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The continuous Skyline query has recently become the subject of the several researches due to its wide spectrum of applications such as multi-criteria decision making, graph analysis network, wireless sensor network and data exploration. In these applications, the datasets are huge and have various dimensions. Moreover, they constantly change as time passes. Therefore, this query is considered as a computation intensive operation that finding the result in a reasonable time is a challenge. In this paper, we present an efficient parallel continuous Skyline approach. In our suggested method, the dataset points are sorted and pruned based on Manhattan distance. Moreover, we use several optimization methods to optimize memory usage in comparison with naive implementation. In addition, besides the applied conventional parallelization methods, we partition the time steps based on the number of available cores. The experimental results for a dataset that contains 800k points with 7 dimensions show considerable speedup.

In this paper, we consider the age of information (AoI) of a discrete time status updating system, focusing on finding the stationary AoI distribution assuming that the Ber/G/1/1 queue is used. Following the standard queueing theory, we show that by invoking a two-dimensional state vector which tracks the AoI and packet age in system simultaneously, the stationary AoI distribution can be derived by analyzing the steady state of the constituted two-dimensional stochastic process. We give the general formula of the AoI distribution and calculate the explicit expression when the service time is also geometrically distributed. The discrete and continuous AoI are compared, we depict the mean of discrete AoI and that of continuous time AoI for system with M/M/1/1 queue. Although the stationary AoI distribution of some continuous time single-server system has been determined before, in this paper, we shall prove that the standard queueing theory is still appliable to analyze the discrete AoI, which is even stronger than the proposed methods handling the continuous AoI., 9 pages, 3 figures, Accepted for presentation in ITW 2021

This paper addresses the Joint Angle and Delay estimation problem of a received multi-carrier frame through multiple antennas. In particular, each source is parameterized through its complex gain, Time-of-arrival (ToA) and Angle-of-arrival (AoA). We develop three novel JADE methods in a partially relaxed model, where one relaxes the interference zone, while focusing on a certain direction at a given time. Computer simulations are given with comparison to the Cramer-Rao Bound.

Network coding has been applied successfully in peer-to-peer systems to shorten the distribution time, particularly in extreme conditions where peers in the network are clustered and separated by physical links with limited bandwidth. In this paper, we focus more closely on the use of source coding, i.e. encoding is done only at the source, as an alternative to network coding to facilitate content distribution under such limited bandwidth configuration. We observe analytically and experimentally that, in this specific case, with appropriately chosen expansion factors, source coding can have comparable performance to network coding in terms of distribution time, yet consuming much less computational resources than the latter approach does.

In the paper a concept of creating a filtering network based on first order nonstationary elements is presented. In each element of the structure two outputs were used: low-pass and high-pass. Replacing gain coefficient k and time constant T with gain function and time function considerably improved the system operation in the time domain. During the research effects of the corresponding stationary, nonstationary and partially stationary structures were compared.

In this paper, a new configuration for a multi-terminal offshore wind farms based VSC-HVDC and large scale photovoltaic system using modified unified adaptive linear neuron (Adaline) algorithm is presented. In the offshore station, the permanent magnet synchronous generator (PMSG)-based wind turbines are tied to the bus via back-to-back converters and a step-up transformer. The large scale photovoltaic power is connected to the dc bus between the offshore voltage source converters (VSC) and onshore VSC-HVDC stations via a modular multilevel converter (MMC). The Power management and the synchronization between two buses are achieved by controlling the two VSC converters. The performance of the topology proposed is tested using Matlab/Simulink during permanent operation mode and in the presence of DC fault.

In this paper, phase unwrapping techniques, which are the most critical step in extracting the Digital Topography Maps of the region in Interferometric Synthetic Aperture Radar imaging, are discussed and compared. Algorithms based on path following method for the phase unwrapping, which is the heart of the interferometric SAR (InSAR/IFSAR) technique were analyzed comparatively using real SAR images. Comparisons of the algorithms in terms of performance, duration, complexity have been made and evaluations on how to cover the missing aspects have been included. In addition, both Matlab and C / C ++ codes of the algorithms are analyzed due to time constraints that may occur while producing the topography map of large regions.

