title: Design of small, safe and robust quadrotor swarms

abstract: Scaling down the size and mass of micro aerial vehicles (MAVs) increases their agility and their ability to operate in tight formations. In addition, smaller robots are safer and, as we will show in this paper, more robust to collisions. This paper addresses the development of a pico quadrotor measuring 11 cm from tip to tip, with a mass of 25g. To increase the robustness of the robot to collisions, the vehicle is equipped with a 2 gram carbon fiber cage that protects it from impact velocities in excess of 4 m/s and also permits recovery after collisions. We present the design of the electrical, mechanical and computational elements, as well as experimental results demonstrating trajectory following with feedback from an external motion camera system, recovery from collisions with walls and other robots, and formation flight.


title: Wireless Technologies in Home and Building Automation

abstract: The use of wireless technologies in automation systems offers attractive benefits, but introduces a number of new technological challenges. The paper discusses these aspects for home and building automation applications. Relevant standards are surveyed. A wireless extension to KNX/EIB based on tunnelling over IEEE 802.15.4 is presented. The design emulates the properties of the KNX/EIB wired medium via wireless communication, allowing a seamless extension. Furthermore, it is geared towards zero-configuration and supports the easy integration of protocol security.


title: Performance comparison of intrusion detection system based anomaly detection using artificial neural network and support vector machine

abstract: This study presents a comparison of the detection accuracy of ANN and SVM on the anomaly-based IDS and uses all the features in the dataset. The experiments were performed on two algorithms using KDDCup99 dataset, preprocessing performed on datasets for normalization and scaling attributes which consist of four categories in the dataset. Artificial Neural Network managed to obtain high accuracy in all categories outperformed SVM with accuracy, DoS 92.20%, 90.60% Probe, R2L 89%, and U2R 90.80%. According to the results obtained from experiments using all the features dataset showed that ANN has better performance than SVM in attack detection accuracy.


title: Extreme Kernel Sparse Learning for Tactile Object Recognition

abstract: Tactile sensors play very important role for robot perception in the dynamic or unknown environment. However, the tactile object recognition exhibits great challenges in practical scenarios. In this paper, we address this problem by developing an extreme kernel sparse learning methodology. This method combines the advantages of extreme learning machine and kernel sparse learning by simultaneously addressing the dictionary learning and the classifier design problems. Furthermore, to tackle the intrinsic difficulties which are introduced by the representer theorem , we develop a reduced kernel dictionary learning method by introducing row-sparsity constraint. A globally convergent algorithm is developed to solve the optimization problem and the theoretical proof is provided. Finally, we perform extensive experimental validations on some public available tactile sequence datasets and show the advantages of the proposed method.


title: Indoor positioning system techniques and security

abstract: Nowadays location based techniques are used various fields such as traffic navigation, map services, etc. Because people spend a lot of time in the indoor place, it is important for users and service providers to get exact indoor positioning information. There are many technologies to get indoor location information like Wi-Fi, Bluetooth, Radio Frequency Identification (RFID), etc. In spite of importance of IPS, there is no standard for IPS techniques. In addition because of characteristic of data, security and privacy problems become issue. This paper introduces the IPS techniques and analyzes each IPS techniques in terms of cost, accuracy, etc. Then introduce related security threats.


title: A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

abstract: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.


title: TxRace: Efficient Data Race Detection Using Commodity Hardware Transactional Memory

abstract: Detecting data races is important for debugging shared-memory multithreaded programs, but the high runtime overhead prevents the wide use of dynamic data race detectors. This paper presents TxRace, a new software data race detector that leverages commodity hardware transactional memory (HTM) to speed up data race detection. TxRace instruments a multithreaded program to transform synchronization-free regions into transactions, and exploits the conflict detection mechanism of HTM for lightweight data race detection at runtime. However, the limitations of the current best-effort commodity HTMs expose several challenges in using them for data race detection: (1) lack of ability to pinpoint racy instructions, (2) false positives caused by cache line granularity of conflict detection, and (3) transactional aborts for non-conflict reasons (e.g., capacity or unknown). To overcome these challenges, TxRace performs lightweight HTM-based data race detection at first, and occasionally switches to slow yet precise data race detection only for the small fraction of execution intervals in which potential races are reported by HTM. According to the experimental results, TxRace reduces the average runtime overhead of dynamic data race detection from 11.68x to 4.65x with only a small number of false negatives.


title: Comparing software metrics tools

abstract: This paper shows that existing software metric tools interpret and implement the definitions of object-oriented software metrics differently. This delivers tool-dependent metrics results and has even implications on the results of analyses based on these metrics results. In short, the metrics-based assessment of a software system and measures taken to improve its design differ considerably from tool to tool. To support our case, we conducted an experiment with a number of commercial and free metrics tools. We calculated metrics values using the same set of standard metrics for three software systems of different sizes. Measurements show that, for the same software system and metrics, the metrics values are tool depended. We also defined a (simple) software quality model for "maintainability" based on the metrics selected. It defines a ranking of the classes that are most critical wrt. maintainability. Measurements show that even the ranking of classes in a software system is metrics tool dependent.


title: A flexible framework for implementing software transactional memory

abstract: We describe DSTM2, a Java™ software library that provides a flexible framework for implementing object-based software transactional memory (STM). The library uses transactional factories to transform sequential (unsynchronized) classes into atomic (transactionally synchronized) ones, providing a substantial improvement over the awkward programming interface of our previous DSTM library. Furthermore, researchers can experiment with alternative STM mechanisms by providing their own factories. We demonstrate this flexibility by presenting two factories: one that uses essentially the same mechanisms as the original DSTM (with some enhancements),and another that uses a completely different approach.Because DSTM2 is packaged as a Java library, a wide range of programmers can easily try it out, and the community can begin to gain experience with transactional programming. Furthermore, researchers will be able to use the body of transactional programs that arises from this community experience to test and evaluate different STM mechanisms simply by supplying new transactional factories. We believe that this flexible approach will help to build consensus about the best ways to implement transactions, and will avoid the premature "lock-in" that may arise if STM mechanisms are baked into compilers before such experimentation is done.


title: A double-blind, randomized, placebo-controlled trial of Lactobacillus helveticus and Bifidobacterium longum for the symptoms of depression

abstract: Objectives:This trial investigated whether probiotics improved mood, stress and anxiety in a sample selected for low mood. We also tested whether the presence or severity of irritable bowel syndrome symptoms, and levels of proinflammatory cytokines, brain-derived neurotrophic factor and other blood markers, would predict or impact treatment response.Method:Seventy-nine participants (10 dropouts) not currently taking psychotropic medications with at least moderate scores on self-report mood measures were randomly allocated to receive either a probiotic preparation (containing Lactobacillus helveticus and Bifidobacterium longum) or a matched placebo, in a double-blind trial for 8 weeks. Data were analysed as intent-to-treat.Results:No significant difference was found between the probiotic and placebo groups on any psychological outcome measure (Cohen’s d range = 0.07–0.16) or any blood-based biomarker. At end-point, 9 (23%) of those in the probiotic group showed a ⩾60% change on the Montgomery–Asberg Depres...


title: Cascaded Pyramid Network for Multi-person Pose Estimation

abstract: The topic of multi-person pose estimation has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, such as occluded keypoints, invisible keypoints and complex background, which cannot be well addressed. In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints. More specifically, our algorithm includes two stages: GlobalNet and RefineNet. GlobalNet is a feature pyramid network which can successfully localize the "simple" keypoints like eyes and hands but may fail to precisely recognize the occluded or invisible keypoints. Our RefineNet tries explicitly handling the "hard" keypoints by integrating all levels of feature representations from the GlobalNet together with an online hard keypoint mining loss. In general, to address the multi-person pose estimation problem, a top-down pipeline is adopted to first generate a set of human bounding boxes based on a detector, followed by our CPN for keypoint localization in each human bounding box. Based on the proposed algorithm, we achieve state-of-art results on the COCO keypoint benchmark, with average precision at 73.0 on the COCO test-dev dataset and 72.1 on the COCO test-challenge dataset, which is a 19% relative improvement compared with 60.5 from the COCO 2016 keypoint challenge.Code (this https URL) and the detection results are publicly available for further research.


title: Truly balanced step recovery diode pulse generator with single power supply

abstract: A space efficient and simple circuit for ultra wideband (UWB) balanced pulse generation is presented. The pulse generator uses a single step recovery diode to provide a truly balanced output. The diode biasing is integrated with the switching circuitry to improve the compactness of the design. Two versions of the circuit with lumped and distributed pulse forming networks have been tested. The pulse parameters for distributed pulse shaping network were: rise/fall time (10-90%) 183 ps, pulse width (50-50%) 340 ps, pulse peak to peak voltage 896 mV (12.05 dBm peak power) and for the lumped case: rise time (10-90%) 272 ps, fall time (90-10%) 566 ps pulse width (50-50%) 511 ps, pulse amplitude /spl plusmn/1.6V (17 dBm peak power). In both cases excellent balance of the two pulses at the output ports can be observed. It should be noted that above parameters were obtained with typical inexpensive RF components. The circuit reduces the complexity of the design because of the lack of broadband baluns required for UWB balanced antennas. The circuit may be used as part of a UWB transmitter.


