title: A degradation model of aluminum electrolytic capacitors for LED drivers

abstract: The failure of aluminum electrolytic capacitors is considered as one of major failure modes of the LED drivers. This paper propose a degradation model of aluminum electrolytic capacitors considers impacts of operation time and temperature.


title: Demonstration of a HV BCD technology with LV CMOS process

abstract: Conventional BCD and high voltage technologies are developed with extra masks and additional thermal drive-in process units included in existing LV platforms. The technology development is time-consuming and the turnaround time for the whole process takes longer. In this paper, a low cost solution with a set of layout design methodology is proposed to accomplish the embedded power BCD technology in the existing 5V CMOS process without any additional process steps.


Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter


title: Stability and traction control of an actively actuated micro‐rover

abstract: This article addresses the issue of enhancing mobility of actively actuated vehicles by the use of optimal force distribution, and by actively controlling the location of the center of mass of the vehicle body. Force distribution in actively actuated vehicles is an underspecified problem, and optimal force distribution leads to improved contact conditions. Actively actuated vehicles also possess the ability to vary their configuration. This capability can be used to locate the center of mass of the system to further enhance mobility by providing sufficient traction, and ensuring vehicle stability, during difficult vehicle maneuvers. In this work, the general idea of control of the location of the center of mass, followed by optimal force distribution, is addressed first. This is followed by an application of these ideas to Gofor, a simple actively actuated wheeled micro-rover system, that has been designed and fabricated at the Jet Propulsion Laboratory. An algorithm to achieve desired traction and stability has been developed. Simulation studies were performed to investigate negotiation of a vertical surface. It was found that one set of wheels readily climbed the vertical surface with the use of active mass and force distribution. This is a significant improvement over traditional wheeled vehicle systems. © 1994 John Wiley & Sons, Inc.


title: The Evolution of First Person Vision Methods: A Survey

abstract: The emergence of new wearable technologies, such as action cameras and smart glasses, has increased the interest of computer vision scientists in the first person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with first person vision (FPV) recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real time, is expected. The current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user-machine interaction, and so on. This paper summarizes the evolution of the state of the art in FPV video analysis between 1997 and 2014, highlighting, among others, the most commonly used features, methods, challenges, and opportunities within the field.


title: Genetic K-means algorithm.

abstract: In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both. To circumvent these expensive operations, we hybridize GA with a classical gradient descent algorithm used in clustering, viz. K-means algorithm. Hence, the name genetic K-means algorithm (GKA). We define K-means operator, one-step of K-means algorithm, and use it in GKA as a search operator instead of crossover. We also define a biased mutation operator specific to clustering called distance-based-mutation. Using finite Markov chain theory, we prove that the GKA converges to the global optimum. It is observed in the simulations that GKA converges to the best known optimum corresponding to the given data in concurrence with the convergence result. It is also observed that GKA searches faster than some of the other evolutionary algorithms used for clustering.


title: ADELFE: a methodology for adaptive multi-agent systems engineering

abstract: Adaptive software is used in situations where either the environment is unpredictable or the system is open. This paper presents a methodology named ADELFE, which is led by the Rational Unified Process (RUP) but is devoted to software engineering of adaptive multi-agent systems. ADELFE guarantees that the software is developed according to the AMAS theory1. We focus this presentation on the additions of ADELFE regarding the three first core workflows of the RUP. Therefore, during the requirements phase, the environment of the studied system must be defined and characterized. Then, in the analysis phase, the engineer is guided to decide to use adaptive multi-agent technology and to identify the agents through the system and the environment models. Finally, the design workflow of ADELFE must provide the cooperative agent?s model and helps the developer to define the local agents? behavior. We illustrate the methodology by applying it to a case study: a timetable design.


title: Avoiding Energy Holes in Wireless Sensor Networks with Nonuniform Node Distribution

abstract: In this paper, we investigate the theoretical aspects of the nonuniform node distribution strategy used to mitigate the energy hole problem in wireless sensor networks (WSNs). We conclude that in a circular multihop sensor network (modeled as concentric coronas) with nonuniform node distribution and constant data reporting, the unbalanced energy depletion among all the nodes in the network is unavoidable. Even if the nodes in the inner coronas of the network have used up their energy simultaneously, the ones in the outermost corona may still have unused energy. This is due to the intrinsic many-to-one traffic pattern of WSNs. Nevertheless, nearly balanced energy depletion in the network is possible if the number of nodes increases in geometric progression from the outer coronas to the inner ones except the outermost one. Based on the analysis, we propose a novel nonuniform node distribution strategy to achieve nearly balanced energy depletion in the network. We regulate the number of nodes in each corona and derive the ratio between the node densities in the adjacent (i + 1)th and ith coronas by the strategy. Finally, we propose (q-switch routing, a distributed shortest path routing algorithm tailored for the proposed nonuniform node distribution strategy. Extensive simulations have been performed to validate the analysis.


title: Towards task scheduling in a cloud-fog computing system

abstract: In recent years, with the advent of the Internet of Things (IoT), fog computing is introduced as a powerful complement to the cloud to handle the IoT's data and communications needs. The interplay and cooperation between the edge (fog) and the core (cloud) has recently received considerable attention. In this paper, we consider task scheduling in a cloud-fog computing system, where a fog provider can exploit the collaboration between its own fog nodes and the rented cloud nodes for efficiently executing users' large-scale offloading applications. We first formulate the task scheduling problem in such cloud-fog environment and then propose a heuristic-based algorithm, whose major objective is achieving the balance between the makespan and the monetary cost of cloud resources. The numerical results show that our proposed algorithm achieves better tradeoff value than other existing algorithms.


title: Unusual Presentation of Carcinoma of Penis

abstract: A case of carcinoma of the penis in a 55-year-old landlord is described. He presented with a fungating growth on the shaft of his penis with an unusual history. The lesion started as a nodule in the coronal sulcus leading to thinning of urinary stream and ultimately retention of urine, which was diagnosed and treated as a case of urethral stricture. Wedge biopsy of the growth revealed the case of squamous cell carcinoma of penis. Ultrasonography and CT scan of pelvis and abdomen proved the disease to be localized to penis and total penectomy was carried out.


title: Expressive speech synthesis using a concatenative synthesizer

abstract: This paper describes an experiment in synthesizing four emotional states - anger, happiness, sadness and neutral – using a concatenative speech synthesizer. To achieve this, five emotionally (i.e., semantically) unbiased target sentences were prepared. Then, separate speech inventories, comprising the target diphones for each of the above emotions, were recorded. Using the 16 different combinations of prosody and inventory during synthesis resulted in 80 synthetic test sentences. The results were evaluated by conducting listening tests with 33 naive listeners. Synthesized anger was recognized with 86.1% accuracy, sadness with 89.1%, happiness with 44.2%, and neutral emotion with 81.8% accuracy. According to our results, anger was classified as inventory dominant and sadness and neutral as prosody dominant. Results were not sufficient to make similar conclusions regarding happiness. The highest recognition accuracies were achieved for sentences synthesized by using prosody and diphone inventory belonging to the same emotion.


title: Retinal Vascular Layers Imaged by Fluorescein Angiography and Optical Coherence Tomography Angiography

abstract: Importance The retinal vasculature is involved in many ocular diseases that cause visual loss. Although fluorescein angiography is the criterion standard for evaluating the retina vasculature, it has risks of adverse effects and known defects in imaging all the layers of the retinal vasculature. Optical coherence tomography (OCT) angiography can image vessels based on flow characteristics and may provide improved information. Objective To investigate the ability of OCT angiography to image the vascular layers within the retina compared with conventional fluorescein angiography. Design, Setting, and Participants In this study, performed from March 14, 2014, through June 24, 2014, a total of 5 consecutive, overlapping B-scan OCT angiography images composed of 216 A-scans were obtained at 216 discrete positions within a region of interest, typically a 2 × 2-mm area of the retina. The flow imaging was based on split-spectrum amplitude decorrelation angiography (SSADA), which can dissect layers of vessels in the retina. These distinct layers were compared with the fluorescein angiograms in 12 healthy eyes from patients at a private practice retina clinic to evaluate the ability to visualize the radial peripapillary capillary network. The proportion of the inner vs outer retinal vascular layers was estimated by 3 masked readers and compared with conventional fluorescein angiograms of the same eyes. Main Outcomes and Measures Outcome measures were visualization of the radial peripapillary capillary network in the fluorescein and SSADA scans and the proportion of the inner retinal vascular plexus vs the outer retinal capillary plexus as seen in SSADA scans that would match the fluorescein angiogram. Results In none of the 12 eyes could the radial peripapillary capillary network be visualized completely around the nerve head by fluorescein angiography, whereas the network was readily visualized in the SSADA scans. The fluorescein angiograms were matched, with a mean proportion of the inner vascular plexus being 95.3% (95% CI, 92.2%-97.8%) vs 4.7% (95% CI, 2.6%-5.7%) for the outer capillary plexus from the SSADA scans. Conclusions and Relevance Fluorescein angiography does not image the radial peripapillary or the deep capillary networks well. However, OCT angiography can image all layers of the retinal vasculature without dye injection. Therefore, OCT angiography, and the findings generated, have the potential to affect clinical evaluation of the retina in healthy patients and patients with disease.


