain up to 13.2dB and the achieved impedance bandwidth is wider than an earlier reported design. FR4 Epoxy with dielectric constant 4.4 and loss tangent 0.02 has been used as substrate material. Antenna has been simulated using HFSS (High Frequency Structure Simulator) as a 3D electromagnetic field simulator, based on finite element method. A good settlement has been found between simulated and measured results. The proposed design is suitable for GSM (890–960MHz), GPS (L1:1575.42MHz, L2:1227.60MHz, L3:1381.05MHz, L4:1379.913MHz, L5:1176.45MHz), DCS (1710–1880MHz), PCS (1850–1990MHz), UMTS(1920–2170MHz), Wi-Fi/WLAN/Hiper LAN/IEEE 802.11 2.4GHz (2412–2484MHz), 3.6GHz (3657.5–3690.0MHz) and 4.9/5.0GHz (4915–5825MHz), Bluetooth (2400–2484MHz), WiMAX 2.3GHz (2.3–2.5GHz), 2.5GHz (2500–2690 MHz), 3.3GHz, 3.5GHz (3400–3600MHz) and 5.8GHz (5.6–5.9GHz) & LTE applications.


title: Measurement of Sexual Arousal in Postoperative Male-to-Female Transsexuals Using Vaginal Photoplethysmography

abstract: Men’s sexual arousal patterns are category-specific: Men typically display significantly greater physiological responses to sexual stimuli depicting members of their preferred gender category. Category-specific patterns of sexual arousal have not been consistently reported in natal women. We used vaginal photoplethysmography to examine patterns of sexual arousal in 11 male-to-female (MtF) transsexuals following sex reassignment surgery (SRS) and in 72 natal women. Subjective arousal was measured with a continuous response lever. Video clips depicting sexual activity between two males, two females, or one male and one female were used as erotic stimuli. All transsexual participants displayed category-specific sexual arousal. Five homosexual transsexual participants (attracted exclusively to males before sex reassignment) showed greater genital and subjective responses to male than to female stimuli, while six nonhomosexual transsexual participants showed the opposite pattern. Vaginal pulse amplitude (VPA) was lower in transsexual participants than in natal women. The mean correlation between VPA and subjective responses was high in nonhomosexual transsexuals, but was significantly lower in homosexual transsexuals and in natal women. One transsexual participant who reported a change in sexual orientation following sex reassignment displayed VPA and subjective responses consistent with her pre-reassignment sexual orientation. We conclude that male-to-female transsexuals display male-typical category-specific sexual arousal following SRS, and that vaginal photoplethysmography is a promising methodology for studying patterns of sexual arousal in postoperative transsexuals.


title: Oxidative & nitrosative stress in depression: Why so much stress?

abstract: Many studies support a crucial role for oxidative & nitrosative stress (O&NS) in the pathophysiology of unipolar and bipolar depression. These disorders are characterized inter alia by lowered antioxidant defenses, including: lower levels of zinc, coenzyme Q10, vitamin E and glutathione; increased lipid peroxidation; damage to proteins, DNA and mitochondria; secondary autoimmune responses directed against redox modified nitrosylated proteins and oxidative specific epitopes. This review examines and details a model through which a complex series of environmental factors and biological pathways contribute to increased redox signaling and consequently increased O&NS in mood disorders. This multi-step process highlights the potential for future interventions that encompass a diverse range of environmental and molecular targets in the treatment of depression.


Anomaly Detection in Time Series of Graphs using ARMA Processes


title: Integrating NLP Using Linked Data

abstract: We are currently observing a plethora of Natural Language Processing tools and services being made available. Each of the tools and services has its particular strengths and weaknesses, but exploiting the strengths and synergistically combining different tools is currently an extremely cumbersome and time consuming task. Also, once a particular set of tools is integrated, this integration is not reusable by others. We argue that simplifying the interoperability of different NLP tools performing similar but also complementary tasks will facilitate the comparability of results and the creation of sophisticated NLP applications. In this paper, we present the NLP Interchange Format (NIF). NIF is based on a Linked Data enabled URI scheme for identifying elements in (hyper-)texts and an ontology for describing common NLP terms and concepts. In contrast to more centralized solutions such as UIMA and GATE, NIF enables the creation of heterogeneous, distributed and loosely coupled NLP applications, which use the Web as an integration platform. We present several use cases of the second version of the NIF specification (NIF 2.0) and the result of a developer study.


