title: Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis.

abstract: Biomechanical analysis is a powerful tool in the evaluation of movement dysfunction in orthopaedic and neurologic populations. Three-dimensional (3D) motion capture systems are widely used, accurate systems, but are costly and not available in many clinical settings. The Microsoft Kinect™ has the potential to be used as an alternative low-cost motion analysis tool. The purpose of this study was to assess concurrent validity of the Kinect™ with Brekel Kinect software in comparison to Vicon Nexus during sagittal plane gait kinematics. Twenty healthy adults (nine male, 11 female) were tracked while walking and jogging at three velocities on a treadmill. Concurrent hip and knee peak flexion and extension and stride timing measurements were compared between Vicon and Kinect™. Although Kinect measurements were representative of normal gait, the Kinect™ generally under-estimated joint flexion and over-estimated extension. Kinect™ and Vicon hip angular displacement correlation was very low and error was large. Kinect™ knee measurements were somewhat better than hip, but were not consistent enough for clinical assessment. Correlation between Kinect™ and Vicon stride timing was high and error was fairly small. Variability in Kinect™ measurements was smallest at the slowest velocity. The Kinect™ has basic motion capture capabilities and with some minor adjustments will be an acceptable tool to measure stride timing, but sophisticated advances in software and hardware are necessary to improve Kinect™ sensitivity before it can be implemented for clinical use.


title: Churn analysis using deep convolutional neural networks and autoencoders

abstract: Customer temporal behavioral data was represented as images in order to perform churn prediction by leveraging deep learning architectures prominent in image classification. Supervised learning was performed on labeled data of over 6 million customers using deep convolutional neural networks, which achieved an AUC of 0.743 on the test dataset using no more than 12 temporal features for each customer. Unsupervised learning was conducted using autoencoders to better understand the reasons for customer churn. Images that maximally activate the hidden units of an autoencoder trained with churned customers reveal ample opportunities for action to be taken to prevent churn among strong data, no voice users.


title: An abstract methodology for developing knowledge management systems

abstract: Powerful organizations are those that manage their power factors efficiently; organizational resources are considered vital power factors, and Knowledge is one of the most important resources to manage. There is no universally accepted Knowledge Management (KM) process, but it is known that establishing the appropriate knowledge flows in the organization is the main goal of organizational KM. A Knowledge Management System (KMS) is an information system which supports the KM process, mainly by providing the required knowledge and enhancing its flow. Organizations increasingly feel the need for appropriate methodologies for developing their target KMSs. However, existing KMS development methodologies are not comprehensive enough to satisfy all organizational needs. In this paper, we propose an abstract KMS development methodology which alleviates the weaknesses of existing methodologies while reusing their strengths. Method engineers can develop concrete methodologies by instantiating the proposed abstract methodology and adding the necessary detail, thus producing bespoke methodologies which are best suited to organizational needs.


title: Continuous Media Sharing in Multimedia Database Systems

abstract: The timeliness and synchronization requirements of multimedia data demand e&ient buffer management and disk access schemes for multimedia database systems. The data rates involved are very high and despite the developmenl of eficient storage and retrieval strategies, disk I/O is a potential bottleneck, which limits the number of concurrent sessions supported by a system. This calls for more eficient use of data that has already been brought into the buffer. We introduce the notion of continuous media caching, which is a simple and novel technique where data that have been played back by a user are preserved in a controlled fashion for use by subsequent users requesting the same data. We present heuristics to determine when continuous media sharing is beneficial and describe the bufler management algorithms. Simulation studies indicate that our technique substantially improves the performance of multimedia database applications where data sharing is possible.


title: Potential of double pilot injection strategies optimized with the design of experiments procedure to improve diesel engine emissions and performance

abstract: Abstract The potential of pilot–pilot–main triple injection strategies versus engine-out emissions, combustion noise and brake specific fuel consumption has been assessed experimentally on a Euro 5 diesel engine with a reduced compression ratio (16.3:1). The engine has been fueled with conventional diesel fuel. The experimental tests on the engine have been carried out in a dynamometer cell under different steady state working conditions, that are representative of passenger car engine applications over the European homologation cycle. Furthermore, in-cylinder analyses of the pressure, heat-release rate, temperature and emissions have been performed in order to obtain more detailed knowledge on the cause-and-effect-relationships between the implemented injection strategies and the results of the experimental tests. The implemented double-pilot injection engine calibrations have been optimized by means of the design of experiments procedure. The plotted data of the engine performance and emissions have been compared with data from the original double-injection schedule, characterized by a retarded main injection timing, in order to intensify the premixed combustion phase. The benefits and the disadvantages of the PCCI concept are preliminarily discussed, on the basis of the experimental pilot–main injection strategy results. The substitution of the pilot–main injection schedule with the triple injection, for light engine loads and low engine speeds, has led to higher mean combustion pressures, lower heat release rates, shorter ignition delays and lower brake specific fuel consumption. Above all, a significant improvement in engine noise and in both CO and HC engine-out emissions has been achieved and the NOx emission have been limited by the application of high EGR rates. When medium engine loads and speeds are analyzed, the considered double-pilot injection strategy allows the NOx emissions to be reduced, compared to the baseline pilot–main injection schedule. However, the combustion noise does not improve and the soot deteriorates, even though the soot penalties are not relevant.


title: PccP: A model for Preserving cloud computing Privacy

abstract: The widespread focus on the Cloud Computing has necessitated the corresponding mechanisms to ensure privacy and security. Various attempts have been made in the past to safeguard the privacy of the individual or agency trying to utilize the services being provided by the cloud. The most challenging task is to provide services to the users while also preserving the privacy of the user's information. In this paper a model that incorporates a three-level architecture, Preserving cloud computing Privacy (PccP) model is proposed which aims to preserve privacy of information pertaining to cloud users. The Consumer Layer deals with all the aspects which relate to enabling the user of the cloud to access the cloud services being provided by the cloud service provider. The Network Interface Layer creates an appropriate mapping between the original IP addresses of the users with a modified IP address, and thereby ensuring the privacy of the IP address of the users. The Privacy Preserved Layer utilizes the functionality of the Unique User Cloud Identity Generator for which an algorithm is proposed in this paper to generate an unique User Service Dependent Identity(USID) with privacy check by establishing mapping among the existing user identity(ID), if any to ID's available in a pool of User ID's to enhance the privacy of sensitive user information. A Privacy check method based on information privacy is being proposed which contributes significantly in maintaining user control over the generated user identities (USID's).


title: The Benefits of Self-Awareness and Attention in Fog and Mist Computing

abstract: Self-awareness facilitates a proper assessment of cost-constrained cyber-physical systems, allocating limited resources where they are most needed. Together, situation awareness and attention are key enablers for self-awareness in efficient distributed sensing and computing networks.


title: Vehicular Networks: A New Challenge for Content-Delivery-Based Applications

abstract: A significant number of promising applications for vehicular ad hoc networks (VANETs) are becoming a reality. Most of these applications require a variety of heterogenous content to be delivered to vehicles and to their on-board users. However, the task of content delivery in such dynamic and large-scale networks is easier said than done. In this article, we propose a classification of content delivery solutions applied to VANETs while highlighting their new characteristics and describing their underlying architectural design. First, the two fundamental building blocks that are part of an entire content delivery system are identified: replica allocation and content delivery. The related solutions are then classified according to their architectural definition. Within each category, solutions are described based on the techniques and strategies that have been adopted. As result, we present an in-depth discussion on the architecture, techniques, and strategies adopted by studies in the literature that tackle problems related to vehicular content delivery networks.