title: An initial analysis of semantic wikis

abstract: Semantic wikis augment wikis with semantic properties that can be used to aggregate and query data through reasoning. Semantic wikis are used by many communities, for widely varying purposes such as organizing genomic knowledge, coding software, and tracking environmental data. Although wikis have been analyzed extensively, there has been no published analysis of the use of semantic wikis. We carried out an initial analysis of twenty semantic wikis selected for their diverse characteristics and content. Based on the number of property edits per contributor, we identified several patterns to characterize community behaviors that are common to groups of wikis.


title: A Two Phases Heuristic Based Method for the MinMax Regret Location Problem

abstract: We are interested in solving the problem of locating a subset of facilities in the case of uncertainties and variations in the system parameters. Dealing with this problem using scenarios based approach needs an important computational effort. The two phases proposed method in this paper combines both exact and heuristic approaches to minimize the maximum regret of the model. We proposed and compared three different heuristics to solve it and applied it to the case of locating emergency vehicles stations. The obtained results show that site selection based on the minimum average travel time is the most promising one.


title: Analysis of Several Image Steganography Techniques in Spatial Domain: A Survey

abstract: Steganography enables user to hide confidential data in any digital medium such that its existence cannot be concealed by the third party. Several research work is being is conducted to improve steganography algorithm's efficiency. Recent trends in computing technology use steganography as an important tool for hiding confidential data. This paper summarizes some of the research work conducted in the field of image steganography in spatial domain along with their advantages and disadvantages. Future research work and experimental results of some techniques is also being discussed. The key goal is to show the powerful impact of steganography in information hiding and image processing domain.


title: The Design and Implementation of Fog Computing Cluster for High-Speed Computation Intensive Real-Time Wireless Sensor Network

abstract: Despite the massive demands of cloud computing and its necessity, there are still several issues drawback due to the obstacles problems in cloud computing such as verbose latency. This paper introduces the concept of FOG computing platform and the benefit of processing and storing data at the edge of the network to minimize the latency in critical Real-time wireless sensor network WSN application which demand bounded latency, such as gas, radiation detector, remote health monitoring and control. The system incorporates a cluster of edge devices stacked as a swarm. To reduce the resource overhead we implement the concept of the container in the resource-constrained Fog cluster instead of the hypervisor. We implement real-time services such as intensive computing, event location tracking and samba server in the Fog cluster. The services are deployed, managed and controlled by swarm manager node. The result of fog computing closed to edge device within the network, compared with cloud computing service. The system improves the application performance by reducing the latency while keeps the other application and data to be relayed to the main cloud data center. The implemented Fog facilities ensure interactive reasonable and satisfied solutions that outperform cloud data center services.


title: TIaaS: Secure Cloud-assisted Traffic Information Dissemination in Vehicular Ad Hoc Networks

abstract: In the recent past, a new concept termed as VANET-based clouds evolved from traditional VANET incorporating both VANET and cloud computing technologies in order to provide vehicle drivers, passengers, and consumers with safe, reliable, and infotainment-rich services while driving on the roads. In this paper, we use a framework of VANET-based clouds proposed by Hussain et al. namely VuC (VANET using Clouds) and define another layer TIaaS (Traffic Information as a Service) atop the cloud computing stack. TIaaS layer provides vehicular nodes (more precisely subscribers) with fine-grained traffic information in a secure way. Additionally our proposed scheme provides security, privacy, and conditional anonymity which are of prime concern in VANET clouds.


title: A simple pragmatic approach to mesh routing using

abstract: 2nd IFIP International Symposium on Wireless Communications and Information Technology in Developing Countries, CSIR, Pretoria, South Africa, 6-7 October 2008


title: Engineering stability in gene networks by autoregulation.

abstract: The genetic and biochemical networks which underlie such things as homeostasis in metabolism and the developmental programs of living cells, must withstand considerable variations and random perturbations of biochemical parameters. These occur as transient changes in, for example, transcription, translation, and RNA and protein degradation. The intensity and duration of these perturbations differ between cells in a population. The unique state of cells, and thus the diversity in a population, is owing to the different environmental stimuli the individual cells experience and the inherent stochastic nature of biochemical processes (for example, refs 5 and 6). It has been proposed, but not demonstrated, that autoregulatory, negative feedback loops in gene circuits provide stability, thereby limiting the range over which the concentrations of network components fluctuate. Here we have designed and constructed simple gene circuits consisting of a regulator and transcriptional repressor modules in Escherichia coli and we show the gain of stability produced by negative feedback.


title: 3D deeply supervised network for automated segmentation of volumetric medical images

abstract: While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our proposed 3D DSN.


title: Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks

abstract: Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demanding, so unfeasible in physical systems for most applications. In this work, we approach an alternative Interactive Machine Learning (IML) strategy for training DNN policies based on human corrective feedback, with a method called Deep COACH (D-COACH). This approach not only takes advantage of the knowledge and insights of human teachers as well as the power of DNNs, but also has no need of a reward function (which sometimes implies the need of external perception for computing rewards). We combine Deep Learning with the COrrective Advice Communicated by Humans (COACH) framework, in which non-expert humans shape policies by correcting the agent's actions during execution. The D-COACH framework has the potential to solve complex problems without much data or time required. Experimental results validated the efficiency of the framework in three different problems (two simulated, one with a real robot), with state spaces of low and high dimensions, showing the capacity to successfully learn policies for continuous action spaces like in the Car Racing and Cart-Pole problems faster than with DRL.


title: The relationship of attachment style to depression, catastrophizing and health care utilization in patients with chronic pain

abstract: Attachment theory and research suggest that patterns of interpersonal relationships may be important determinants of illness behavior, care seeking, and treatment response in individuals with chronic health problems, including chronic pain. Attachment styles have been shown to be associated with psychological adjustment in the context of chronic illness, but little research has been conducted so far examining these relationships in patients with chronic pain. We assessed 111 patients with chronic pain participating in a multidisciplinary pain treatment program to determine if attachment style is associated with pain, depression, catastrophizing and physical disability at pre-treatment and 12-month follow-up, and with change in health care utilization pre-treatment to follow-up. At both pre-treatment and follow-up, fearful attachment style was associated with significantly greater depression and catastrophizing, and secure attachment was associated with significantly lower levels of depression. Preoccupied attachment style was associated with greater than weekly pain-related visits at 12 months follow-up, even after controlling for depression, catastrophizing and pre-treatment pain-related health care utilization. The findings suggest that attachment style may be a useful construct for examining factors affecting adjustment and treatment response of patients with chronic pain.


title: An efficient classification model for detecting advanced persistent threat

abstract: Among most of the cyber attacks that occured, the most drastic are advanced persistent threats. APTs are differ from other attacks as they have multiple phases, often silent for long period of time and launched by adamant, well-funded opponents. These targeted attacks mainly concentrated on government agencies and organizations in industries, as are those involved in international trade and having sensitive data. APTs escape from detection by antivirus solutions, intrusion detection and intrusion prevention systems and firewalls. In this paper we proposes a classification model having 99.8% accuracy, for the detection of APT.


title: MATLAB for Adaptive Noise Cancellation from ECG Signal

abstract: The medical monitoring devices are more sensitive for the biomedical signal recording and need more accurate results for every diagnosis. The low frequency signal is destroyed by power line interference of 50 Hz noise, this noise is also source of interference for biomedical signal recording. The frequency of power line interference 50 Hz is nearly equal to the frequency of ECG, so this 50 Hz noise can destroyed the output of ECG signal. One way to remove the noise is to filter the signal with a notch filter at 50 Hz. However, due to slight variations in the power supply to the hospital, the exact frequency of the power supply might (hypothetically) wander between 47 Hz and 53 Hz. A static filter would need to remove all the frequencies between 47 and 53 Hz, which could excessively degrade the quality of the ECG since the heart beat would also likely have frequency components in the rejected range. To circumvent this potential loss of information, an adaptive filter has been used. The adaptive filter would take input both from the patient and from the power supply directly and would thus be able to track the actual frequency of the noise as it fluctuates [2].