title: Designing A Catalog Management System? An Ontology Approach

abstract: When enterprises collaborate, a common frame of understanding of all products and catalogs in each organization is indispensable. Suppliers who virtually collaborate should be able to share product related data, create new products, and update old products from their own catalog. In this paper, we describe the development of a model for presenting electronic catalog based on OWL ontology language. This model uses WordNet ontology to distinguish classes and the relationships between them. We use SPARQL query language to introduce three types of search for the catalog management system. The concepts of this classification system are mapped to the concept of current standard classification systems such as UNSPSC, ECL@SS, and etc. We use VSM to diagnose class of one product. For customization aspect of electronic catalog, we introduce a semantic recommendation procedure which is more efficient when applied to Internet shopping malls. The suggested procedure recommends the semantic products to the customers and is originally based on Web usage mining, product classification, association rule mining, and frequently purchasing. We applied the procedure to the data set of MovieLens Company for performance evaluation, and some experimental results are provided. The experimental results have shown superior performance in terms of coverage and precision.


title: Topical advances in wound care.

abstract: Abstract There are fundamental differences between acute wounds that proceed to uncomplicated healing and those that become chronic wounds. Non-healing or chronic wounds can result from a combination of overlapping factors that prevent healing, including local tissue ischemia, repetitive trauma and ischemia/reperfusion injury, presence of tissue necrosis, impaired cellular and systemic host response to stress, and critical bacterial contamination. The bacterial burden in the wound contributes to a sustained inflammatory state, which inhibits normal progression to the proliferative phase of healing, thereby preventing restoration of tissue integrity. Appropriate wound bed preparation removes local barriers to healing and optimizes the tissue environment to achieve wound healing. It is an essential element of wound management that advances endogenous healing as well as the efficacy of topical and other wound therapy. This article will summarize a systematic approach to wound bed preparation using the "TIME" principle, and will highlight important advances in topical wound care.


title: A secure identity-based capability system

abstract: The author presents the design of an identity-based capability protection system called ICAP, which is aimed at a distributed system in a network environment. The semantics of traditional capabilities are modified to incorporate subject identities. This enables the monitoring, mediating, and recording of capability propagations to enforce security policies. It also supports administrative activities such as traceability. The author has developed an exception-list approach to achieve rapid revocation and the idea of capability propagation trees for complete revocation. Compared with existing capability system designs, ICAP requires much less storage and has the potential of lower cost and better real-time performance. The author proposes to expand R.Y. Kain and C.E. Landwehr's (1987) design taxonomy of capability-based systems to cover a wider range of designs. >


title: Turing Computability with Neural Nets

abstract: Abstract This paper shows the existence of a finite neural network, made up of sigmoidal neurons, which simulates a universal Turing machine. It is composed of less than 10 5 synchronously evolving processors, interconnected linearly. High-order connections are not required.


The Myths and Facts behind Cyber Security Risks for Industrial Control Systems


title: Semantic security against web application attacks

abstract: In this paper, we propose a method of detecting and classifying web application attacks. In contrast to current signature-based security methods, our solution is an ontology based technique. It specifies web application attacks by using semantic rules, the context of consequence and the specifications of application protocols. The system is capable of detecting sophisticated attacks effectively and efficiently by analyzing the specified portion of a user request where attacks are possible. Semantic rules help to capture the context of the application, possible attacks and the protocol that was used. These rules also allow inference to run over the ontological models in order to detect, the often complex polymorphic variations of web application attacks. The ontological model was developed using Description Logic that was based on the Web Ontology Language (OWL). The inference rules are Horn Logic statements and are implemented using the Apache JENA framework. The system is therefore platform and technology independent. Prior to the evaluation of the system the knowledge model was validated by using OntoClean to remove inconsistency, incompleteness and redundancy in the specification of ontological concepts. The experimental results show that the detection capability and performance of our system is significantly better than existing state of the art solutions. The system successfully detects web application attacks whilst generating few false positives. The examples that are presented demonstrate that a semantic approach can be used to effectively detect zero day and more sophisticated attacks in a real-world environment.


title: Patterns of entry and correction in large vocabulary continuous speech recognition systems

abstract: A study was conducted to evaluate user performance andsatisfaction in completion of a set of text creation tasks usingthree commercially available continuous speech recognition systems.The study also compared user performance on similar tasks usingkeyboard input. One part of the study (Initial Use) involved 24users who enrolled, received training and carried out practicetasks, and then completed a set of transcription and compositiontasks in a single session. In a parallel effort (Extended Use),four researchers used speech recognition to carry out real worktasks over 10 sessions with each of the three speech recognitionsoftware products. This paper presents results from the Initial Usephase of the study along with some preliminary results from theExtended Use phase. We present details of the kinds of usabilityand system design problems likely in current systems and severalcommon patterns of error correction that we found.


