title: Inline Pseudoelliptic ${\rm TE}_{01\delta}$-Mode Dielectric Resonator Filters Using Multiple Evanescent Modes to Selectively Bypass Orthogonal Resonators

abstract: A new class of dielectric resonator filters with in-line structure and pseudoelliptic response is presented in this paper. The basic idea consists of using TE01δ single-mode resonators cascaded along an evanescent mode waveguide and oriented along orthogonal directions. By exploiting multiple evanescent modes, which can alternatively excite or bypass a resonator depending on its orientation, cross-coupling between nonadjacent dielectric pucks can be established and controlled. Various configurations, such as triplets, quadruplets, and quintuplets, can be obtained by properly orienting the various pucks and by exploiting a prescribed set of evanescent modes. In contrast with conventional techniques, pseudoelliptic filters can be implemented without using cumbersome cross-coupled architectures or reduced spurious performance multimode resonators. The experimental results of two sixth-order filters with two arbitrarily located transmission zeros, and a fifth-order filter with three transmission zeros validate the proposed filter class.


title: The use of biomedicine, complementary and alternative medicine, and ethnomedicine for the treatment of epilepsy among people of South Asian origin in the UK

abstract: BackgroundStudies have shown that a significant proportion of people with epilepsy use complementary and alternative medicine (CAM). CAM use is known to vary between different ethnic groups and cultural contexts; however, little attention has been devoted to inter-ethnic differences within the UK population. We studied the use of biomedicine, complementary and alternative medicine, and ethnomedicine in a sample of people with epilepsy of South Asian origin living in the north of England.MethodsInterviews were conducted with 30 people of South Asian origin and 16 carers drawn from a sampling frame of patients over 18 years old with epilepsy, compiled from epilepsy registers and hospital databases. All interviews were tape-recorded, translated if required and transcribed. A framework approach was adopted to analyse the data.ResultsAll those interviewed were taking conventional anti-epileptic drugs. Most had also sought help from traditional South Asian practitioners, but only two people had tried conventional CAM. Decisions to consult a traditional healer were taken by families rather than by individuals with epilepsy. Those who made the decision to consult a traditional healer were usually older family members and their motivations and perceptions of safety and efficacy often differed from those of the recipients of the treatment. No-one had discussed the use of traditional therapies with their doctor. The patterns observed in the UK mirrored those reported among people with epilepsy in India and Pakistan.ConclusionThe health care-seeking behaviour of study participants, although mainly confined within the ethnomedicine sector, shared much in common with that of people who use global CAM. The appeal of traditional therapies lay in their religious and moral legitimacy within the South Asian community, especially to the older generation who were disproportionately influential in the determination of treatment choices. As a second generation made up of people of Pakistani origin born in the UK reach the age when they are the influential decision makers in their families, resort to traditional therapies may decline. People had long experience of navigating plural systems of health care and avoided potential conflict by maintaining strict separation between different sectors. Health care practitioners need to approach these issues with sensitivity and to regard traditional healers as potential allies, rather than competitors or quacks.


title: The German Dunkelfeld Project: A Pilot Study to Prevent Child Sexual Abuse and the Use of Child Abusive Images

abstract: Abstract Introduction Sexual interest toward prepubescents and pubescents (pedophilia and hebephilia) constitutes a major risk factor for child sexual abuse (CSA) and viewing of child abusive images, i.e., child pornography offenses (CPO). Most child sexual exploitation involving CSA and CPO are undetected and unprosecuted in the “Dunkelfeld” (German: “dark field”). Aim This study assesses a treatment program to enhance behavioral control and reduce associated dynamic risk factors (DRF) in self‐motivated pedophiles/hebephiles in the Dunkelfeld. Methods Between 2005 and 2011, 319 undetected help‐seeking pedophiles and hebephiles expressed interest in taking part in an anonymous and confidential 1‐year‐treatment program using broad cognitive behavioral methodology in the Prevention Project Dunkelfeld. Therapy was assessed using nonrandomized waiting list control design (n = 53 treated group [TG]; n = 22 untreated control group [CG]). Main Outcome Measures Self‐reported pre‐/posttreatment DRF changes were assessed and compared with CG. Offending behavior characteristics were also assessed via self‐reporting. Results No pre‐/postassessment changes occurred in the control group. Emotional deficits and offense‐supportive cognitions decreased in the TG; posttherapy sexual self‐regulation increased. Treatment‐related changes were distributed unequally across offender groups. None of the offending behavior reported for the TG was identified as such by the legal authorities. However, five of 25 CSA offenders and 29 of 32 CPO offenders reported ongoing behaviors under therapy. Conclusions Therapy for pedophiles/hebephiles in the Dunkelfeld can alter child sexual offending DRF and reduce‐related behaviors. Unidentified, unlawful child sexual exploitative behaviors are more prevalent in this population than in officially reported recidivism. Further research into factors predictive of problematic sexual behaviors in the Dunkelfeld is warranted. Beier KM, Grundmann D, Kuhle LF, Scherner G, Konrad A, and Amelung T. The German Dunkelfeld Project: A pilot study to prevent child sexual abuse and the use of child abusive images. J Sex Med 2015;12:529–542.


title: Systematic evaluation of sandboxed software deployment for real-time software on the example of a self-driving heavy vehicle

abstract: Companies developing and maintaining software-only products like web shops aim for establishing persistent links to their software running in the field. Monitoring data from real usage scenarios allows for a number of improvements in the software life-cycle, such as quick identification and solution of issues, and elicitation of requirements from previously unexpected usage. While the processes of continuous integration, continuous deployment, and continuous experimentation using sandboxing technologies are becoming well established in said software-only products, adopting similar practices for the automotive domain is more complex mainly due to real-time and safety constraints. In this paper, we systematically evaluate sandboxed software deployment in the context of a self-driving heavy vehicle that participated in the 2016 Grand Cooperative Driving Challenge (GCDC) in The Netherlands. We measured the system's scheduling precision after deploying applications in four different execution environments. Our results indicate that there is no significant difference in performance and overhead when sandboxed environments are used compared to natively deployed software. Thus, recent trends in software architecting, packaging, and maintenance using microservices encapsulated in sandboxes will help to realize similar software and system engineering for cyber-physical systems.


A Case Study for Blockchain in Healthcare : “ MedRec ” prototype for electronic health records and medical research data


title: Next-generation synthetic gene networks

abstract: Synthetic biology is focused on the rational construction of biological systems based on engineering principles. During the field's first decade of development, significant progress has been made in designing biological parts and assembling them into genetic circuits to achieve basic functionalities. These circuits have been used to construct proof-of-principle systems with promising results in industrial and medical applications. However, advances in synthetic biology have been limited by a lack of interoperable parts, techniques for dynamically probing biological systems and frameworks for the reliable construction and operation of complex, higher-order networks. As these challenges are addressed, synthetic biologists will be able to construct useful next-generation synthetic gene networks with real-world applications in medicine, biotechnology, bioremediation and bioenergy.


title: ARAP++: an extension of the local/global approach to mesh parameterization

abstract: Mesh parameterization is one of the fundamental operations in computer graphics (CG) and computeraided design (CAD). In this paper, we propose a novel local/global parameterization approach, ARAP++, for singleand multi-boundary triangular meshes. It is an extension of the as-rigid-as-possible (ARAP) approach, which stitches together 1-ring patches instead of individual triangles. To optimize the spring energy, we introduce a linear iterative scheme which employs convex combination weights and a fitting Jacobian matrix corresponding to a prescribed family of transformations. Our algorithm is simple, efficient, and robust. The geometric properties (angle and area) of the original model can also be preserved by appropriately prescribing the singular values of the fitting matrix. To reduce the area and stretch distortions for high-curvature models, a stretch operator is introduced. Numerical results demonstrate that ARAP++ outperforms several state-of-the-art methods in terms of controlling the distortions of angle, area, and stretch. Furthermore, it achieves a better visualization performance for several applications, such as texture mapping and surface remeshing.


title: AirExGlove — A novel pneumatic exoskeleton glove for adaptive hand rehabilitation in post-stroke patients

abstract: Stroke is one of the leading causes of disability worldwide: post-stroke disabilities affect the upper and lower limbs, significantly undermining a subject's autonomy in the Activities of Daily Living (ADLs). Among post-stroke disabilities, one of the most impairing and widespread conditions is the clenched fist deformity: the subject experiences a permanent contraction of the hand, resulting in a closed hand rest pose. In this paper, the authors propose a novel light-weight inflatable soft exoskeleton device, called the AirExGlove, to deliver high-dosage, adaptive and gradual rehabilitation therapy to patients affected by clenched fist deformity. Our system is lightweight, low-cost, adaptable to any hand size and unobtrusive. The system has been extensively tested to assess the hand-opening range in which it can operate according to the severity of the patient condition, which is typically ranked on the Modified Ashworth Scale (MAS) scale. Experimental analysis demonstrates the suitability of the glove for patients affected by post-stroke muscle spasticity scoring up to 3 out of 4 in the MAS scale. Preliminary testing with clenched-fist patient confirmed a higher level of ergonomics of the system in comparison with rigid-linked robotic systems.