title: Weakly-supervised Semantic Parsing with Abstract Examples

abstract: Semantic parsers translate language utterances to programs, but are often trained from utterance-denotation pairs only. Consequently, parsers must overcome the problem of spuriousness at training time, where an incorrect program found at search time accidentally leads to a correct denotation. We propose that in small well-typed domains, we can semi-automatically generate an abstract representation for examples that facilitates information sharing across examples. This alleviates spuriousness, as the probability of randomly obtaining a correct answer from a program decreases across multiple examples. We test our approach on CNLVR, a challenging visual reasoning dataset, where spuriousness is central because denotations are either TRUE or FALSE, and thus random programs have high probability of leading to a correct denotation. We develop the first semantic parser for this task and reach 83.5% accuracy, a 15.7% absolute accuracy improvement compared to the best reported accuracy so far.


title: Scaling and assessment of data quality

abstract: The various physical factors affecting measured diffraction intensities are discussed, as are the scaling models which may be used to put the data on a consistent scale. After scaling, the intensities can be analysed to set the real resolution of the data set, to detect bad regions (e.g. bad images), to analyse radiation damage and to assess the overall quality of the data set. The significance of any anomalous signal may be assessed by probability and correlation analysis. The algorithms used by the CCP4 scaling program SCALA are described. A requirement for the scaling and merging of intensities is knowledge of the Laue group and point-group symmetries: the possible symmetry of the diffraction pattern may be determined from scores such as correlation coefficients between observations which might be symmetry-related. These scoring functions are implemented in a new program POINTLESS.


title: Role of Climate Variability in Modulating the Surface Water and Groundwater Interaction over the Southeast United States

abstract: AbstractThis paper presents an investigation of the role of climatic variability on interannual groundwater and streamflow variability in the southeast United States. For this purpose, streamflow and associated groundwater levels are analyzed for 20 basins that are minimally affected by reservoirs and groundwater pumping. Using the spatially averaged monthly precipitation time series obtained from the Precipitation-elevation Regressions on Independent Slopes Model (PRISM), this paper identifies the recharge and discharge periods that influence the groundwater levels during the winter [January, February, March (JFM)] and summer [July, August, September (JAS)] seasons. Recharge-discharge dependency analyses indicate that precipitation during the previous 3 months influences the groundwater level in a given month. Streamflow in any given month depends on the groundwater level during the previous 3 months. Principal component analysis (PCA) on the precipitation, temperature, streamflow, and groundwater data i...


title: A 51-nW 32.7-kHz CMOS relaxation oscillator with half-period pre-charge compensation scheme for ultra-low power systems

abstract: This paper presents a temperature and supply voltage variation-tolerant CMOS relaxation oscillator which is suitable for ultra-low power systems. A low-power low-cost half-period pre-charge compensation scheme is proposed to eliminate influences of the delay of the comparator and the RS latch on the frequency stability of the relaxation oscillator. In a clock period consisting of four working stages including normal charge, discharge, pre-charge and hold stage, the threshold voltage of comparators is adjusted dynamically. A 32.7-kHz relaxation oscillator with the proposed half-period pre-charge compensation scheme is implemented in a TSMC 0.18-μm CMOS process, occupying a silicon area of 0.048 mm2. Simulation results show that the proposed relaxation oscillator consumes 51-nW at room temperature from 0.6-V power supply, and temperature stability of 43.1 ppm/°C from −55 °C to 125 °C and ±0.60% frequency variation with supply voltage from 0.5 V to 1.0 V are achieved.


title: Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping

abstract: Over the last few years, 3D imaging of plant geometry has become of significant importance for phenotyping and plant breeding. Several sensing techniques, like 3D reconstruction from multiple images and laser scanning, are the methods of choice in different research projects. The use of RGBcameras for 3D reconstruction requires a significant amount of post-processing, whereas in this context, laser scanning needs huge investment costs. The aim of the present study is a comparison between two current 3D imaging low-cost systems and a high precision close-up laser scanner as a reference method. As low-cost systems, the David laser scanning system and the Microsoft Kinect Device were used. The 3D measuring accuracy of both low-cost sensors was estimated based on the deviations of test specimens. Parameters extracted from the volumetric shape of sugar beet taproots, the leaves of sugar beets and the shape of wheat ears were evaluated. These parameters are compared regarding accuracy and correlation to reference measurements. The evaluation scenarios were chosen with respect to recorded plant parameters in current phenotyping projects. In the present study, low-cost 3D imaging devices have been shown to be highly reliable for the demands of plant phenotyping, with the potential to be implemented in automated application procedures, while saving acquisition costs. Our study confirms that a carefully selected low-cost sensor is able to replace an expensive laser scanner in many plant phenotyping scenarios.


title: Preparing for success: readiness models for rural telehealth.

abstract: BACKGROUND ::: Readiness is an integral and preliminary step in the successful implementation of telehealth services into existing health systems within rural communities. ::: ::: ::: METHODS AND MATERIALS ::: This paper details and critiques published international peer-reviewed studies that have focused on assessing telehealth readiness for rural and remote health. Background specific to readiness and change theories is provided, followed by a critique of identified telehealth readiness models, including a commentary on their readiness assessment tools. ::: ::: ::: RESULTS ::: Four current readiness models resulted from the search process. The four models varied across settings, such as rural outpatient practices, hospice programs, rural communities, as well as government agencies, national associations, and organizations. All models provided frameworks for readiness tools. Two specifically provided a mechanism by which communities could be categorized by their level of telehealth readiness. ::: ::: ::: DISCUSSION ::: Common themes across models included: an appreciation of practice context, strong leadership, and a perceived need to improve practice. Broad dissemination of these telehealth readiness models and tools is necessary to promote awareness and assessment of readiness. This will significantly aid organizations to facilitate the implementation of telehealth.


title: A new approach to content-based file type detection

abstract: File type identification and file type clustering may be difficult tasks that have an increasingly importance in the field of computer and network security. Classical methods of file type detection including considering file extensions and magic bytes can be easily spoofed. Content-based file type detection is a newer way that is taken into account recently. In this paper, a new content-based method for the purpose of file type detection and file type clustering is proposed that is based on the PCA and neural networks. The proposed method has a good accuracy and is fast enough.


title: Longer is Better: On the Role of Test Sequence Length in Software Testing

abstract: In the presence of an internal state, often it is required a sequence of function calls to test software. In fact, to cover a particular branch of the code, a sequence of previous function calls might be required to put the internal state in the appropriate configuration. Internal states are not only present in object-oriented software, but also in procedural software(e.g., static variables in C programs). In the literature, there are many techniques to test this type of software. However, to our best knowledge, the properties related to choosing the length of these sequences have received only little attention in the literature. In this paper, we analyse the role that the length plays in software testing, in particular branch coverage. We show that on “difficult” software testing benchmarks longer test sequences make their testing trivial. Hence, we argue that the choice of the length of the test sequences is very important in software testing.


Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information.


title: A survey of network anomaly detection techniques

abstract: Information and Communication Technology (ICT) has a great impact on social wellbeing, economic growth and national security in todays world. Generally, ICT includes computers, mobile communication devices and networks. ICT is also embraced by a group of people with malicious intent, also known as network intruders, cyber criminals, etc. Confronting these detrimental cyber activities is one of the international priorities and important research area. Anomaly detection is an important data analysis task which is useful for identifying the network intrusions. This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering. The paper also discusses research challenges with the datasets used for network intrusion detection. HighlightsMaps different types of anomalies with network attacks.Provides an up-to-date taxonomy of network anomaly detection.Evaluates effectiveness of different categories of techniques.Explores recent research related to publicly available network intrusion evaluation datasets.


title: Video steganography techniques: Taxonomy, challenges, and future directions

abstract: Nowadays, video steganography has become important in many security applications. The performance of any steganographic method ultimately relies on the imperceptibility, hiding capacity, and robustness. In the past decade, many video steganography methods have been proposed; however, the literature lacks of sufficient survey articles that discuss all techniques. This paper presents a comprehensive study and analysis of numerous cutting edge video steganography methods and their performance evaluations from literature. Both compressed and raw video steganographic methods are surveyed. In the compressed domain, video steganographic techniques are categorized according to the video compression stages as venues for data hiding such as intra frame prediction, inter frame prediction, motion vectors, transformed and quantized coefficients, and entropy coding. On the other hand, raw video steganographic methods are classified into spatial and transform domains. This survey suggests current research directions and recommendations to improve on existing video steganographic techniques.


title: Paracrine-mediated osteoclastogenesis by the osteosarcoma MG63 cell line: is RANKL/RANK signalling really important?