Sudoku is a very popular mathematical and logical curiosity with abundant variations. Many artificial intelligence (AI) programs that imitate human expertise have been developed to solve sudoku puzzles. However, most popular methods, such as Recurrent Relational Networks and Double Q-learning, are primarily reinforcement learning (RL) algorithms. These algorithms are rather complex since they consist of two or more fundamental models for achieving high accuracy. To circumvent this drawback, this paper proposes a simple Multi-Layer Perceptron (MLP) model that not only produces correct results but is easy enough for implementation as well. Since the MLP model is a supervised learning method and is widely used in classification problems, this paper first discusses how the training data is generated. It then demonstrates a coding method that can be used to convert 4 × 4 sudoku puzzles to classification problems. Afterward, it shows the MLP settings and how the predictions from the model can be transformed into legal solutions. This model has been thoroughly tested on sudoku puzzles with various levels of difficulty, and the resulting data illustrate that its accuracy is no worse than those reinforcement learning methods and other non-AI algorithms. Moreover, with slight modification, the MLP model can be utilized on larger scales, such as 9 × 9 sudoku puzzles, or even harder ones.

Wireless Power Transfer (WPT) is a new technology for charging the electric vehicles that can make free humans from annoying wires. In recent years, WPT systems have been developed and have significant advances. Design of an inductive power transfer (IPT) has several sections; the two important of which are design of the power electronic devices and design of the pad topology and electromagnetic issues. In this paper, we have discussed the second section and examined parameters that affect the magnetic coupling coefficient between the two pads especially for DD Pad structure.

High speed transmission of information suffers inevitably by intersymbol interference (ISI) induced by multipath propagation over frequency selective channels. ISI has been a major obstacle preventing the successful implementation of physical layer network coding (PLNC) systems in these environments. Another major obstacle is the high computational complexity of existing algorithms dealing with ISI, which is a determining factor in the design of relay nodes. In this paper, we propose a novel and computationally efficient decision feedback equalizer structure that is utilized at the end node and in conjunction with a simple amplify and forward (AF) technique employed at the relay. We derive the optimal equations for the decision feedback equalizer (DFE) coefficients and evaluate the end-to-end bit error rate performance in frequency selective channels, and provide both analytical and numerical simulation results that demonstrate the feasibility of the proposed approach under realistic channel conditions. The obtained results show that E2E performance is dominated by the channel with most severe frequency selectivity.

In this paper, the output regulation of multi-agent systems over signed digraphs is discussed. We consider antagonistic interactions among subsystems and propose a distributed feedback paradigm to achieve the output regulation goal. The reference and disturbance signals are produced by exosystems, which can be considered as a leader. However, not all subsystems have access to the information of the leader for constructing a feedback law to achieve the output regulation objective. The paper presents a distributed feedback strategy that makes all subsystems track the reference signal in a bipartite manner while rejecting the inflicted disturbance. Finally, the theoretical findings of this paper are validated through a numerical example.

Non-motorized transport is becoming increasingly important in urban development of cities in China. How to evaluate the non-motorized transport popularity of urban roads is an interesting question to study. The great amount of tracking data generated by smart mobile devices give us opportunities to solve this problem. This study aims to provide a data driven method for evaluating the popularity (walkability and bikeability) of urban non-motorized transport system. This paper defines a p-index to evaluate the popular degree of road segments which is based on the cycling, running, and walking GPS track data from outdoor activities logging applications. According to the p-index definition, this paper evaluates the non-motorized transport popularity of urban area in Wuhan city within different temporal periods., 5 pages, 4 figures, CPGIS2018-The 26th International Conference on Geoinformatics

Super-resolution (SR) algorithms for multiview videos aim at refining the low-resolution (LR) views using high-frequency detail information from adjacent high-resolution (HR) views. However, to refine a LR view, the state-of-the-art methods only exploit the spatial redundancy of a multiview video. To take full advantage of the spatial and temporal redundancy, a new spatiotemporal SR algorithm is proposed in this paper. Depth-image-based rendering (DIBR) establishes coordinate correspondences between different views. Motion estimation handles object motion between different frames. The high-frequency details are extracted in transform domain and added to LR images. In order to merge the details from different HR images, a combination of global and local weights is proposed. The effectiveness of the proposed algorithm is proved theoretically and shown experimentally.

