Journal of Electrical and Computer Engineering The latest articles from Hindawi © 2017 , Hindawi Limited . All rights reserved. Compact Design of Circularly Polarized Antenna with Vertical Slotted Ground for RFID Reader Application Sun, 03 Dec 2017 00:00:00 +0000 A novel compact circular polarization (CP) microstrip antenna is proposed for UHF ultrahigh frequency (UHF) radio frequency identification (RFID) reader applications. The proposed antenna is composed of a corner truncated square-ring radiating patch on a substrate and a vertical slotted ground surrounding four sides of the antenna. A new feeding scheme is designed from flexible impedance matching techniques. The impedance bandwidths for  dB and 3 dB axial ratio (AR) bandwidth are 12.1% (794.5–896.5 MHz) and 2.5% (833.5–854.5 MHz), respectively. Hesheng Cheng, Jin Zhang, Hexia Cheng, and Qunli Zhao Copyright © 2017 Hesheng Cheng et al. All rights reserved. A Vessel Positioning Algorithm Based on Satellite Automatic Identification System Sun, 03 Dec 2017 00:00:00 +0000 Vessels can obtain high precision positioning by using the global navigation satellite system (GNSS), but when the ship borne GNSS receiver fails, the existence of an alternative positioning system is important for the navigation safety of vessel. In this paper, a localization method based on the signals transmitted by satellite-based automatic identification system (AIS) is proposed for vessel in GNSS-denied environments. In the proposed method, the positioning model is a modification on the basis of time difference and frequency difference of arrival measurements by introducing an additional measurement, and the measurement is obtained through the interactive multiple model algorithm. The performance of the proposed strategy is evaluated through simulations, and the results validate the feasibility and reliability of vessel localization based on satellite-based AIS. Shexiang Ma, Jie Wang, Xin Meng, and Junfeng Wang Copyright © 2017 Shexiang Ma et al. All rights reserved. A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems Sun, 03 Dec 2017 00:00:00 +0000 This work presents a design exploration framework for developing a high level Artificial Neural Network (ANN) for fault detection in hardware systems. ANNs can be used for fault detection purposes since they have excellent characteristics such as generalization capability, robustness, and fault tolerance. Designing an ANN in order to be used for fault detection purposes includes different parameters. Through this work, those parameters are presented and analyzed based on simulations. Moreover, after the development of the ANN, in order to evaluate it, a case study scenario based on Networks on Chip is used for detection of interrouter link faults. Simulation results with various synthetic traffic models show that the proposed work can detect up to 96–99% of interrouter link faults with a delay less than 60 cycles. Added to this, the size of the ANN is kept relatively small and they can be implemented in hardware easily. Synthesis results indicate an estimated amount of 0.0523 mW power consumption per neuron for the implemented ANN when computing a complete cycle. Andreas G. Savva, Theocharis Theocharides, and Chrysostomos Nicopoulos Copyright © 2017 Andreas G. Savva et al. All rights reserved. Improved Collaborative Representation Classifier Based on -Regularized for Human Action Recognition Mon, 20 Nov 2017 00:00:00 +0000 Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs) and extracting deep convolutional neural network (DCNN) features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC) based on -regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM. Shirui Huo, Tianrui Hu, and Ce Li Copyright © 2017 Shirui Huo et al. All rights reserved. An Experimental Study and Concept Evaluation on Tree-Interior Imaging Radar Using Sinusoidal Template-Based Focusing Algorithm Wed, 01 Nov 2017 00:00:00 +0000 An algorithm for detecting cavities inside a tree-body is presented with simulation and measured examples. The details of the imaging algorithm that is based on sinusoidal template focusing routine are given. First, the algorithm is tested with the simulation scenario for which perfect reconstruction of the simulated cavity structure together with tree-body is successfully formed in MATLAB programming environment. Then, the algorithm is applied to the measurement data that have been collected from a laboratory set-up. Collected backscattered measurements from the tree-body (with cavity) structure are used to generate the image of the scene by the help of our proposed algorithm. The resultant radar images of the measured data collected from the laboratory arrangement have shown the applicability of the developed algorithm for the detection of cavity structures inside tree-bodies. Betül Yılmaz, Serhat Gökkan, and Caner Özdemir Copyright © 2017 Betül Yılmaz et al. All rights reserved. Cross-Corpus Speech Emotion Recognition Based on Multiple Kernel Learning of Joint Sample and Feature Matching Wed, 01 Nov 2017 00:00:00 +0000 Cross-corpus speech emotion recognition, which learns an accurate classifier for new test data using old and labeled training data, has shown promising value in speech emotion recognition research. Most previous works have explored two learning strategies independently for cross-corpus speech emotion recognition: feature matching and sample reweighting. In