title: Hotspot localization and prediction in wireless cellular networks via spatial traffic fitting

abstract: With the proliferation of bandwidth-demanding mobile applications in the era of 5G, the aggregation of a few users may lead to extremely high load in cellular base stations, producing traffic hotspot in wireless networks. Therefore the higher requirement is imposed on the flexibility of a 5G network, namely the capability of performing rapid capacity enhancement in hotspot area, which makes hotspot localization and critical prediction functions. In this paper, we proposed to localize hotspots with Gaussian Random Field (GRF)-based spatial traffic density model deduced from load data of base stations, together with the prediction with Holt-Winters. We measured the spatial traffic in a specific area within a short time span and forecasted the spatial traffic density distribution. Numeric results show the proposed approach can localize hotspot efficiently, and during traffic peak hours, hotspot prediction is of high success rate.


title: Log-Based Cloud Monitoring System for OpenStack

abstract: Cloud systems are fast emerging remote computing environment, providing their users with computational resources and services on demand over the Internet. OpenStack is one representative open source platform which helps in building private clouds for the organization. Monitoring the activities in the cloud is a task of utmost importance and log files are often used for it. Logs management plays a very important role in determining various issues in the cloud network or helps in troubleshooting the issues. Often logs are cumbersome to manage and difficult to read. In this paper, we propose a system which monitor the logs of OpenStack components (i.e., Nova, Neutron, Cinder) in real-time and generate an alert for the information, debug, error, warning, and trace messages as soon as an issue recorded in the logs. It helps in easy readability to the administrators. Apart from that our logic also helps to generate warning messages prior to the different resource quota like virtual CPUs, Floating IP’s, etc. A feature like that can help the administrator to increase the quota or release some and avoid the inconvenience caused to the users creates virtual machines. We develop our proposed system and demonstrate its feasibility and performances through simulation.


Scalable Multiplex Network Embedding


title: Program synthesis using natural language

abstract: Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.


title: Choosing a Motivation Construct

abstract: Motivation is a key concept and concern to occupational therapists; however, no true occupational therapy motivational model exists. Previous studies of motivation by occupational therapists have tended to focus on unidimensional, content theories of motivation, which do not adequately explain this construct. The authors present a review of several major theories of motivation and propose one — Lawler's motivation model — as an appropriate model for guiding occupational therapy practice and research. Based on expectancy theory, this model incorporates many conceptual factors consistent with occupational therapy theories and beliefs.


title: Patients' Characteristics, Histopathological Findings, and Tumor Stage in Different Types of Malignant Melanoma: A Retrospective Multicenter Study.

abstract: Cutaneous malignant melanoma (CMM) is currently the most fatal of skin cancers accounting for 50000 deaths annually. Five distinct melanomas are described histopathologically: superficial spreading, lentigo maligna, nodular, acral lentiginous and mucosal melanoma. The aim of this study was to investigate the characteristics of patients with various types of malignant melanoma and evaluate histopathological findings. In this retrospective study, we obtained our data from the records of 111 patients with melanoma. Biopsied specimens were collected and re-evaluated. Demographic information and histopathological findings were noted. SPSS 16 was used for analyzing data. Chi-square and one-way ANOVA was conducted for comparing categorical and numerical variables respectively. The mean age of patients was 59.33±14.68 years old. Most common melanoma type was acral lentiginous (40.5%), followed by nodular (35.1%) and mucosal (10.8%). The highest tumor thickness was viewed in nodular melanoma followed by mucosal melanoma. The highest rate of metastasis, microsatellitosis, perineural invasion and Clark level of the invasion were reported in nodular and acral lentiginous respectively. The most frequent rate of ulceration and vascular invasion was reported in mucosal melanoma. Distribution of melanoma types varies largely in different regions. Lack of classic presentations in some types necessitate specific public education about warning signs. Histopathological and pathological characteristics in melanoma can aid in better staging and management of the tumor.


title: More than a Feeling: The MiFace Framework for Defining Facial Communication Mappings

abstract: Facial expressions transmit a variety of social, grammatical, and affective signals. For technology to leverage this rich source of communication, tools that better model the breadth of information they convey are required. MiFace is a novel framework for creating expression lexicons that map signal values to parameterized facial muscle movements. In traditional mapping paradigms using posed photographs, naive judges select from predetermined label sets and movements are inferred by trained experts. The set of generally accepted expressions established in this way is limited to six basic displays of affect. In contrast, our approach generatively simulates muscle movements on a 3D avatar. By applying natural language processing techniques to crowdsourced free-response labels for the resulting images, we efficiently converge on an expression's value across signal categories. Two studies returned 218 discriminable facial expressions with 51 unique labels. The six basic emotions are included, but we additionally define such nuanced expressions as embarrassed, curious, and hopeful.


title: Supervised Recurrent Hashing for Large Scale Video Retrieval

abstract: Hashing for large-scale multimedia is a popular research topic, attracting much attention in computer vision and visual information retrieval. Previous works mostly focus on hashing the images and texts while the approaches designed for videos are limited. In this paper, we propose a \textit{Supervised Recurrent Hashing} (SRH) that explores the discriminative representation obtained by deep neural networks to design hashing approaches. The long-short term memory (LSTM) network is deployed to model the structure of video samples. The max-pooling mechanism is introduced to embedding the frames into fixed-length representations that are fed into supervised hashing loss. Experiments on UCF-101 dataset demonstrate that the proposed method can significantly outperforms several state-of-the-art methods.


title: Methodological proposal for process mining projects

abstract: Process mining is a discipline that allows organisations to discover, analyse and improve their business processes. Although the techniques, algorithms and software packages intended for the application of this discipline have been evolving positively, challenges have appeared as it is applied to process improvement projects. This is because most of the methodological approaches developed for its application provide general guidelines, but they do not define the specific steps and tactics for the challenges that a practitioner must go through when facing a process redesign project through process mining. The current development contributes to improving the applicability of this discipline by designing a methodology that allows professionals and organizations to conduct process mining projects. The methodology is illustrated by applying to three case studies, after which it was subjected to evaluation by the personnel of the companies that participated in its application. The limitations of the designed methodology and future research perspectives are finally discussed.


title: Attention-based Context Aggregation Network for Monocular Depth Estimation

abstract: Depth estimation is a traditional computer vision task, which plays a crucial role in understanding 3D scene geometry. Recently, deep-convolutional-neural-networks based methods have achieved promising results in the monocular depth estimation field. Specifically, the framework that combines the multi-scale features extracted by the dilated convolution based block (atrous spatial pyramid pooling, ASPP) has gained the significant improvement in the dense labeling task. However, the discretized and predefined dilation rates cannot capture the continuous context information that differs in diverse scenes and easily introduce the grid artifacts in depth estimation. In this paper, we propose an attention-based context aggregation network (ACAN) to tackle these difficulties. Based on the self-attention model, ACAN adaptively learns the task-specific similarities between pixels to model the context information. First, we recast the monocular depth estimation as a dense labeling multi-class classification problem. Then we propose a soft ordinal inference to transform the predicted probabilities to continuous depth values, which can reduce the discretization error (about 1% decrease in RMSE). Second, the proposed ACAN aggregates both the image-level and pixel-level context information for depth estimation, where the former expresses the statistical characteristic of the whole image and the latter extracts the long-range spatial dependencies for each pixel. Third, for further reducing the inconsistency between the RGB image and depth map, we construct an attention loss to minimize their information entropy. We evaluate on public monocular depth-estimation benchmark datasets (including NYU Depth V2, KITTI). The experiments demonstrate the superiority of our proposed ACAN and achieve the competitive results with the state of the arts.


title: Context-Aware Filtering and Visualization of Web Service Clusters

abstract: Web service filtering is an efficient approach to address some big challenges in service computing, such as discovery, clustering and recommendation. The key operation of the filtering process is measuring the similarity of services. Several methods are used in current similarity calculation approaches such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches do not consider domain-specific contexts in measuring similarity because they have failed to capture the semantic similarity of Web services in a given domain and this has affected their filtering performance. In this paper, we propose a context-aware similarity method that uses a support vector machine and a domain dataset from a context-specific search engine query. Our filtering approach uses a spherical associated keyword space algorithm that projects filtering results from a three-dimensional sphere to a two-dimensional (2D) spherical surface for 2D visualization. Experimental results show that our filtering approach works efficiently.


title: The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers

abstract: Solving mathematical word problems (MWPs) automatically is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics. Despite the long history dated back to the $1960$s, MWPs have regained intensive attention in the past few years with the advancement of Artificial Intelligence (AI). Solving MWPs successfully is considered as a milestone towards general AI. Many systems have claimed promising results in self-crafted and small-scale datasets. However, when applied on large and diverse datasets, none of the proposed methods in the literature achieves high precision, revealing that current MWP solvers still have much room for improvement. This motivated us to present a comprehensive survey to deliver a clear and complete picture of automatic math problem solvers. In this survey, we emphasize on algebraic word problems, summarize their extracted features and proposed techniques to bridge the semantic gap, and compare their performance in the publicly accessible datasets. We also cover automatic solvers for other types of math problems such as geometric problems that require the understanding of diagrams. Finally, we identify several emerging research directions for the readers with interests in MWPs.