Transfer of resonance radiation in purely scattering media - I. Semi-infinite medium.


title: Querying the Semantic Web with Corese Search Engine

abstract: This paper presents an ontology-based approach for web querying, using semantic metadata. We propose a query language based on ontologies and emphasize its ability to express approximate queries, useful for an efficient information retrieval on the web. We present the Corese search engine dedicated to RDF(S) metadata and illustrate it through several real-world applications.


title: A Convolutional Encoder Model for Neural Machine Translation

abstract: The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. In this paper we present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the entire source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT'16 English-Romanian translation we achieve competitive accuracy to the state-of-the-art and we outperform several recently published results on the WMT'15 English-German task. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT'14 English-French translation. Our convolutional encoder speeds up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM baseline.


title: DiskFiltration: Data Exfiltration from Speakerless Air-Gapped Computers via Covert Hard Drive Noise

abstract: Air-gapped computers are disconnected from the Internet physically and logically. This measure is taken in order to prevent the leakage of sensitive data from secured networks. In the past, it has been shown that malware can exfiltrate data from air-gapped computers by transmitting ultrasonic signals via the computer's speakers. However, such acoustic communication relies on the availability of speakers on a computer. In this paper, we present 'DiskFiltration,' a covert channel which facilitates the leakage of data from an air-gapped compute via acoustic signals emitted from its hard disk drive (HDD). Our method is unique in that, unlike other acoustic covert channels, it doesn't require the presence of speakers or audio hardware in the air-gapped computer. A malware installed on a compromised machine can generate acoustic emissions at specific audio frequencies by controlling the movements of the HDD's actuator arm. Digital Information can be modulated over the acoustic signals and then be picked up by a nearby receiver (e.g., smartphone, smartwatch, laptop, etc.). We examine the HDD anatomy and analyze its acoustical characteristics. We also present signal generation and detection, and data modulation and demodulation algorithms. Based on our proposed method, we developed a transmitter on a personal computer and a receiver on a smartphone, and we provide the design and implementation details. We also evaluate our covert channel on various types of internal and external HDDs in different computer chassis and at various distances. With DiskFiltration we were able to covertly transmit data (e.g., passwords, encryption keys, and keylogging data) between air-gapped computers to a smartphone at an effective bit rate of 180 bits/minute (10,800 bits/hour) and a distance of up to two meters (six feet).


title: Modeling and identification of spool dynamics in an industrial electro-hydraulic valve

abstract: Approximate models of spool dynamics are proposed to deal with dynamic delays observed for an industrial electro-hydraulic valve. The suggested model consists of a linear block and two static nonlinearities. A method for identification of parameters is suggested that allows to deal with a case when only pressure in a hydraulic cylinder is measured but not the displacement of the spool. The approach is verified experimentally.


title: Learning Bayesian Belief Network Classifiers: Algorithms and System

abstract: This paper investigates the methods for learning predictive classifiers based on Bayesian belief networks (BN) - primarily unrestricted Bayesian networks and Bayesian multi-nets. We present our algorithms for learning these classifiers, and discuss how these methods address the overfitting problem and provide a natural method for feature subset selection. Using a set of standard classification problems, we empirically evaluate the performance of various BN-based classifiers. The results show that the proposed BN and Bayes multinet classifiers are competitive with (or superior to) the best known classifiers, based on both BN and other formalisms; and that the computational time for learning and using these classifiers is relatively small. These results argue that BN-based classifiers deserve more attention in the data mining community.