this paper, we show that both strategies are important and inevitable when the distribution difference is substantially large for training and test data. We therefore put forward a novel multiple kernel learning of joint sample and feature matching (JSFM-MKL) to model them in a unified optimization problem. Experimental results demonstrate that the proposed JSFM-MKL outperforms the competitive algorithms for cross-corpus speech emotion recognition. Ping Yang Copyright © 2017 Ping Yang. All rights reserved. Wireless and Mobile Networks: Security and Privacy Issues Wed, 01 Nov 2017 00:00:00 +0000 Arun Kumar Sangaiah, Marimuthu Karuppiah, and Xiong Li Copyright © 2017 Arun Kumar Sangaiah et al. All rights reserved. Multiorder Fusion Data Privacy-Preserving Scheme for Wireless Sensor Networks Mon, 16 Oct 2017 07:15:18 +0000 Privacy-preserving in wireless sensor networks is one of the key problems to be solved in practical applications. It is of great significance to solve the problem of data privacy protection for large-scale applications of wireless sensor networks. The characteristics of wireless sensor networks make data privacy protection technology face serious challenges. At present, the technology of data privacy protection in wireless sensor networks has become a hot research topic, mainly for data aggregation, data query, and access control of data privacy protection. In this paper, multiorder fusion data privacy-preserving scheme (MOFDAP) is proposed. Random interference code, random decomposition of function library, and cryptographic vector are introduced for our proposed scheme. In multiple stages and multiple aspects, the difficulty of cracking and crack costs are increased. The simulation results demonstrate that, compared with the typical Slice-Mix-AggRegaTe (SMART) algorithm, the algorithm proposed in this paper has a better data privacy-preserving ability when the traffic load is not very heavy. Mingshan Xie, Yong Bai, Mengxing Huang, and Zhuhua Hu Copyright © 2017 Mingshan Xie et al. All rights reserved. Research on HILS Technology Applied on Aircraft Electric Braking System Wed, 11 Oct 2017 09:53:50 +0000 On the basis of analyzing the real-time feature of hardware-in-the-loop simulation of aircraft braking system, a new simulation method based on MATLAB/RTW (Real-Time Workshop) and DSP is introduced. The purpose of this research is to develop a digital control unit with antilock brake system control algorithm for aircraft braking system using HILS. DSP is used as simulator. Using this method, a detailed mathematical modeling of system is proposed first. Studies on reducing sampling time with model simplification and modeling for applying to I/O interface of DSP and HILS are conducted. Compared with other methods, this method is low cost and convenient to implement. By using these methods, we can complete HIL simulation of aircraft braking under various experimental conditions, modify its control laws, and test its braking performance. The results have demonstrated that this platform has high reliability. The algorithm is verified by real-time closed loop test with HILS system and the results are presented. Suying Zhou, Hui Lin, and Bingqiang Li Copyright © 2017 Suying Zhou et al. All rights reserved. Study on Electrophysiological Signal Monitoring of Plant under Stress Based on Integrated Op-Amps and Patch Electrode Tue, 10 Oct 2017 09:59:54 +0000 Electrophysiological signal in plant is a weak electrical signal, which can fluctuate with the change of environment. An amplification detection system was designed for plant electrical signal acquisition by using integrated op-amps (CA3140, AD620, and INA118), patch electrode, data acquisition card (NI USB-6008), computer, and shielded box. Plant electrical signals were also studied under pressure and flooding stress. The amplification detection system can make nondestructive acquisition for Aquatic Scindapsus and Guaibcn with high precision, high sensitivity, low power consumption, high common mode rejection ratio, and working frequency bandwidth. Stress experiments were conducted through the system; results show that electrical signals were produced in the leaf of Aquatic Scindapsus under the stress of pressure. Electrical signals in the up-leaf surface of Aquatic Scindapsus were stronger than the down-leaf surface. Electrical signals produced in the leaf of Guaibcn were getting stronger when suffering flooding stress. The more the flooding stress was severe, the faster the electrical signal changed, the longer the time required for returning to a stable state was, and the greater the electrical signal got at the stable state was. Weiming Cai and Qingke Qi Copyright © 2017 Weiming Cai and Qingke Qi. All rights reserved. Visual Sensor Based Image Segmentation by Fuzzy Classification and Subregion Merge Mon, 25 Sep 2017 09:07:51 +0000 The extraction and tracking of targets in an image shot by visual sensors have been studied extensively. The technology of image segmentation plays an important role in such tracking systems. This paper presents a new approach to color image segmentation based on fuzzy color extractor (FCE). Different from many existing methods, the proposed approach provides a new classification of pixels in a source color image which usually classifies an individual pixel into several subimages by fuzzy sets. This approach shows two unique features: the spatial proximity