Survey of research papers is not an easy task for novice researchers, because they are not always good at finding all appropriate keywords for the survey. Moreover, it is not easy for them to understand positions of papers in their research fields instantly, even when they use famous search engines like Google Scholar, it may often take a long time for them to find scholarly literature. On the other hand, many researchers have presented citation visualization techniques for surveying research papers. However, it is still often difficult to observe the complicated relations across multiple research fields or traverse the entire relations in their interest. In this paper, we proposed a visualization technique for citation networks applying topic-based paper clustering. Our technique categorizes papers applying LDA (Latent Dirichlet Allocation), and constructs clustered networks consisting of the papers.

This paper examines the current CT2 system and proposes a simple method of capacity expansion using embedded ADPCM coding. Existing objective measures of speech quality are analyzed based on a simple error model.

Re documentation and design recovery are two important areas of reverse engineering. Detection of recurring organizations of classes and communicating objects, called Software Patterns, supports this process [1]. Many approaches to detect Software Patterns which have been published in the past years suffer from the problems of necessity of reference library, performance and language compatibility. This paper presents a model to solve those problems in software pattern detection. The proposed model solves the problem of necessity of reference library by detecting software patterns using Formal Concept Analysis (FCA). The proposed model solves the problem of performance by using the most efficient algorithm CMCG (Concept-Matrix based Concepts Generation) for the construction of concept lattice, which is the core data structure of FCA [3]. The proposed model solves the problem of language compatibility by using the language independent Meta model called MOOSE for taking the input information. The validity of this model was proved in theory and by experiment.

In technical study programs on the bachelor level the focus is mainly directed on the scientific education in theory and practice. Publication is usually started during the PhD phase when students write their first scientific papers. During the last years some very successful projects were made at the university of applied sciences Regensburg to motivate bachelor students doing their first own small publications. The results of these projects are presented in this paper and ideas how students can publish are given.

Machine vision based automatic apparatus is a necessary component of an automated production line. In this paper, taking can-end surface defect detection as an example, we presented a visual inspector and proposed a new surface defect detection framework. Firstly, the Region of Interest (ROI) of the input image is got by circle detection and prior radius knowledge of can-end. Then, the saliency map and texture filtering image are computed to overcome the problems of irregular texture and non-uniform illumination. Finally, all suspected defects are obtained by saliency map segmentation, connected component analysis. Following, the size, the average saliency value and the mean texture filtering value of each suspected defective region are calculated, and the 3-dimensional features are used to distinguish true defects in the central panel region of can-end. We employ the designed equipment to grab can-end images and do tests divided into four groups on the grabbed images. The accuracy is 98.16% and the execution time is about 65.7 milliseconds. Experimental results demonstrate that the proposed method can inspect faint and low contrast defect accurately. Meanwhile, the proposed method is simple and computationally efficient and meets the high speed of industrial automation.

Video synopsis provides a means for fast browsing of activities in video. Principal background selection (PBS) is an important step in video synopsis. Existing methods make PBS in an offline way and at a high memory cost. In this paper we propose a novel background selection method, ``online principal background selection'' (OPBS). The OPBS selects n principal backgrounds from N backgrounds in an online fashion with a low memory cost, making it possible to build an efficient online video synopsis system. Another advantage is that, with OPBS, the selected backgrounds are related to not only background changes over time but also video activities. Experimental results demonstrate the advantages of the proposed OPBS.

This work describes a general concept for modeling weather phenomena as renewable energy resources. The model considers all typical effects, like time-variance, realistic rates of change, the causality relation between different weather phenomena and satisfying the reference distribution functions. The focus of this work is to design a continuous model, similar to a weather generator, with a simple analytical description. This paper illustrates the concept by a specific implementation for global horizontal irradiance, air temperature and wind speed. We describe the model itself and the data analysis to determine the model parameters. This specific implementation and the concept is validated by designing a model from a 8 year reference dataset. The results show a similar behavior to the recorded data. Only rare, extreme phenomenas are modeled limitedly.