title: Beyond parallel programming with domain specific languages

abstract: Today, almost all computer architectures are parallel and heterogeneous; a combination of multiple CPUs, GPUs and specialized processors. This creates a challenging problem for application developers who want to develop high performance programs without the effort required to use low-level, architecture specific parallel programming models (e.g. OpenMP for CMPs, CUDA for GPUs, MPI for clusters). Domain-specific languages (DSLs) are a promising solution to this problem because they can provide an avenue for high-level application-specific abstractions with implicit parallelism to be mapped directly to low level architecture-specific programming models; providing both high programmer productivity and high execution performance. In this talk I will describe an approach to building high performance DSLs, which is based on DSL embedding in a general purpose programming language, metaprogramming and a DSL infrastructure called Delite. I will describe how we transform DSL programs into efficient first-order low-level code using domain specific optimization, parallelism and locality optimization with parallel patterns, and architecture-specific code generation. All optimizations and transformations are implemented in Delite: an extensible DSL compiler infrastucture that significantly reduces the effort required to develop new DSLs. Delite DSLs for machine learning, data querying, graph analysis, and scientific computing all achieve performance competitive with manually parallelized C++ code.


title: Comparative Study of Deep Learning Software Frameworks

abstract: Deep learning methods have resulted in significant performance improvements in several application domains and as such several software frameworks have been developed to facilitate their implementation. This paper presents a comparative study of five deep learning frameworks, namely Caffe, Neon, TensorFlow, Theano, and Torch, on three aspects: extensibility, hardware utilization, and speed. The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings. The speed performance metrics used here include the gradient computation time, which is important during the training phase of deep networks, and the forward time, which is important from the deployment perspective of trained networks. For convolutional networks, we also report how each of these frameworks support various convolutional algorithms and their corresponding performance. From our experiments, we observe that Theano and Torch are the most easily extensible frameworks. We observe that Torch is best suited for any deep architecture on CPU, followed by Theano. It also achieves the best performance on the GPU for large convolutional and fully connected networks, followed closely by Neon. Theano achieves the best performance on GPU for training and deployment of LSTM networks. Caffe is the easiest for evaluating the performance of standard deep architectures. Finally, TensorFlow is a very flexible framework, similar to Theano, but its performance is currently not competitive compared to the other studied frameworks.


title: MAC Essentials for Wireless Sensor Networks

abstract: The wireless medium being inherently broadcast in nature and hence prone to interferences requires highly optimized medium access control (MAC) protocols. This holds particularly true for wireless sensor networks (WSNs) consisting of a large amount of miniaturized battery-powered wireless networked sensors required to operate for years with no human intervention. There has hence been a growing interest on understanding and optimizing WSN MAC protocols in recent years, where the limited and constrained resources have driven research towards primarily reducing energy consumption of MAC functionalities. In this paper, we provide a comprehensive state-of-the-art study in which we thoroughly expose the prime focus of WSN MAC protocols, design guidelines that inspired these protocols, as well as drawbacks and shortcomings of the existing solutions and how existing and emerging technology will influence future solutions. In contrast to previous surveys that focused on classifying MAC protocols according to the technique being used, we provide a thematic taxonomy in which protocols are classified according to the problems dealt with. We also show that a key element in selecting a suitable solution for a particular situation is mainly driven by the statistical properties of the generated traffic.


title: Baby's leukemia recedes after novel cell therapy

abstract: A London baby with end-stage leukemia has received a remarkable new cancer treatment: off-the shelf T cells with several gene modifications. Doctors say it9s too early to know whether she9s cured, but the announcement advances a frontier in cancer immunotherapy, in which the body9s immune system tackles the disease. For the past several years, researchers have been modifying T cells so they can attack leukemia, but the cells must be painstakingly isolated from the patients themselves and grown in a lab. Drug companies and many doctors dream of using off-the-shelf cells to make the therapy more like a regular drug. Now, by harnessing advances in genome editing to slice and dice genes in donor T cells, researchers have created a new type of cancer immunotherapy.


title: KittingBot: A Mobile Manipulation Robot for Collaborative Kitting in Automotive Logistics

abstract: Individualized manufacturing of cars requires kitting: the collection of individual sets of part variants for each car. This challenging logistic task is frequently performed manually by warehouseman. We propose a mobile manipulation robotic system for autonomous kitting, building on the Kuka Miiwa platform which consists of an omnidirectional base, a 7 DoF collaborative iiwa manipulator, cameras, and distance sensors. Software modules for detection and pose estimation of transport boxes, part segmentation in these containers, recognition of part variants, grasp generation, and arm trajectory optimization have been developed and integrated. Our system is designed for collaborative kitting, i.e. some parts are collected by warehouseman while other parts are picked by the robot. To address safe human-robot collaboration, fast arm trajectory replanning considering previously unforeseen obstacles is realized. The developed system was evaluated in the European Robotics Challenge 2, where the Miiwa robot demonstrated autonomous kitting, part variant recognition, and avoidance of unforeseen obstacles.


title: Adolescents’ use of sexually explicit internet material and their sexual attitudes and behavior: Parallel development and directional effects.

abstract: Although research has repeatedly demonstrated that adolescents' use of sexually explicit Internet material (SEIM) is related to their endorsement of permissive sexual attitudes and their experience with sexual behavior, it is not clear how linkages between these constructs unfold over time. This study combined 2 types of longitudinal modeling, mean-level development and cross-lagged panel modeling, to examine (a) developmental patterns in adolescents' SEIM use, permissive sexual attitudes, and experience with sexual behavior, as well as whether these developments are related; and (b) longitudinal directionality of associations between SEIM use on the 1 hand and permissive sexual attitudes and sexual behavior on the other hand. We used 4-wave longitudinal data from 1,132 7th through 10th grade Dutch adolescents (M(age) T1 = 13.95; 52.7% boys) and estimated multigroup models to test for moderation by gender. Mean-level developmental trajectories showed that boys occasionally and increasingly used SEIM over the 18-month study period, which co-occurred with increases in their permissive attitudes and their experience with sexual behavior. Cross-lagged panel models revealed unidirectional effects from boys' SEIM use on their subsequent endorsement of permissive attitudes, but no consistent directional effects between their SEIM use and sexual behavior. Girls showed a similar pattern of increases in experience with sexual behavior, but their SEIM use was consistently low and their endorsement of permissive sexual attitudes decreased over the 18-month study period. In contrast to boys, girls' SEIM use was not longitudinally related to their sexual attitudes and behavior. Theoretical and practical implications of these gender-specific findings are discussed. (PsycINFO Database Record


title: Black-box macromodeling and its EMC applications

abstract: The main idea of black-box macromodeling is to approximate the dynamic behavior of complex systems in terms of lowcomplexity models or equivalent circuits. Such compact models can be derived through robust numerical algorithms, such as the Vector Fitting scheme, starting from frequency- or time-domain responses of the system, and without any specific knowledge of its internal structure. The excellent accuracy that can be achieved, combined with the reduced size of the models, has led to a widespread adoption of this approach in several electrical and electronic applications, allowing designers to perform numerical simulations at the system level with high efficiency. This paper reviews the basics of black-box macromodeling and illustrates several application scenarios that are relevant for the EMC community, including Signal and Power Integrity, lossy transmission line modeling, electromagnetic full-wave simulation, network equivalencing and transformer modeling.


title: Posterior short-segment fixation with or without fusion for thoracolumbar burst fractures. a five to seven-year prospective randomized study.