title: Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning

abstract: Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an economical alternative, with which training phase could hardly generate satisfactory performance unfortunately. In order to generate high-quality annotated data with a low time cost for accurate segmentation, in this paper, we propose a novel annotation enrichment strategy, which expands existing coarse annotations of training data to a finer scale. Extensive experiments on the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural networks trained with the enriched annotations from our framework yield a significant improvement over that trained with the original coarse labels. It is highly competitive to the performance obtained by using human annotated dense annotations. The proposed method also outperforms among other state-of-the-art weakly-supervised segmentation methods.


title: Multidomain Document Layout Understanding using Few Shot Object Detection

abstract: We try to address the problem of document layout understanding using a simple algorithm which generalizes across multiple domains while training on just few examples per domain. We approach this problem via supervised object detection method and propose a methodology to overcome the requirement of large datasets. We use the concept of transfer learning by pre-training our object detector on a simple artificial (source) dataset and fine-tuning it on a tiny domain specific (target) dataset. We show that this methodology works for multiple domains with training samples as less as 10 documents. We demonstrate the effect of each component of the methodology in the end result and show the superiority of this methodology over simple object detectors.


title: Improving Super Resolution Methods Via Incremental Residual Learning

abstract: Recently, Convolutional Neural Networks (CNNs) have shown promising performance in super-resolution (SR). However, these methods operate primarily on Low Resolution (LR) inputs for memory efficiency but this limits, as we demonstrate, their ability to (i) model high frequency information; and (ii) smoothly translate from LR to High Resolution (HR) space. To this end, we propose a novel Incremental Residual Learning (IRL) framework to address these mentioned issues. In IRL, first we select a typical SR pre-trained network as a master branch. Next we sequentially train and add residual branches to the main branch, where each residual branch is learned to model accumulated residuals of all previous branches. We plug state of the art methods in IRL framework and demonstrate consistent performance improvement on public benchmark datasets to set a new state of the art for SR at only ≈ 20% increase in training time.1


title: A 300-nW Sensitive, 50-nA DC-DC Converter for Energy Harvesting Applications

abstract: A maximum-power-point-tracking DC-DC boost converter to harvest energy from sub- $\mu{\rm W}$ power sources is presented. For available input-power levels below 1 $\mu{\rm W}$ , voltage boosting is achieved by operating all circuits in the sub-threshold region, and by switching the DC-DC converter at tens of Hz, thereby reducing switching losses. The paper further explores the possibility of energizing the DC-DC inductor for an optimum duration, such that switching and resistive losses are minimized. The sub- $\mu{\rm W}$ energy harvesting circuit uses an area of 0.2 ${\rm mm}^{2}$ on a standard 180 nm CMOS process, and utilizes an auxiliary voltage source for start-up. The designed and fabricated system is more than 50% efficient when the available power is greater than 2 $\mu{\rm W}$ . The circuit can harvest energy whenever the available power is more than 0.3 $\mu{\rm W}$ . Efficiency at 0.3 $\mu{\rm W}$ is 25%, at 0.5 $\mu{\rm W}$ is 37% and at 1 $\mu{\rm W}$ is 48%. The complete IC consumes 50 nA for internal operations and the input voltage can be as low as 70 mV.


title: NTRU Prime : reducing attack surface at low cost

abstract: Several ideal-lattice-based cryptosystems have been broken by recent attacks that exploit special structures of the rings used in those cryptosystems. The same structures are also used in the leading proposals for post-quantum lattice-based cryptography, including the classic NTRU cryptosystem and typical Ring-LWE-based cryptosystems.


title: Distributed Vector Representations of Words in the Sigma Cognitive Architecture

abstract: Recently reported results with distributed-vector word representations in natural language processing make them appealing for incorporation into a general cognitive architecture like Sigma. This paper describes a new algorithm for learning such word representations from large, shallow information resources, and how this algorithm can be implemented via small modifications to Sigma. The effectiveness and speed of the algorithm are evaluated via a comparison of an external simulation of it with state-of-the-art algorithms. The results from more limited experiments with Sigma are also promising, but more work is required for it to reach the effectiveness and speed of the simulation.


title: An incremental growing neural gas learns topologies

abstract: An incremental and growing network model is introduced which is able to learn the topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. We propose a new algorithm for a SOM which can learn new input data (plasticity) without degrading the previously trained network and forgetting the old input data (stability). We report the validation of this model on experiments using a synthetic problem, the IRIS database and the handwriting digit recognition problem over a portion of the NIST database. Finally we show how to use this network for clustering and semi-supervised clustering.


title: Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices

abstract: We developed an application for Android™-based mobile devices that allows real-time electrocardiogram (ECG) monitoring and automated arrhythmia detection by analyzing ECG parameters. ECG data provided by pre-recorded files or acquired live by accessing a Shimmer™ sensor node via Bluetooth™ can be processed and evaluated. The application is based on the Pan-Tompkins algorithm for QRS-detection and contains further algorithm blocks to detect abnormal heartbeats. The algorithm was validated using the MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia databases. More than 99% of all QRS complexes were detected correctly by the algorithm. Overall sensitivity for abnormal beat detection was 89.5% with a specificity of 80.6%. The application is available for download and may be used for real-time ECG-monitoring on mobile devices.


title: Design compact planar monopole antenna for LTE mobile handset

abstract: Development of Mobile handset applications, new standard for cellular networks have been defined. In this Paper, author intend to propose a Novel mobile Antenna that can cover more of LTE (Long Term Evolution) Bands (4G cellular networks). The proposed antenna uses structure of planar monopole antenna. Bandwidth of antenna is 0.87–0.99 GHz, 1.65–3.14 GHz and has high efficiency unlike the previous structures. The dimension of the antenna is 18mm×21mm and has FR4 substrate by 1.5mm thickness that is very compact antenna respect to the other expressed antenna.


title: The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust

abstract: The online merchant of an e-marketplace consists of an intermediary, providing the market infrastructure, and the community of sellers conducting business within that infrastructure. Typically, consumers willingly buy from unknown sellers within an e-marketplace, despite the apparent risk, since they trust the institutional mechanisms furnished by the relatively well-known intermediary. Consumers' trust in one component of the e-marketplace merchant may not only affect their trust in the other, but also influence the way consumers make online purchases. This paper explores the impact of trust on consumer behavior in e-marketplaces. An empirical study has been conducted to accomplish our research objectives, using a questionnaire survey of 222 active e-marketplace shoppers in Korea. The results reveal that consumer trust in an intermediary has a strong influence upon both attitudinal loyalty and purchase intentions, although consumer trust in the community of sellers has no significant effect on the two constructs representing consumer behavior. In addition, it was found that trust is transferred from an intermediary to the community of sellers, implying that the trustworthiness of the intermediary plays a critical role in determining the extent to which consumers trust and accept the sellers in the e-marketplace. This paper offers some implications from the findings of the research.


title: Lossy Image Compression with Compressive Autoencoders

abstract: We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to address this need, but are difficult to optimize directly due to the inherent non-differentiabilty of the compression loss. We here show that minimal changes to the loss are sufficient to train deep autoencoders competitive with JPEG 2000 and outperforming recently proposed approaches based on RNNs. Our network is furthermore computationally efficient thanks to a sub-pixel architecture, which makes it suitable for high-resolution images. This is in contrast to previous work on autoencoders for compression using coarser approximations, shallower architectures, computationally expensive methods, or focusing on small images.


title: The socialbot network: when bots socialize for fame and money

abstract: Online Social Networks (OSNs) have become an integral part of today's Web. Politicians, celebrities, revolutionists, and others use OSNs as a podium to deliver their message to millions of active web users. Unfortunately, in the wrong hands, OSNs can be used to run astroturf campaigns to spread misinformation and propaganda. Such campaigns usually start off by infiltrating a targeted OSN on a large scale. In this paper, we evaluate how vulnerable OSNs are to a large-scale infiltration by socialbots: computer programs that control OSN accounts and mimic real users. We adopt a traditional web-based botnet design and built a Socialbot Network (SbN): a group of adaptive socialbots that are orchestrated in a command-and-control fashion. We operated such an SbN on Facebook---a 750 million user OSN---for about 8 weeks. We collected data related to users' behavior in response to a large-scale infiltration where socialbots were used to connect to a large number of Facebook users. Our results show that (1) OSNs, such as Facebook, can be infiltrated with a success rate of up to 80%, (2) depending on users' privacy settings, a successful infiltration can result in privacy breaches where even more users' data are exposed when compared to a purely public access, and (3) in practice, OSN security defenses, such as the Facebook Immune System, are not effective enough in detecting or stopping a large-scale infiltration as it occurs.


title: Second case of Beare–Stevenson syndrome with anFGFR2 Ser372Cys mutation

abstract: Beare–Stevenson syndrome is characterized by cutis gyrata, acanthosis nigricans, skin furrows, skin tags, craniosynostosis, Crouzonoid-like features in some cases and cloverleaf skull in others, anogenital anomalies, and prominent umbilical stump. Reported causes are an FGFR2 Tyr375Cys mutation in nine cases and an FGFR2 Ser372Cys mutation in one case. Here, we report on a second patient with the FGFR2 Ser372Cys mutation. © 2008 Wiley-Liss, Inc.