abstract: Although in the past little attention has been paid to the influence of osteosarcoma cells in osteoclast function, recent studies suggest a close relationship between osteosarcoma aggressiveness and osteoclastic activity. The present study addresses the paracrine effects of MG63 cells, a human osteosarcoma-derived cell line, on the differentiation of peripheral blood osteoclast precursor cells (PBMC). PBMC were cultured for 21 days in the presence of conditioned media from MG63 cell cultures (CM) collected at 48 h (CM_MG1), 7 days (CM_MG2) and 14 days (CM_MG3). MG63 cell cultures displayed the expression of ALP and BMP-2 and, also, the osteoclastogenic genes M-CSF and RANKL, although with a low expression of RANKL. PBMC cultures supplemented with CM presented an evident osteoclastogenic behavior, which was dependent on the culture period of the MG63 cells. The inductive effect appeared to be more relevant for the differentiation and activation genes, c-myc and c-src, and lower for genes associated with osteoclast function. In addition, PBMC cultures displayed increased functional parameters, including calcium phosphate resorbing activity. Assessment of the PBMC cultures in the presence of U0126, PDTC, and indomethacin suggested that in addition to MEK and NFkB pathways, other signaling mechanisms, probably not involving RANKL/RANK interaction, might be activated in the presence of conditioned medium from MG63. In conclusion, MG63 cell line appears to induce a significant paracrine-mediated osteoclastogenic response. Understanding the mechanisms underlying the interaction of osteosarcoma cells and osteoclasts may contribute to the development of new potential approaches in the treatment of such bone metabolic diseases.


title: Implementing the right to child protection: a challenge for developing countries

abstract: The UN Convention on the Rights of the Child (CRC) is ratified by all but two countries--Somalia and the USA. Those states that have ratified have made efforts to implement it with varied results. From ratification to development of child protection policy and legislation can be illustrated by what happens in Africa where to meet their obligations under the CRC countries have produced national programmes of action. The process of developing a programme and planning its implementation has often been spearheaded by UNICEF. Furthermore Domestic laws have been revised and child-friendly systems such as Councils and Commissions have been created. In some countries such as Kenya and Uganda comprehensive Children Acts now exist and policies on children and young people have been formulated. Childrens parliaments too are common practice in many countries on the continent. Governments are working to improve the effectiveness of their departments that deal with childrens issues and many have undertaken investigations into the problems that affect children. Other international treaties that deal with specific issues--such as children in commercial sex armed conflict and child labour--have been developed by different arms of the UN in collaboration with international organisations such as Save the Children Alliance and Anti-Slavery International and have been signed and ratified by governments. (excerpt)


title: Archeoguide: an augmented reality guide for archaeological sites

abstract: The paper discusses Archeoguide which offers personalized augmented reality tours of archaeological sites. It uses outdoor tracking, mobile computing, 3D visualization and augmented reality techniques to enhance information presentation, reconstruct ruined sites, and simulate ancient life.


title: Frequency and Pattern Reconfigurable Slot Antenna

abstract: A novel frequency and pattern reconfigurable slot antenna is proposed. Slot antenna produces a bidirectional radiation pattern, thus a reflector is positioned behind of the antenna to generate a directional radiation pattern. Two switches are placed in the slot to produce three reconfigurable frequency bands: at 1.82 GHz, 1.93 GHz and 2.10 GHz. The introduction of four slits at the edge of the ground plane offers pattern reconfigurability. To tune the beam angles, three switches are inserted into each slit. By manipulating the phase of upper and lower slits in the ground plane, the proposed antenna is capable of reconfiguring its beam to three different angles at 0°, -15° and +15°. The simulated and measured results of reflection coefficient and radiation patterns are presented and compared.


title: An Overview of the Research on Texture Based Plant Leaf Classification

abstract: Plant classification has a broad application prospective in agriculture and medicine, and is especially significant to the biology diversity research. As plants are vitally important for environmental protection, it is more important to identify and classify them accurately. Plant leaf classification is a technique where leaf is classified based on its different morphological features. The goal of this paper is to provide an overview of different aspects of texture based plant leaf classification and related things. At last we will be concluding about the efficient method i.e. the method that gives better performance compared to the other methods.


title: Model of Customer Churn Prediction on Support Vector Machine

abstract: Abstract To improve the prediction abilities of machine learning methods, a support vector machine (SVM) on structural risk minimization was applied to customer churn prediction. Researching customer churn prediction cases both in home and foreign carries, the method was compared with artifical neural network, decision tree, logistic regression, and naive bayesian classifier. It is found that the method enjoys the best accuracy rate, hit rate, covering rate, and lift coefficient, and therefore, provides an effective measurement for customer churn prediction.


title: Object Recognition in Aerial Images Using Convolutional Neural Networks

abstract: There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time.


A Weighted Majority Voting based on NMI for Cluster Analysis


title: Accurate wild animal recognition using PCA, LDA and LBPH

abstract: In this paper, the performances of image recognition methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Patterns Histograms (LBPH) are tested and compared for the image recognition of the input animal images. The main idea of this paper is to present an independent, comparative study and some of the benefits and drawbacks of these most popular image recognition methods. Two sets of experiments are conducted for relative performance evaluations. In the first part of our experiments, the recognition accuracy of PCA, LDA and LBPH is demonstrated. The overall time execution for animal recognition process is evaluated in the second set of our experiments. We conduct tests on created animal database. The all algorithms have been tested on 300 different subjects (60 images for each class). The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set.


title: Echo State Networks-Based Reservoir Computing for MNIST Handwritten Digits Recognition

abstract: Reservoir Computing is an attractive paradigm of recurrent neural network architecture, due to the ease of training and existing neuromorphic implementations. Successively applied on speech recognition and time series forecasting, few works have so far studied the behavior of such networks on computer vision tasks. Therefore we decided to investigate the ability of Echo State Networks to classify the digits of the MNIST database. We show that even if ESNs are not able to outperform state-of-the-art convolutional networks, they allow low error thanks to a suitable preprocessing of images. The best performance is obtained with a large reservoir of 4,000~neurons, but committees of smaller reservoirs are also appealing and might be further investigated.


title: Understanding the effect of PCB layout on circuit performance in a high frequency gallium nitride based point of load converter

abstract: The introduction of enhancement mode gallium nitride based power devices such as the eGaN®FET offers the potential to achieve higher efficiencies and higher switching frequencies than possible with Silicon MOSFETs. With the improvements in switching performance and low parasitic packaging provided by eGaN FETs, the PCB layout becomes critical to converter performance. This paper will study the effect of PCB layout parasitic inductance on efficiency and peak device voltage stress for an eGaN FET based point of load (POL) converter operating at a switching frequency of 1 MHz, an input voltage range of 12-28 V, an output voltage of 1.2 V, and an output current up to 20 A. This work will also compare the parasitic inductances of conventional PCB layouts and propose an improved PCB design providing a 40% decrease in parasitic inductance over the best conventional PCB design.


title: Alice, Greenfoot, and Scratch -- A Discussion

abstract: This article distills a discussion about the goals, mechanisms, and effects of three environments which aim to support the acquisition and development of computing concepts (problem solving and programming) in pre-University and non-technical students: Alice, Greenfoot, and Scratch. The conversation started in a special session on the topic at the 2010 ACM SIGCSE Symposium on Computer Science Education and continued during the creation of the resulting Special Issue of the ACM Transactions on Computing Education.


CROSSING AND BRIDGING SPACES IN A SECOND LANGUAGE: A CORPUS-BASED INVESTIGATION OF MOTION TALK BY AMERICAN LEARNERS OF RUSSIAN


title: Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm

abstract: Most tensor problems are NP-hard, and low-rank tensor completion is much more difficult than low-rank matrix completion. In this paper, we propose a time and space-efficient low-rank tensor completion algorithm by using the scaled latent nuclear norm for regularization and the Frank-Wolfe (FW) algorithm for optimization. We show that all the steps can be performed efficiently. In particular,FW's linear subproblem has a closed-form solution which can be obtained from rank-one SVD. By utilizing sparsity of the observed tensor,we only need to maintain sparse tensors and a set of small basis matrices. Experimental results show that the proposed algorithm is more accurate, much faster and more scalable than the state-of-the-art.


title: Erratum to: Fungal diversity notes 111–252—taxonomic and phylogenetic contributions to fungal taxa

abstract: Erratum to: Fungal Diversity Notes 111–252 - taxonomic and phylogenetic contributions to fungal taxa.


title: Remote Heart Rate Measurement from Face Videos under Realistic Situations

abstract: Heart rate is an important indicator of people's physiological state. Recently, several papers reported methods to measure heart rate remotely from face videos. Those methods work well on stationary subjects under well controlled conditions, but their performance significantly degrades if the videos are recorded under more challenging conditions, specifically when subjects' motions and illumination variations are involved. We propose a framework which utilizes face tracking and Normalized Least Mean Square adaptive filtering methods to counter their influences. We test our framework on a large difficult and public database MAHNOB-HCI and demonstrate that our method substantially outperforms all previous methods. We also use our method for long term heart rate monitoring in a game evaluation scenario and achieve promising results.


title: Identifying Non-Explicit Citing Sentences for Citation-Based Summarization.