abstract: BACKGROUND ::: The impact of fusion as a supplement to short-segment instrumentation for the treatment of thoracolumbar burst fractures is unclear. We conducted a controlled clinical trial to define the effect of fusion on lumbar spine and patient-related functional outcomes. ::: ::: ::: METHODS ::: From 2000 to 2002, seventy-three consecutive patients with a single-level Denis type-B burst fracture involving the thoracolumbar spine and a load-sharing score of <or=6 were managed with posterior pedicle screw instrumentation. The patients were randomly assigned to treatment with posterolateral fusion (fusion group, n = 37) or without posterolateral fusion (nonfusion group, n = 36). The patients were followed for at least five years after surgery and were assessed with regard to clinical and radiographic outcomes. Clinical outcomes were evaluated with use of the Frankel scale, the motor score of the American Spinal Injury Association, a visual analog scale, and the Short Form-36 (SF-36) questionnaire. Radiographic outcomes were assessed on the basis of the local kyphosis angle and loss of kyphosis correction. ::: ::: ::: RESULTS ::: No significant difference in radiographic or clinical outcomes was noted between the patients managed with the two techniques. Both operative time and blood loss were significantly less in the nonfusion group compared with the fusion group (p < 0.05). Twenty-five of the thirty-seven patients in the fusion group still had some degree of donor-site pain at the time of the latest examination. ::: ::: ::: CONCLUSIONS ::: Posterolateral bone-grafting is not necessary when a Denis type-B thoracolumbar burst fracture associated with a load-sharing score of <or=6 is treated with short-segment pedicle screw fixation.


title: Face2Face: Real-Time Face Capture and Reenactment of RGB Videos

abstract: We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time.


title: Different Contributions of the Human Amygdala and Ventromedial Prefrontal Cortex to Decision-Making

abstract: The somatic marker hypothesis proposes that decision-making is a process that depends on emotion. Studies have shown that damage of the ventromedial prefrontal (VMF) cortex precludes the ability to use somatic (emotional) signals that are necessary for guiding decisions in the advantageous direction. However, given the role of the amygdala in emotional processing, we asked whether amygdala damage also would interfere with decision-making. Furthermore, we asked whether there might be a difference between the roles that the amygdala and VMF cortex play in decision-making. To address these two questions, we studied a group of patients with bilateral amygdala, but not VMF, damage and a group of patients with bilateral VMF, but not amygdala, damage. We used the “gambling task” to measure decision-making performance and electrodermal activity (skin conductance responses, SCR) as an index of somatic state activation. All patients, those with amygdala damage as well as those with VMF damage, were (1) impaired on the gambling task and (2) unable to develop anticipatory SCRs while they pondered risky choices. However, VMF patients were able to generate SCRs when they received a reward or a punishment (play money), whereas amygdala patients failed to do so. In a Pavlovian conditioning experiment the VMF patients acquired a conditioned SCR to visual stimuli paired with an aversive loud sound, whereas amygdala patients failed to do so. The results suggest that amygdala damage is associated with impairment in decision-making and that the roles played by the amygdala and VMF in decision-making are different.


title: An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

abstract: A high-performance differential global positioning system (GPS) receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.


title: The Possible-Word Constraint in the Segmentation of Continuous Speech

abstract: We propose that word recognition in continuous speech is subject to constraints on what may constitute a viable word of the language. This Possible-Word Constraint (PWC) reduces activation of candidate words if their recognition would imply word status for adjacent input which could not be a word - for instance, a single consonant. In two word-spotting experiments, listeners found it much harder to detectapple,for example, infapple(where [f] alone would be an impossible word), than invuffapple(wherevuffcould be a word of English). We demonstrate that the PWC can readily be implemented in a competition-based model of continuous speech recognition, as a constraint on the process of competition between candidate words; where a stretch of speech between a candidate word and a (known or likely) word boundary is not a possible word, activation of the candidate word is reduced. This implementation accurately simulates both the present results and data from a range of earlier studies of speech segmentation.


Understanding and Improving Distal Pointing Interaction


title: An overview of cancer treatment by terahertz radiation

abstract: Cancer claimed the death of over 7.6 million people each year all over the world. Many attempts have being practiced in clinical applications, yet some new technologies are believed to make up for the shortcomings of existing methods. Some latest advances have aroused interests in terahertz imaging and the biological effect as promising diagnostic methods for cancer. This review, however, taps into the biological effects of terahertz radiation as well as the possible mechanism to reveal the potentials and prospects of THz in cancer treatment.


title: A Framework for Exploiting Data Availability to Optimize Communication

abstract: This paper presents a global analysis framework for determining the availability of data on a virtual processor grid. The data availability information obtained is useful for optimizing communication when generating SPMD programs for distributed address-space multiprocessors. We introduce a new kind of array section descriptor, called an Available Section Descriptor, which represents the mapping of an array section onto the processor grid. We present an array data-flow analysis procedure, based on interval analysis, for determining data availability at each statement. Several communication optimizations, including redundant communication elimination, are also described. An advantage of our approach is that it is independent of actual data partitioning and representation of explicit communication.


title: On Influential Node Discovery in Dynamic Social Networks

abstract: The problem of maximizing influence spread has been widely studied in social networks, because of its tremendous number of applications in determining critical points in a social network for information dissemination. All the techniques proposed in the literature are inherently static in nature, which are designed for social networks with a fixed set of links. However, many forms of social interactions are transient in nature, with relatively short periods of interaction. Any influence spread may happen only during the period of interaction, and the probability of spread is a function of the corresponding interaction time. Furthermore, such interactions are quite fluid and evolving, as a result of which the topology of the underlying network may change rapidly, as new interactions form and others terminate. In such cases, it may be desirable to determine the influential nodes based on the dynamic interaction patterns. Alternatively, one may wish to discover the most likely starting points for a given infection pattern. We will propose methods which can be used both for optimization of information spread, as well as the backward tracing of the source of influence spread. We will present experimental results illustrating the effectiveness of our approach on a number of real data sets.


A FUZZY LOGIC CLUSTER FORMATION PROTOCOL FOR WIRELESS SENSOR NETWORKS


title: Soccer: Who Has the Ball? Generating Visual Analytics and Player Statistics

abstract: The world of sports intrinsically involves fast and complex events that are difficult for coaches, trainers and players to analyze, and also for audiences to follow. In sports, talent identification and selection are imperative for the development of future elite level performers. Current scenarios involve word-of-mouth, coaches and recruiters scouring through hours of videos and many times manual annotation of these videos. In this paper, we propose an approach that automatically generates visual analytics from videos specifically for soccer to help coaches and recruiters identify the most promising talents. We use (a) Convolutional Neural Networks (CNNs) to localize soccer players in a video and identify players controlling the ball, (b) Deep Convolutional Generative Adversarial Networks (DCGAN) for data augmentation, (c) a histogram based matching to identify teams and (d) frame-by-frame prediction and verification analyses to generate visual analytics. We compare our approach with state-of-the-art approaches and achieve an accuracy of 86.59% on identifying players controlling the ball and an accuracy of 84.73% in generating the game analytics and player statistics.


title: Cascaded Dual-Buck Inverter With Reduced Number of Inductors

abstract: A dual-buck inverter (DBI) possesses no shoot-through worries and can boost efficiency by using power mosfet s. The main drawback of a DBI is the use of more inductors than in a traditional full-bridge inverter. This greater number of inductors decreases the power density and increases system cost. In this paper, a modified cascaded full-bridge DBI is proposed. It retains all the advantages of the conventional cascaded full-bridge DBI, possesses no shoot-through issues, and can be operated with no pulse-width-modulation dead-times. It can be implemented with power mosfet without reverse recovery issues of a body diode to boost efficiency. The cascaded modules in the proposed inverter share current limiting inductors. Therefore, the number of inductors and inductor footprints, the complexity of the circuit layout, required inductance, and magnetic volume can all be decreased. To show the advantages of the proposed inverter, detailed theoretical analysis and experimental results are provided.


Disabling pansclerotic morphea of childhood – unusual case and management challenges


title: iBFS: Concurrent Breadth-First Search on GPUs

abstract: Breadth-First Search (BFS) is a key graph algorithm with many important applications. In this work, we focus on a special class of graph traversal algorithm - concurrent BFS - where multiple breadth-first traversals are performed simultaneously on the same graph. We have designed and developed a new approach called iBFS that is able to run i concurrent BFSes from i distinct source vertices, very efficiently on Graphics Processing Units (GPUs). iBFS consists of three novel designs. First, iBFS develops a single GPU kernel for joint traversal of concurrent BFS to take advantage of shared frontiers across different instances. Second, outdegree-based GroupBy rules enables iBFS to selectively run a group of BFS instances which further maximizes the frontier sharing within such a group. Third, iBFS brings additional performance benefit by utilizing highly optimized bitwise operations on GPUs, which allows a single GPU thread to inspect a vertex for concurrent BFS instances. The evaluation on a wide spectrum of graph benchmarks shows that iBFS on one GPU runs up to 30x faster than executing BFS instances sequentially, and on 112 GPUs achieves near linear speedup with the maximum performance of 57,267 billion traversed edges per second (TEPS).


title: Spam Filtering with Naive Bayes -- Which Naive Bayes?