title: High-Efficient Circularly Polarized Magnetoelectric Dipole Antenna for 5G Applications Using Dual-Polarized Split-Ring Resonator Lens

abstract: A circularly polarized magnetoelectric dipole antenna with high efficiency based on printed ridge gap waveguide is presented. The antenna gain is improved by using a wideband lens in front of the antennas. The lens consists of three layers dual-polarized mu-near zero (MNZ) inclusions. Each layer consists of a $3\times4$ MNZ unit cell. The measured results indicate that the magnitude of $S_{11}$ is below −10 dB in the frequency range of 29.5–37 GHz. The resulting 3-dB axial ratio is over a frequency range of 32.5–35 GHz. The measured realized gain of the antenna is more than 10 dBi over a frequency band of 31–35 GHz achieving a radiation efficiency of 94% at 34 GHz.


title: Performance Analysis of a Low-Power High-Speed Hybrid 1-bit Full Adder Circuit

abstract: In this paper, a hybrid 1-bit full adder design employing both complementary metal–oxide–semiconductor (CMOS) logic and transmission gate logic is reported. The design was first implemented for 1 bit and then extended for 32 bit also. The circuit was implemented using Cadence Virtuoso tools in 180-and 90-nm technology. Performance parameters such as power, delay, and layout area were compared with the existing designs such as complementary pass-transistor logic, transmission gate adder, transmission function adder, hybrid pass-logic with static CMOS output drive full adder, and so on. For 1.8-V supply at 180-nm technology, the average power consumption (4.1563 $\mu $ W) was found to be extremely low with moderately low delay (224 ps) resulting from the deliberate incorporation of very weak CMOS inverters coupled with strong transmission gates. Corresponding values of the same were 1.17664 $\mu $ W and 91.3 ps at 90-nm technology operating at 1.2-V supply voltage. The design was further extended for implementing 32-bit full adder also, and was found to be working efficiently with only 5.578-ns (2.45-ns) delay and 112.79- $\mu $ W (53.36- $\mu $ W) power at 180-nm (90-nm) technology for 1.8-V (1.2-V) supply voltage. In comparison with the existing full adder designs, the present implementation was found to offer significant improvement in terms of power and speed.


title: Ultra-wideband (UWB) Wilkinson power divider with ultra-narrow dual-notched bands using embedded CPW resonators

abstract: In this paper, an ultra-wideband (UWB) Wilkinson power divider with ultra-narrow dual-notched bands is proposed. The multi-mode UWB characteristic is achieved using stepped-impedance open-circuited stub (SIOS) and broadside-coupled microstrip/CPW (BCMC) transition. Then, to cancel the interferences from existed wireless local-area network (WLAN) signals (i.e., 5.2 and 5.8 GHz), two pairs of embedded CPW resonators are employed. To verify the mechanisms mentioned above, an UWB power divider with dual-notched bands is implemented and fabricated. The measurement exhibits dual-notched bands with center frequencies of 5.28 and 5.86 GHz, which has merits of 10-dB notched FBW of 1.5% and 0.68%, respectively.


title: Predicting Stock Price Direction using Support Vector Machines

abstract: Support Vector Machine is a machine learning technique used in recent studies to forecast stock prices. This study uses daily closing prices for 34 technology stocks to calculate price volatility and momentum for individual stocks and for the overall sector. These are used as parameters to the SVM model. The model attempts to predict whether a stock price sometime in the future will be higher or lower than it is on a given day. We find little predictive ability in the short-run but definite predictive ability in the long-run.


title: Solid-State Transformer and MV Grid Tie Applications Enabled by 15 kV SiC IGBTs and 10 kV SiC MOSFETs Based Multilevel Converters

abstract: Medium-voltage (MV) SiC devices have been developed recently which can be used for three-phase MV grid tie applications. Two such devices, 15 kV SiC insulated-gate bipolar transistor (IGBT) and 10 kV SiC MOSFET, have opened up the possibilities of looking into different converter topologies for the MV distribution grid interface. These can be used in MV drives, active filter applications, or as the active front end converter for solid-state transformers (SSTs). The transformerless intelligent power substation (TIPS) is one such application for these devices. TIPS is proposed as a three-phase SST interconnecting a 13.8 kV distribution grid with a 480 V utility grid. It is an all SiC device-based multistage SST. This paper focuses on the advantages, design considerations, and challenges associated with the operation of converters using these devices keeping TIPS as the topology of reference. The efficiency of the TIPS topology is also calculated using the experimentally measured loss data of the devices and the high-frequency transformer. Experimental results captured on a developed prototype of TIPS along with its measured efficiency are also given.


title: A Unified Approach to the Change of Resolution: Space and Gray-Level

abstract: It is shown that by defining a suitable measure for the comparison of images, changes in resolution can be treated with the same tool as changes in color resolution. A gray-tone image, for example, can be compared to a half-tone image having only two colors (black and white), but of higher spatial resolution. A graph-theoretical definition of the basic measure used is introduced. This is followed by application to spatial resampling and gray-level requantization. This results in a hybrid treatment of resolution, and the possibility of trading spatial for gray-level resolution and vice versa. >


title: On Becoming a Strategic Partner : The Role of Human Resources in Gaining Competitive Advantage by

abstract: Although managers cite human resources as a firm's most important asset, many organizational decisions do not reflect this belief. This article uses the value, rareness, imitability, and organization (VRIO) framework to examine the role that the human resource (HR) function plays in developing a sustainable competitive advantage. Why some popularly cited sources of sustainable competitive advantage are not, and what aspects of a firm's human resources can provide a source of sustainable competitive advantage are discussed. The role of the HR executive as a strategic partner in developing and maintaining competitive advantage within the firm is also examined. © 1998 John Wiley & Sons, Inc.


title: Robust number plate detector based on stroke width transform and neural network

abstract: The number plate detection is a key step affecting the overall performance of the number plate recognition system. In the paper a novel algorithm for this purpose is proposed. The approach is based on the detection of text areas using the stroke width transform. More plate candidates are detected using the specifically developed image preprocessing scheme based on set of morphological operators and contour analysis. The final number plate candidates are properly classified using the neural network which is learned from the training dataset. Experiment results indicate on the high performance of the developed methodology.


title: Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra , Portugal

abstract: Abstract: The expansion of cities entails the abandonment of forest and agricultural lands, and these lands‘ conversion into urban areas, which results in substantial impacts on ecosystems. Monitoring these changes and planning urban development can be successfully achieved using multitemporal remotely sensed data, spatial metrics, and modeling. In this paper, urban land use change analysis and modeling was carried out for the Concelhos of Setubal and Sesimbra in Portugal. An existing land cover map for the year 1990, together with two derived land cover maps from multispectral satellite images for the years 2000 and 2006, were utilized using an object-oriented classification approach. Classification accuracy assessment revealed satisfactory results that fulfilled minimum standard accuracy levels. Urban land use dynamics, in terms of both patterns and quantities, were studied using selected landscape metrics and the Shannon Entropy index. Results show that urban areas increased by 91.11% between 1990 and 2006. In contrast, the change was only 6.34% between 2000 and 2006. The entropy value was 0.73 for both municipalities in 1990, indicating a high rate of urban sprawl in the area. In 2006, this value, for both Sesimbra and Setubal, reached almost 0.90. This is demonstrative of a tendency toward intensive urban sprawl. Urban land use change for the year 2020 was modeled using a Cellular Automata based approach. The predictive power of the model was successfully validated using Kappa variations. Projected land cover changes show a growing tendency in urban land use, which might threaten areas that are currently reserved for natural parks and agricultural lands.


title: Where to Kill the Cyber Kill-Chain: An Ontology-Driven Framework for IoT Security Analytics

abstract: In this paper we propose an ontology-based framework for the Internet of Things (IoT) to safeguard against Advanced Persistent Threats (APTs). The framework grasps the understanding of attack kill-chain, leveraged attack patterns and vulnerabilities and aligns them with network semantics to gauge their applicability on IoT systems. Followed by that, it automatically infers efficient solutions for changing attack tactics by performing cost-benefit analysis of viable countermeasures through rule-based ontology reasoning. Our work leverages existing ontologies of well-known Cyber Threat Intelligence (CTI) standards by extending them with new concepts and aligning with a novel IoT ontology. The framework automatically extracts relevant information from XML-based threat feeds, populates it as ontology instances and maps it with IoT configurations to perform the desired reasoning. The practicality of approach has been illustrated by evaluating a sample IoT network against a variety of real-world APTs.