title: Agent-Based Integrated Decision Making for Autonomous Vehicles in Urban Traffic

abstract: We present an approach for integrated decision making of vehicle agents in urban traffic systems. The planning process for a vehicle agent is broken down into two stages: strategic planning for selection of the optimal route and tactical planning for passing the current street in the most optimal manner. Vehicle routing is considered as a stochastic shortest path problem with imperfect knowledge about network conditions. Tactical planning is considered as a problem of collaborative learning with neighbor vehicles.We present planning algorithms for both stages and demonstrate interconnections between them; as well, an example illustrates how the proposed approach may reduce travel time of vehicle agents in urban traffic.


title: A direct texture placement and editing interface

abstract: The creation of most models used in computer animation and computer games requires the assignment of texture coordinates, texture painting, and texture editing. We present a novel approach for texture placement and editing based on direct manipulation of textures on the surface. Compared to conventional tools for surface texturing, our system combines UV-coordinate specification and texture editing into one seamless process, reducing the need for careful initial design of parameterization and providing a natural interface for working with textures directly on 3D surfaces.A combination of efficient techniques for interactive constrained parameterization and advanced input devices makes it possible to realize a set of natural interaction paradigms. The texture is regarded as a piece of stretchable material, which the user can position and deform on the surface, selecting arbitrary sets of constraints and mapping texture points to the surface; in addition, the multi-touch input makes it possible to specify natural handles for texture manipulation using point constraints associated with different fingers. Pressure can be used as a direct interface for texture combination operations. The 3D position of the object and its texture can be manipulated simultaneously using two-hand input.


title: Design of RF power combiner/divider based on surface acoustic wave longitudinally coupled resonators

abstract: A compact filtering power divider/combiner using surface acoustic wave (SAW) technology for low frequency applications is presented. The proposed power divider/combiner is based on parallel connected SAW coupled resonator filters providing a dual functional capability as well as a higher level of miniaturization. The designed structure is a 3 way power divider/combiner that exhibits a passband-filtering response. Simulated results demonstrated a return loss and insertion loss of 3.2 dB and 25 dB around 87 MHz with a relative bandwidth of 1%. The total area of the designed power combined/divider is 8.4×4.6 mm2 which is a relative size reduction of more than 70% in comparison to traditional Wilkinson power dividers operating at same frequency.


title: Cyber security challenges in Smart Cities: Safety, security and privacy

abstract: The world is experiencing an evolution of Smart Cities. These emerge from innovations in information technology that, while they create new economic and social opportunities, pose challenges to our security and expectations of privacy. Humans are already interconnected via smart phones and gadgets. Smart energy meters, security devices and smart appliances are being used in many cities. Homes, cars, public venues and other social systems are now on their path to the full connectivity known as the “Internet of Things.” Standards are evolving for all of these potentially connected systems. They will lead to unprecedented improvements in the quality of life. To benefit from them, city infrastructures and services are changing with new interconnected systems for monitoring, control and automation. Intelligent transportation, public and private, will access a web of interconnected data from GPS location to weather and traffic updates. Integrated systems will aid public safety, emergency responders and in disaster recovery. We examine two important and entangled challenges: security and privacy. Security includes illegal access to information and attacks causing physical disruptions in service availability. As digital citizens are more and more instrumented with data available about their location and activities, privacy seems to disappear. Privacy protecting systems that gather data and trigger emergency response when needed are technological challenges that go hand-in-hand with the continuous security challenges. Their implementation is essential for a Smart City in which we would wish to live. We also present a model representing the interactions between person, servers and things. Those are the major element in the Smart City and their interactions are what we need to protect.


title: Towards interactive narrative medicine.

abstract: Interactive Storytelling technologies have attracted significant interest in the field of simulation and serious gaming for their potential to provide a principled approach to improve user engagement in training scenarios. In this paper, we explore the use of Interactive Storytelling to support Narrative Medicine as a reflective practice. We describe a workflow for the generation of virtual narratives from high-level descriptions of patients' experiences as perceived by physicians, which can help to objectivize such perceptions and support various forms of analysis.


title: Design of an energy-based controller for a 2D ball segway

abstract: A ball segway is a ballbot-type robot that has a car-like structure. It can move with three omnidirectional-wheel mechanisms to drive the ball while maintaining balance. To obtain stable balancing and transferring simultaneously of the 2D ball segway which is an underactuated system, a control law is designed based on energy method. The energy storage function is formulated to prove the passivity property of the system. Simulation results show the effectiveness of our approach.