Apache Spark and Apache Kafka at the Rescue of Distributed RDF Stream Processing Engines


title: A 2.4GHz SiGe low phase-noise VCO using on chip tapped inductor

abstract: An on-chip tapped inductor technique is shown to be an efficient method for reducing oscillator phase noise. Higher signal amplitude can be achieved while avoiding breakdown and without penalty in area or tuning range. A commercial SiGe BJT process was used to fabricate the 2.4GHz VCO. The measured phase noise at 1MHz offset frequency is -128 dBc/Hz with 23% tuning range. The VCO dissipates 16.5mA at a 2.5V supply voltage.


title: FIFS: Fine-Grained Indoor Fingerprinting System

abstract: WLAN-based indoor location fingerprinting has been attractive owing to the advantages of open access and high accuracy. Most fingerprinting-based systems so far rely on the received signal strength (RSS), which can be easily measured at the receiver with commercial WLAN equipment. However, RSS is a coarse value which simply measures the received power for a whole channel. Thus, it fluctuates over time in typical indoor environments with rich multipath effects and not unique for a specific location. In this paper, we present the design, implementation, and evaluation of a Fine-grained Indoor Fingerprinting System (FIFS). FIFS explores a PHYlayer Channel State Information (CSI) that specifies the channel status over all the subcarriers for location fingerprinting in WLAN. The system leverages the CSI values including different amplitudes and phases at multiple propagation paths, known as the frequency diversity, to uniquely manifest a location. Moreover, the multiple antennas provides the spatial diversity that can be further augmented in fingerprinting. We also present a coherence bandwidth-enhanced probability algorithm with a correlation filter to map object to the fingerprints. We conducted experiments in two typical indoor scenarios with commercial IEEE 802.11 NICs. The experimental results demonstrate that the overall positioning accuracy can be improved compared with the RSS-based Horus system.


title: The Antimicrobial Resistance Crisis: Causes, Consequences, and Management

abstract: The antimicrobial resistance (AMR) crisis is the increasing global incidence of infectious diseases affecting the human population, which are untreatable with any known antimicrobial agent. This crisis will have a devastating cost on human society as both debilitating and lethal diseases increase in frequency and scope. Three major factors determine this crisis: (1) the increasing frequency of AMR phenotypes among microbes is an evolutionary response to the widespread use of antimicrobials; (2) the large and globally connected human population allows pathogens in any environment access to all of humanity; and (3) the extensive and often unnecessary use of antimicrobials by humanity provides the strong selective pressure that is driving the evolutionary response in the microbial world. Of these factors, the size of the human population is least amenable to rapid change. In contrast, the remaining two factors may be affected, so offering a means of managing the crisis: the rate at which AMR, as well as virulence factors evolve in microbial world may be slowed by reducing the applied selective pressure. This may be accomplished by radically reducing the global use of current and prospective antimicrobials. Current management measures to legislate the use of antimicrobials and to educate the healthcare world in the issues, while useful, have not comprehensively addressed the problem of achieving an overall reduction in the human use of antimicrobials. We propose that in addition to current measures and increased research into new antimicrobials and diagnostics, a comprehensive education program will be required to change the public paradigm of antimicrobial usage from that of a first line treatment to that of a last resort when all other therapeutic options have failed.


Multi-Modal Multi-Task Learning for Automatic Dietary Assessment


title: Depth-based view synthesis using pixel-level image inpainting

abstract: Depth-based view synthesis can produce novel realistic images of a scene by view warping and image inpainting. This paper presents a depth-based view synthesis approach performing pixel-level image inpainting. The proposed approach provides great flexibility in pixel manipulation and prevents random effects in texture propagation. By analyzing the process generating image holes in view warping, we firstly classify such areas into simple holes and disocclusion areas. Based on depth information constraints and different strategies for random propagation, an approximate nearest-neighbor match based pixel-level inpainting is introduced to complete holes from the two classes. Experimental results demonstrate that the proposed view synthesis method can effectively produce smooth textures and reasonable structure propagation. The proposed depth-based pixel-level inpainting is well suitable to multi-view video and other higher dimensional view synthesis settings.


title: AirExpress: Enabling Seamless In-band Wireless Multi-hop Transmission

abstract: This paper describes the design and implementation of AirExpress, a system that enables in-band wireless cut-through transmission. Unlike wired cut-through, wireless cut-through can reduce latency and improve throughput performance of the network at the same time. In AirExpress, all the forwarders along the cut-through path forward the signal they received immediately without decoding. The hierarchical structure of AirExpress enables its interference cancellation ability to handle all kinds of interference among the radios. Novel MAC and routing algorithms based on cut-through transmission are also proposed to support the realization of AirExpress in multi-hop mesh networks. AirExpress is implemented on an NI-based SDR platform. Through experiments in the 2.4GHz ISM band, we show throughput gain of up to 3.4 times with a 4-hop AirExpress system. Trace driven evaluation of AirExpress on the NS3 platform shows an average throughput gain of 2.85 for AirExpress over optimal TDMA.


title: Combined Nonlinear Echo Cancellation and Residual Echo Suppression

abstract: We describe a combined nonlinear acoustic echo cancellation and residual echo suppression system. The echo canceler uses parallel Hammerstein branches consisting of fixed nonlinear basis functions and linear adaptive filters. The residual echo suppressor uses an Artificial Neural Network for modeling of the residual echo spectrum from spectral features computed from the far-end signal. We show that modeling nonlinear effects both in the echo canceler and in the echo suppressor leads to an increased performance of the combined system.


title: SEVA3D: Using Arti cial Neural Networks to Autonomous Vehicle Parking Control

abstract: This paper describes the simulation system proposed in order to study and to implement intelligent autonomous vehicle control. The developed system can automatically drive a vehicle, implementing a robust control system capable of simulating in a realistic way autonomous parking in a parallel parking space. The system controls the vehicles based on the reading of sonar sensors and uses a neural network to automatically generate acceleration and steering commands, parking it in a parallel parking space. The controller was implemented using a Jordan-Net based neural network, and the results obtained in our simulations demonstrated that the proposed controller is perfectly able to correctly park the vehicle in different situations.


title: DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

abstract: Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient. On the other end, DeePMD-kit is interfaced with high-performance classical molecular dynamics and quantum (path-integral) molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus, upon training, the potential energy and force field models can be used to perform efficient molecular simulations for different purposes. As an example of the many potential applications of the package, we use DeePMD-kit to learn the interatomic potential energy and forces of a water model using data obtained from density functional theory. We demonstrate that the resulted molecular dynamics model reproduces accurately the structural information contained in the original model.


Integrating Function , Geometry , Appearance for Scene Parsing


Advanced Interactions with Heatmaps for Analyzing


title: Attendance generating system using RFID and GSM

abstract: In this paper, we propose a new model for parent alerting and automatic attendance marking using of RFID (Radio-Frequency Identification) and GSM(Global System for Mobile Communication). This system will enable automated and reliable attendance, informing parents and school administration about the same. For implementing this system each student and professor will be having a unique RFID card. A micro controller is attached to the classroom door which is used to match the RFID of the student with the RFID present in the database. If they are match the door will open. This is done by using a GSM module.


title: Survey of graph database models

abstract: Graph database models can be defined as those in which data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models took off in the eighties and early nineties alongside object-oriented models. Their influence gradually died out with the emergence of other database models, in particular geographical, spatial, semistructured, and XML. Recently, the need to manage information with graph-like nature has reestablished the relevance of this area. The main objective of this survey is to present the work that has been conducted in the area of graph database modeling, concentrating on data structures, query languages, and integrity constraints.


title: A metric for software readability

abstract: In this paper, we explore the concept of code readability and investigate its relation to software quality. With data collected from human annotators, we derive associations between a simple set of local code features and human notions of readability. Using those features, we construct an automated readability measure and show that it can be 80% effective, and better than a human on average, at predicting readability judgments. Furthermore, we show that this metric correlates strongly with two traditional measures of software quality, code changes and defect reports. Finally, we discuss the implications of this study on programming language design and engineering practice. For example, our data suggests that comments, in of themselves, are less important than simple blank lines to local judgments of readability.


title: Fault injection attack on deep neural network

abstract: Deep neural network (DNN), being able to effectively learn from a training set and provide highly accurate classification results, has become the de-facto technique used in many mission-critical systems. The security of DNN itself is therefore of great concern. In this paper, we investigate the impact of fault injection attacks on DNN, wherein attackers try to misclassify a specified input pattern into an adversarial class by modifying the parameters used in DNN via fault injection. We propose two kinds of fault injection attacks to achieve this objective. Without considering stealthiness of the attack, single bias attack (SBA) only requires to modify one parameter in DNN for misclassification, based on the observation that the outputs of DNN may linearly depend on some parameters. Gradient descent attack (GDA) takes stealthiness into consideration. By controlling the amount of modification to DNN parameters, GDA is able to minimize the fault injection impact on input patterns other than the specified one. Experimental results demonstrate the effectiveness and efficiency of the proposed attacks.


title: Monitoring and Billing of A Lightweight Cloud System Based on Linux Container

abstract: Nowadays, more and more enterprises and research institutes choose to build mini-datacenters and deploy private cloud environments to meet growing business and research needs. To make users can run different application frameworks on the same datacenter, Cao et al. [5] proposed a new service model named ClaaS (Cluster as a Service) and developed a lightweight prototype system named Docklet which is based on LXC (Linux Container). Docklet faces a problem of resources waste and abuse due to our free policy. This paper introduces the monitoring and billing modules of Docklet in order to solve this problem. Monitoring module provides users and administrators with a clear, real-time and detailed monitoring interface to understand the statues of running applications and the usage of physical resources. Billing module uses these data to remind users to release unnecessary resources. An experiment and observations show that our proposed monitoring method is effective and lightweight and our proposed billing model increases the utilization of physical resources of a mini-datacenter.