Teaching children with autism inclusion readiness skills using video self-modeling with an iPad


title: Asian rhinoplasty.

abstract: Asian rhinoplasty is one of the most challenging ethnic rhinoplasties that plastic surgeons perform because of the thick skin and soft-tissue envelope. There are three goals: pleasing the patient, achieving an aesthetically appealing result, and preserving a natural look. Of these goals, the most arduous is to satisfy the patient, as many patients have unrealistic goals and may desire an extremely narrow Western nose. Furthermore, patients may bring in celebrity or model photographs and expect that outcome, even though it may not be suitable for their face or appear over-resected and pinched. The surgeon's most important task is to attempt to persuade the patient that this result is nonfunctional, esthetically unfit, and difficult to achieve with their skin. For ethnic surgery, a clear and thorough grasp of nasal anatomy, function, and surgical techniques is paramount. An extensive preoperative discussion, including expectations, outcomes, and a detailed list of potential complications with the patient can prevent physician-patient miscommunication. Before surgery, it is essential to review the office examination, previous operative summary, photographs, nasal analysis sheet, problem list, and plan before proceeding with the surgical treatment.


title: Curvature preserving fingerprint ridge orientation smoothing using Legendre polynomials

abstract: Smoothing fingerprint ridge orientation involves a principal discrepancy. Too little smoothing can result in noisy orientation fields (OF), too much smoothing will harm high curvature areas, especially singular points (SP). In this paper we present a fingerprint ridge orientation model based on Legendre polynomials. The motivation for the proposed method can be found by analysing the almost exclusively used method in literature for orientation smoothing, proposed by Witkin and Kass (1987) more than two decades ago. The main contribution of this paper argues that the vectorial data (sine and cosine data) should be smoothed in a coupled way and the approximation error should not be evaluated employing vectorial data. For evaluating the proposed method we use a Poincare-Index based SP detection algorithm. The experiments show, that in comparison to competing methods the proposed method has improved orientation smoothing capabilities, especially in high curvature areas.


title: A practical public key cryptosystem provably secure against adaptive chosen ciphertext attack

abstract: A new public key cryptosystem is proposed and analyzed. The scheme is quite practical, and is provably secure against adaptive chosen ciphertext attack under standard intractability assumptions. There appears to be no previous cryptosystem in the literature that enjoys both of these properties simultaneously.


Using the Technology Organization Environment Framework for Adoption and Implementation of Cloud Computing in Institutions of Higher Learning in Kenya


title: Dynamic scheduling of network updates (Extended version)

abstract: We present Dionysus, a system for fast, consistent network updates in software-defined networks. Dionysus encodes as a graph the consistency-related dependencies among updates at individual switches, and it then dynamically schedules these updates based on runtime differences in the update speeds of different switches. This dynamic scheduling is the key to its speed; prior update methods are slow because they pre-determine a schedule, which does not adapt to runtime conditions. Testbed experiments and data-driven simulations show that Dionysus improves the median update speed by 53–88% in both wide area and data center networks compared to prior methods.


title: Large age-gap face verification by feature injection in deep networks

abstract: A new large-scale Large Age Gap dataset is collected, that includes 1010 identities.A new Deep Convolutional Neural Network (DCNN) architecture is proposed.The proposed DCNN includes a feature injection layer that performs feature fusion.The proposed DCNN outperforms state-of-the-art methods on LAG dataset. This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task. Fine-tuning is performed in a Siamese architecture using a contrastive loss function. A feature injection layer is introduced to boost verification accuracy, showing the ability of the DCNN to learn a similarity metric leveraging external features. Experimental results on the LAG dataset show that our method is able to outperform the face verification solutions in the state of the art considered.


title: A current-mode buck converter with a pulse-skipping soft-start circuit

abstract: This paper presents a soft-start circuit that adopts a pulse-skipping control to prevent inrush current and output voltage overshoot during the start-up period of dc-dc converters. The purpose of the pulse-skipping control is to significantly restrain the increasing rate of the reference voltage of the error amplifier. Thanks to the pulse-skipping mechanism and the duty cycle minimization, the soft-start-up time can be extended and the restriction of the charging current and the capacitance can be relaxed. The proposed soft-start circuit is fully integrated on chip without external components, leading to a reduction in PCB area and cost. A current-mode buck converter is implemented with TSMC 0.35-μm 2P4M CMOS process. Simulation results show the output voltage of the buck converter increases smoothly and inrush current is less than 300 mA.


Classification and Regression Tree Methods (In Encyclopedia of Statistics in Quality and Reliability,


title: A Survey of General-Purpose Computation on Graphics Hardware

abstract: The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware a compelling platform for computationally demanding tasks in a wide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping general-purpose computation to graphics hardware. We begin with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping general-purpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in general-purpose application development on graphics hardware. This survey should be of particular interest to researchers who are interested in using the latest GPGPU applications in their systems of interest.


Using Machine Learning Methods and Linguistic Features in Single-Document Extractive Summarization


title: Cloudlet-based just-in-time indexing of IoT video

abstract: As video cameras proliferate, the ability to scalably capture and search their data becomes important. Scalability is improved by performing video analytics on cloudlets at the edge of the Internet, and only shipping extracted index information and meta-data to the cloud. In this setting, we describe interactive data exploration (IDE), which refers to human-in-the-loop content-based retrospective search using predicates that may not have been part of any prior indexing. We also describe a new technique called just-in-time indexing (JITI) that improves response times in IDE.


title: An X-band to Ka-band SPDT switch using 200 nm SiGe HBTs

abstract: This paper presents the design and measured performance of an X-band to Ka-band SiGe HBT SPDT switch. The proposed SPDT switch was fabricated using a 200 nm, 150 GHz peak f T silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) BiCMOS technology. The SPDT switch design uses diode-connected SiGe HBTs in a series-shunt configuration to improve the switch bandwidth and isolation. Between 8 and 40 GHz, this SPDT switch achieves an insertion loss of less than 4.3 dB, an isolation of more than 20.3 dB, and a return loss of more than 9 dB.


title: Towards Understanding Sparse Filtering: A Theoretical Perspective

abstract: In this paper we present a theoretical analysis to understand sparse filtering, a recent and effective algorithm for unsupervised learning. The aim of this research is not to show whether or how well sparse filtering works, but to understand why and when sparse filtering does work. We provide a thorough theoretical analysis of sparse filtering and its properties, and further offer an experimental validation of the main outcomes of our theoretical analysis. We show that sparse filtering works by explicitly maximizing the entropy of the learned representation through the maximization of the proxy of sparsity, and by implicitly preserving mutual information between original and learned representations through the constraint of preserving a structure of the data, specifically the structure defined by relations of neighborhoodness under the cosine distance. Furthermore, we empirically validate our theoretical results with artificial and real data sets, and we apply our theoretical understanding to explain the success of sparse filtering on real-world problems. Our work provides a strong theoretical basis for understanding sparse filtering: it highlights assumptions and conditions for success behind this feature distribution learning algorithm, and provides insights for developing new feature distribution learning algorithms.


title: Meta-analysis of the impact of positive psychological capital on employee attitudes, behaviors, and performance

abstract: The positive core construct of psychological capital (or simply PsyCap), consisting of the psychological resources of hope, efficacy, resilience, and optimism, has recently been demonstrated to be open to human resource development (HRD) and performance management. The research stream on PsyCap has now grown to the point that a quantitative summary analysis of its impact on employee attitudes, behaviors, and especially performance is needed. The present meta-analysis included 51 independent samples (representing a total of N � 12,567 employees) that met the inclusion criteria. The results indicated the expected significant positive relationships between PsyCap and desirable employee attitudes (job satisfaction, organizational commitment, psychological well-being), desirable employee behaviors (citizenship), and multiple measures of performance (self, supervisor evaluations, and objective). There was also a significant negative relationship between PsyCap and undesirable employee attitudes (cynicism, turnover intentions, job stress, and anxiety) and undesirable employee behaviors (deviance). A sub-analysis found no major differences between the types of performance measures used (i.e., between self, subjective, and objective). Finally, the analysis of moderators revealed the relationship between PsyCap and employee outcomes were strongest in studies conducted in the United States and in the service sector. These results provide a strong evidence-based recommendation for the use of PsyCap in HRD and performance programs. Theoretical contributions, future research directions, and practical guidelines for HRD conclude the article.


title: Decentralized deduplication in san cluster file systems

abstract: File systems hosting virtual machines typically contain many duplicated blocks of data resulting in wasted storage space and increased storage array cache footprint. Deduplication addresses these problems by storing a single instance of each unique data block and sharing it between all original sources of that data. While deduplication is well understood for file systems with a centralized component, we investigate it in a decentralized cluster file system, specifically in the context of VM storage. ::: ::: We propose DEDE, a block-level deduplication system for live cluster file systems that does not require any central coordination, tolerates host failures, and takes advantage of the block layout policies of an existing cluster file system. In DEDE, hosts keep summaries of their own writes to the cluster file system in shared on-disk logs. Each host periodically and independently processes the summaries of its locked files, merges them with a shared index of blocks, and reclaims any duplicate blocks. DEDE manipulates metadata using general file system interfaces without knowledge of the file system implementation. We present the design, implementation, and evaluation of our techniques in the context of VMware ESX Server. Our results show an 80% reduction in space with minor performance overhead for realistic workloads.


title: Matrix Tri-Factorization with Manifold Regularizations for Zero-Shot Learning

abstract: Zero-shot learning (ZSL) aims to recognize objects of unseen classes with available training data from another set of seen classes. Existing solutions are focused on exploring knowledge transfer via an intermediate semantic embedding (e.g., attributes) shared between seen and unseen classes. In this paper, we propose a novel projection framework based on matrix tri-factorization with manifold regularizations. Specifically, we learn the semantic embedding projection by decomposing the visual feature matrix under the guidance of semantic embedding and class label matrices. By additionally introducing manifold regularizations on visual data and semantic embeddings, the learned projection can effectively captures the geometrical manifold structure residing in both visual and semantic spaces. To avoid the projection domain shift problem, we devise an effective prediction scheme by exploiting the test-time manifold structure. Extensive experiments on four benchmark datasets show that our approach significantly outperforms the state-of-the-arts, yielding an average improvement ratio by 7.4% and 31.9% for the recognition and retrieval task, respectively.


title: Securing the Internet of Things

abstract: This paper presents security of Internet of things. In the Internet of Things vision, every physical object has a virtual component that can produce and consume services Such extreme interconnection will bring unprecedented convenience and economy, but it will also require novel approaches to ensure its safe and ethical use. The Internet and its users are already under continual attack, and a growing economy-replete with business models that undermine the Internet's ethical use-is fully focused on exploiting the current version's foundational weaknesses.