abstract: Naive Bayes is very popular in commercial and open-source anti-spam e-mail filters. There are, however, several forms of Naive Bayes, something the anti-spam literature does not always acknowledge. We discuss five dierent versions of Naive Bayes, and compare them on six new, non-encoded datasets, that contain ham messages of particular Enron users and fresh spam messages. The new datasets, which we make publicly available, are more realistic than previous comparable benchmarks, because they maintain the tempo- ral order of the messages in the two categories, and they emulate the varying proportion of spam and ham messages that users receive over time. We adopt an experimental procedure that emulates the incremental training of person- alized spam filters, and we plot roc curves that allow us to compare the dierent versions of nb over the entire tradeo between true positives and true negatives.


title: Construction of Vietnamese SentiWordNet by using Vietnamese Dictionary

abstract: SentiWordNet is an important lexical resource supporting sentiment analysis in opinion mining applications. In this paper, we propose a novel approach to construct a Vietnamese SentiWordNet (VSWN). SentiWordNet is typically generated from WordNet in which each synset has numerical scores to indicate its opinion polarities. Many previous studies obtained these scores by applying a machine learning method to WordNet. However, Vietnamese WordNet is not available unfortunately by the time of this paper. Therefore, we propose a method to construct VSWN from a Vietnamese dictionary, not from WordNet. We show the effectiveness of the proposed method by generating a VSWN with 39,561 synsets automatically. The method is experimentally tested with 266 synsets with aspect of positivity and negativity. It attains a competitive result compared with English SentiWordNet that is 0.066 and 0.052 differences for positivity and negativity sets respectively.


title: Annotated probabilistic temporal logic

abstract: The semantics of most logics of time and probability is given via a probability distribution over threads, where a thread is a structure specifying what will be true at different points in time (in the future). When assessing the probabilities of statements such as “Event a will occur within 5 units of time of event b,” there are many different semantics possible, even when assessing the truth of this statement within a single thread. We introduce the syntax of annotated probabilistic temporal (APT) logic programs and axiomatically introduce the key notion of a frequency function (for the first time) to capture different types of intrathread reasoning, and then provide a semantics for intrathread and interthread reasoning in APT logic programs parameterized by such frequency functions. We develop a comprehensive set of complexity results for consistency checking and entailment in APT logic programs, together with sound and complete algorithms to check consistency and entailment. The basic algorithms use linear programming, but we then show how to substantially and correctly reduce the sizes of these linear programs to yield better computational properties. We describe a real world application we are developing using APT logic programs.


title: Engineering and characterization of a superfolder green fluorescent protein

abstract: Existing variants of green fluorescent protein (GFP) often misfold when expressed as fusions with other proteins. We have generated a robustly folded version of GFP, called 'superfolder' GFP, that folds well even when fused to poorly folded polypeptides. Compared to 'folding reporter' GFP, a folding-enhanced GFP containing the 'cycle-3' mutations and the 'enhanced GFP' mutations F64L and S65T, superfolder GFP shows improved tolerance of circular permutation, greater resistance to chemical denaturants and improved folding kinetics. The fluorescence of Escherichia coli cells expressing each of eighteen proteins from Pyrobaculum aerophilum as fusions with superfolder GFP was proportional to total protein expression. In contrast, fluorescence of folding reporter GFP fusion proteins was strongly correlated with the productive folding yield of the passenger protein. X-ray crystallographic structural analyses helped explain the enhanced folding of superfolder GFP relative to folding reporter GFP.


title: Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

abstract: Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, Bayesian network etc can be applied on the educational data for predicting the student's performance in examination. This prediction will help to identify the weak students and help them to score better marks. The C4.5, ID3 and CART decision tree algorithms are applied on engineering student's data to predict their performance in the final exam. The outcome of the decision tree predicted the number of students who are likely to pass, fail or promoted to next year. The results provide steps to improve the performance of the students who were predicted to fail or promoted. After the declaration of the results in the final examination the marks obtained by the students are fed into the system and the results were analyzed for the next session. The comparative analysis of the results states that the prediction has helped the weaker students to improve and brought out betterment in the result.


title: Knowledge Graph Embedding: A Locally and Temporally Adaptive Translation-Based Approach

abstract: A knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. The construction of knowledge graphs in the past decades facilitates many applications, such as link prediction, web search analysis, question answering, and so on. Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, for example, TransE, TransH, and TransR, learn the embedding representation by defining a global margin-based loss function over the data. However, the loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidates, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this article, a locally adaptive translation method for knowledge graph embedding, called TransA, is proposed to find the loss function by adaptively determining its margin over different knowledge graphs. Then the convergence of TransA is verified from the aspect of its uniform stability. To make the embedding methods up-to-date when new vertices and edges are added into the knowledge graph, the incremental algorithm for TransA, called iTransA, is proposed by adaptively adjusting the optimal margin over time. Experiments on four benchmark data sets demonstrate the superiority of the proposed method, as compared to the state-of-the-art ones.


title: A monopulse slot array antenna based on dual-layer substrate integrated waveguide (SIW)

abstract: In this paper, a monopulse slot array antenna working at Ka-band based on dual-layer substrate integrated waveguide (SIW) is presented. The 16×16 slot array antenna with feeding network and the monopulse sum-difference comparator is designed on separated layers which takes the advantage of compact size and high aperture efficiency. The honeycomb SIW (HCSIW) is adopted to design the novel right-angle corner and 90 degree 3-dB coupler. The simulated bandwidth (S11 of sum port < −10 dB) of the antenna is about 9.43%. The maximum gain of sum pattern at 35 GHz is around 26.1 dB with sidelobe suppression of −27 dB in both E-plane and H-plane. This proposed antenna presents a good candidate for the directional-finding systems which are restricted by aperture size.


title: Matrix Completion With Noise

abstract: On the heels of compressed sensing, a remarkable new field has very recently emerged. This field addresses a broad range of problems of significant practical interest, namely, the recovery of a data matrix from what appears to be incomplete, and perhaps even corrupted, information. In its simplest form, the problem is to recover a matrix from a small sample of its entries, and comes up in many areas of science and engineering including collaborative filtering, machine learning, control, remote sensing, and computer vision to name a few. This paper surveys the novel literature on matrix completion, which shows that under some suitable conditions, one can recover an unknown low-rank matrix from a nearly minimal set of entries by solving a simple convex optimization problem, namely, nuclear-norm minimization subject to data constraints. Further, this paper introduces novel results showing that matrix completion is provably accurate even when the few observed entries are corrupted with a small amount of noise. A typical result is that one can recover an unknown n x n matrix of low rank r from just about nr log^2 n noisy samples with an error which is proportional to the noise level. We present numerical results which complement our quantitative analysis and show that, in practice, nuclear norm minimization accurately fills in the many missing entries of large low-rank matrices from just a few noisy samples. Some analogies between matrix completion and compressed sensing are discussed throughout.


title: Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition

abstract: Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain-computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity, low signal-to-noise ratio, and closely spaced frequency bands of interest, we investigate the effectiveness of recently introduced multivariate extensions of empirical mode decomposition (MEMD) in motor imagery BCI. We show that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation. Comparative analysis with other state of the art methods on both synthetic benchmark examples and a well established BCI motor imagery dataset support the analysis.


title: Antecedents of customer satisfaction in mobile commerce: Exploring the moderating effect of customization

abstract: The purpose of this paper is to determine statistically significant drivers of customer satisfaction in mobile commerce and to test the moderating effects of customization on the relationships between customer satisfaction and its predictors.,The sample comprised 224 respondents. Confirmatory factor analysis was used to test the validity of the model, and moderated regression analysis was applied to determine main and interaction effects.,Trust, perceived usefulness, mobility, and perceived enjoyment were found to be significant drivers of customer satisfaction. The results also indicate the statistical significance of two interaction effects: customization moderates the influence of mobility and the influence of trust on customer satisfaction.,The study was conducted in a single time period and in a developing country where m-commerce is still not widely used. Future models should include new variables. Comparison between different age or gender groups would also be useful.,The findings are useful for m-commerce providers who are developing marketing campaigns, where the focus should be on promoting the mobility aspect of m-commerce, in particular its usefulness to consumers and its security. M-commerce activities should be developed and redesigned to better meet consumers’ specific demands and needs.,M-commerce customer satisfaction studies are rare. The developed model has five potential antecedents of satisfaction: trust, social influence, perceived usefulness, mobility, and perceived enjoyment. New insights are provided into the moderating role of customization.


title: Reliable file sharing in distributed operating system using web RTC

abstract: Since, the evolution of distributed operating system, distributed file system is come out to be important part in operating system. P2P is a reliable way in Distributed Operating System for file sharing. It was introduced in 1999, later it became a high research interest topic. Peer to Peer network is a type of network, where peers share network workload and other load related tasks. A P2P network can be a period of time connection, where a bunch of computers connected by a USB (Universal Serial Bus) port to transfer or enable disk sharing i.e. file sharing. Currently P2P requires special network that should be designed in P2P way. Nowadays, there is a big influence of browsers in our life. In this project we are going to study of file sharing mechanism in distributed operating system in web browsers, where we will try to find performance bottlenecks which our research will going to be an improvement in file sharing by performance and scalability in distributed file systems. Additionally, we will discuss the scope of Web Torrent file sharing and free-riding in peer to peer networks.


title: Mesh Segmentation - A Comparative Study

abstract: Mesh segmentation has become an important component in many applications in computer graphics. In the last several years, many algorithms have been proposed in this growing area, offering a diversity of methods and various evaluation criteria. This paper provides a comparative study of some of the latest algorithms and results, along several axes. We evaluate only algorithms whose code is available to us, and thus it is not a comprehensive study. Yet, it sheds some light on the vital properties of the methods and on the challenges that future algorithms should face.