title: ICDAR2017 Robust Reading Challenge on COCO-Text

abstract: This report presents the final results of the ICDAR 2017 Robust Reading Challenge on COCO-Text. A challenge on scene text detection and recognition based on the largest real scene text dataset currently available: the COCO-Text dataset. The competition is structured around three tasks: Text Localization, Cropped Word Recognition and End-To-End Recognition. The competition received a total of 27 submissions over the different opened tasks. This report describes the datasets and the ground truth, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.


title: ICDAR2017 Competition on Document Image Binarization (DIBCO 2017)

abstract: DIBCO 2017 is the international Competition on Document Image Binarization organized in conjunction with the ICDAR 2017 conference. The general objective of the contest is to identify current advances in document image binarization of machine-printed and handwritten document images using performance evaluation measures that are motivated by document image analysis and recognition requirements. This paper describes the competition details including the evaluation measures used as well as the performance of the 26 submitted methods along with a brief description of each method.


title: A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

abstract: This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.


title: 3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN

abstract: Deep learning on point clouds has made a lot of progress recently. Many point cloud dedicated deep learning frameworks, such as PointNet and PointNet++, have shown advantages in accuracy and speed comparing to those using traditional 3D convolution algorithms. However, nearly all of these methods face a challenge, since the coordinates of the point cloud are decided by the coordinate system, they cannot handle the problem of 3D transform invariance properly. In this paper, we propose a general framework for point cloud learning. We achieve transform invariance by learning inner 3D geometry feature based on local graph representation, and propose a feature extraction network based on graph convolution network. Through experiments on classification and segmentation tasks, our method achieves state-of-the-art performance in rotated 3D object classification, and achieve competitive performance with the state-of-the-art in classification and segmentation tasks with fixed coordinate value.


title: AMPds: A public dataset for load disaggregation and eco-feedback research

abstract: A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.


title: Automatic Registration of Terrestrial Laser Scanning Point Clouds using Panoramic Reflectance Images

abstract: This paper presents a new approach to the automatic registration of terrestrial laser scanning (TLS) point clouds using panoramic reflectance images. The approach follows a two-step procedure that includes both pair-wise registration and global registration. The pair-wise registration consists of image matching (pixel-to-pixel correspondence) and point cloud registration (point-to-point correspondence), as the correspondence between the image and the point cloud (pixel-to-point) is inherent to the reflectance images. False correspondences are removed by a geometric invariance check. The pixel-to-point correspondence and the computation of the rigid transformation parameters (RTPs) are integrated into an iterative process that allows for the pair-wise registration to be optimised. The global registration of all point clouds is obtained by a bundle adjustment using a circular self-closure constraint. Our approach is tested with both indoor and outdoor scenes acquired by a FARO LS 880 laser scanner with an angular resolution of 0.036° and 0.045°, respectively. The results show that the pair-wise and global registration accuracies are of millimetre and centimetre orders, respectively, and that the process is fully automatic and converges quickly.


title: Arch Sex Behav DOI 10.1007/s10508-006-9041-7 ORIGINAL PAPER The Sexual Excitation/Sexual Inhibition Inventory for Women: Psychometric Properties

abstract: This article reports on the development of a new questionnaire designed to assess the propensity for sexual excitation and sexual inhibition in women: The Sexual Excitation/Sexual Inhibition Inventory for Women (SESII-W). The theoretical model underlying this research, the Dual Control Model, postulates that sexual response depends on a balance between excitatory and inhibitory mechanisms and that individuals vary in their propensity for excitation and inhibition. This study describes the development and initial validation of the SESII-W in a sample of 655 women (M age, 33.9 years). Factor analysis identified eight factors and two higher-order factors: one related to sexual excitation and one to sexual inhibition. The measure demonstrated good test-retest reliability and discriminant and convergent validity. Our data underscore that a number of factors affect women's sexual arousal and these appear to be related to opposing processes of sexual excitation and sexual inhibition. Theoretical issues, possible gender differences, and the value of using qualitative data to inform questionnaire development are discussed.


title: High-Performance Front-End Converter for Avionics Applications

abstract: The distributed power systems of future commercial aircraft will adopt variable frequency generation (360 to 800 Hz). Front-end converters in the system will be required to have a high efficiency and a low total harmonic distortion (THD) of the input current. This paper explains the design of a zero-voltage-switching (ZVS) active-clamped isolated low-harmonic SEPIC rectifier for such applications. Utilization of the transformer leakage inductance for ZVS and a single-layer transformer design contribute to a high efficiency. An accurate averaged switch model has been developed, which shows that the control-to-input-current transfer function of this converter does not exhibit resonances observed in the conventional SEPIC. As a result, for closed-loop operation using average current control, damping of the coupling capacitor is not required. Operating at a switching frequency of 200 kHz, an experimental 100 W, 28 V output rectifier achieves a THD of 3-4% and efficiency exceeding 90% over the entire line frequency range.


title: A Review of Data Fusion Techniques

abstract: The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed. These methods and algorithms are presented using three different categories: (i) data association, (ii) state estimation, and (iii) decision fusion.


title: Sensor-based Bayesian detection of anomalous living patterns in a home setting

abstract: In this paper, we present an automated behavior analysis system developed to assist the elderly and individuals with disabilities who live alone, by learning and predicting standard behaviors to improve the efficiency of their healthcare. Established behavioral patterns have been recorded using wireless sensor networks composed by several event-based sensors that captured raw measures of the actions of each user. Using these data, behavioral patterns of the residents were extracted using Bayesian statistics. The behavior was statistically estimated based on three probabilistic features we introduce, namely sensor activation likelihood, sensor sequence likelihood, and sensor event duration likelihood. Real data obtained from different home environments were used to verify the proposed method in the individual analysis. The results suggest that the monitoring system can be used to detect anomalous behavior signs which could reflect changes in health status of the user, thus offering an opportunity to intervene if required.


title: Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization

abstract: Abstract Fine-grained image recognition is a challenging computer vision problem, due to the small inter-class variations caused by highly similar subordinate categories, and the large intra-class variations in poses, scales and rotations. In this paper, we prove that selecting useful deep descriptors contributes well to fine-grained image recognition. Specifically, a novel Mask-CNN model without the fully connected layers is proposed. Based on the part annotations, the proposed model consists of a fully convolutional network to both locate the discriminative parts (e.g., head and torso), and more importantly generate weighted object/part masks for selecting useful and meaningful convolutional descriptors. After that, a three-stream Mask-CNN model is built for aggregating the selected object- and part-level descriptors simultaneously. Thanks to discarding the parameter redundant fully connected layers, our Mask-CNN has a small feature dimensionality and efficient inference speed by comparing with other fine-grained approaches. Furthermore, we obtain a new state-of-the-art accuracy on two challenging fine-grained bird species categorization datasets, which validates the effectiveness of both the descriptor selection scheme and the proposed Mask-CNN model.


title: Analytical model of voltage division inside stacked-FET switch

abstract: An analytical model of the voltage division inside a MOSFET stack is presented. The voltage division can be described by a simple closed-form analytical expression. This approach provides not only a much faster calculation, but also a more clear understanding of the influence of single FET parameters on the FET stack behavior. Analytical expressions for the off capacitance (C off ) and breakdown voltage (BV) of a FET stack are then derived. By increasing the number of the stacked FETs, a minimum C off and a maximum BV are predicted. The analytical model is validated against both numerical calculation and measurements.


title: Tree2Tree Neural Translation Model for Learning Source Code Changes

abstract: The way developers edit day-to-day code tend to be repetitive and often use existing code elements. Many researchers tried to automate this tedious task of code changes by learning from specific change templates and applied to limited scope. The advancement of Neural Machine Translation (NMT) and the availability of the vast open source software evolutionary data open up a new possibility of automatically learning those templates from the wild. However, unlike natural languages, for which NMT techniques were originally designed, source code and the changes have certain properties. For instance, compared to natural language source code vocabulary can be virtually infinite. Further, any good change in code should not break its syntactic structure. Thus, deploying state-of-the-art NMT models without domain adaptation may poorly serve the purpose. To this end, in this work, we propose a novel Tree2Tree Neural Machine Translation system to model source code changes and learn code change patterns from the wild. We realize our model with a change suggestion engine: CODIT. We train the model with more than 30k real-world changes and evaluate it with 6k patches. Our evaluation shows the effectiveness of CODIT in learning and suggesting abstract change templates. CODIT also shows promise in suggesting concrete patches and generating bug fixes.