Exploiting the Semantic Web for Unsupervised Natural Language Semantic Parsing


title: Precise Condition Synthesis for Program Repair

abstract: Due to the difficulty of repairing defect, many research efforts have been devoted into automatic defect repair. Given a buggy program that fails some test cases, a typical automatic repair technique tries to modify the program to make all tests pass. However, since the test suites in real world projects are usually insufficient, aiming at passing the test suites often leads to incorrect patches. This problem is known as weak test suites or overfitting. In this paper we aim to produce precise patches, that is, any patch we produce has a relatively high probability to be correct. More concretely, we focus on condition synthesis, which was shown to be able to repair more than half of the defects in existing approaches. Our key insight is threefold. First, it is important to know what variables in a local context should be used in an "if" condition, and we propose a sorting method based on the dependency relations between variables. Second, we observe that the API document can be used to guide the repair process, and propose document analysis technique to further filter the variables. Third, it is important to know what predicates should be performed on the set of variables, and we propose to mine a set of frequently used predicates in similar contexts from existing projects. Based on the insight, we develop a novel program repair system, ACS, that could generate precise conditions at faulty locations. Furthermore, given the generated conditions are very precise, we can perform a repair operation that is previously deemed to be too overfitting: directly returning the test oracle to repair the defect. Using our approach, we successfully repaired 18 defects on four projects of Defects4J, which is the largest number of fully automatically repaired defects reported on the dataset so far. More importantly, the precision of our approach in the evaluation is 78.3%, which is significantly higher than previous approaches, which are usually less than 40%.


title: Object-level document analysis of PDF files

abstract: The PDF format is commonly used for the exchange of documents on the Web and there is a growing need to understand and extract or repurpose data held in PDF documents. Many systems for processing PDF files use algorithms designed for scanned documents, which analyse a page based on its bitmap representation. We believe this approach to be inefficient. Not only does the rasterization step cost processing time, but information is also lost and errors can be introduced. Inspired primarily by the need to facilitate machine extraction of data from PDF documents, we have developed methods to extract textual and graphic content directly from the PDF content stream and represent it as a list of "objects" at a level of granularity suitable for structural understanding of the document. These objects are then grouped into lines, paragraphs and higher-level logical structures using a novel bottom-up segmentation algorithm based on visual perception principles. Experimental results demonstrate the viability of our approach, which is currently used as a basis for HTML conversion and data extraction methods.


title: Online least-squares policy iteration for reinforcement learning control

abstract: Reinforcement learning is a promising paradigm for learning optimal control. We consider policy iteration (PI) algorithms for reinforcement learning, which iteratively evaluate and improve control policies. State-of-the-art, least-squares techniques for policy evaluation are sample-efficient and have relaxed convergence requirements. However, they are typically used in offline PI, whereas a central goal of reinforcement learning is to develop online algorithms. Therefore, we propose an online PI algorithm that evaluates policies with the so-called least-squares temporal difference for Q-functions (LSTD-Q). The crucial difference between this online least-squares policy iteration (LSPI) algorithm and its offline counterpart is that, in the online case, policy improvements must be performed once every few state transitions, using only an incomplete evaluation of the current policy. In an extensive experimental evaluation, online LSPI is found to work well for a wide range of its parameters, and to learn successfully in a real-time example. Online LSPI also compares favorably with offline LSPI and with a different flavor of online PI, which instead of LSTD-Q employs another least-squares method for policy evaluation.


title: An analysis of different image preprocessing techniques for determining the centroids of circular marks using hough transform

abstract: In this paper, different image preprocessing methods are compared based on their ability to remove noise and to segment the images. Two filters, namely, Median filter and Wiener filter, and seven image segmentation methods, namely, Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Canny edge detection, basic global thresholding (BGT) and Otsu's global thresholding (OGT) are applied on the images of eight different signal-to-noise ratios (SNRs) ranging from 2.7 to 17.8. First, the preprocessing results are qualitatively compared by visual inspection for image SNRs of 17.8 (high) and 2.7 (low). Then the effects of different preprocessing methods are quantitatively analyzed by determining the accuracy of centroid detection of circular marks using Hough transform. The quantitative comparison showed that Median filter plus BGT or OGT give better results than other methods for low SNRs, and Wiener filter plus LoG detector provided higher accuracy compared to other methods for high SNRs. The application of this work is in many areas, for example, biomedical imaging, flow diagnostics and computer vision, where we detect the sizes or locations of circular objects in images.


Ultra Low Power Capless Low-Dropout Voltage Regulator ( Master Thesis Extended Abstract )


title: The coming of age of chaperone-mediated autophagy

abstract: Chaperone-mediated autophagy (CMA) was the first studied process that indicated that degradation of intracellular components by the lysosome can be selective - a concept that is now well accepted for other forms of autophagy. Lysosomes can degrade cellular cytosol in a nonspecific manner but can also discriminate what to target for degradation with the involvement of a degradation tag, a chaperone and a sophisticated mechanism to make the selected proteins cross the lysosomal membrane through a dedicated translocation complex. Recent studies modulating CMA activity in vivo using transgenic mouse models have demonstrated that selectivity confers on CMA the ability to participate in the regulation of multiple cellular functions. Timely degradation of specific cellular proteins by CMA modulates, for example, glucose and lipid metabolism, DNA repair, cellular reprograming and the cellular response to stress. These findings expand the physiological relevance of CMA beyond its originally identified role in protein quality control and reveal that CMA failure with age may aggravate diseases, such as ageing-associated neurodegeneration and cancer.


title: A performance evaluation of probabilistic vs. deterministic spiking neural network

abstract: This paper aims to present a comparison between probabilistic and deterministic spiking neural network for a back Propagation classification algorithm. To have a fair comparison, neuron models and structures are considered identical in both of the networks. The networks are trained and tested with the Iris database. According to the simulation results, the probabilistic network converges faster than the deterministic one, where it is also more sensitive to the input variations. The simulation results show a precision of 90% 88% for the probabilistic and the deterministic networks correspondingly, which is in consistence with the similar results for linear neural networks.


Bilateral ultrasound-guided erector spinae plane blocks in breast cancer and reconstruction surgery


title: Data Poisoning Attacks in Contextual Bandits

abstract: We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards in the data, an attacker can force the bandit algorithm to pull a target arm for a target contextual vector. The target arm and target contextual vector are both chosen by the attacker. That is, the attacker can hijack the behavior of a contextual bandit. We also investigate the feasibility and the side effects of such attacks, and identify future directions for defense. Experiments on both synthetic and real-world data demonstrate the efficiency of the attack algorithm.


title: An integrated self-healable electronic skin system fabricated via dynamic reconstruction of a nanostructured conducting network

abstract: Electronic skin devices capable of monitoring physiological signals and displaying feedback information through closed-loop communication between the user and electronics are being considered for next-generation wearables and the 'Internet of Things'. Such devices need to be ultrathin to achieve seamless and conformal contact with the human body, to accommodate strains from repeated movement and to be comfortable to wear. Recently, self-healing chemistry has driven important advances in deformable and reconfigurable electronics, particularly with self-healable electrodes as the key enabler. Unlike polymer substrates with self-healable dynamic nature, the disrupted conducting network is unable to recover its stretchability after damage. Here, we report the observation of self-reconstruction of conducting nanostructures when in contact with a dynamically crosslinked polymer network. This, combined with the self-bonding property of self-healing polymer, allowed subsequent heterogeneous multi-component device integration of interconnects, sensors and light-emitting devices into a single multi-functional system. This first autonomous self-healable and stretchable multi-component electronic skin paves the way for future robust electronics.