Analysing the traversability from the point cloud input is a critical task for mobile robot navigation. However, there is no mature method for mobile robots to exploring complicated 3D environments, especially those with multi-layered roads. This paper proposes a new traversability map based on the point cloud input, and the map will help analyze where the mobile robot can move in the whole space. The proposed 3D traversability map consists of nodes and edges generated according to the mobile robot’s geometry and driving performance. Nodes represent where the robot can be placed, and edges indicate the cost of moving to another node. The traversability map is stored in digraph form and is convenient to be applied to path planning. Experiments show the successful results of traversability map generation and path planning with D algorithm in different situations.

Truncated multiplication can be used to significantly reduce the power dissipation for applications that do not require correctly-rounded results. This paper presents an efficient method for truncated multiplication called hybrid-correction truncation that utilizes the advantages of two previous methods to obtain lower average and maximum absolute error. Comparisons are presented contrasting power, area, and delay for all three methods compared to standard parallel multipliers. Estimates indicate that hybrid truncated multipliers dissipate slightly less power and consume slightly less area than previous methods for truncated multiplication. In addition, utilization of the hybrid truncation method can provide a method for altering the implementation within certain limits to meet a given precision.

Sign language is the most natural and expressive way for the hearing impaired. Its most appealing application is the development of more effective and friendly interfaces for human-machine interaction. Gestures are a natural and powerful way of communication. A hand gesture recognition system can provide an opportunity for a mute person to communicate with normal people without the need of an interpreter. In this paper we proposed a novel technology for hand gesture recognition which is based on thinning of segmented image.

IoT contains a large amount of data as well as privacy sensitive information. This information can be easily identified by the various cyber-attackers. Attackers may steel the data or they can introduce viruses and other malicious software to damage the network. So to overcome these issues, we need to establish an efficient security mechanism for data protection in IoT systems. Advanced Encryption standard is one of the popular security protocols for IoT systems. The main disadvantage of this standard is power consumption for large sized IoT networks. In this paper we proposed an novel mechanism for secure communication in large sized IoT systems. In our design, we use a Hash based Message Authentication Code (HMAC) and a privacy-preserving communication protocol for chaos-based encryption. We observe that proposed mechanism consumes relatively less power over AES with less storage resources.

Given an image, scene understanding is the process of segmenting and identifying the objects present, and classifying the overall scene. Several frameworks already exist to perform these tasks coherently but training of their probabilistic models is time consuming thereby limiting their scalability. This paper presents a scalable framework adopting an object-based approach. The steps taken by the algorithm are saliency detection for unsupervised object discovery, graph-cut for object segmentation, bag-of-features for object classification and binary decision trees for scene classification. A region of interest (ROI) detector is proposed to automatically provide object location priors from saliency maps for graph-cut. We tested our system on a novel NUS/NTU dataset and compared the scene classification accuracy using different classifiers. Unlike other existing frameworks, the proposed algorithm is scalable and can easily accommodate more object and scene classes.

According to the environment adaptability require-ment of an autonomous agent system, this paper studies the mapping relationships between the dynamic environment aspects and task scheduling, and then, gives the definitions and computation models of the task, quality of scheduling (QoS) and fitness. Considering the dynamic and inaccurate parameters of environment, a new Fuzzy-logic based Environment Adaptive Scheduler (FEAS) is proposed. In FEAS, the Multi-State Fuzzy Controller (MSFC) can make decision with the environment aspects to adjust the scheduling quality of the multi task groups so as to ensure that the execution process can suit to dynamic environment as possible. Finally, an experiment to make an agent system with FEAS avoids obstacle autonomously is carried out. And the results prove that such task scheduling method we studied can improve its adaptability of an agent system.

The high-speed railway load is a single-phase load with high power, which can generate large negative sequence current (NSC). Many compensation methods have been applied to compensate NSC which is a big power quality problem of railway power supply system, and each method has its own characteristics. A hybrid compensation system is proposed in this paper. The system consists of two different compensation methods. One is based on railway power conditioner(RPC) with modular multilevel converter(MMC) structure(MRPC), and the other is based on Steinmetz compensation principle with magnetic static var compensation (MSVC). The MRPC has a fast response speed but expensive price, while the MSVC has a low response speed but much cheaper price. Compared with MRPC-ONLY compensation method, the hybrid compensation system has a lower cost by reducing the compensation capacity of MRPC with MSVC. Firstly, the compensation principles of the hybrid compensation system is analyzed in detail. Then, the collaboration control strategy of compensation system is proposed. Moreover, the cost of the hybrid compensation system and the MRPC-ONLY compensation method are compared. Finally, simulation and experiment results are both presented to verify the effectiveness of the hybrid compensation system and its control method.