title: Fifth Generation (5G) Wireless Technology “Revolution in Telecommunication”

abstract: After the up rise of 4G wireless mobile technology takes place; researchers, mobile operator industries representative, academic institutions have started to look into the advancement (technological) towards 5G communication networks due to some main demands that are meliorated data rates, better capacity, minimized latency and better QoS (Quality of Service). To established the 5G mobile communication technological foundation, various research works or projects entailing main mobile infrastructure manufacturers, academia and international mobile network operators have been introduced recently. Nevertheless, 5G mobile services to be made available for use, their architecture, and their performance have not been evidently explicated. In this paper, we represent thorough overview of 5G the next generation mobile technology. We mainly throws light on 5G network architecture, 5G radio spectrum, ultra-dense radio access networks (UDRAN), traffic offloading of mobile, cognitive radio (CR), software defined radio (SDR), software defined networking (SDN), mixed infrastructure, and 5G network impact on the society.


title: A Dual-Broadband Dual-Polarized Fylfot-Shaped Antenna for Mobile Base Stations Using MIMO Over-Lapped Antenna Subarrays

abstract: This paper proposes a novel design of a dual-broadband dual-polarized antenna for mobile communication base stations. The proposed antenna is Fylfot-shaped and covers 0.7–0.96 and 1.7–2.7 GHz simultaneously (with S11 ≤ −10 dB), which are the two common frequency bands for mobile communication systems. Three resonant frequencies are produced in the proposed design where two of them are independently controlled to achieve the dual-broadband performance. A prototype of the proposed antenna has been developed and tested. The results demonstrate that the antenna has high isolation between its ports, high polarization purity, and stable radiation patterns across both the frequency bands. A new linear array is proposed and implemented. Unlike reported dual-band base station antenna arrays, the proposed array uses a novel technique of two MIMO overlapped subarrays with shared radiating antenna elements. The overlapped subarrays have excellent frequency diversity performance with low envelope correlation coefficients. The array has been found to have the same performance as a standard conventional base station antenna but with 25% less number of antenna elements, which leads to a smaller size and lower cost. The simple antenna structure and the novel smart MIMO overlapped antenna subarrays technique give the design the privilege to be an ideal candidate for a low-cost directive base station antenna.


title: Local Laplacian filters: edge-aware image processing with a Laplacian pyramid

abstract: The Laplacian pyramid is ubiquitous for decomposing images into multiple scales and is widely used for image analysis. However, because it is constructed with spatially invariant Gaussian kernels, the Laplacian pyramid is widely believed as being unable to represent edges well and as being ill-suited for edge-aware operations such as edge-preserving smoothing and tone mapping. To tackle these tasks, a wealth of alternative techniques and representations have been proposed, e.g., anisotropic diffusion, neighborhood filtering, and specialized wavelet bases. While these methods have demonstrated successful results, they come at the price of additional complexity, often accompanied by higher computational cost or the need to post-process the generated results. In this paper, we show state-of-the-art edge-aware processing using standard Laplacian pyramids. We characterize edges with a simple threshold on pixel values that allows us to differentiate large-scale edges from small-scale details. Building upon this result, we propose a set of image filters to achieve edge-preserving smoothing, detail enhancement, tone mapping, and inverse tone mapping. The advantage of our approach is its simplicity and flexibility, relying only on simple point-wise nonlinearities and small Gaussian convolutions; no optimization or post-processing is required. As we demonstrate, our method produces consistently high-quality results, without degrading edges or introducing halos.


title: A MV intelligent gate driver for 15kV SiC IGBT and 10kV SiC MOSFET

abstract: This paper presents an Intelligent Medium-voltage Gate Driver (IMGD) for 15kV SiC IGBT and 10kV SiC MOSFET devices. The high voltage-magnitude and high dv/dt(> 30kV/μs) of these MV SiC devices, pose design challenge in form of isolation and EMI. This problem is solved by development of a <; 1pF isolation capacitance power-supply. But due to applied high stress, smaller short-circuit withstand time and the criticality of the application, these devices need to be monitored, well protected, active gate-driven and safely shut-down. This paper presents an EMI hardened IMGD built around a CPLD, sensing and optical interfacing unit. It provides advanced gate-driving, added protection and optically isolated state-monitoring features. The device operating conditions such as module temperature and Vds(on) can be data-logged. They can be used for diagnosis/prognosis purposes such as to predict failure and safely shut-down the system. This paper describes the functionality of different building blocks. The 15kV SiC IGBT has higher second switching slope above its punch-through level which is moderated without increasing losses by using digitally controlled active gate-driving. The shoot-through protection time can be reduced below withstand time by advanced gate driving. Soft turn-on and over-current triggered gate-voltage reduction helps reducing blanking time and quick turn-off reduces the protection response time. In this paper, the IMGD is high side tested at 5kV with device state monitoring on. The active gate-driving is tested at 6kV.


title: 6D hands: markerless hand-tracking for computer aided design

abstract: Computer Aided Design (CAD) typically involves tasks such as adjusting the camera perspective and assembling pieces in free space that require specifying 6 degrees of freedom (DOF). The standard approach is to factor these DOFs into 2D subspaces that are mapped to the x and y axes of a mouse. This metaphor is inherently modal because one needs to switch between subspaces, and disconnects the input space from the modeling space. In this paper, we propose a bimanual hand tracking system that provides physically-motivated 6-DOF control for 3D assembly. First, we discuss a set of principles that guide the design of our precise, easy-to-use, and comfortable-to-use system. Based on these guidelines, we describe a 3D input metaphor that supports constraint specification classically used in CAD software, is based on only a few simple gestures, lets users rest their elbows on their desk, and works alongside the keyboard and mouse. Our approach uses two consumer-grade webcams to observe the user's hands. We solve the pose estimation problem with efficient queries of a precomputed database that relates hand silhouettes to their 3D configuration. We demonstrate efficient 3D mechanical assembly of several CAD models using our hand-tracking system.


title: Knowledge Extraction and Annotation for Cross-Domain Textual Case-Based Reasoning in Biologically Inspired Design

abstract: Biologically inspired design (BID) is a methodology for designing technological systems by analogy to designs of biological systems. Given that knowledge of many biological systems is available mostly in the form of textual documents, the question becomes how can we extract design knowledge about biological systems from textual documents for potential use in designing engineering systems? In earlier work, we described how annotating biology articles with partial Structure-Behavior-Function models helps users access documents relevant to a given design problem and understand the biological systems for potential transfer of their causal mechanisms to engineering problems. In this paper, we present an automated technique instantiated in the IBID system for extracting partial SBF models of biological systems from their natural language documents for potential use in biologically inspired design.


title: Visualizing the behavior of object-oriented systems

abstract: A new and distinct variety of miniature rose plant characterized by a profusion of exhibition form blooms that ae an unusual combination of colors, consisting of red and light pink with a strong fruity fragrance, on a strong plant with exceptional disease resistance.


title: Support Vector Machine with Inverse Fringe as Feature for MNIST Dataset

abstract: In this paper we proposed SVM algorithm for MNIST dataset with fringe and its complementary version, inverse fringe as feature for SVM. MNIST data-set is consists of 60000 examples of training set and 10000 examples of test set. In our experiments we started with using fringe distance map as feature and found that the accuracy of system on trained data is 99.99% and on test data it is 97.14%, using inverse fringe distance map as feature and found that the accuracy of system on trained data is 99.92% and on test data is 97.72% and using combination of above two feature as feature and found that the accuracy of system on trained data is 100 and on test data is 97.55%.


title: A data-oriented survey of context models

abstract: Context-aware systems are pervading everyday life, therefore context modeling is becoming a relevant issue and an expanding research field. This survey has the goal to provide a comprehensive evaluation framework, allowing application designers to compare context models with respect to a given target application; in particular we stress the analysis of those features which are relevant for the problem of data tailoring. The contribution of this paper is twofold: a general analysis framework for context models and an up-to-date comparison of the most interesting, data-oriented approaches available in the literature.


title: Design and optimization of DBSCAN Algorithm based on CUDA

abstract: DBSCAN is a very classic algorithm for data clus- tering, which is widely used in many fields. However, with the data scale growing much more bigger than before, the traditional serial algorithm can not meet the performance requirement. Recently, parallel computing based on CUDA has developed very fast and has great advantage on big data. This paper summarizes the algorithms proposed before and improves the performance of the old DBSCAN algorithm by using CUDA and parallel computing. The algorithm uses shared memory as much as possible compared with other algorithms and it has very good scalability. A data set is tested on the new version of DBSCAN. Finally, we analyze the results and give a conclusion that our algorithm is approximately 97 times faster than the serial version.


title: Service Learning for Counselors: Integrating Education, Training, and the Community.