Knowledge Engineering Ontologies , Constructivist Epistemology , Computer Rhetoric : A Trivium for the Knowledge Age


title: Helical antenna for handheld UHF RFID reader

abstract: In this paper, we describe a compact high gain circularly polarized antenna for handheld UHF RFID reader. The antenna is an axial mode helix operating in a backfire mode with ground plane on top and reflector on the bottom. The described antenna has small footprint (85 mm in diameter) and delivers maximum linear gain ≫ 6 dBil, standing wave ratio ≪ 2, and axial ratio ≪ 2 dB in 60 MHz band centered around 895 MHz (approximate bandwidth 7%). The antenna can be easily tuned to cover any desired portion of the global UHF RFID band (860–960 MHz). The antenna is an attractive solution for handheld readers to maximize tag read range while providing circular polarization. We also review existing antenna solutions for handheld readers and discuss link budget and forward and reverse link tradeoffs.


title: A 90-nm CMOS Threshold-Compensated RF Energy Harvester

abstract: This paper presents an efficient energy harvester for RF-powered sensor networks. The circuit is based on an improved multi-stage rectifier, which exploits a fully passive threshold self-compensation scheme to overcome the limitation due to the input dead zone. A CAD-oriented design methodology is also proposed, which is aimed at maximizing the overall power conversion efficiency of the harvester through an optimum trade-off among matching losses, power reflection and rectifier efficiency. According to the proposed methodology, a 915-MHz harvester comprising an integrated input matching network and a 17-stage self-compensated rectifier has been designed and fabricated in a 90-nm CMOS technology. The rectifier exhibits a remarkably low input power threshold, as it is able to deliver a 1-V dc output voltage to a capacitive load with a very small input power of -24 dBm (4 μW). When driving a 1-MΩ load, the device can supply a 1.2-V output with an input power of -18.8 dBm (13.1 μW). The achieved results exceed the performance of previously reported RF multi-stage rectifiers in standard analog CMOS technology.


title: Public perception towards genetically modified crops and food in Kenya

abstract: The purpose of this study was to assess public perception of Genetically Modified (GM) Crops and Foods in Kenya. The specific objectives were to find out the influence of background factors and Agro-ecological zones on the public perception towards GMCs and Foods in the country. A survey was carried out in Kenya covering all the 8 provinces between November 2007 and June 2008, targeting adult civilian population (aged 18+ years). The researchers set out to attain a sample size of 700 subjects so as to achieve a sampling error rate of ±3.7%. Accordingly, four clusters comprising of general consumers, farmers, academicians, and resource persons were selected for the study. Efforts were made to ensure that the sample drawn was representative of the Kenyan population through Simple Random Sampling, Systematic Sampling, Convenient and Snowball sampling as was appropriate. Specifically, the survey instrument was designed to gather information on the participants' background, ecological zones and perception towards GMCs and foods. Overall, well over one half (58%) of the respondents had positive perceptions towards GMCs and foods. Further, results of the study indicated that positive perception towards GMCs and foods was significantly related not only to the participant's demographics but also to their Agro-ecological zones. Findings of the study suggest the need for a well designed and effective program to create awareness and educate the public about various issues relating to GMCs and foods. Various stakeholder such as the government, scientists, Non Governmental Organizations and the private sector need to communicate proactively with the public, especially women, elderly people etc, with complete and accurate information about GMCs and Foods.


title: An Architecture for Ambient Intelligent Environments

abstract: This paper presents the proposal of an architecture for developing systems that interact with Ambient Intelligence (AmI) environments. This architecture has been proposed as a consequence of a methodology for the inclusion of Artificial Intelligence in AmI environments (ISyRAmI - Intelligent Systems Research for Ambient Intelligence). The ISyRAmI architecture considers several modules. The first is related with the acquisition of data, information and even knowledge. This data/information/knowledge deals with our AmI environment and can be acquired in different ways (from raw sensors, from the web, from experts). The second module is related with the storage, conversion, and handling of the data/information/knowledge. It is understood that incorrectness, incompleteness, and uncertainty are present in the data/information/knowledge. The third module is related with the intelligent operation on the data/information/knowledge of our AmI environment. Here we include knowledge discovery systems, expert systems, planning, multi-agent systems, simulation, optimization, etc. The last module is related with the actuation in the AmI environment, by means of automation, robots, intelligent agents and users.


title: The importance of students' attention to program state: a case study of debugging behavior

abstract: To develop a model of students' debugging processes, I conducted a qualitative analysis of young students engaged in debugging computer programs they had written in the programming language Scratch. I present a microgenetic analysis that tracks how one student's attention to elements of computer program state shifted during his debugging process. I present evidence that this student had relevant domain knowledge and claim that his changing attention within the problem, and not his domain knowledge, mediated his debugging process. I hypothesize that a key competence in debugging is learning to identify what elements of program state are important to pay attention to and that this attention, and not only domain knowledge, mediates the debugging process. This hypothesis is consistent with a model of physics reasoning and learning from the Knowledge in Pieces theoretical framework and in this research I build upon education research outside of computer science. The case study analyzes the debugging process of a student entering the sixth grade, but I document an isomorphic case from a pair of college students to show that this pattern extends beyond this age.


title: BlindIDS: Market-Compliant and Privacy-Friendly Intrusion Detection System over Encrypted Traffic

abstract: The goal of network intrusion detection is to inspect network traffic in order to identify threats and known attack patterns. One of its key features is Deep Packet Inspection (DPI), that extracts the content of network packets and compares it against a set of detection signatures. While DPI is commonly used to protect networks and information systems, it requires direct access to the traffic content, which makes it blinded against encrypted network protocols such as HTTPS. So far, a difficult choice was to be made between the privacy of network users and security through the inspection of their traffic content to detect attacks or malicious activities. This paper presents a novel approach that bridges the gap between network security and privacy. It makes possible to perform DPI directly on encrypted traffic, without knowing neither the traffic content, nor the patterns of detection signatures. The relevance of our work is that it preserves the delicate balance in the security market ecosystem. Indeed, security editors will be able to protect their distinctive detection signatures and supply service providers only with encrypted attack patterns. In addition, service providers will be able to integrate the encrypted signatures in their architectures and perform DPI without compromising the privacy of network communications. Finally, users will be able to preserve their privacy through traffic encryption, while also benefiting from network security services. The extensive experiments conducted in this paper prove that, compared to existing encryption schemes, our solution reduces by 3 orders of magnitude the connection setup time for new users, and by 6 orders of magnitude the consumed memory space on the DPI appliance.


title: Scalable Distributed Service Integrity Attestation for Software-as-a-Service Clouds

abstract: Software-as-a-service (SaaS) cloud systems enable application service providers to deliver their applications via massive cloud computing infrastructures. However, due to their sharing nature, SaaS clouds are vulnerable to malicious attacks. In this paper, we present IntTest, a scalable and effective service integrity attestation framework for SaaS clouds. IntTest provides a novel integrated attestation graph analysis scheme that can provide stronger attacker pinpointing power than previous schemes. Moreover, IntTest can automatically enhance result quality by replacing bad results produced by malicious attackers with good results produced by benign service providers. We have implemented a prototype of the IntTest system and tested it on a production cloud computing infrastructure using IBM System S stream processing applications. Our experimental results show that IntTest can achieve higher attacker pinpointing accuracy than existing approaches. IntTest does not require any special hardware or secure kernel support and imposes little performance impact to the application, which makes it practical for large-scale cloud systems.


title: Recent progress on the elliptic curve discrete logarithm problem

abstract: We survey recent work on the elliptic curve discrete logarithm problem. In particular we review index calculus algorithms using summation polynomials, and claims about their complexity.


title: Relationship of core selfevaluations traits—self-esteem, generalized self-efficacy, locus of control, and emotional stability—with job satisfaction and job performance: A meta-analysis

abstract: Recently, Judge, Locke, and Durham (1997) proposed a higher order construct they termed core self-evaluations or, more simply, positive self-concept. According to Judge et al. (1997), this construct is a broad dispositional trait that is indicated by four more specific traits—self-esteem, generalized self-efficacy, locus of control, and emotional stability (low neuroticism). The core selfevaluations construct was originally proposed as a potential explanatory variable in the dispositional source of job satisfaction. Subsequently, Judge and colleagues also have argued that the construct should be related to work motivation and, ultimately, to job performance (Judge, Erez, & Bono, 1998). Investigations of a link between core self-evaluations and job performance, however, are lacking. Despite a lack of studies linking the core self-evaluations factor to job satisfaction and, especially, to job performance, three of the core traits (self-esteem, locus of control, and emotional stability) appear to be the most widely studied personality traits in personality and applied psychology.1 Yet, with the exception of emotional stability and job performance, we have found no metaanalyses of the relationship between any of these traits with either job satisfaction or job performance.2 Thus, the purpose of the present study is to provide a quantitative review of the literature that examines the relationship of the four core self-evaluation traits with job satisfaction and job performance. This study determines whether general relationships exist and, if so, what the magnitudes of these relationships are. In the next section, we provide a brief review of the four traits and discuss the possible relationship of these traits with both job satisfaction and job performance.