Voltage instability has been a cause of several recent major power outages worldwide. This paper presents a successive mixed integer programming (MIP) based method of reactive power control planning to increase voltage stability margin under a set of contingencies. The backward/forward search algorithm and voltage stability margin sensitivities are used to select candidate locations for switched shunt and series capacitors. Optimal locations and amounts of new reactive power controls are obtained by solving a sequence of mixed integer programming problems. The effectiveness of the method is illustrated using the New England 39 bus system. The results show that the method works satisfactorily to plan reactive power controls.

Image classification is a well known example of a complex problem that can be tackled using deep learning. In the latest period, convolutional neural networks were successfully applied in order to solve the problem. Moreover, recently, the research directions have been focusing more on developing lightweight models that can still achieve good classification performance. In this paper we propose a lightweight solution for solving an image classification problem. The solution is tested on an open dataset for fruits recognition. We achieve state of the art performance in terms of both classification accuracy and execution speed.

This paper presents a wind power generation system using a Permanent Magnet Synchronous Machine (PMSM). The whole system consists of a wind turbine, permanent magnet synchronous machine, three phase diode rectifier, boost converter, a voltage source inverter and an LCL filter. The ac output from the PMSM is sent to the diode rectifier for conversion to DC and a boost converter is used for stabilization of the DC output voltage. The DC output voltage is then converted into a single phase ac output with the voltage source inverter, in which the gating signals are provided by an advanced modulation technique called Harmonic Injection Modulation (HIM). Finally, the LCL filter is employed for the elimination of output harmonics. The system setup and the simulation results were obtained using the PSIM simulation software.

Radio frequency identification (RFID) systems are emerging platforms that support a variety of pervasive applications. RFID tags can be used to label items and enable item-level monitoring. The problem of identifying missing tags in RFID systems has attracted wide attention due to its practical importance (e.g., anti-theft). This paper presents P-MTI: a Physical-layer Missing Tag Identification scheme that effectively makes use of the lower-layer information and dramatically improves operational efficiency. Unlike conventional approaches, P-MTI looks into the aggregated tag responses instead of focusing on individual tag responses and extracts useful information from physical-layer collisions. P-MTI leverages the sparsity of missing tag events and reconstructs tag responses through compressive sensing. We prototype P-MTI using the USRP software defined radio and Intel WISP platform. We also evaluate the performance of P-MTI with extensive simulations and compare to previous approaches. The experiment results show the promising performance of P-MTI in identification accuracy, time efficiency, as well as robustness over noisy channels.

The high energy consumption has become one bottleneck in the development of the data centers (DCs), where the main energy consumers are the cooling system and the servers. Therefore, the joint optimization for the energy efficiency of the cooling system and the servers is a crucial problem, while most of previous works on energy saving only studies one of these two components in an isolated manner. In this paper, we propose a real-time strategy, rTCS (real-time Task Classification and Scheduling strategy), to jointly optimize the energy efficiency of these two components in the scenario where the tasks arrive dynamically. Strategy rTCS first labels the tasks to classify them according to their run time and end time with a time complexity of O(1) and a bounded space complexity. Then, rTCS schedules the tasks in real time based on their labels and the energy consumption model of the DC. Simulation results show that rTCS can effectively improve the energy efficiency of DCs.