and color similarity, and it mainly consists of two algorithms: CreateSubImage and MergeSubImage. We apply the FCE to segment colors of the test images from the database at UC Berkeley in the RGB, HSV, and YUV, the three different color spaces. The comparative studies show that the FCE applied in the RGB space is superior to the HSV and YUV spaces. Finally, we compare the segmentation effect with Canny edge detection and Log edge detection algorithms. The results show that the FCE-based approach performs best in the color image segmentation. Huidong He, Xiaoqian Mao, Wei Li, Linwei Niu, and Genshe Chen Copyright © 2017 Huidong He et al. All rights reserved. A Student Information Management System Based on Fingerprint Identification and Data Security Transmission Tue, 19 Sep 2017 07:14:00 +0000 A new type of student information management system is designed to implement student information identification and management based on fingerprint identification. In order to ensure the security of data transmission, this paper proposes a data encryption method based on an improved AES algorithm. A new -box is cleverly designed, which can significantly reduce the encryption time by improving ByteSub, ShiftRow, and MixColumn in the round transformation of the traditional AES algorithm with the process of look-up table. Experimental results show that the proposed algorithm can significantly improve the encryption time compared with the traditional AES algorithm. Pengtao Yang, Guiling Sun, Jingfei He, Peiyao Zhou, and Jiangjiang Liu Copyright © 2017 Pengtao Yang et al. All rights reserved. Epipolar Plane Image Rectification and Flat Surface Detection in Light Field Tue, 19 Sep 2017 00:00:00 +0000 Flat surface detection is one of the most common geometry inferences in computer vision. In this paper we propose detecting printed photos from original scenes, which fully exploit angular information of light field and characteristics of the flat surface. Unlike previous methods, our method does not need a prior depth estimation. The algorithm rectifies the mess epipolar lines in the epipolar plane image (EPI). Then feature points are extracted from light field data and used to compute an energy ratio in the depth distribution of the scene. Based on the energy ratio, a feature vector is constructed and we obtain robust detection of flat surface. Apart from flat surface detection, the kernel rectification algorithm in our method can be expanded to inclined plane refocusing and continuous depth estimation for flat surface. Experiments on the public datasets and our collections have demonstrated the effectiveness of the proposed method. Lipeng Si, Hao Zhu, and Qing Wang Copyright © 2017 Lipeng Si et al. All rights reserved. Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm Mon, 11 Sep 2017 08:07:03 +0000 Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to improve neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in two-dimensional histogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix is taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and sampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms. Wuli Wang, Liming Duan, and Yong Wang Copyright © 2017 Wuli Wang et al. All rights reserved. Machine Intelligence in Signal Sensing, Processing, and Recognition Wed, 06 Sep 2017 00:00:00 +0000 Lei Zhang, Sunil Kr. Jha, Zhixin Yang, Zhenbing Zhao, and Bhupendra Nath Tiwari Copyright © 2017 Lei Zhang et al. All rights reserved. Multi-Input Convolutional Neural Network for Flower Grading Thu, 31 Aug 2017 00:00:00 +0000 Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%. Yu Sun, Lin Zhu, Guan Wang, and Fang Zhao Copyright © 2017 Yu Sun et al. All rights reserved. Vulnerability Analysis of Interdependent Scale-Free Networks with Complex Coupling Mon, 14 Aug 2017 00:00:00 +0000 Recent studies have shown that random nodes are vulnerable in interdependent networks with simple coupling. However, relationships in actual networks are interrelated and complex coupling. This paper analyzes the vulnerability of interdependent scale-free networks with complex coupling based on the BA model. The results indicate that these networks have the same vulnerability against the maximum node attack, the load of the maximum node attack, and the random node attack, which explain that the coupling relationship between network nodes is an important factor in network design. Chunjie Cao, Zhiqiang Zhang, Jingzhang Sun, Xianpeng Wang, and Mengxing Huang Copyright © 2017 Chunjie Cao et al. All rights reserved. A New Generalized Orthogonal Matching Pursuit Method Sun, 13 Aug 2017 06:29:00 +0000 To improve the reconstruction performance of the generalized orthogonal matching pursuit, an improved method is proposed. Columns are selected from the sensing matrix by generalized orthogonal matching pursuit, and indices of the columns are added to the estimated support set to reconstruct a sparse signal. Those columns contain error columns that can reduce the reconstruction performance. Therefore, the proposed algorithm adds a backtracking process to remove the low-reliability columns from the selected column set. For any -sparse signal, the proposed method firstly computes the correlation between the columns of the sensing matrix and the residual vector and then selects columns that correspond to the largest correlation in magnitude and adds their indices to the estimated support