abstract: Identifying background (context) information in scientific articles can help scholars understand major contributions in their research area more easily. In this paper, we propose a general framework based on probabilistic inference to extract such context information from scientific papers. We model the sentences in an article and their lexical similarities as a Markov Random Field tuned to detect the patterns that context data create, and employ a Belief Propagation mechanism to detect likely context sentences. We also address the problem of generating surveys of scientific papers. Our experiments show greater pyramid scores for surveys generated using such context information rather than citation sentences alone.


title: A simple rule for axon outgrowth and synaptic competition generates realistic connection lengths and filling fractions

abstract: Neural connectivity at the cellular and mesoscopic level appears very specific and is presumed to arise from highly specific developmental mechanisms. However, there are general shared features of connectivity in systems as different as the networks formed by individual neurons in Caenorhabditis elegans or in rat visual cortex and the mesoscopic circuitry of cortical areas in the mouse, macaque, and human brain. In all these systems, connection length distributions have very similar shapes, with an initial large peak and a long flat tail representing the admixture of long-distance connections to mostly short-distance connections. Furthermore, not all potentially possible synapses are formed, and only a fraction of axons (called filling fraction) establish synapses with spatially neighboring neurons. We explored what aspects of these connectivity patterns can be explained simply by random axonal outgrowth. We found that random axonal growth away from the soma can already reproduce the known distance distribution of connections. We also observed that experimentally observed filling fractions can be generated by competition for available space at the target neurons--a model markedly different from previous explanations. These findings may serve as a baseline model for the development of connectivity that can be further refined by more specific mechanisms.


title: Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI

abstract: The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. Many stock prediction studies focus on using macroeconomic indicators, such as CPI and GDP, to train the prediction model. However, daily data of the macroeconomic indicators are almost impossible to obtain. Thus, those methods are difficult to be employed in practice. In this paper, we propose a method that directly uses prices data to predict market index direction and stock price direction. An extensive empirical study of the proposed method is presented on the Korean Composite Stock Price Index (KOSPI) and Hang Seng Index (HSI), as well as the individual constituents included in the indices. The experimental results show notably high hit ratios in predicting the movements of the individual constituents in the KOSPI and HIS.


Estimating Uncertainty in Quantitative Text Analysis


title: Hydro-economic models : Concepts , design , applications , and future prospects

abstract: Future water management will shift from building new water supply systems to better operating existing ones. The variation of water values in time and space will increasingly motivate efforts to address water scarcity and reduce water conflicts. Hydro-economic models represent spatially distributed water resource systems, infrastructure, management options and economic values in an integrated manner. In these tools water allocations and management are either driven by the economic value of water or economically evaluated to provide policy insights and reveal opportunities for better management. A central concept is that water demands are not fixed requirements but rather functions where quantities of water use at different times have varying total and marginal economic values. This paper reviews techniques to characterize the economic value of water use and include such values in mathematical models. We identify the key steps in model design and diverse problems, formulations, levels of integration, spatial and temporal scales, and solution techniques addressed and used by over 80 hydro-economic modeling efforts dating back 45-years from 23 countries. We list current limitations of the approach, suggest directions for future work, and recommend ways to improve policy relevance.


title: Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

abstract: Objective. Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. Approach. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. Main results. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Significance. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.


title: A novel device for left atrial appendage exclusion: the third-generation atrial exclusion device.

abstract: OBJECTIVE ::: Occlusion of the left atrial appendage is proposed to reduce the risk of stroke in patients with atrial fibrillation. The third-generation atrial exclusion device, modified to provide uniform distribution of pressure at appendage exclusion, was assessed for safety and effectiveness in a canine model and compared with a surgical stapler. ::: ::: ::: METHODS ::: The atrial exclusion device consists of 2 parallel, straight, rigid titanium tubes and 2 nitinol springs with a knit-braided polyester fabric. Fourteen mongrel dogs were implanted with the device at the base of the left atrial appendage via a median sternotomy. In each dog, the right atrial appendage was stapled with a commercial apparatus for comparison. The animals were evaluated at 7 days (n = 3), 30 days (n = 5), and 90 days (n = 6) after implantation by epicardial echocardiography, left atrial and coronary angiography, gross pathology, and histology. ::: ::: ::: RESULTS ::: Left atrial appendage exclusion was complete and achieved without hemodynamic instability, and coronary angiography revealed that the left circumflex artery was patent in all cases. A new endothelial tissue layer developed on the occluded orifice of the left atrium 90 days after implantation. This endothelial layer was not evident on the stapled right atrial appendage. ::: ::: ::: CONCLUSION ::: In dogs, the third-generation atrial exclusion device achieved easy, reliable, and safe exclusion of the left atrial appendage with favorable histologic results. Clinical application could provide a new therapeutic option for reducing the risk of stroke in patients with atrial fibrillation.


title: Predicting Learning and Affect from Multimodal Data Streams in Task-Oriented Tutorial Dialogue.

abstract: Learners experience a wide array of cognitive and affective states during tutoring. Detecting and responding to these states is a core problem of adaptive learning environments that aim to foster motivation and increase learning. Recognizing learner affect through nonverbal behavior is particularly challenging, as students display affect across numerous modalities. This study utilizes an automatically extracted set of multimodal nonverbal behaviors and task actions to predict learning and affect in a data set of sixty-three computer-mediated human tutoring sessions. Predictive models of post-session self-reported engagement, frustration, and learning were evaluated with leave-one-out cross-validation. Nonverbal behaviors conditioned on task events and typing were found to be more predictive than incoming student self-efficacy and pretest score. Face and gesture were predictive of engagement and frustration, while face and posture was predictive of learning. The nonverbal model features captured moments when students were most active on the task, such as writing and testing the Java program. These results provide initial evidence linking affect, moment-bymoment multimodal nonverbal behavior, and task performance during tutoring. They improve understanding of learner affect and enable automated tutorial interventions that adapt to student states as a highly effective human tutor would.


title: Modeling and simulation of sensor orientation errors in garments

abstract: We report in this paper on a novel modeling and simulation approach to predict orientation errors of garment-attached sensors and their effect on posture classification. Such errors occur frequently in smart garment implementations and can reduce sensor information quality for movement and posture recognition. A kinematic model of the human upper-body was developed to simulate upper limb postures and the output of virtual 3D acceleration sensors. The model was enhanced with a statistical approximation of garment-related orientation errors. We derived this model from acceleration sensor deviations between skin- and garment-attached units. The feasibility of our body model and the garment-attached sensor deviation was validated in experimental data. We compared the classification performance for ten posture types that are frequently used in shoulder rehabilitation. In a validation set of 7 participants we observed similar classifier confusions and a relative error of 2.6% (SD:±3.2%) between simulation and experiment. We utilized the model to estimate classification performance for further simulated textile error distributions. Our simulations showed that classification performance depends on low deviations of an acceleration sensor at the lower arm, while a sensor at the upper arm was less critical. Moreover, we included magnetic field sensors in our simulation. With the help of this additional modality our posture classification performance increased by 18%. We conclude that simulation of skin- and garment-attached sensors is a feasible approach to expedite design and development process of smart garments.


title: Active-Clamped Ripple-Free DC/DC Converter Using an Input–Output Coupled Inductor

abstract: A new input-output coupled inductor (IOCI) is proposed for reducing current ripples and magnetic components. Moreover, a current-source-type circuit using active-clamp mechanism and a current doubler with synchronous rectifier are presented to achieve high efficiency in low input-output voltage applications. The configuration of the IOCI is realized by three windings on a common core, and has the properties of an input inductor at the input-side and two output inductors at the output- side. An active clamped ripple-free dc-dc converter using the proposed IOCI is analyzed in detail and optimized for high power efficiency. Experimental results for 80 W (5 V/16 A) at a constant switching frequency of 100 kHz are obtained to show the performance of the proposed converter.


title: The physiological basis of transcranial motor cortex stimulation in conscious humans

abstract: Transcranial stimulation of the human motor cortex can evoke several different kinds of descending activity depending on the type of stimulation, the intensity of stimulation and the area of the cortex being stimulated. Thus, transcranial magnetic stimulation preferentially activates different structures than transcranial electrical stimulation. In addition, the response to magnetic stimulation depends on the direction of the induced current in the brain, the waveform of the stimulating current, and the shape of the coil. Stimulation of the lower limb area of motor cortex recruits different elements than stimulation of the upper limb area. These differences occur because different structures in the motor cortex have a differential threshold to the different techniques of stimulation. We have had the opportunity to perform a series of direct recordings of the corticospinal volley evoked by the different techniques of transcranial stimulation from the epidural space of conscious patients with chronically implanted spinal electrodes. These recordings provide insights about the physiological basis of the excitatory and inhibitory phenomena produced by transcranial stimulation.