Spam Detection Techniques : A Review


title: Barriers and facilitators of disclosures of domestic violence by mental health service users: qualitative study

abstract: Background ::: ::: Mental health service users are at high risk of domestic violence but this is often not detected by mental health services. ::: ::: Aims ::: ::: To explore the facilitators and barriers to disclosure of domestic violence from a service user and professional perspective. ::: ::: Method ::: ::: A qualitative study in a socioeconomically deprived south London borough, UK, with 18 mental health service users and 20 mental health professionals. Purposive sampling of community mental health service users and mental healthcare professionals was used to recruit participants for individual interviews. Thematic analysis was used to determine dominant and subthemes. These were transformed into conceptual maps with accompanying illustrative quotations. ::: ::: Results ::: ::: Service users described barriers to disclosure of domestic violence to professionals including: fear of the consequences, including fear of Social Services involvement and consequent child protection proceedings, fear that disclosure would not be believed, and fear that disclosure would lead to further violence; the hidden nature of the violence; actions of the perpetrator; and feelings of shame. The main themes for professionals concerned role boundaries, competency and confidence. Service users and professionals reported that the medical diagnostic and treatment model with its emphasis on symptoms could act as a barrier to enquiry and disclosure. Both groups reported that enquiry and disclosure were facilitated by a supportive and trusting relationship between the individual and professional. ::: ::: Conclusions ::: ::: Mental health services are not currently conducive to the disclosure of domestic violence. Training of professionals in how to address domestic violence to increase their confidence and expertise is recommended.


title: Effect of social commerce factors on user purchase behavior: An empirical investigation from renren.com

abstract: The UGC and the platform supports are significantly impact on the consumer's purchasing intention.Empirically tested the effect of third-party infomediaries, product uncertainty and seller uncertainty on the consumers purchase intention.Enrich social support theory for studying consumers interactive behavior that affects the purchasing behavior in social commerce. As a new type of e-commerce, social commerce is an emerging marketing form in which business is conducted via social networking platforms. It is playing an increasingly important role in influencing consumers' purchase intentions. Social commerce uses friendships on social networking platforms, such as Facebook and Twitter, as the vehicle for social sharing about products or sellers to induce interest in a product, thereby increasing the purchase intention. In this paper, we develop and validate a conceptual model of how social factors, such as social support, seller uncertainty, and product uncertainty, influence users' purchasing behaviors in social commerce. This study aims to provide an understanding of the relationship between user behavior and social factors on social networking platforms. Using the largest social networking website in China, renren.com, this study finds that social support, seller uncertainty, and product uncertainty affect user behaviors. The results further show that social factors can significantly enhance users' purchase intentions in social shopping.


title: Distributed Generation placement and sizing method to improve the voltage stability margin in a distribution system

abstract: Recently, integration of Distributed Generation (DG) in distribution systems has increased to high penetration levels. The impact of DG units on the voltage stability margins has become significant. Optimization techniques are tools which can be used to locate and size the DG units in the system, so as to utilize these units optimally within certain limits and constraints. Thus, the impact of DG units on some issues, such as voltage stability and voltage profile, can be utilized positively. The ultimate goal of this paper is to propose a method of locating and sizing DG units so as to improve the voltage stability margin. The load and renewable DG generation probabilistic nature are considered in this study. The DG units' placement and sizing is formulated using mixed integer non-linear programming (MINLP), with an objective function of improving the stability margin; the constraints are the system voltage limits, feeders' capacity, and the DG penetration level.


title: Beyond Batch Processing: Towards Real-Time and Streaming Big Data

abstract: Today, big data are generated from many sources, and there is a huge demand for storing, managing, processing, and querying on big data. The MapReduce model and its counterpart open source implementation Hadoop, has proven itself as the de facto solution to big data processing, and is inherently designed for batch and high throughput processing jobs. Although Hadoop is very suitable for batch jobs, there is an increasing demand for non-batch requirements like: interactive jobs, real-time queries, and big data streams. Since Hadoop is not suitable for these non-batch workloads, new solutions are proposed to these new challenges. In this article, we discussed two categories of these solutions: real-time processing, and stream processing of big data. For each category, we discussed paradigms, strengths and differences to Hadoop. We also introduced some practical systems and frameworks for each category. Finally, some simple experiments were performed to approve effectiveness of new solutions compared to available Hadoop-based solutions.


title: Accurate and robust localization of duplicated region in copy–move image forgery

abstract: Copy---move image forgery detection has recently become a very active research topic in blind image forensics. In copy---move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy---move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy---move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using "dense" MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy---move image forgery with higher accuracy, especially when the size of the duplicated region is small.


title: Automatic conversion of IFC datasets to geometrically and semantically correct CityGML LOD3 buildings

abstract: Although the international standard CityGML has five levels of detail (LODs), the vast majority of available models are the coarse ones (up to LOD2, i.e. block-shaped buildings with roofs). LOD3 and LOD4 models, which contain architectural details such as balconies, windows and rooms, rarely exist because, unlike coarser LODs, their construction requires several datasets that must be acquired with different technologies, and often extensive manual work is needed. In this article we investigate an alternative to obtaining CityGML LOD3 models: the automatic conversion from already existing architectural models (stored in the IFC format). Existing conversion algorithms mostly focus on the semantic mappings and convert all the geometries, which yields CityGML models having poor usability in practice (spatial analysis, for instance, is not possible). We present a conversion algorithm that accurately applies the correct semantics from IFC models and that constructs valid CityGML LOD3 buildings by performing a series of geometric operations in 3D. We have implemented our algorithm and we demonstrate its effectiveness with several real-world datasets. We also propose specific improvements to both standards to foster their integration in the future.


A Dissimilarity Measure for Comparing Subsets of Data: Application to Multivariate Time Series ∗


title: Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings

abstract: This paper describes our system for the CoNLL 2016 Shared Task’s supplementary task on Discourse Relation Sense Classification. Our official submission employs a Logistic Regression classifier with several cross-argument similarity features based on word embeddings and performs with overall F-scores of 64.13 for the Dev set, 63.31 for the Test set and 54.69 for the Blind set, ranking first in the Overall ranking for the task. We compare the feature-based Logistic Regression classifier to different Convolutional Neural Network architectures. After the official submission we enriched our model for Non-Explicit relations by including similarities of explicit connectives with the relation arguments, and part of speech similarities based on modal verbs. This improved our Non-Explicit result by 1.46 points on the Dev set and by 0.36 points on the Blind set.


title: Model-based security testing: a taxonomy and systematic classification

abstract: Model-based security testing relies on models to test whether a software system meets its security requirements. It is an active research field of high relevance for industrial applications, with many approaches and notable results published in recent years. This article provides a taxonomy for model-based security testing approaches. It comprises filter criteria i.e.i¾?model of system security, security model of the environment and explicit test selection criteria as well as evidence criteria i.e.i¾?maturity of evaluated system, evidence measures and evidence level. The taxonomy is based on a comprehensive analysis of existing classification schemes for model-based testing and security testing. To demonstrate its adequacy, 119 publications on model-based security testing are systematically extracted from the five most relevant digital libraries by three researchers and classified according to the defined filter and evidence criteria. On the basis of the classified publications, the article provides an overview of the state of the art in model-based security testing and discusses promising research directions with regard to security properties, coverage criteria and the feasibility and return on investment of model-based security testing. Copyright © 2015 John Wiley & Sons, Ltd.