In this paper, an ultrasound-measurement-based method of detecting stenosises in fallopian tubal models is presented. The proposed method employs silicone rubber tubes and custom phantoms made of polymer gels as models of fallopian tubes. When the targets are the former (or latter), stenosis models are formed by lumps of polyester sewing threads (or polymer gels with high reflection intensity). The proposed method copes with detecting stenosis models as classification problems by applying support vector machines (SVMs for short). It prepare data for SVM learning and stenosis detection from waveforms in the range specified by two time points at which waves are probably reflected by inner upper and lower surfaces of tubal models. The data preparation is conducted by generating the frequency distribution of the number of regular short intervals into which this range is divided versus normalized values associated with amplitude of the waveforms in such intervals. When it is explored whether given data are prepared from a tube with the stenosis model, a discrimination model constructed by SVM learning works as a two-class classifier. Using the model as a multi-class classifier makes it possible to distinguish data of tubes with stenosis models from each other. It is revealed from experimental results that the proposed method has substantially high capability in detecting stenosis models.

It is widely acknowledged that network slicing can tackle the diverse use cases and connectivity services of the forthcoming next-generation mobile networks (5G). Resource scheduling is of vital importance for improving resource-multiplexing gain among slices while meeting specific service requirements for radio access network (RAN) slicing. Unfortunately, due to the performance isolation, diversified service requirements, and network dynamics (including user mobility and channel states), resource scheduling in RAN slicing is very challenging. In this paper, we propose an intelligent resource scheduling strategy (iRSS) for 5G RAN slicing. The main idea of an iRSS is to exploit a collaborative learning framework that consists of deep learning (DL) in conjunction with reinforcement learning (RL). Specifically, DL is used to perform large time-scale resource allocation, whereas RL is used to perform online resource scheduling for tackling small time-scale network dynamics, including inaccurate prediction and unexpected network states. Depending on the amount of available historical traffic data, an iRSS can flexibly adjust the significance between the prediction and online decision modules for assisting RAN in making resource scheduling decisions. Numerical results show that the convergence of an iRSS satisfies online resource scheduling requirements and can significantly improve resource utilization while guaranteeing performance isolation between slices, compared with other benchmark algorithms.

Enabling software systems to adjust their protection in continuously changing environments with imperfect context information is a grand challenging problem. The issue of uncertain reasoning based on imperfect information has been overlooked in traditional logic programming with classical negation when applied to dynamic systems. This paper sketches a non-monotonic approach based on Answer Set Programming to reason with imperfect context data in adaptive security where there is little or no knowledge about certainty of the actions and events.

This paper presents a robust two stage maximum power point tracking (MPPT) system of the photovoltaic (PV) module using an improved restricted control set model predictive control (iRCS-MPC) technique. The suggested work is improved in two aspects; a revision in conventional P&O algorithm is made by employing three distinct step sizes for different conditions, and an improvement in conventional MPC algorithm. The improved MPC algorithm is based on the single step prediction horizon that provides less computational load and swift tracking of maximum power point (MPP) by applying the control pulses directly to the converter switch. The computer aided experimental results for various environmental scenarios revealed that compared with the conventional method (conventional P&O + MPC), for the PV power and inductor current, the undershoot and overshoot is decreased to 68% and 35% respectively under stiff environmental conditions. In addition, the settling time needed to reach a stable state is significantly reduced in the proposed system. The viability of the solution suggested is verified in MATLAB/Simulink and by hardware experimentation.

In this paper a novel and robust method for automatic iris segmentation based on local areas is described. Such method is composed of three main parts, (a) Find the local rectangle region which has the minimum intensity mean and extend it to locate pupil, (b) Select two small local sector areas including the outer boundaries of iris to locate outer iris, (c) Translate the iris from polar coordinates into Cartesian coordinates and normalize it to fixed size to compensate the stretching of the iris texture as the pupil changes in size and remove the non-concentricity of the iris and the pupil. The method was implemented using CASIA iris image databases. The experimental results show that the proposed method has an encouraging result with an overall accuracy of 98.42%

As an important radio resource management scheme in wireless cellular network, scheduling plays an important role in balancing the system throughput and user fairness. This paper studies the scheduling scheme in a special scenario, where the ordinary users and onboard high-speed train users are co-served by the same basestation (BS) in cellular network. Orthogonal frequency-division multiple-access (OFDMA)-based cellular communication systems such as the Third-Generation Partnership Project (3GPP) long-term evolution (LTE) is considered, thus the radio resources are scheduled in both frequency and time domains among different users. In the addressed scenario, it is shown that none of the max C/I, round robin (RR), proportional fair (PF) algorithms can provide good performance in terms of user fairness. Therefore, a new scheduling algorithm based on the original PF scheme is proposed, which not only improves the user fairness greatly but also causes no degradation to the system throughput. The performance of the proposed scheme is simulated and compared with other scheduling schemes in terms of both system throughput and user fairness.

Demographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. By considering the category-unbalance problem, we predict users' demographic information and propose an optimization method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level).

This paper introduces a system to track visitors' positions and movement patterns through museum art galleries with the goal of assisting the curators and other Museum Professionals (MPs) in the tedious task of analyzing visitors' activities, behaviors and experiences. We use multiple LIDARs as a primary sensing technology in supporting our proposed system. We also describe how valuable information related to visitor behaviors can be autonomously collected and analyzed using our system. Additionally, we present a solution to visualize the visitors' movement patterns and preferences with respect to the exhibits. Finally, we tested our system in an art gallery of a real museum to validate its effectiveness.

Diabetes and lung disease are some of the most common medical conditions in the world. The economic costs and social burdens brought by these two diseases are considerable. Even though there are proven methodologies for diagnosing each disease individually in practice, there does not exist a single non-invasive methodology/procedure that can detect both diseases. With recent advancements made in machine learning and pattern recognition, the Convolutional Neural Network (CNN) has been widely used in many recognition applications due to its high efficiency and performance. Therefore, in this paper we propose an approach using CNN for non-invasive multi-disease classification called Multi-Disease CNN (MD-CNN). Facial images are first captured using our specially designed device. Next, four facial blocks are extracted located at specific regions on the face. Finally, the facial blocks are concatenated and used as input for our MD-CNN. Based on three datasets consisting of healthy control, diabetes and lung disease, the proposed method achieved an average accuracy of 73%. When compared to other classifiers not employing a deep learning architecture, MD-CNN produced the highest result. This show a potentially new way to perform multi-disease classification.

In recent years, chaos-based image cipher has been widely studied and a growing number of schemes based on permutation-substitution architecture have been proposed. To better meet the challenge of real-time secure image communication applications, this paper suggests a new image encryption scheme using a parallel substitution. In the permutation stage, the Arnold cat map is employed to shuffle the pixel positions so as to erase the strong relationship between adjacent pixels. In the substitution stage, the scrambled image is firstly decomposed into eight bit-planes which are then parallelly mixed with keystreams generated by the chaotic logistic map. Theoretically, the parallel substitution strategy runs eight times faster than the serial strategy on an 8-thread processor as the volume of data processed by each substitution unit is 1/8 of that of the input image. Experimental results show that the proposed parallel scheme runs more than five times faster than the serial scheme. Extensive security analysis is carried out with detailed analysis, demonstrating the satisfactory security of the proposed scheme.

This paper investigates the radio irregularity (RI) phenomenon and its impact on communication performance in wireless sensor networks (WSNs). Based on the theoretical analysis, we find that the RI phenomenon induces the asymmetric links problem. According to this discovery, we propose a novel HCT (Hop-count Correction Tree) scheme to handle this problem. HCT utilizes graph theoretical method to get a search tree and correct the hop count error that appears in the adjacent nodes. Practical results obtained from testbed experiments demonstrate that this solution can greatly improve localization accuracy in the presence of RI.

International audience; In this paper, we demonstrate how methods based on interval arithmetic and interval analysis can be used to achieve numerical certification of the kinematic calibration of a parallel robots. We introduce our work by describing the usual calibration methods and the motivations for a numerical certification. Then, we briefly present the interval methods we used and the kinematic calibration problem. In the main part, we develop our certified approach of this problem in the case of a Gough platform, and we show with numerical examples how this approach avoids wrong solutions produced by classical approach. Details on implementation and performance are also given.