title: A Compact UWB Bandpass Filter With Embedded SIR as Band Notch Structure

abstract: In this letter, an ultrawideband (UWB) bandpass filter with a band notch is proposed. The UWB BPF (3.1-10.6 GHz) is realized by cascading a distributed high-pass filter and an elliptic low-pass filter with an embedded stepped impedance resonator (SIR) to achieve a band notch characteristic. The notch band is obtained at 5.22 GHz. It is shown that the notch frequency can be tuned by changing the impedance ratio of the embedded SIR. A fabricated prototype of the proposed UWB bandpass filter is developed. The inband and out-of-band performance obtained by measurement, EM simulation, and that with an equivalent circuit model are in good agreement.


title: Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy.

abstract: This study evaluated mindtulness-based cognitive therapy (MBCT), a group intervention designed to train recovered recurrendy depressed patients to disengage from dysphoria-activated depressogenic thinking that may mediate relapse/recurrence. Recovered recurrently depressed patients (« = 145) were randomized to continue with treatment as usual or, in addition, to receive MBCT. Relapse/recurrence to major depression was assessed over a 60-week study period. For patients with 3 or more previous episodes of depression (77% of the sample), MBCT significantly reduced risk of relapse/recur rence. For patients with only 2 previous episodes, MBCT did not reduce relapse/recurrence. MBCT offers a promising cost-efficient psychological approach to preventing relapse/recurrence in recovered recurrently depressed patients.


title: A 12Gb/s, 8.6µApp input sensitivity, monolithic-integrated fully differential optical receiver in CMOS 45nm SOI process

abstract: A high-sensitivity, fully-differential optical receiver for high-density photonic interconnects is presented. To realize fully-differential operation, a 3-dB power splitter and SiGe photodetector are integrated with the receiver, all in a CMOS 45nm SOI process. The proposed receiver improves sensitivity by suppressing common-mode and supply noise through fully-differential (FD) operation, achieving 12Gb/s at BER PP while consuming 4.3mW. To understand the effectiveness of the proposed solution, we compare it to a conventional single-ended (SE) receiver on the same test-chip. Measured sensitivity is >2× better than the closest state-of-the-art design, achieving same energy per bit at higher data-rate.


title: Simultaneous segmentation and reconstruction: a level set method approach for limited view computed tomography.

abstract: Purpose: An iterative tomographic reconstruction algorithm that simultaneously segments and reconstructs the reconstruction domain is proposed and applied to tomographic reconstructions from a sparse number of projection images. ::: ::: Methods: The proposed algorithm uses a two-phase level set method segmentation in conjunction with an iterative tomographic reconstruction to achieve simultaneous segmentation and reconstruction. The simultaneous segmentation and reconstruction is achieved by alternating between level set function evolutions and per-region intensity value updates. To deal with the limited number of projections, a priori information about the reconstruction is enforced via penalized likelihood function. Specifically, smooth function within each region (piecewise smooth function) and bounded function intensity values for each region are assumed. Such a priori information is formulated into a quadratic objective function with linear bound constraints. The level set function evolutions are achieved by artificially time evolving the level set function in the negative gradient direction; the intensity value updates are achieved by using the gradient projection conjugate gradient algorithm. ::: ::: Results: The proposed simultaneous segmentation and reconstruction results were compared to “conventional” iterative reconstruction (with no segmentation), iterative reconstruction followed by segmentation, and filtered backprojection. Improvements of 6%–13% in the normalized root mean square error were observed when the proposed algorithm was applied to simulated projections of a numerical phantom and to real fan-beam projections of the Catphan phantom, both of which did not satisfy the a priori assumptions. ::: ::: Conclusions: The proposed simultaneous segmentation and reconstruction resulted in improved reconstruction image quality. The algorithm correctly segments the reconstruction space into regions, preserves sharp edges between different regions, and smoothes the noise within each region. The proposed algorithm framework has the flexibility to be adapted to different a priori constraints while maintaining the benefits achieved by the simultaneous segmentation and reconstruction.


title: Arabic Nominals in HPSG: A Verbal Noun Perspective

abstract: Semitic languages exhibit rich nonconcatenative morphological operations, which can generate a myriad of derived lexemes. Especially, the feature rich, root-driven morphology in the Arabic language demonstrates the construction of several verb-derived nominals (verbal nouns) such as gerunds, active participles, passive participles, locative participles, etc. Although HPSG is a successful syntactic theory, it lacks the representation of complex nonconcatenative morphology. In this paper, we propose a novel HPSG representation for Arabic nominals and various verb-derived nouns. We also present the lexical type hierarchy and derivational rules for generating these verb-derived nominals using the HPSG framework.


title: Multiple extended objects tracking with object-local occupancy grid maps

abstract: Improved sensors in the automotive field are leading to multi-object tracking of extended objects becoming more and more important for advanced driver assistance systems and highly automated driving. This paper proposes an approach that combines a PHD filter for extended objects, viz. objects that originate multiple measurements while also estimating the shape of the objects via constructing an object-local occupancy grid map and then extracting a polygonal chain. This allows tracking even in traffic scenarios where unambiguous segmentation of measurements is difficult or impossible. In this work, this is achieved using multiple segmentation assumptions by applying different parameter sets for the DBSCAN clustering algorithm. The proposed algorithm is evaluated using simulated data and real sensor data from a test track including highly accurate D-GPS and IMU data as a ground truth.


title: A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation

abstract: Estimating count and density maps from crowd images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning. In addition, techniques developed for crowd counting can be applied to related tasks in other fields of study such as cell microscopy, vehicle counting and environmental survey. The task of crowd counting and density map estimation is riddled with many challenges such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Nevertheless, over the last few years, crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios. The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets. In this paper, we provide a comprehensive survey of recent Convolutional Neural Network (CNN) based approaches that have demonstrated significant improvements over earlier methods that rely largely on hand-crafted representations. First, we briefly review the pioneering methods that use hand-crafted representations and then we delve in detail into the deep learning-based approaches and recently published datasets. Furthermore, we discuss the merits and drawbacks of existing CNN-based approaches and identify promising avenues of research in this rapidly evolving field.


title: Early Life Stress Alters Behavior, Immunity, and Microbiota in Rats: Implications for Irritable Bowel Syndrome and Psychiatric Illnesses

abstract: Background Adverse early life events are associated with a maladaptive stress response system and might increase the vulnerability to disease in later life. Several disorders have been associated with early life stress, ranging from depression to irritable bowel syndrome. This makes the identification of the neurobiological substrates that are affected by adverse experiences in early life invaluable. Methods The purpose of this study was to assess the effect of early life stress on the brain-gut axis. Male rat pups were stressed by separating them from their mothers for 3 hours daily between postnatal days 2–12. The control group was left undisturbed with their mothers. Behavior, immune response, stress sensitivity, visceral sensation, and fecal microbiota were analyzed. Results The early life stress increased the number of fecal boli in response to a novel stress. Plasma corticosterone was increased in the maternally separated animals. An increase in the systemic immune response was noted in the stressed animals after an in vitro lipopolysaccharide challenge. Increased visceral sensation was seen in the stressed group. There was an alteration of the fecal microbiota when compared with the control group. Conclusions These results show that this form of early life stress results in an altered brain-gut axis and is therefore an important model for investigating potential mechanistic insights into stress-related disorders including depression and IBS.


title: Inception Recurrent Convolutional Neural Network for Object Recognition

abstract: Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields. In this paper, we introduce a new deep learning model called an Inception- Recurrent Convolutional Neural Network (IRCNN), which utilizes the power of an inception network combined with recurrent layers in DCNN architecture. We have empirically evaluated the recognition performance of the proposed IRCNN model using different benchmark datasets such as MNIST, CIFAR-10, CIFAR- 100, and SVHN. Experimental results show similar or higher recognition accuracy when compared to most of the popular DCNNs including the RCNN. Furthermore, we have investigated IRCNN performance against equivalent Inception Networks and Inception-Residual Networks using the CIFAR-100 dataset. We report about 3.5%, 3.47% and 2.54% improvement in classification accuracy when compared to the RCNN, equivalent Inception Networks, and Inception- Residual Networks on the augmented CIFAR- 100 dataset respectively.


title: Hybrid memory cube new DRAM architecture increases density and performance

abstract: Multi-core processor performance is limited by memory system bandwidth. The Hybrid Memory Cube is a three-dimensional DRAM architecture that improves latency, bandwidth, power and density. Through-silicon vias (TSVs), 3D packaging and advanced CMOS performance enable a new approach to memory system architecture. Heterogeneous die are stacked with significantly more connections, thereby reducing the distance signals travel.