abstract: Service learning integrates classroom instruction with community service to augment learning. This method of instruction facilitates humanistic counseling skills by enhancing counselor awareness and knowledge of unique needs and strengths of diverse peoples while promoting personal, social, civic, and professional responsibility. This article describes applications of service learning for counseling students and professionals. ********** Service learning and community service are important components of higher education (Montrose & Ross, 2000). Service learning is a structured learning experience that facilitates the acquisition of awareness, knowledge, and skills while promoting a commitment to personal, social, civic, and professional responsibility. It is a form of experiential education that directly involves the participant in the phenomena being studied (Kendall, Duley, Rubin, Little, & Permaul, 1986). The purpose of this method of instruction is to increase understanding of concepts studied in a classroom environment by providing students with opportunities for direct exposure to problems, issues, and strengths of communities. Service learning also emphasizes the development of positive and collaborative relationships. Community service, although closely aligned with service learning, differs in a significant way. Community service is volunteer work that aims to contribute to the welfare of others and to provide benefit to an organization and/or a community. However, community service does not require a structured learning process or linkage to an academic curriculum, nor does it require a focus on mutuality and collaboration. Community service may have educational benefits (e.g., teaching students to be socially responsible); however, it does not allow for structured time to process what students experience during service activities or for use of skills acquired in the classroom in real-life situations (Burns, 1998). In this article, we describe service learning and experiential education, foundations of service learning in counseling, roles of service learning in counselor education, and applications for professional counselors. In addition, we provide examples of service-learning curricular programs. SERVICE LEARNING AND EXPERIENTIAL EDUCATION Service learning is an instructional activity or strategy designed to achieve various learning objectives, yield outcomes integrated in the context of learning, offer applied learning opportunities, and provide communities and participants with the opportunity to work together on the basis of the tenets of collaboration, inclusion, mutuality, and cooperation (Bums, 1998; Ward & Wolf-Wendel, 1997). Service learning emphasizes working with the community rather than for the community. Didactic instruction, case examples, technology, and other training methods are less capable of actualizing the principles of mutuality, equality, and community that are the basis for service learning (Burnett, Hamel, & Long, 2004). Service learning is a form of experiential education that enables students to acquire multiple competencies (Kendall et al., 1986). Kolb (1984) described experiential education as a learning strategy that enables participants to develop affective or emotional competencies through concrete experience, improve perceptual or awareness competencies through reflection, develop symbolic or abstract competencies through practice in conceptualization, and enhance behavioral or action abilities through testing theoretical constructs in practice. Combining the principles of service learning with the theoretical foundations of experiential education provides an avenue for bridging the theory--practice gap in the training of students and the continuing education of professionals. FOUNDATIONS OF SERVICE LEARNING IN COUNSELING This service-learning model emphasizes the development of a community-centered perspective, enhancement of counselor self-awareness, utilization of peer learning, and promotion of contact between groups (Burnett et al. …


title: Advanced Fault Ride-Through Technique for PMSG Wind Turbine Systems Using Line-Side Converter as STATCOM

abstract: In this paper, a hybrid control scheme for energy storage systems (ESS) and braking choppers for fault ride-through capability and a suppression of the output power fluctuation is proposed for permanent-magnet synchronous generator (PMSG) wind turbine systems. During grid faults, the dc-link voltage is controlled by the ESS instead of the line-side converter (LSC), whereas the LSC is exploited as a STATCOM to inject reactive current into the grid for assisting in the grid voltage recovery. The validity of the proposed system is verified by experimental results for a reduced-scale wind turbine simulator as well as simulation results for a 2-MW PMSG wind turbine system.


title: Statistics in Semiconductor Test: Going beyond Yield

abstract: The quantity and complexity of data generated at each test manufacturing step can be daunting. This article, which emerged from a tutorial presented at ITC 2008, explains the application of statistics to help process that data and provides examples of how test has shifted from descriptive to predictive methods.


title: Efficient logic architecture in training gradient boosting decision tree for high-performance and edge computing

abstract: This study proposes a logic architecture for the high-speed and power efficiently training of a gradient boosting decision tree model of binary classification. We implemented the proposed logic architecture on an FPGA and compared training time and power efficiency with three general GBDT software libraries using CPU and GPU. The training speed of the logic architecture on the FPGA was 26-259 times faster than the software libraries. The power efficiency of the logic architecture was 90-1,104 times higher than the software libraries. The results show that the logic architecture suits for high-performance and edge computing.


title: Power Electronics in Wind Turbine Systems

abstract: The global electrical energy consumption is still rising and there is a steady demand to increase the power capacity. The production, distribution and the use of the energy should be as technological efficient as possible and incentives to save energy at the end-user should be set up. The deregulation of energy has lowered the investment in larger power plants, which means the need for new electrical power sources may be very high in the near future. Two major technologies will play important roles to solve the future problems. One is to change the electrical power production sources from the conventional, fossil (and short term) based energy sources to renewable energy resources. The other is to use high efficient power electronics in power systems, power production and end-user application. This paper discuss the most emerging renewable energy source, wind energy, which by means of power electronics is changing from being a minor energy source to be acting as an important power source in the energy system. By that wind power is also getting an added value in the power system operation.


title: Un-Crafting: De-Constructive Engagements with Interactive Artifacts

abstract: Crafting interactive artifacts is typically associated with synthesizing, making, constructing, putting things together. In this paper, we discuss how these activities can be contrasted with de-synthesizing activities that revolve around decomposition, dissection, and taking things apart. Based on framings that emphasize how related practices are valuable in engineering processes, we aim to unlock the potential of de-constructive engagements with interactive technologies as a material driven (design) practice. We propose un-crafting as a framework of four modes of taking interactive artifacts apart (i.e., un-crafting for material exposition, material inspiration, material inquiry, and material exploration) aiming to pinpoint de-constructive episodes inherent to design processes, as well as to encourage the refinement of respective techniques and methods.


DEFINING AND DEVELOPING MEASURES OF LEAN PRODUCTION


title: Projected Gradient Methods for Nonnegative Matrix Factorization

abstract: Nonnegative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two projected gradient methods for NMF, both of which exhibit strong optimization properties. We discuss efficient implementations and demonstrate that one of the proposed methods converges faster than the popular multiplicative update approach. A simple Matlab code is also provided.


title: Software development cost estimation approaches – A survey

abstract: This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertisedbased techniques, learningdoriented techniques, dynamicsdbased models, regressiondbased models, and compositedBayesian techniques for integrating expertisedbased and regressiondbased models. Experience to date indicates that neuraldnet and dynamicsdbased techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates.


title: Evolutionary Foundations of Cultural Variation : Evoked Culture and Mate Preferences

abstract: We articulate an evolutionary perspective on cultural variation, centering on the concept of evoked culture. We then demonstrate how the framework of evoked culture has been used to predict and explain cultural variation and report new tests of hypotheses about cultural variation in mate preferences. These tests demonstrate the predictive power of ecological variables such as parasite prevalence that are implicated by evolutionary psychological theories. New empirical tests provided little support for the predictions advanced by competing social role theories (e.g., Eagly & Wood, 1999), with some findings running opposite to those predicted by such theories. We propose that a well-articulated evolutionary perspective on cultural variation may be particularly useful because it can specify how variation in cultural practice itself may emerge. We conclude that discussions of cultural variation should move beyond false dichotomies of social versus biological and suggest that evolutionary psychology provides f...


title: Definition of Inflammation, Causes of Inflammation and Possible Anti-inflammatory Strategies

abstract: Current definition of inflammation by its cardinal signs is obsolete and unsuitable for guiding adequate therapeutic strategies. Furthermore, present theory of the inflammatory process regarding vascular phenomena as essential for generation of cardinal signs is invalid and unable to explain well established empirical facts, particularly the extent of the osmotic pressure and temperature variations within the inflamed tissue. From five cardinal signs, there is actually just one specific macroscopic sign of inflammation, namely localized edema. Further, the driving force for tissue fluid accumulation is defined in biochemical terms and as such taken for the definition of the inflammatory process. Inflammation may be defined as a degenerative process which is intense enough to cause local accumulation of low molecular weight catabolic products, which in turn elevates tissue osmotic pressure that attracts extra fluid, with or without heat release sufficient for significant elevation of tissue temperature. This process is in a sharp contrast to the pathogenesis of burns, where externally applied heat causes a process that is in essence opposite to inflammation, bearing only some superficial similarities with the latter. The inflammatory process is itself a pathological process, whereas the natural anti-inflammatory response that ensues after acute inflammation tends to reverse tissue homeostasis towards normality and should therefore be regarded as a true defensive reaction of the affected tissue. Based on the therapeutic principle of reverse thermodynamics, heat application to the inflamed tissue is an obvious, yet non-exclusive therapeutic choice that follows from the given universal definition of inflammation.