title: Dry Electrode Optimization for Wrist-based Electrodermal Activity Monitoring

abstract: Recent years of healthcare research has shown an increasing demand and interest in wearable personal healthcare systems. This, when combined with the superior utility of Electrodermal activity (EDA) for diverse applications ranging from market research to human stress and sleep quality analysis to seizure detection have driven significant interest towards optimizing dry electrode designs for EDA monitoring. The conventional wet adhesive Ag/AgCl electrodes, which is the gold standard, is used almost universally and provide excellent signal quality. However, they are less comfortable and not suitable for wearable scenarios that involve continuous long-term monitoring. This study focuses on identifying an optimum dry electrode configuration for monitoring EDA from the wrist. It is hypothesized that parameters like electrode material, interelectrode distance and anatomical location of measurement influence the dry electrode design for EDA detection. Accordingly, stainless steel, silver, brass and gold electrodes were fabricated with geometry and dimensions similar to that of commercially available standard wet electrodes. The fabricated electrodes were further investigated with interelectrode separations of 2 cm and 4 cm, both on the ventral and dorsal surfaces of the wrist at 6 cm distance from the carpus, thereby constituting 16 dry electrode configurations. An experimental protocol that spanned over 10 hours per subject was designed to investigate these configurations systematically and to identify the one that yielded the highest correlation with the gold standard. Also, the stabilization period for these dry electrode configurations were quantified as the time taken by the EDA signal to reach within ± 5% of its final value. The experimental study done on 7 subjects (3 females and 4 males, age: 23.3 ± 2.8 years; mean ± SD) illustrated that silver electrodes worn on the dorsal surface of the wrist with an interelectrode separation of 4 cm performed consistently well on all subjects with an average Pearson correlation coefficient of 0.899 ± 0.036, following an average stabilization time of 27.1 minutes.


title: Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set

abstract: Recent years have witnessed an ever-mounting interest in the research of sparse representation. The framework, Sparse Representation-based Classification (SRC), has been widely applied as a classifier in numerous domains, among which Synthetic Aperture Radar (SAR) target recognition is really challenging because it still is an open problem to interpreting the SAR image. In this paper, SRC is utilized to classify a 10-class moving and stationary target acquisition and recognition (MSTAR) target, which is a standard SAR data set. Before the classification, the sizes of the images need to be normalized to maintain the useful information, target and shadow, and to suppress the speckle noise. Specifically, a preprocessing method is recommended to extract the feature vectors of the image, and the feature vectors of the test samples can be represented by the sparse linear combination of basis vectors generated by the feature vectors of the training samples. Then the sparse representation is solved by l 1 -norm minimization. Finally, the identities of the test samples are inferred by the reconstructive errors calculated through the sparse coefficient. Experimental results demonstrate the good performance of SRC. Additionally, the average recognition rate under different feature spaces and the recognition rate of each target are discussed.


title: Discovery of research interests of authors over time using a topic model

abstract: With a growing number of Web documents, many approaches have been proposed for knowledge discovery on Web documents. The documents do not always provide keywords or categories, so unsupervised approaches are desirable, and topic modeling is such an approach for knowledge discovery without using labels. Further, Web documents usually have time information such as publish years, so knowledge patterns over time can be captured by incorporating the time information. In this paper, we propose a new topic model called the Author Topic-Flow (ATF) model whose objective is to capture temporal patterns of research interests of authors over time, where each topic is associated with a research domain. The design of the ATF model is based on the hypothesis that direct topic flows are better than indirect topic flows in the state-of-the-art Temporal Author Topic (TAT) model, which is the most similar approach to ours. We prove the hypothesis by showing the effectiveness of the ATF model compared to the TAT model.


title: A deployable sampling strategy for data race detection

abstract: Dynamic data race detection incurs heavy runtime overheads. Recently, many sampling techniques have been proposed to detect data races. However, some sampling techniques (e.g., Pacer) are based on traditional happens-before relation and incur a large basic overhead. Others utilize hardware to reduce their sampling overhead (e.g., DataCollider) and they, however, detect a race only when the race really occurs by delaying program executions. In this paper, we study the limitations of existing techniques and propose a new data race definition, named as Clock Races, for low overhead sampling purpose. The innovation of clock races is that the detection of them does not rely on concrete locks and also avoids heavy basic overhead from tracking happens-before relation. We further propose CRSampler (Clock Race Sampler) to detect clock races via hardware based sampling without directly delaying program executions, to further reduce runtime overhead. We evaluated CRSampler on Dacapo benchmarks. The results show that CRSampler incurred less than 5% overhead on average at 1% sampling rate. Whereas, Pacer and DataCollider incurred larger than 25% and 96% overhead, respectively. Besides, at the same sampling rate, CRSampler detected significantly more data races than that by Pacer and DataCollider.


title: Design of Conical Transmission Line Power Combiners Using Tapered Line Matching Sections

abstract: An axially symmetric power combiner, which utilizes a tapered conical impedance matching network to transform ten 50-Omega inputs to a central coaxial line over the X-band, is presented. The use of a conical line allows standard transverse electromagnetic design theory to be used, including tapered impedance matching networks. This, in turn, alleviates the problem of very low impedance levels at the common port of conical line combiners, which normally requires very high-precision manufacturing and assembly. The tapered conical line is joined to a tapered coaxial line for a completely smooth transmission line structure. Very few full-wave analyses are needed in the design process since circuit models are optimized to achieve a wide operating bandwidth. A ten-way prototype was developed at X-band with a 47% bandwidth, very low losses, and excellent agreement between simulated and measured results.


Modeling Strategic Partnerships Using the E3value Ontology: A Field Study in the Banking Industry


title: Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

abstract: Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification (also called "semantic image segmentation"). We show that responses at the final layer of DCNNs are not sufficiently localized for accurate object segmentation. This is due to the very invariance properties that make DCNNs good for high level tasks. We overcome this poor localization property of deep networks by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). Qualitatively, our "DeepLab" system is able to localize segment boundaries at a level of accuracy which is beyond previous methods. Quantitatively, our method sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 71.6% IOU accuracy in the test set. We show how these results can be obtained efficiently: Careful network re-purposing and a novel application of the 'hole' algorithm from the wavelet community allow dense computation of neural net responses at 8 frames per second on a modern GPU.


title: A voxel-based morphometry study of semantic dementia: relationship between temporal lobe atrophy and semantic memory. Ann Neurol 47: 36–45

abstract: The cortical anatomy of 6 patients with semantic dementia (the temporal lobe variant of frontotemporal dementia) was contrasted with that of a group of age-matched normal subjects by using voxel-based morphometry, a technique that identifies changes in gray matter volume on a voxel-by-voxel basis. Among the circumscribed regions of neuronal loss, the left temporal pole (Brodmann area 38) was the most significantly and consistently affected region. Cortical atrophy in the left hemisphere also involved the inferolateral temporal lobe (Brodmann area 20/21) and fusiform gyrus. In addition, the right temporal pole (Brodmann area 38), the ventromedial frontal cortex (Brodmann area 11/32) bilaterally, and the amygdaloid complex were affected, but no significant atrophy was measured in the hippocampus, entorhinal, or caudal perirhinal cortex. The degree of semantic memory impairment across the 6 cases correlated significantly with the extent of atrophy of the left anterior temporal lobe but not with atrophy in the adjacent ventromedial frontal cortex. These results confirm that the anterior temporal lobe is critically involved in semantic processing, and dissociate its function from that of the adjacent frontal region.


title: Fake News: A Survey of Research, Detection Methods, and Opportunities

abstract: The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news analysis, detection and intervention. This survey comprehensively and systematically reviews fake news research. The survey identifies and specifies fundamental theories across various disciplines, e.g., psychology and social science, to facilitate and enhance the interdisciplinary research of fake news. Current fake news research is reviewed, summarized and evaluated. These studies focus on fake news from four perspective: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) the credibility of its creators and spreaders. We characterize each perspective with various analyzable and utilizable information provided by news and its spreaders, various strategies and frameworks that are adaptable, and techniques that are applicable. By reviewing the characteristics of fake news and open issues in fake news studies, we highlight some potential research tasks at the end of this survey.


Mining Inference Formulas by Goal-Directed Random Walks


title: Design of Class-E Amplifier With MOSFET Linear Gate-to-Drain and Nonlinear Drain-to-Source Capacitances

abstract: This paper presents expressions for the waveforms and design equations to satisfy the ZVS/ZDS conditions in the class-E power amplifier, taking into account the MOSFET gate-to-drain linear parasitic capacitance and the drain-to-source nonlinear parasitic capacitance. Expressions are given for power output capability and power conversion efficiency. Design examples are presented along with the PSpice-simulation and experimental waveforms at 2.3 W output power and 4 MHz operating frequency. It is shown from the expressions that the slope of the voltage across the MOSFET gate-to-drain parasitic capacitance during the switch-off state affects the switch-voltage waveform. Therefore, it is necessary to consider the MOSFET gate-to-drain capacitance for achieving the class-E ZVS/ZDS conditions. As a result, the power output capability and the power conversion efficiency are also affected by the MOSFET gate-to-drain capacitance. The waveforms obtained from PSpice simulations and circuit experiments showed the quantitative agreements with the theoretical predictions, which verify the expressions given in this paper.