In this paper, we explore building classifiers to detect Salsa dance step primitives in choreographies available in the Huawei 3DLife data set. These can collectively be an important component of dance tuition systems that support e-learning. A dance step is reasoned as the shortest possible extract of bodily motion that can uniquely identify a particularly repeatable movement through time. The representation of dance steps adopted is a concatenation of vectorized matrices involving the 3D coordinates of tracked body joints. Under this modeling context, a Salsa dance performance is seen as an ordered sequence of Salsa dance steps, requiring a multiple of the variables allocated in the representation of a single step. Following a previous work by Masurelle & Essid that discusses the classification of six Salsa dance steps from 3DLife, we show that it is possible to obtain better classifiers under a similar experimental protocol in terms of both test accuracy and F-measure. By carefully re-annotating the data in 3DLife, we refocus on the six-step classification problem and then extend the protocol to the case of 20 dance steps. In comparison to common classifiers of the trade operating on full-dimensions, we show that it is possible to produce more accurate models by computing a subspace of the data. At the same time it is possible to reduce problematic bias in resulting models due to the uneven distribution of samples across step data classes. We provide and discuss experimental findings to support both hypotheses for the two experimental settings.

The Probabilistic Index Map (PIM) model was originally proposed for video processing to extract background of video frames. In this paper, we introduce the PIM model for texture segmentation. We first extract texture features based on Laws and Gabor filters respectively. Then we present a Fuzzy K-Means method to generate the index map and palette, and use the PIM model to improve the segmentation accuracy. Based on the comparison of experimental results produced using different features and different resolutions, we show the proposed method is effective for texture segmentation.

Low power VLSI circuit design faces the challenging issues of static power dissipation as transistor count doubles for every couple of years. static power dissipation also known as leakage power dissipation which increases in scaled down threshold voltage circuits. Downsizing of CMOS innovation improved the speed and simultaneously leakage currents are left over as struggle. This unconstrained leakage current ought to be decreased for untroubled working of the circuit. This paper proposed novel static power reduction strategy with the literature survey. The research study mainly concentrated on circuit performance parameters like speed, power and power delay product of the specific circuit. This work presents logic gates designed and evaluated by mentor graphics tools with 22 nm CMOS Technology.

In this paper, a method combining detection, motion planning and control of human tracking mobile robots is discussed. Using face recognition and detection of a pair of human legs via a stereo vision camera and 2D laser scanner, respectively, the human position is estimated. Based on the differential flatness property of the kinematic model of the mobile robot, the trajectory of the robot to the moving human is planned in real-time. The combined motion planning and control method is used for tracking a detected human.

Credit and debit cards enable financial transactions at unattended "pay-at-the-pump" gas station terminals across North America. Attackers discreetly open these pumps and install skimmers, which copy sensitive card data. While EMV (“chip-and-PIN”) has made substantial inroads in traditional retailers, such systems have virtually no deployment at pay-at-the-pump terminals due to dramatically higher costs and logistical/regulatory constraints, leaving consumers vulnerable in these contexts. In an effort to improve security, station owners have deployed security indicators such as low-cost tamper-evident seals, and technologists have developed skimmer detection apps for mobile phones. Not only do these solutions put the onus on consumers to notice and react to security concerns at the pump, but the efficacy of these solutions has not been measured. In this paper, we evaluate the indicators available to consumers to detect skimmers. We perform a comprehensive teardown of all known skimmer detection apps for iOS and Android devices, and then conduct a forensic analysis of real-world gas pump skimmer hardware recovered by multiple law enforcement agencies. Finally, we analyze anti-skimmer mechanisms deployed by pump owners/operators, and augment this investigation with an analysis of skimmer reports and accompanying security measures collected by the Florida Department of Agriculture and Consumer Services over four years, making this the most comprehensive long-term study of such devices. Our results show that common gas pump security indicators are not only ineffective at empowering consumers to detect tampering, but may be providing a false sense of security. Accordingly, stronger, reliable, inexpensive measures must be developed to protect consumers and merchants from fraud.

Fuzzy neural networks have features which make them useful for knowledge engineering, namely: fast learning; good generalisation; good explanation facilities in the form of fuzzy rules; abilities to accommodate both data and existing fuzzy knowledge about the problem under consideration. This paper presents a current project on using genetic algorithms for optimisation of the structure of a fuzzy neural network called FuNN, for finding the best adaptation mode and for its automated design. Experiments on speech data are reported as part of the project which is aimed at building adaptive speech recognition systems.