title: Understanding software process improvement in global software development: a theoretical framework of human factors

abstract: Presently, most of the software development organizations are adopting the phenomena of Global Software Development (GSD), mainly because of the significant return on investment it produces. However, GSD is a complex phenomenon and there are many challenges associated with it, especially that related to Software Process Improvement (SPI). The aim of this work is to identify humans' related success factors and barriers that could impact the SPI process in GSD organizations and proposed a theoretical framework of the factors in relation to SPI implementation. We have adopted the Systematic Literature Review (SLR) method in order to investigate the success factors and barriers. Using the SLR approach, total ten success factors and eight barriers were identified. The paper also reported the Critical Success Factors (CSFs) and Critical Barriers (CBs) for SPI implementation following the criteria of the factors having a frequency ≥ 50% as critical. Our results reveal that five out of ten factors are critical for SPI program. Moreover, total three barriers were ranked as the most critical barriers. Based on the analysis of the identified factors, we have presented a theoretical framework that has highlighted an association between the identified factors and the implementation of the SPI program in GSD environment.


title: End-to-End Reinforcement Learning for Automatic Taxonomy Induction

abstract: We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e., detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy. In our approach, the representations of term pairs are learned using multiple sources of information and used to determine \textit{which} term to select and \textit{where} to place it on the taxonomy via a policy network. All components are trained in an end-to-end manner with cumulative rewards, measured by a holistic tree metric over the training taxonomies. Experiments on two public datasets of different domains show that our approach outperforms prior state-of-the-art taxonomy induction methods up to 19.6\% on ancestor F1.


title: The Future Role of Digital Technologies in Emerging Technology-Based Retail Environments

abstract: The retail industry is continuously affected by advances in digital technologies, which dramatically change the competitive scenario. On the one hand consumers expect to find technology-enriched retail environments, on the other, retailers achieve advantages through the use of new tools for market seizing and sensing. The aim of this paper is to provide a clearer understanding of the innovative forces affecting the sector, by focusing on the evaluation of innovation trends in the sectors by classifying and analyzing patents. These insights could provide academics and practitioners with an overview of the critical areas and help with the prediction of future trends.


title: A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

abstract: Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.


Can Conversational Agents Express Big Five Personality Traits through Language?: Evaluating a Psychologically-Informed Language Generator


title: The focused plenoptic camera

abstract: Plenoptic cameras, constructed with internal microlens arrays, focus those microlenses at infinity in order to sample the 4D radiance directly at the microlenses. The consequent assumption is that each microlens image is completely defocused with respect to to the image created by the main camera lens and the outside object. As a result, only a single pixel in the final image can be rendered from it, resulting in disappointingly low resolution. In this paper, we present a new approach to lightfield capture and image rendering that interprets the microlens array as an imaging system focused on the focal plane of the main camera lens. This approach captures a lightfield with significantly higher spatial resolution than the traditional approach, allowing us to render high resolution images that meet the expectations of modern photographers. Although the new approach samples the lightfield with reduced angular density, analysis and experimental results demonstrate that there is sufficient parallax to completely support lightfield manipulation algorithms such as refocusing and novel views


title: Integrated microwave filters in MCM-D

abstract: Thin film multilayer Multichip Module technology (MCM-D) may be used to realise microwave circuits such as capacitors and inductors. These may be combined to form passive filter structures. Such filters are an essential element of microwave circuits. On monolithic microwave integrated circuits, these structures require a large area resulting in a high cost. In this paper we present microwave filters integrated in multilayer MCM-D technology. As MCM-D also provides the interconnect between the individual microwave components, an integrated microwave Junction is realised, reducing the global system cost. The inductive and capacitive elements that are used to realise the filters are presented. The design of a low pass and a band pass filter using these elements is discussed. Finally, measurements of realised filters are presented.


title: Binarized Neural Networks

abstract: We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At train-time the binary weights and activations are used for computing the parameter gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. To validate the effectiveness of BNNs, we conducted two sets of experiments on the Torch7 and Theano frameworks. On both, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10 and SVHN datasets. We also report our preliminary results on the challenging ImageNet dataset. Last but not least, we wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for training and running our BNNs is available on-line.


title: When symbolism overtakes quality: Materialists consumers disregard product quality when faced with luxury brands

abstract: Consumers use extrinsic and intrinsic cues to set preferences and make purchase decisions. However, the extent to which luxury-related extrinsic cues determine consumer preferences and whether the relative weighting of extrinsic vs. intrinsic cues depends on consumers’ values is still unclear. We investigated how luxury vs. non-luxury brands affect consumer preferences, and how this impact is moderated by consumers’ materialistic values. Results from Experiment 1 showed that materialistic and non-materialistic participants similarly appreciated products with luxurious brands. However, compared with non-materialistic participants, materialistic participants devaluated products that were tagged as non-luxurious brands. In Experiment 2, we investigated how product quality interacts with brands and whether materialistic values moderated this interaction. Materialistic participants paid more attention to brand-related cues than to quality-related cues, whereas non-materialistic participants considered these cues similarly. Taken together, the results of these two studies suggest that materialism influences the way extrinsic (i.e., brand) and intrinsic (i.e., quality) information is combined during product evaluation. These results highlight the importance of materialism in consumer decision-making, especially in the context of luxury consumption.


title: Collaborative Intent Prediction with Real-Time Contextual Data

abstract: Intelligent personal assistants on mobile devices such as Apple’s Siri and Microsoft Cortana are increasingly important. Instead of passively reacting to queries, they provide users with brand new proactive experiences that aim to offer the right information at the right time. It is, therefore, crucial for personal assistants to understand users’ intent, that is, what information users need now. Intent is closely related to context. Various contextual signals, including spatio-temporal information and users’ activities, can signify users’ intent. It is, however, challenging to model the correlation between intent and context. Intent and context are highly dynamic and often sequentially correlated. Contextual signals are usually sparse, heterogeneous, and not simultaneously available. We propose an innovative collaborative nowcasting model to jointly address all these issues. The model effectively addresses the complex sequential and concurring correlation between context and intent and recognizes users’ real-time intent with continuously arrived contextual signals. We extensively evaluate the proposed model with real-world data sets from a commercial personal assistant. The results validate the effectiveness the proposed model, and demonstrate its capability of handling the real-time flow of contextual signals. The studied problem and model also provide inspiring implications for new paradigms of recommendation on mobile intelligent devices.


title: Photometric stereo with non-parametric and spatially-varying reflectance

abstract: We present a method for simultaneously recovering shape and spatially varying reflectance of a surface from photometric stereo images. The distinguishing feature of our approach is its generality; it does not rely on a specific parametric reflectance model and is therefore purely ldquodata-drivenrdquo. This is achieved by employing novel bi-variate approximations of isotropic reflectance functions. By combining this new approximation with recent developments in photometric stereo, we are able to simultaneously estimate an independent surface normal at each point, a global set of non-parametric ldquobasis materialrdquo BRDFs, and per-point material weights. Our experimental results validate the approach and demonstrate the utility of bi-variate reflectance functions for general non-parametric appearance capture.


On the Use of Hierarchical Subtrace Mining for Efficient Local Process Model Mining


Opinion Mining and Sentiment Analysis - An Assessment of Peoples' Belief: A Survey


title: A Sharp PageRank Algorithm with Applications to Edge Ranking and Graph Sparsification

abstract: We give an improved algorithm for computing personalized PageRank vectors with tight error bounds which can be as small as Ω(n − p ) for any fixed positive integer p. The improved PageRank algorithm is crucial for computing a quantitative ranking of edges in a given graph. We will use the edge ranking to examine two interrelated problems – graph sparsification and graph partitioning. We can combine the graph sparsification and the partitioning algorithms using PageRank vectors to derive an improved partitioning algorithm.


title: Analysis of Voltage Stability of Power Systems Using the Bifurcation Theory

abstract: In order to research power system voltage stability, based on the basic concepts and principles of the bifurcation theory, the qualitative behavior of power system voltage stability dynamics as modeled by differential algebraic equations is discussed. Bifurcation phenomena and the effect of these bifurcations on the voltage stability analysis are overviewed in power systems. The application of static and dynamic bifurcation in voltage stability studies is discussed in detail, respectively. Two main kinds of bifurcations, that is, saddle-node bifurcation(SNB), Hopf bifurcation(HB) which result in voltage instability, are analyzed emphatically. The advantages and disadvantages of the computation methods are compared. The effect of interaction of bifurcations on voltage stability is also discussed briefly. Multi-parameters problems are intrinsically important in power system voltage stability, and the issues involving higher bifurcation will certainly emerge, other further research fields of bifurcation theory applied in voltage stability analysis is predicted in the future.


title: Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks

abstract: Can we make a famous rap singer like Eminem sing whatever our favorite song? Singing style transfer attempts to make this possible, by replacing the vocal of a song from the source singer to the target singer. This paper presents a method that learns from unpaired data for singing style transfer using generative adversarial networks.