title: Extended Functionality Attacks on IoT Devices: The Case of Smart Lights

abstract: In this paper we consider the security aspects of Internet of Things (IoT) devices, which bridge the physical and virtual worlds. We propose a new taxonomy of attacks, which classifies them into four broad categories. The most interesting category (which we call functionality extension attacks) uses the designed functionality of the IoT device to achieve a totally different effect. To demonstrate this type of attack, we consider the case of smart lights (whose original functionality is just to control the color and intensity of the lights in a particular room) and show how to use them to achieve unrelated effects. In the first attack, we use smart lights as a covert LIFI communication system to exfiltrate data from a highly secure (or even fully airgapped) office building. We implemented the attack and were able to read the leaked data from a distance of over 100 meters using only cheap and readily available equipment. In another attack, we showed that an attacker can strobe the lights at a frequency which may trigger seizures in people suffering from photosensitive epilepsy (in the same way that rapidly flashing video games can cause such seizures). In our experiments, we have tested both high-end and lower-end smart light systems, ranging from an expensive Philips HUE system to a cheap system manufactured by LimitlessLED. In addition, we consider other weaknesses of the systems we tested, and propose feasible remedies for the problems we found.


title: Investigation and Comparison of 3GPP and NYUSIM Channel Models for 5G Wireless Communications

abstract: Channel models describe how wireless channel parameters behave in a given scenario, and help evaluate link- and system-level performance. A proper channel model should be able to faithfully reproduce the channel parameters obtained in field measurements and accurately predict the spatial and temporal channel impulse response along with large-scale fading. This paper compares two popular channel models for next generation wireless communications: the 3rd Generation Partnership Project (3GPP) TR 38.900 Release 14 channel model and the statistical spatial channel model NYUSIM developed by New York University (NYU). The two channel models employ different modeling approaches in many aspects, such as the line-of-sight probability, path loss, and clustering methodology. Simulations are performed using the two channel models to analyze the channel eigenvalue distribution and spectral efficiency leveraging the analog/digital hybrid beamforming methods found in the literature. Simulation results show that the 3GPP model produces different eigenvalue and spectral efficiency distributions for mmWave bands, as compared to the outcome from NYUSIM that is based on massive amounts of real-world measured data in New York City. This work shows NYUSIM is more accurate for realistic simulations than 3GPP in urban environments.


title: Multiple intelligences: Can they be measured?

abstract: AbstractThis paper is about issues relating to the assessment of multiple intelligences. The first section introduces the authors' work on building measures of multiple intelligences and moral sensitivities. It also provides a conceptual definition of multiple intelligences based on Multiple Intelligences theory by Howard Gardner (1983). The second section discusses the context specificity of intelligences and alternative approaches to measuring multiple intelligences. The third section analyses the validity of self-evaluation instruments and provides a case example of building such an instrument. The paper ends with concluding remarks.Key words: Giftedness, multiple intelligences theory, MIPQ, CFA, Bayesian modeling(ProQuest: ... denotes formula omitted.)IntroductionIn this paper, we introduce our work on building measures of multiple intelligences and moral sensitivities based on the Multiple Intelligences theory of Howard Gardner (1983, 1993). We have developed several instruments for self-assessment that can be used in educational settings (Tirri & Nokelainen, 2011). Gardner's theory of Multiple Intelli- gences (MI) focuses on the concept of an 'intelligence', which he defines as "the ability to solve problems, or to create products, that are valued within one or more cultural settings" (Gardner, 1993, p. x). Gardner lists seven intelligences that meet his criteria for an intelligence, namely linguistic, logical-mathematical, musical, spatial, bodily kines- thetic, interpersonal, and intrapersonal (Gardner, 1993, p. xi).In a broad sense, Gardner views his theory as a contribution to the tradition advocated by Thurstone (1960) and Guilford (1967) because all these theories argue for the existence of a number of factors, or components, of intelligence. All these theories also view intel- ligence as being broader and multidimensional rather than a single, general capacity for conceptualization and problem-solving. Gardner differs from the other pluralists, howev- er, in his attempt to base MI theory upon neurological, evolutionary, and cross-cultural evidence (Gardner, 1993, p. xii). In the first edition of his MI theory, thirty years ago, Gardner (1983) adopted a very individualistic point of view in exploring various intelli- gences. In a newer edition of MI theory, however, Gardner (1993) places more emphasis on the cultural and contextual factors involved in the development of the seven intelli- gences. Gardner retained the original seven intelligences, but acknowledged the possibil- ity of adding new intelligences to the list. For example, he has worked on an eighth intel- ligence - the intelligence of the naturalist - to be included in his list of multiple intelli- gences (Gardner, 1995, p. 206).Robert Sternberg identifies Gardner's MI theory as a systems approach, similar to his own triarchic theory. Although he appreciates Gardner's assessments at a theoretical level, he believes them to be a psychometric nightmare. The biggest challenge for advo- cates of Gardner's approach, then, is to demonstrate the psychometric soundness of their instrument. Sternberg is calling for hard data that would show that the theory works operationally in a way that will satisfy scientists as well as teachers. Sternberg's own theory promises the broader measurement implied by the triarchic theory (Sternberg, 1985). His theory provides "process scores for componential processing, coping with novelty, automatization, and practical-contextual intelligence, and content scores for the verbal, quantitative, and figural content domains" (Sternberg, 1991, p. 266).Sternberg's observations on Gardner's theory should be kept in mind in attempts to create tests based on his theory. However, in the educational setting his theory can be used as a framework in planning a program that would meet the needs of different learn- ers (Tirri, 1997). Gardner has shown a special interest in how schools encourage the different intelligences in students (Gardner, 1991). …


title: Battling the digital forensic backlog through data deduplication

abstract: In recent years, technology has become truly pervasive in everyday life. Technological advancement can be found in many facets of life, including personal computers, mobile devices, wearables, cloud services, video gaming, web-powered messaging, social media, Internet-connected devices, etc. This technological influence has resulted in these technologies being employed by criminals to conduct a range of crimes — both online and offline. Both the number of cases requiring digital forensic analysis and the sheer volume of information to be processed in each case has increased rapidly in recent years. As a result, the requirement for digital forensic investigation has ballooned, and law enforcement agencies throughout the world are scrambling to address this demand. While more and more members of law enforcement are being trained to perform the required investigations, the supply is not keeping up with the demand. Current digital forensic techniques are arduously time-consuming and require a significant amount of man power to execute. This paper discusses a novel solution to combat the digital forensic backlog. This solution leverages a deduplication-based paradigm to eliminate the reacquisition, redundant storage, and reanalysis of previously processed data.


title: The Zone of Proximal Development in In-Hand Manipulation Skills of Nondysfunctional 3- and 4-Year-Old Children

abstract: The purpose of this study was to assess the influence of verbal and visual cues on 28 nondysfunctional 3- and 4-year-old children's performance of in-hand manipulation skills. During the uncued version of the test (pretest), the children were presented with play activities known to elicit in-hand manipulation skills in young children but were given no cues for specific manipulative skill use. Within 1 week of the pretest, after the children were randomly assigned to groups, a posttest was given in which the children were provided with verbal or visual cues. The results indicate that both visual and verbal cues were effective in increasing the groups' test scores, but no significant differences were found based on the type of cue provided. Approximately 30% of the children in the study showed marked improvement on the posttest scores, whereas the others showed little change. The children who had lower pretest scores showed greater score increases on the posttest than did the children who scored somewhat higher initially. The use of an uncued test followed by a cued test may be an effective method for the identification of those children who are most likely to show improvement in these manipulation skills with intervention. This study's findings lend tentative support to Vygotsky's (1978) zone of proximal development theory and extends the application of his work into the domain of fine motor skills.


title: Reconfigurable Planar SIW Cavity Resonator and Filter

abstract: A new reconfigurable frequency cavity compatible with planar technology has been designed, manufactured and measured. An original tuning solution is proposed, based upon the insertion of vertical capacitive posts integrated within SIW cavities. One extremity of each post is connected to a floating metallic ring, located on the substrate supporting the SIW cavity. The frequency agility is obtained once the metallic ring is connected to the ground plane by short-circuited the corresponding annular slot using surface actuators. Such combination of volumetric cavity and planar activation leads to reconfigurable filters with high Q values and simplified tuning control conditions


title: A near-optimal probing strategy for workpiece localization

abstract: This paper addresses an optimal planning problem for workpiece localization with coordinate measurements. The fundamental issue is to find the best probing locations and a suitable sampling size, such that the uncertainty of the localization error is within a predefined limited bound. First, we introduce two sequential optimization algorithms to incrementally increase the localization accuracy, defined by the determinant of the information matrix of the measurements. Then, a reliability analysis method is incorporated for finding a sample size that is sufficient to reduce the uncertainty of the localization error to a limited bound. By combining these two analysis tools, we present a near-optimal probing strategy for finding the best probing locations and a suitable sampling size. With this strategy, given the desired translation and orientation error bounds and desired confidence limit, we can experimentally determine the least number of points needed to measure. Simulation and experimental results show the efficiency of the proposed probing strategy.


title: Comparison of Compact Polarimetric Synthetic Aperture Radar Modes

abstract: Compact polarimetry is a technique that allows construction of pseudo quad-pol information from dual-polarization synthetic aperture radar (SAR) systems. Compact polarimetry showed promise of being able to reduce the complexity, cost, mass, and data rate of a SAR system while attempting to maintain many capabilities of a fully polarimetric system. In this paper, we study different transmit/receive configurations to determine which polarimetric configurations allow for superior reconstruction of the fully polarimetric data. We discuss modifications of the original reconstruction algorithm proposed by Souyris , which show potential to better reconstruct fully polarimetric data.