title: Enhancing bank security system using Face Recognition, Iris Scanner and Palm Vein Technology

abstract: The objective of this paper is to design a bank locker security system which is using Face Recognition, Iris Scanner and Palm Vein Technology (PVR) for securing valuable belongings. A face recognition system is a system which identifies and authenticates the image of the authorized user by using MATLAB software. The images of a person entering a unrestricted zone are taken by the camera and software compares the image with an existing database of valid users. Iris Recognition system uses generous characteristics present in human body. This technology is employed for biometric authentication in ATM’s, Immigration & border control, public safety, hospitality and tourism etc. This paper serves techniques so that capability of palm vein recognition system can be improved using modified vascular pattern thinning algorithm. Palm Vein Recognition (PVR) is a technology which recognizes palm vein pattern of an individual and matches it with the data stored in database for authentication. This is a very reliable technique and has a very good accuracy and is considered as the most secure and better technique for security purposes.


title: ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

abstract: Automatically annotating column types with knowledge base(KB) concepts is a critical task to gain a basic understandingof web tables. Current methods rely on either table metadatalike column name or entity correspondences of cells in theKB, and may fail to deal with growing web tables with in-complete meta information. In this paper we propose a neu-ral network based column type annotation framework namedColNetwhich is able to integrate KB reasoning and lookupwith machine learning and can automatically train Convolu-tional Neural Networks for prediction. The prediction modelnot only considers the contextual semantics within a cell us-ing word representation, but also embeds the semantics of acolumn by learning locality features from multiple cells. Themethod is evaluated with DBPedia and two different web ta-ble datasets, T2Dv2 from the general Web and Limaye fromWikipedia pages, and achieves higher performance than thestate-of-the-art approaches.


title: Robust trajectory tracking control of a quadrotor UAV

abstract: In this paper, we present two control strategies of a quadrotor Unmanned Aerial Vehicle (UAV). The dynamics of the system is controlled to achieve a complex trajectory tracking. A controller using the integral backstepping is developed for the stabilization of the six degrees of freedom (6DoF) of the system. Furthermore, we propose a hybrid strategy by combining the integral backstepping and the PID linear controller. For this, we divide the dynamic system into two subsystems; each one with 3DoF. For the stabilization of the rotational dynamics, the PID controller is proposed. The Integral Backstepping is applied for the longitudinal, lateral and vertical position stabilization. Finally, some numerical simulations are conducted to illustrate the robustness of both strategies.


title: Krill-eye : Superposition compound eye for wide-angle imaging via GRIN lenses

abstract: We propose a novel wide angle imaging system inspired by compound eyes of animals. Instead of using a single lens, well compensated for aberration, we used a number of simple lenses to form a compound eye which produces practically distortion-free, uniform images with angular variation. The images formed by the multiple lenses are superposed on a single surface for increased light efficiency. We use GRIN (gradient refractive index) lenses to create sharply focused images without the artifacts seen when using reflection based methods for X-ray astronomy. We show the theoretical constraints for forming a blur-free image on the image sensor, and derive a continuum between 1 : 1 flat optics for document scanners and curved sensors focused at infinity. Finally, we show a practical application of the proposed optics in a beacon to measure the relative rotation angle between the light source and the camera with ID information.


title: Zadoff–Chu Sequence Design for Random Access Initial Uplink Synchronization in LTE-Like Systems

abstract: The autocorrelation of a Zadoff-Chu (ZC) sequence with a non-zero cyclically shifted version of itself is zero. Due to the interesting property, ZC sequences are widely used in the LTE air interface in the primary synchronization signal (PSS), random access preamble (PRACH), uplink control channel (PUCCH) etc. However, this interesting property of ZC sequence is not useful in the random access initial uplink synchronization problem due to some specific structures of the underlying problem. In particular, the state of the art uplink synchronization algorithms do not perform equally for all ZC sequences. In this work, we show a systematic procedure to choose the ZC sequences that yield the optimum performance of the uplink synchronization algorithms. At first, we show that the uplink synchronization is a sparse signal recovery problem on an overcomplete basis. Next, we use the theory of sparse recovery algorithms and identify a factor that controls performance of the algorithms. We then suggest a ZC sequence design procedure to optimally choose this factor. The simulation results show that the performance of most of the state of the art uplink synchronization algorithms improve significantly when the ZC sequences are chosen by using the proposed technique.


title: A retrieved context account of spacing and repetition effects in free recall.

abstract: Repeating an item in a list benefits recall performance, and this benefit increases when the repetitions are spaced apart (Madigan, 1969; Melton, 1970). Retrieved context theory incorporates two mechanisms that account for these effects: contextual variability and study-phase retrieval. Specifically, if an item presented at position i is repeated at position j, this leads to retrieval of its context from its initial presentation at i (study-phase retrieval) and this retrieved context will be used to update the current state of context (contextual variability). Here we consider predictions of a computational model that embodies retrieved context theory, the context maintenance and retrieval model (CMR; Polyn, Norman, & Kahana, 2009). CMR makes the novel prediction that subjects are more likely to successively recall items that follow a shared repeated item (e.g. i + 1, j + 1) because both items are associated with the context of the repeated item presented at i and j. CMR also predicts that the probability of recalling at least one of two studied items should increase with the items’ spacing (Lohnas, Polyn, & Kahana, 2011). We tested these predictions in a new experiment and CMR’s predictions were upheld. These findings suggest that retrieved context theory offers an integrated explanation for repetition and spacing effects in free recall tasks.


title: GPU-NB: A Fast CUDA-Based Implementation of Naïve Bayes

abstract: The advent of the Web 2.0 has given rise to an interesting phenomenon: there is currently much more data than what can be effectively analyzed without relying on sophisticated automatic tools. Some of these tools, which target the organization and extraction of useful knowledge from this huge amount of data, rely on machine learning and data or text mining techniques, specifically automatic document classification algorithms. However, these algorithms are still a computational challenge because of the volume of data that needs to be processed. Some of the strategies available to address this challenge are based on the parallelization of ADC algorithms. In this work, we present GPU-NB, a parallel version of one of the most widely used document classification algorithms, the Naive Bayes, that uses graphics processing units (GPUs). In our evaluation using 6 different document collections, we show that the GPU-NB can maintain the same classification effectiveness (in most cases) while increasing the efficiency by up to 34x faster than its sequential version using CPU. GPU-NB is also up to 11x faster than a CPU-based parallel implementation of Naive Bayes running with 4 threads. Moreover, assuming an optimistic behavior of the CPU parallelization, GPU-NB should outperform the CPU-based implementation with up to 32 cores, at a small fraction of the cost. We also show that the efficiency of the GPU-NB parallelization is impacted by features of the document collections, particularly the number of classes, although the density of the collection (average number of occurrences of terms per document) has a significant impact as well.


title: Special Section on CAD/Graphics 2013: Tooth segmentation on dental meshes using morphologic skeleton

abstract: Tooth segmentation has an important role in computer-aided orthodontics. However, fine segmentation results remain difficult to obtain because of various tooth shapes, complex tooth arrangements, and especially, tooth-crowding problems. Most published approaches or commercial solutions in this area are either interaction-intensive or inaccurate, and thus, we propose a novel tooth segmentation approach based on morphologic skeleton for scanned dental meshes. Strict single-vertex width boundaries are obtained through improved morphologic skeleton technique. The skeleton describes the topological relationship among different dental parts on meshes and is exploited by automatic adjacent teeth separation. The morphologic skeleton technique eliminates dependence on a complex, precise mesh feature estimation and is implemented efficiently. The characteristics of the skeleton also facilitate effective teeth separation. Our techniques significantly reduce user interactions and are robust to various levels of tooth-crowding problems. We have conducted experiments on clinical dental models, thus demonstrating the effectiveness of the proposed approach.


title: Assessing Novelty of Research Articles Using Fuzzy Cognitive Maps

abstract: In this paper, we compare and analyze the novelty of a scientific paper (text document) of a specific domain. Our experiments utilize the standard Latent Dirichlet Allocation (LDA) topic modeling algorithm to filter the redundant documents and the Ontology of a specific domain which serves as the knowledge base for that domain, to generate cognitive maps for the documents. We report results based on the distance measure such as the Euclidean distance measure that analyses the divergence of the concepts between the documents.


title: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

abstract: This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f/spl isin/C/sup N/ and a randomly chosen set of frequencies /spl Omega/. Is it possible to reconstruct f from the partial knowledge of its Fourier coefficients on the set /spl Omega/? A typical result of this paper is as follows. Suppose that f is a superposition of |T| spikes f(t)=/spl sigma//sub /spl tau//spl isin/T/f(/spl tau/)/spl delta/(t-/spl tau/) obeying |T|/spl les/C/sub M//spl middot/(log N)/sup -1/ /spl middot/ |/spl Omega/| for some constant C/sub M/>0. We do not know the locations of the spikes nor their amplitudes. Then with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the /spl lscr//sub 1/ minimization problem. In short, exact recovery may be obtained by solving a convex optimization problem. We give numerical values for C/sub M/ which depend on the desired probability of success. Our result may be interpreted as a novel kind of nonlinear sampling theorem. In effect, it says that any signal made out of |T| spikes may be recovered by convex programming from almost every set of frequencies of size O(|T|/spl middot/logN). Moreover, this is nearly optimal in the sense that any method succeeding with probability 1-O(N/sup -M/) would in general require a number of frequency samples at least proportional to |T|/spl middot/logN. The methodology extends to a variety of other situations and higher dimensions. For example, we show how one can reconstruct a piecewise constant (one- or two-dimensional) object from incomplete frequency samples - provided that the number of jumps (discontinuities) obeys the condition above - by minimizing other convex functionals such as the total variation of f.