title: High strength bubble artificial muscles for walking assistance

abstract: We present a high strength artificial muscle for inclusion in an orthotic device that can deliver power at the knee joint with the aim of improving knee flexion in people with reduced mobility. The bubble artificial muscle (BAM) was developed by adapting traditional pleated pneumatic artificial muscles to achieve high contraction and tensile force. Initial gravimetric evaluation of a single BAM was performed, achieving a contraction of 23.7% while lifting a mass of 2.5 kg (over 100 times the mass of the actuator). To demonstrate the actuator's suitability for an orthosis, a device consisting of three parallel BAMs was tested with a physical pendular leg model based upon a human leg. A maximum knee flexion of 47.25 degrees was achieved, about 70% of the peak knee flexion of the average of young healthy subjects (68 degrees). 8.19 Nm of torque was delivered during testing, which is approximately 33% of the knee flexion moment during swing phase of a gait cycle. This high moment is achieved despite the low mass of the device (206.6 g). This work shows the suitability of BAMs for wearable orthotic devices such as soft assistive suits, where softness, lightness and high stress and strain are essential.


title: A Mining and Visualizing System for Large-Scale Chinese Technical Standards

abstract: A technical standard is an established norm or requirement in regard to technical systems. It is usually a formal document that establishes uniform engineering or technical criteria, methods, processes and practices. Every year, more than 10K standards are released by standardization organizations, such as International Standardization Organization (ISO), American National Standards Institute (ANSI), and China National Institute of Standardization (CNIS). Thereinto, China publishes more than 4K national and industrial standards per year. Confronted with the large amount of standards, how to manage and make full use of these standards efficiently and effectively has become a major issue to be addressed. In this paper, we introduce a mining and visualizing system for large-scale Chinese technical standards from spatial and temporal perspectives. Specifically, we propose an s-index metric to measure the contribution of a drafting unit. Moreover, we develop a multiple dimensional mining and visualizing analysis system for standardization experts and users to explore regional differences and temporal trends of standards in China. Our system has run on 95K Chinese standards spanning from 2001 to 2016 and provided online service. The extensive experiments and case study demonstrate the effectiveness of our proposed system.


title: Conical quad-mode antenna with integrated tapered slot antennas for wide-field polarimetry

abstract: This paper presents a conical quad-mode antenna for irregular sparse antenna arrays. The antenna integrates tapered slot antennas with two orthogonal dipole antennas and a conical monopole element to achieve good polarimetric performance over an ultra-wide field-of-view coverage. It is shown that the presented antenna element is able to detect two orthogonal field components up to scan angles of 80° from zenith over a 50% relative bandwidth.


title: Sparse Deep Nonnegative Matrix Factorization

abstract: Nonnegative matrix factorization is a powerful technique to realize dimension reduction and pattern recognition through single-layer data representation learning. Deep learning, however, with its carefully designed hierarchical structure, is able to combine hidden features to form more representative features for pattern recognition. In this paper, we proposed sparse deep nonnegative matrix factorization models to analyze complex data for more accurate classification and better feature interpretation. Such models are designed to learn localized features or generate more discriminative representations for samples in distinct classes by imposing $L_1$-norm penalty on the columns of certain factors. By extending one-layer model into multi-layer one with sparsity, we provided a hierarchical way to analyze big data and extract hidden features intuitively due to nonnegativity. We adopted the Nesterov's accelerated gradient algorithm to accelerate the computing process with the convergence rate of $O(1/k^2)$ after $k$ steps iteration. We also analyzed the computing complexity of our framework to demonstrate their efficiency. To improve the performance of dealing with linearly inseparable data, we also considered to incorporate popular nonlinear functions into this framework and explored their performance. We applied our models onto two benchmarking image datasets, demonstrating our models can achieve competitive or better classification performance and produce intuitive interpretations compared with the typical NMF and competing multi-layer models.


title: A 0.5-V 28-nm 256-kb Mini-Array Based 6T SRAM With Vtrip-Tracking Write-Assist

abstract: This paper presents a 28-nm 256-kb 6T static random access memory operating down to near-threshold regime. The cell array is built on foundry 4-by-2 mini-array with split single-ended large signal sensing to enable an ultra-short local bit-line of 4-b length to improve variation tolerance and performance, and to reduce disturb while maintaining manufacturability. The design employs threshold power gating to facilitate lower NAP (Sleep) mode voltage/power and faster wake-up for the cell array, and low-swing global read bit-line (GRBL) with integrated low-swing voltage precharger to improve read performance and reduce the dynamic read power. A cell Vtrip-tracking write-assist (VTWA) lowers the selected sub-array supply to cell inverter trip voltage to enhance write-ability while providing PVT tracking capability to ensure adequate data retention margin for unselected cells in the selected sub-array. The 256-kb test chip is implemented in UMC 28-nm high- $\kappa $ metal-gate (H $\kappa $ MG) CMOS technology with macro area of $1058.22\times 374.76~\mu \text{m}^{2}$ . Error-free full functionality is achieved from 0.9 down to 0.5 V (limited by read VMIN without redundancy. The low-swing GRBL reduces dynamic power by 6.5% (8.0%) at 0.9 V (0.6 V). The VTWA improves the write VMIN by 75 mV (from 0.525 to 0.45 V). The measured maximum operation frequency is 735 MHz (20 MHz) at 0.9 V (0.5 V), TT corner, 25°.


title: 95µW 802.11g/n compliant fully-integrated wake-up receiver with −72dBm sensitivity in 14nm FinFET CMOS

abstract: A 2.4GHz fully-integrated Wi-Fi compliant wake-up receiver in 14nm FinFET technology is presented. The receiver achieves −72dBm sensitivity and +20dBr adjacent channel interference rejection for 62.5kbps at 10−3 BER while consuming 95µW. The OOK-modulated wake-up packet can be transmitted using any legacy OFDM Wi-Fi transmitter.


title: Development of robot leg composed of parallel linkage and elastic spring for dynamic locomotion

abstract: Musculoskeletal structures in many animals are flexible, which are very important to achieve dynamic locomotion such as running and jumping. For this reason, in recent research of robotics, elastic element is introduced in robot structure in order to improve dynamic performance of robots. In this paper, we propose a robot leg composed of parallel linkage and elastic tension spring. A prototype of the leg has been developed and it is evaluated by jumping simulations and experiments. As results of the experiments, higher and more efficient jumping is realized by the proposed leg comparing to the leg without elastic spring.


title: Gene selection using hybrid particle swarm optimization and genetic algorithm

abstract: Selecting high discriminative genes from gene expression data has become an important research. Not only can this improve the performance of cancer classification, but it can also cut down the cost of medical diagnoses when a large number of noisy, redundant genes are filtered. In this paper, a hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) method is used for gene selection, and Support Vector Machine (SVM) is adopted as the classifier. The proposed approach is tested on three benchmark gene expression datasets: Leukemia, Colon and breast cancer data. Experimental results show that the proposed method can reduce the dimensionality of the dataset, and confirm the most informative gene subset and improve classification accuracy.


title: From Local Geometry to Global Structure: Learning Latent Subspace for Low-resolution Face Image Recognition

abstract: In this letter, we propose a novel approach for learning coupled mappings to improve the performance of low-resolution (LR) face image recognition. The coupled mappings aim to project the LR probe images and high-resolution (HR) gallery images into a unified latent subspace, which is efficient to measure the similarity of face images with different resolutions. In the training phase, we first construct local optimization for each training sample according to the relationship of neighboring data points. The local optimization aims to: (1) ensure the consistency for each LR face image and corresponding HR one; (2) model the intrinsic geometric structure between each given sample and its neighbors; and (3) preserve the discriminative information across different subjects. We finally incorporate the local optimizations together for building the global structure. The coupled mappings can be learned by solving a standard eigen-decomposition problem, which avoids the small-sample-size problem. Experimental results demonstrate the effectiveness of the proposed method on public face databases.


title: A Millimeter-Wave (23–32 GHz) Wideband BiCMOS Low-Noise Amplifier

abstract: This paper presents a 23-32 GHz wideband BiCMOS low-noise amplifier (LNA). The LNA utilizes coupled-resonators to provide a wideband load. To our knowledge, the proposed LNA achieves the widest bandwidth with minimum power consumption using 0.18 ?m BiCMOS technology in K-band. Analytical expressions for the wideband input matching, gain, noise figure and linearity are presented. The LNA is implemented using 0.18 ?m BiCMOS technology and occupies an area of 0.25 mm2 . It achieves a voltage gain of 12 dB, 3-dB bandwidth of 9 GHz, noise figure between 4.5-6.3 dB, linearity higher than -6.4 dBm with a power consumption of 13 mW from a 1.5 V supply.