title: Automatic Rooftop Extraction in Nadir Aerial Imagery of Suburban Regions Using Corners and Variational Level Set Evolution

abstract: Building profile extraction from aerial imagery constitutes a key element in numerous geospatial applications. Rooftop detection has been addressed through a variety of approaches that are, however, rarely capable of coping with conditions such as arbitrary illumination, variant reflections, and complex building profiles. This paper proposes a new method for extracting 2-D rooftop footprints from nadir aerial imagery through a fully automatic approach that handles arbitrary illumination, variant reflections, and complex building profiles without shape priors. The proposed method combines the strength of energy-based approaches with distinctiveness of corners. Corners are assessed using multiple color and color-invariance spaces. A rooftop outline is generated from selected corner candidates and further refined to fit the best possible boundaries through level-set curve evolution that is enhanced via a mean squared error map. Experimental results confirm the ability of the presented system to effectively extract rooftop profiles with an overall average shape accuracy of 84%, correctness of 94%, completeness of 92 %, and quality of 88%.


title: Video Captioning by Adversarial LSTM

abstract: In this paper, we propose a novel approach to video captioning based on adversarial learning and Long-Short Term Memory (LSTM). With this solution concept we aim at compensating for the deficiencies of LSTM-based video captioning methods that generally show potential to effectively handle temporal nature of video data when generating captions, but that also typically suffer from exponential error accumulation. Specifically, we adopt a standard Generative Adversarial Network (GAN) architecture, characterized by an interplay of two competing processes: a "generator", which generates textual sentences given the visual content of a video, and a "discriminator" which controls the accuracy of the generated sentences. The discriminator acts as an "adversary" towards the generator and with its controlling mechanism helps the generator to become more accurate. For the generator module, we take an existing video captioning concept using LSTM network. For the discriminator, we propose a novel realization specifically tuned for the video captioning problem and taking both the sentences and video features as input. This leads to our proposed LSTM-GAN system architecture, for which we show experimentally to significantly outperform the existing methods on standard public datasets.


title: Evulvalution: The Portrayal of Women's External Genitalia and Physique across Time and the Current Barbie Doll Ideals

abstract: Media images of the female body commonly represent reigning appearance ideals of the era in which they are published. To date, limited documentation of the genital appearance ideals in mainstream media exists. Analysis 1 sought to describe genital appearance ideals (i.e., mons pubis and labia majora visibility, labia minora size and color, and pubic hair style) and general physique ideals (i.e., hip, waist, and bust size, height, weight, and body mass index [BMI]) across time based on 647 Playboy Magazine centerfolds published between 1953 and 2007. Analysis 2 focused exclusively on the genital appearance ideals embodied by models in 185 Playboy photographs published between 2007 and 2008. Taken together, results suggest the perpetuation of a “Barbie Doll” ideal characterized by a low BMI, narrow hips, a prominent bust, and hairless, undefined genitalia resembling those of a prepubescent female.


title: Biological Soft Robotics.

abstract: In nature, nanometer-scale molecular motors are used to generate force within cells for diverse processes from transcription and transport to muscle contraction. This adaptability and scalability across wide temporal, spatial, and force regimes have spurred the development of biological soft robotic systems that seek to mimic and extend these capabilities. This review describes how molecular motors are hierarchically organized into larger-scale structures in order to provide a basic understanding of how these systems work in nature and the complexity and functionality we hope to replicate in biological soft robotics. These span the subcellular scale to macroscale, and this article focuses on the integration of biological components with synthetic materials, coupled with bioinspired robotic design. Key examples include nanoscale molecular motor-powered actuators, microscale bacteria-controlled devices, and macroscale muscle-powered robots that grasp, walk, and swim. Finally, the current challenges and future opportunities in the field are addressed.


title: Chromosome breakage in human peripheral lymphocytes after radioactive iodine (125I) treatment.

abstract: Recent interest in the use of /sup 125/I (half-life 60 days) in prefcrence to /sup 131/I (half-life 8 days) in the treatment of thyrotoxicosis has prompted studies of possible undesirable extrathyroidal nadiation damage. The frequency of chromosome breakage in peripheral lymphocytes is one measure of this damage. A study in patients treated with /sup 125/I is described which shows as high a frequency of chromosome aberrations as is found after /sup 131/I therapy. The implications of this observation are discussed. (auth)


title: Design of GaN-Based MHz Totem-Pole PFC Rectifier

abstract: The totem-pole bridgeless power factor correction (PFC) rectifier has recently been recognized as a promising front-end candidate for applications like servers and telecommunication power supplies. This paper begins with a discussion of the advantages of using emerging high-voltage gallium-nitride (GaN) devices in totem-pole PFC rectifiers rather than traditional PFC rectifiers. The critical-mode operation is used in the totem-pole PFC rectifier in order to achieve both high frequency and high efficiency. Then, several high-frequency issues and detailed design considerations are introduced, including extending zero-voltage-switching operation for the entire line-cycle, a variable on-time strategy for zero-crossing distortion suppression, and interleaving control for ripple current cancellation. The volume reduction of differential-mode electromagnetic interference filters is also presented, which benefits greatly from MHz high-frequency operation and multiphase interleaving. Finally, a dual-phase interleaved GaN-based MHz totem-pole PFC rectifier is demonstrated with 99% peak efficiency and 220 W/in $^{\mathrm {{3}}}$ power density.


title: Visual Cryptography

abstract: In this paper we consider a new type of cryptographic scheme, which can decode concealed images without any cryptographic computations. The scheme is perfectly secure and very easy to implement. We extend it into a visual variant of the k out of n secret sharing problem, in which a dealer provides a transparency to each one of the n users; any k of them can see the image by stacking their transparencies, but any k-1 of them gain no information about it.


A Self-Service Supporting Business Intelligence and Big Data Analytics Architecture


Articulatory-based conversion of foreign accents with deep neural networks.


title: Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques

abstract: Abstract — Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. Several methods such as Liner Predictive Predictive Coding (LPC), Hidden Markov Model (HMM), Artificial Neural Network (ANN) and etc are evaluated with a view to identify a straight forward and effective method for voice signal. The extraction and matching process is implemented right after the Pre Processing or filtering signal is performed. The non-parametric method for modelling the human auditory perception system, Mel Frequency Cepstral Coefficients (MFCCs) are utilize as extraction techniques. The non linear sequence alignment known as Dynamic Time Warping (DTW) introduced by Sakoe Chiba has been used as features matching techniques. Since it’s obvious that the voice signal tends to have different temporal rate, the alignment is important to produce the better performance.This paper present the viability of MFCC to extract features and DTW to compare the test patterns.


title: Made in America: A Social History of American Culture and Character

abstract: Our nation began with the simple phrase "We the People." But who were and are "We"? Who were we in 1776, in 1865, or in 1968, and is there any continuity in character between the we of those years and the nearly 300 million people living in the radically different America of today? With "Made in America", Claude S. Fischer draws on decades of historical, psychological, and social research to answer that question by tracking the evolution of American character and culture over three centuries. He explodes myths - such as that contemporary Americans are more mobile and less religious than their ancestors, or that they are more focused on money and consumption - and reveals instead how greater security and wealth have only reinforced the independence, egalitarianism, and commitment to community that characterized our people from the earliest years.


title: Game Theory for Security: An Important Challenge for Multiagent Systems

abstract: The goal of this paper is to introduce a real-world challenge problem for researchers in multiagent systems and beyond, where our collective efforts may have a significant impact on activities in the real-world. The challenge is in applying game theory for security: Our goal is not only to introduce the problem, but also to provide exemplars of initial successes of deployed systems in this challenge problem arena, some key open research challenges and pointers to getting started in this research.


title: Software architectural principles in contemporary mobile software: from conception to practice

abstract: Distills the architectural principles found in Android.Traces back those principles to their conception in software-architecture research.Reports on characteristics of Android ecosystem of apps by mining hundreds of apps in several app markets.Reflects on deviations from how architectural concepts have been prescribed in architecture literature. The meteoric rise of mobile software that we have witnessed in the past decade parallels a paradigm shift in its design, construction, and deployment. In particular, we argue that today's mobile software, with its rich ecosystem of apps, would have not been possible without the pioneering advances in software architecture research in the decade that preceded it. We describe the drivers that elevated software architecture to the centerpiece of contemporary mobile software. We distill the architectural principles found in Android, the predominant mobile platform with the largest market share, and trace those principles to their conception at the turn of century in software architecture literature. Finally, to better understand the extent to which Android's ecosystem of apps employs architectural concepts, we mine the reverse-engineered architecture of hundreds of Android apps in several app markets and report on those results.


title: Adaptive Controller for Drive System PMSG in Wind Turbine

abstract: This paper proposes adaptive Maximum Power Point Tracking (MPPT) controller for Permanent Magnet Synchronous Generator (PMSG) wind turbine and direct power control for grid side inverter for transformer less integration of wind energy. PMSG wind turbine with two back to back voltage source converters are considered more efficient, used to make real and reactive power control. The optimal control strategy has introduced for integrated control of PMSG Maximum Power Extraction, DC link voltage control and grid voltage support controls. Simulation model using MATLAB Simulink has developed to investigate the performance of proposed control techniques for PMSG wind turbine steady and variable wind conditions. This paper shows that the direct driven grid connected PMSG system has excellent performances and confirms the feasibility of the proposed techniques. While the wind turbine market continues to be dominated by conventional gear-driven wind turbine systems, the direct drive is attracting attention. PM machines are more attractive and superior with higher efficiency and energy yield, higher reliability, and power-to-weight ratio compared with electricity-excited machines.