title: A Neural Theory of Visual Attention: Bridging Cognition and Neurophysiology

abstract: A neural theory of visual attention (NTVA) is presented. NTVA is a neural interpretation of C. Bundesen’s (1990) theory of visual attention (TVA). In NTVA, visual processing capacity is distributed across stimuli by dynamic remapping of receptive fields of cortical cells such that more processing resources (cells) are devoted to behaviorally important objects than to less important ones. By use of the same basic equations used in TVA, NTVA accounts for a wide range of known attentional effects in human performance (reaction times and error rates) and a wide range of effects observed in firing rates of single cells in the primate visual system. NTVA provides a mathematical framework to unify the 2 fields of research—formulas bridging cognition and neurophysiology. This article presents a neural theory of visual attention: NTVA. The theory proposes a close link between attentional function at the behavioral and at the cellular level. By use of the same basic equations used in TVA (theory of visual attention; Bundesen, 1990), the theory accounts for both a large portion of the attentional effects reported in the psychological literature and a large portion of the attentional effects demonstrated in individual neurons. NTVA thus provides a mathematical framework to unify these two fields of research. NTVA is a further development of TVA (Bundesen, 1990). TVA is a formal, computational theory that accounts for a wide range of attentional effects in mind and behavior reported in the psychological literature. At the heart of TVA are two equations, and NTVA is a neural interpretation of these equations. The equations jointly describe two mechanisms of attentional selection: filtering (selection of objects) and pigeonholing (selection of features). In NTVA, filtering affects the number of cells (cortical neurons) in which an object is represented, whereas pigeonholing is a multiplicative scaling of the level of activation in cells coding for particular features (see Figure 1). The total activation representing a visual categorization of the form “object x has feature i” is directly proportional to both the number of neurons representing the categorization (which is controlled by filtering) and the level of activation of the individual neurons representing the categorization (controlled by pigeonholing), and Equation 1 of TVA essentially expresses this fact. Filtering is done in such a way that the number of cells in which an object is represented increases with the behavioral importance of the object (parallel processing with differential allocation of resources). More specifically, the probability that a cortical neuron represents a particular object within its classical receptive field (RF) equals the attentional weight of the object divided by the sum of the attentional weights across all objects in the RF. Equation 2 of TVA describes how attentional weights are computed, and logically this computation must occur before processing resources (cells) can be distributed in accordance with the weights. Accordingly, in NTVA, a normal perceptual cycle consists of two waves: a wave of unselective processing followed by a wave of selective processing. During the first wave, cortical processing resources are distributed at random (unselectively) across the visual field. At the end of the first wave, an attentional weight has been computed for each object in the visual field and stored in a saliency map. The weights are used for reallocation of attention (visual processing capacity) by dynamic remapping of RFs of cortical neurons such that the number of neurons allocated to an object increases with the attentional weight of the object. Hence, during the second wave, cortical processing is selective in the sense that the amount of processing resources (number of neurons) allocated to an object depends on the attentional weight of the object. Because more processing resources are devoted to behaviorally important objects than to less important ones, the important objects are more likely to become encoded into visual short-term memory (VSTM). The VSTM system is conceived as a (Kwinners-take-all) feedback mechanism that sustains activity in the neurons that have won the attentional competition. This article contains three main sections. In the first main section, we review the equations describing the basic mechanisms of selection in TVA and summarize how TVA has been applied to a broad range of findings on human performance in visual recognition and attention tasks. The neural interpretation of TVA, NTVA, is presented in the second main section. We first develop the neural interpretation of Equation 1 of TVA in general terms. We then present a set of simple networks for performing the attentional operations of NTVA. We show how the computations of the networks correspond to the original equations of TVA, and


title: Learning-based occupancy behavior detection for smart buildings

abstract: In this article, we propose a novel method to detect the occupancy behavior of a building through the temperature and/or possible heat source information, which can be used for energy reduction, security monitoring for emerging smart buildings. Our work is based on a realistic building simulation program, EnergyPlus, from Department of Energy. EnergyPlus can model the various time-series inputs to a building such as ambient temperature, heating, ventilation, and air-conditioning (HVAC) inputs, power consumption of electronic equipment, lighting and number of occupants in a room sampled in each hour and produce resulting temperature traces of zones (rooms). The new approach is based on a learning based approach in which a recurrent neutral network (RNN) is trained to detect the number of people in a room based on the room temperature and other information such as ambient temperature, and other related heat sources. We applied the Elman's recurrent neural network (ELNN), which has local feedbacks in each layer. We use an empirical formula to calculate the RNN layer number and layer size to configure RNN architecture to avoid overfitting and under-fitting problems. Experimental results from a case study of a 5-zone building show that ELNN can lead to very accurate occupancy behavior estimation. The error level, in terms of number of people, can be as low as 0.0056 on average and 0.288 at maximum when we consider ambient, room temperatures and HVAC powers as detectable information. Without knowing HVAC powers, estimation error can still be 0.044 on average, and only 0.71% estimated points have errors greater than 0.5.


title: TreeDroid: a tree automaton based approach to enforcing data processing policies

abstract: Current approaches to security policy monitoring are based on linear control flow constraints such as 'runQuery' may be evaluated only after 'sanitize'. However, realistic security policies must be able to conveniently capture data flow constraints as well. An example is a policy stating that arguments to the function 'runQuery' must be either constants, outputs of a function 'sanitize', or concatenations of any such values. We present a novel approach to security policy monitoring that uses tree automata to capture constraints on the way data is processed along an execution. We present a »-calculus based model of the framework, investigate some of the models meta-properties, and show how it can be implemented using labels corresponding to automaton states to reflect the computational histories of each data item. We show how a standard denotational semantics induces the expected monitoring regime on a simple "while" language. Finally we implement the framework for the Dalvik VM using TaintDroid as the underlying data flow tracking mechanism, and evaluate its functionality and performance on five case studies.


Semantic segmentation of mixed crops using deep convolutional neural network.


title: Adding Semantics to Data-Driven Paraphrasing

abstract: We add an interpretable semantics to the paraphrase database (PPDB). To date, the relationship between phrase pairs in the database has been weakly defined as approximately equivalent. We show that these pairs represent a variety of relations, including directed entailment (little girl/girl) and exclusion (nobody/someone). We automatically assign semantic entailment relations to entries in PPDB using features derived from past work on discovering inference rules from text and semantic taxonomy induction. We demonstrate that our model assigns these relations with high accuracy. In a downstream RTE task, our labels rival relations from WordNet and improve the coverage of a proof-based RTE system by 17%.


title: Hybrid excited claw pole electric machine

abstract: The paper presents the concept and results of simulation and experimental research of a claw pole generator with hybrid excitation. Hybrid excitation is performed with a conventional coil located between two parts of the claw-shape rotor and additional permanent magnets which are placed on claw poles. Within the research first a simulation and next constructed experimental model has been developed on the basis of the mass-produced vehicle alternator. Experimental researches have shown that — at a suitable rotational speed — it is possible to self-excite of the generator without any additional source of electrical power.


title: A testbed for adaptive security for IoT in eHealth

abstract: Wireless Body Area Sensor Networks (WBASNs) are networks of low-power sensing objects that collect and send vital signs of a patient using low-rate communication media. They have been originally created to improve the efficiency of e-health applications and they constitute now an important part of the Internet of Things (IoT) by bringing humans into the IoTs. The ASSET (Adaptive Security for Smart Internet of Things in eHealth) project [1] develops risk-based adaptive security methods and mechanisms for IoT in eHealth. The project requires a real-life testbed to evaluate accurately the adaptive security solutions in realistic simulation and use case scenarios. This paper describes the setup of a testbed for adaptive security for the IoT using current commercial off-the-shelf products and open source software. The particular features of the proposed testbed with regard to those published in the literature are underlined. The paper also discusses the validation of the setup through the study of the impact of antenna orientation on energy consumption. To this purpose, an estimation strategy of the energy consumption using the Holt-Winters prediction method has been developed. This will particularly be useful when studying the feasibility of the adaptive lightweight security solutions that will be part of the ASSET project.