title: SiGe technology and circuits for automotive radar applications

abstract: Recent advancements in SiGe device development enable the realization of 77GHz automotive radar systems using relatively low-cost silicon technology. This paper will discuss technology requirements for the radar design and also present examples of receiver and transmitter circuit implementations.


title: Definition-based Versus Contextualized Vocabulary Learning

abstract: Ability to infer the meaning of unknown words encountered while reading plays an important role in learners’ foreign language word knowledge development. The current study was an attempt to investigate the efficiency of two vocabulary learning procedures, i.e. definition-based (decontextualized) versus contextualized. In the contextualized group there were 53 students of grade one in high school and pre-university who were taught meaning inferencing strategies in which they read their textbooks, identified unknown words in the texts, and inferred the meanings of the unknown words. There were also another 54 students in the decontextualized group who learnt the new words' translations out of context before a new lesson was taught. The findings of the study revealed that the students in decontextualized group slightly outperformed the contextualized group on the vocabulary post-test. It is concluded that definition-based learning was more effective in improving learner’s vocabulary knowledge.


title: Exploring reverse engineering symptoms in Android apps

abstract: The appearance of the Android platform and its popularity has resulted in a sharp rise in the number of reported vulnerabilities and consequently in the number of mobile threats. Leveraging openness of Android app markets and the lack of security testing, malware authors commonly plagiarize Android applications (e.g., through code reuse and repackaging) boosting the amount of malware on the markets and consequently the infection rate. In this study, we present AndroidSOO, a lightweight approach for the detection of repackaging symptoms on Android apps. In this work, we introduce and explore novel and easily extractable attribute called String Offset Order. Extractable from string identifiers list in the .dex file, the method is able to pinpoint symptoms of reverse engineered Android apps without the need for complex further analysis. We performed extensive evaluation of String Order metric to assess its capabilities on datasets made available by three recent studies: Android Malware Genome Project, DroidAnalytics and Drebin. We also performed a large-scale study of over 5,000 Android applications extracted from Google Play market and over 80 000 samples from Virus Total service.


title: Design and control of an exoskeleton for the elderly and patients

abstract: Recently, the exoskeletal power assistive equipment, which is a kind of wearable robot, has been widely developed to help human body motion. The exoskeleton for elderly people and patients, however, has some limitations due to the weight and volume of the equipment. In this paper, a tendon-driven exoskeletal power assistive device, exoskeleton for patients and the old by the Sogang University (EXPOS), is proposed as a feasible solution. In case of EXPOS, the caster walker carries heavy items such as motors, drivers, controllers, and batteries so that the weight and volume of the wearable exoskeleton are minimized. The tendon-connecting motors and pulleys of hip and knee generate the assistive power according to the requirement of the users. In this paper, the design concepts of the EXPOS, sensing techniques, and control methods are discussed


title: Dynamic Analysis of Slide Vane Compressor Stator Based on Finite Element Method

abstract: Mode analysis is important for slide vane compressor stator. Because of period force may set up resonance in the stator. In this paper, the stress distributions and vibration modes of stator of sliding vane compressor were studied through finite element method (FEM). The first-order vibration mode to five-order vibration mode of stator were computed. The results showed that the resonance frequencies are much larger than the working frequency. It is significant for sliding vane compressor design, fatigue life prediction and reliability assessment.


title: Using PCA to Efficiently Represent State Spaces

abstract: Reinforcement learning algorithms need to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces. This is known as the curse of dimensionality. By projecting the agent's state onto a low-dimensional manifold, we can represent the state space in a smaller and more efficient representation. By using this representation during learning, the agent can converge to a good policy much faster. We test this approach in the Mario Benchmarking Domain. When using dimensionality reduction in Mario, learning converges much faster to a good policy. But, there is a critical convergence-performance trade-off. By projecting onto a low-dimensional manifold, we are ignoring important data. In this paper, we explore this trade-off of convergence and performance. We find that learning in as few as 4 dimensions (instead of 9), we can improve performance past learning in the full dimensional space at a faster convergence rate.


Vibration Analysis of a Solar Powered UAV


title: Recommendation with Social Relationships via Deep Learning

abstract: While users trust the selections of their social friends in recommendation systems, the preferences of friends do not necessarily match. In this study, we introduce a deep learning approach to learn both about user preferences and the social influence of friends when generating recommendations. In our model we design a deep learning architecture by stacking multiple marginalized Denoising Autoencoders. We define a joint objective function to enforce the latent representation of social relationships in the Autoencoder's hidden layer to be as close as possible to the users' latent representation when factorizing the user-item matrix. We formulate a joint objective function as a minimization problem to learn both user preferences and friends' social influence and we present an optimization algorithm to solve the joint minimization problem. Our experiments on four benchmark datasets show that the proposed approach achieves high recommendation accuracy, compared to other state-of-the-art methods.


title: Traffic Analysis of Encrypted Messaging Services: Apple iMessage and Beyond

abstract: Instant messaging services are quickly becoming the most dominant form of communication among consumers around the world. Apple iMessage, for example, handles over 2 billion messages each day, while WhatsApp claims 16 billion messages from 400 million international users. To protect user privacy, many of these services typically implement end-to-end and transport layer encryption, which are meant to make eavesdropping infeasible even for the service providers themselves. In this paper, however, we show that it is possible for an eavesdropper to learn information about user actions, the language of messages, and even the length of those messages with greater than 96% accuracy despite the use of state-of-the-art encryption technologies simply by observing the sizes of encrypted packets. While our evaluation focuses on Apple iMessage, the attacks are completely generic and we show how they can be applied to many popular messaging services, including WhatsApp, Viber, and Telegram.


title: Design of a New Hysteretic PWM Controller for All Types of DC-to-DC Converters

abstract: A new control method using a hysteretic PWM controller for all types of converters and its proper design method are presented. The triangular voltage obtained from a simple RC network connected between comparator output and converter output is superimposed to the output voltage and as a feedback signal to a hysteretic comparator. Since the hysteretic PWM controller essentially has derivative characteristics and has no error amplifier, the presented method provides no steady-state error voltage on the output and excellent dynamic performances for the load current transient by choosing proper values of time constants in the RC network. Performances of the proposed controller are experimentally verified for the buck, buck-boost and boost converters.


title: Acetylcholine-Related Bowel Dysmotility in Homozygous Mutant NCX/HOX11L.1-Deficient (NCX/) Mice—Evidence That Acetylcholine is Implicated in Causing Intestinal Neuronal Dysplasia

abstract: Abstract Background/purpose Homozygous mutant Ncx/Hox11L.1 -deficient (Ncx−/−) mice develop mega-ileo-ceco-colon (mega-ICC) with a caliber change in the proximal colon. The authors investigated the mechanism of intestinal dysmotility in these mice. Methods Five-week-old Ncx−/− mice with mega ICC were compared with age-matched BDF1 control mice. Jejunum, ileum, and colon were excised from all mice and 1.0-cm-long strips of each organ, each with a resting tension of 0.5 g , were suspended in an organ bath filled with Tyrode's solution at 37°C and bubbled with a mixture of 95% oxygen and 5% carbon dioxide. Contractile responses to acetylcholine chloride (ACh), histamine, serotonin, and barium chloride (BaCl 2 ) were recorded isometrically. Results For ACh, Ncx−/− mice had decreased distal colon circular muscle contraction only at lower doses and decreased distal colon longitudinal muscle contraction for all doses compared with controls ( P P P P 2 was the same for both Ncx−/− mice and controls. Conclusions Only ACh differentially affected muscle contraction in Ncx−/− mice in the proximal and distal colon. Thus, ACh is implicated in causing the bowel dysmotility seen in Ncx−/− mice and human IND.


title: SCCIR: Smart Cities Critical Infrastructure Response Framework

abstract: Critical infrastructures play important roles in ensuring the wellbeing of the populace. Protecting critical infrastructures and ensuring their continued operation will be an important part of future Smart City ecosystems. Minimising the destruction of failing critical infrastructure components or system components that are geographically close critical services is essential. Equally important are the system of systems relationships that a failing system has, as these could render a minor system critical under certain circumstances. Infrastructure failure is usually brought under control through system adaptation e.g. using sensor area networks to close valves or by emergency response e.g. extinguishing fires. Current response procedures rely on antiquated information sharing techniques and provide little or no opportunity to effect change within the failing infrastructures systems. There may also be minimal understanding of the important systems of systems role that is being provided by components of the failing system. This paper details our initial work on the Smart Cities Critical Infrastructure response framework. The goal of our framework is to provide a response strategy to first responders based on smart cities information flows. Where these information flows have been compromised we propose a robust infrastructure state preservation system as to provide an interface to a failing critical infrastructure.


title: CAIS: A Copy Adjustable Incentive Scheme in Community-Based Socially Aware Networking

abstract: Socially aware networking (SAN) is a new communication paradigm, in which the social characteristics of mobile nodes are exploited to improve the performance of data distribution. In SAN, mobile carriers may exhibit selfish behaviors and refuse to relay messages for others for various reasons, such as limited resources (e.g., buffer, energy, and bandwidth) or social relationships. Several incentive schemes have recently been investigated to stimulate selfish users for cooperation in data forwarding. However, a majority of the existing methods have not fully studied nodes' social relationships in their selfish behaviors. In this paper, we propose a copy adjustable incentive scheme (CAIS), which adopts the virtual credit concept to stimulate selfish nodes to cooperate in data forwarding. In CAIS, we consider a network in which the nodes are divided into certain communities based on their social relationships. Then, we apply two types of credits, i.e., social credit and nonsocial credit, to reward the nodes when they relay data for other nodes inside their community or outsiders, respectively. Based on our mechanism, the number of messages a node can replicate to other nodes is adjusted according to its cooperation level and earned credits. To further improve the performance of CAIS, a single-copy data replication policy is employed, which manages the credit distribution of each node according to its available resources. The results of our extensive experiments using both synthetic and trace-driven simulations illustrate that CAIS copes well with node selfishness in community-based networks and outperforms other benchmark protocols with high data delivery ratio, low communication overhead, and short data delivery latency.


title: An inside-out origin for the eukaryotic cell

abstract: BackgroundAlthough the origin of the eukaryotic cell has long been recognized as the single most profound change in cellular organization during the evolution of life on earth, this transition remains poorly understood. Models have always assumed that the nucleus and endomembrane system evolved within the cytoplasm of a prokaryotic cell.ResultsDrawing on diverse aspects of cell biology and phylogenetic data, we invert the traditional interpretation of eukaryotic cell evolution. We propose that an ancestral prokaryotic cell, homologous to the modern-day nucleus, extruded membrane-bound blebs beyond its cell wall. These blebs functioned to facilitate material exchange with ectosymbiotic proto-mitochondria. The cytoplasm was then formed through the expansion of blebs around proto-mitochondria, with continuous spaces between the blebs giving rise to the endoplasmic reticulum, which later evolved into the eukaryotic secretory system. Further bleb-fusion steps yielded a continuous plasma membrane, which served to isolate the endoplasmic reticulum from the environment.ConclusionsThe inside-out theory is consistent with diverse kinds of data and provides an alternative framework by which to explore and understand the dynamic organization of modern eukaryotic cells. It also helps to explain a number of previously enigmatic features of cell biology, including the autonomy of nuclei in syncytia and the subcellular localization of protein N-glycosylation, and makes many predictions, including a novel mechanism of interphase nuclear pore insertion.


End-to-End Deep Learning Framework for Speech Paralinguistics Detection Based on Perception Aware Spectrum


title: Morphological Segmentation of 2-D Barcode Gray Scale Image

abstract: Segmentation is a key process of 2-D barcode identification. In this paper we propose two 2-D barcode image segmentation algorithms under complex background. The first algorithm is an expansion of multi-scale morphology reconstruction and it can acquire a good segmentation result. However, this algorithm is not suitable for fast real-time image processing due to large computation. To fix the shortcoming of the first algorithm we put forward a fast real-time image segmentation algorithm. The proposed approach is applied in experiments on 2D barcodes with complicated background. In experiments, the second proposed method can maintain image segmentation accuracy while significantly reducing the time consumption.


title: DMM-Stream: A Density Mini-Micro Clustering Algorithm for Evolving Data Streams

abstract: Clustering real-time stream data is an important and challenging problem. The existing algorithms have not considered the distribution of data inside micro cluster, specifically when data points are non uniformly distributed inside micro cluster. In this situation, a large radius of micro cluster has to be considered which leads to lower quality. In this paper, we present a density-based clustering algorithm, DMM-Stream, for evolving data streams. It is an online-offline algorithm which considers the distribution of data inside micro cluster. In DMM-Stream, we introduce mini-micro cluster for keeping summary information of data points inside micro cluster. In our method, based on the distribution of the dense areas inside the micro cluster at least one representative point, either micro cluster itself or its mini-micro clusters’ centers, are sent to the offline phase. By choosing a proper mini-micro and micro center, we increase cluster quality while maintaining the time complexity. A pruning strategy is also used to filter out the real data from noise by introducing dense and sparse mini-micro and micro cluster. Our performance study over real and synthetic data sets demonstrates effectiveness of our method.


title: Analysis of Multilingual Sequence-to-Sequence speech recognition systems

abstract: This paper investigates the applications of various multilingual approaches developed in conventional hidden Markov model (HMM) systems to sequence-to-sequence (seq2seq) automatic speech recognition (ASR). On a set composed of Babel data, we first show the effectiveness of multi-lingual training with stacked bottle-neck (SBN) features. Then we explore various architectures and training strategies of multi-lingual seq2seq models based on CTC-attention networks including combinations of output layer, CTC and/or attention component re-training. We also investigate the effectiveness of language-transfer learning in a very low resource scenario when the target language is not included in the original multi-lingual training data. Interestingly, we found multilingual features superior to multilingual models, and this finding suggests that we can efficiently combine the benefits of the HMM system with the seq2seq system through these multilingual feature techniques.


title: SPECIES-SPECIFIC DIFFERENCES OF PYRENOIDS IN CHLORELLA (CHLOROPHYTA)1

abstract: The structure of the pyrenoid supports the separation of Chlorella species into two groups based on cell wall chemistry and suggests evolutionary relationships. Chlorella species with a glucan-type wall exhibit quite diverse pyrenoid structures, which may indicate that these species are not closely related. Those species with glucosamine cell walls (C. kessleri, C. sorokiniana, C. vulgaris) are virtually identical in pyrenoid morphology, indicating a closer evolutionary relationship. In the species with glucosamine walls, the thylakoid that penetrates into the pyrenoid matrix, is unijormly double-layered. Pyrenoids in the species with glucan walls show various features: 1) a pyrenoid matrix only, 2) a pyrenoid traversed by a few discs of double thylakoids with many adhering pyrenoglobuli, 3) a pyrenoid penetrated with tubelike structures or 4) a pyrenoid penetrated with many single undulating thylakoids. The pyrenoid structure of the symbiotic Chlorella in Paramecium bursaria resembles those of free-living Chlorella with glucosamine walls.


title: Internal crowdsourcing: conceptual framework, structured review, and research agenda

abstract: The use of IT-enabled crowdsourcing with employees in enterprises has increased substantially in recent years. This phenomenon, which we refer to as ‘internal crowdsourcing’, is distinct both from external crowdsourcing with end users and from hierarchy-based work with employees. A literature stream has emerged that corresponds with the increased relevance of internal crowdsourcing in practice. The purpose of this review paper of internal crowdsourcing is to provide conceptual development, synthesise the literature, and provide a research agenda. In the review reported in this paper, we systematically analysed and critically reviewed the literature in this domain published thus far (74 papers). We found useful findings and insights into a new and relevant IT-enabled phenomenon. At the same time, we also found conflicting definitions and conceptualisation, as well as research efforts that are not well integrated. The paper supports future research on internal crowdsourcing by providing improved conceptualisation, consolidating insights, and identifying important areas for future research.


title: A Novel Read Scheme for Read Disturbance Suppression in 3D NAND Flash Memory

abstract: A new read scheme is proposed to suppress read disturbance in unselected strings of three-dimensional (3D) vertical channel flash memories. This new scheme decreases the channel potential difference between select word-line (WL) and adjacent WL by more than 20% and the read disturb due to hot carrier injection in adjacent WL of selected WL is suppressed effectively by about 95%. Meanwhile, boosted channel potential during read operation has been preserved to improve soft programming read disturbance by more than 85% in non-adjacent unselected memory cells, owing to the reduced electric field across tunnel oxide. Compared with the conventional scheme, the proposed scheme leads to a significant improvement in read disturbance characteristics with a shorter read period as well as a simplified waveform of read operation.


title: Testing estimation of water surface in Italian rice district from MODIS satellite data

abstract: Abstract Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R 2 = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies.


title: Computer-aided Diagnosis: How to Move from the Laboratory to the Clinic

abstract: If it is easy to work on computer-aided diagnosis (CAD) development, because an accessible system to validate and combine algorithms has been created, and if high performing systems that will surface can be easily plugged into all clinical viewing software, then CAD will thrive.