set in each iteration. Secondly, the proposed algorithm projects the measurements onto the space that consists of those selected columns and calculates the projection coefficient vector. When the size of the support set is larger than , the proposed method will select high-reliability indices using a search strategy from the support set. Finally, the proposed method updates the estimated support set using the selected high-reliability indices. The simulation results demonstrate that the proposed algorithm has a better recovery performance. Liquan Zhao and Yulong Liu Copyright © 2017 Liquan Zhao and Yulong Liu. All rights reserved. Spherical Simplex-Radial Cubature Quadrature Kalman Filter Tue, 08 Aug 2017 07:46:56 +0000 A spherical simplex-radial cubature quadrature Kalman filter (SSRCQKF) is proposed in order to further improve the nonlinear filtering accuracy. The Gaussian probability weighted integral of the nonlinear function is decomposed into spherical integral and radial integral, which are approximated by spherical simplex cubature rule and arbitrary order Gauss-Laguerre quadrature rule, respectively, and the novel spherical simplex-radial cubature quadrature rule is obtained. Combined with the Bayesian filtering framework, the general form and the specific form of SSRCQKF are put forward, and the numerical simulation results indicate that the proposed algorithm can achieve a higher filtering accuracy than CKF and SSRCKF. Zhaoming Li and Wenge Yang Copyright © 2017 Zhaoming Li and Wenge Yang. All rights reserved. Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization Sun, 06 Aug 2017 07:55:37 +0000 Subpixel mapping technology can determine the specific location of different objects in the mixed pixel and effectively solve the uncertainty of the ground features spatial distribution in traditional classification technology. Existing methods based on linear optimization encounter the premature and local convergence of the optimization algorithm. This paper proposes a subpixel mapping method based on modified binary quantum particle swarm optimization (MBQPSO) to solve the above issues. The initial subpixel mapping imagery is obtained according to spectral unmixing results. We focus mainly on the discretization of QPSO, which is implemented by modifying the discrete update process of particle location, to minimize the objective function, which is formulated based on different connected regional perimeter calculating methods. To reduce time complexity, a target optimization strategy of global iteration combined with local iteration is performed. The MBQPSO is tested on standard test functions and results show that MBQPSO has the best performance on global optimization and convergent rate. Then, we analyze the proposed algorithm qualitatively and quantitatively by simulated and real experiment; results show that the method combined with MBQPSO and objective function, which is formulated based on the gap length between region and background, has the best performance in accuracy and efficiency. Shuhan Chen, Xiaorun Li, and Liaoying Zhao Copyright © 2017 Shuhan Chen et al. All rights reserved. An Acquisition Algorithm with NCCFR for BOC Modulated Signals Thu, 03 Aug 2017 00:00:00 +0000 With the development of satellite navigation technology, BOC (Binary Offset Carrier) signals are proposed and applied in navigation system. However, in the advantages of enhancing the utilized rating of the band resource, some new problems are also emerging in the acquisition processing. On the basis of analyzing the limitations of the existing methods in suppressing side peaks, a NCCFR (New Cross-Correlation Function Reconstruction) algorithm is proposed, in which different modulation coefficients are used to construct correlation function with a shifter phase. The simulation results show that the new algorithm can suppress first side peaks and restrain other side peaks. Yongxin Feng, Fang Liu, Xudong Yao, and Xiaoyu Zhang Copyright © 2017 Yongxin Feng et al. All rights reserved. Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network Wed, 02 Aug 2017 08:18:31 +0000 According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient. Zhuqing Bi, Chenming Li, Xujie Li, and Hongmin Gao Copyright © 2017 Zhuqing Bi et al. All rights reserved. Reordering Features with Weights Fusion in Multiclass and Multiple-Kernel Speech Emotion Recognition Thu, 27 Jul 2017 00:00:00 +0000 The selection of feature subset is a crucial aspect in speech emotion recognition problem. In this paper, a Reordering Features with Weights Fusion (RFWF) algorithm is proposed for selecting more effective and compact feature subset. The RFWF algorithm fuses the weights reflecting the relevance, complementarity, and redundancy between features and classes comprehensively and implements the reordering of features to construct feature subset with excellent emotional recognizability. A binary-tree structured multiple-kernel SVM classifier is adopted in emotion recognition. And different feature subsets are selected in different nodes of the classifier. The highest recognition accuracy of the five emotions in Berlin database is 90.549% with only 15 features selected by RFWF. The experimental results show the effectiveness of RFWF in building feature subset and the utilization of different feature subsets for specified emotions can improve the overall recognition performance. Xiaoqing Jiang, Kewen Xia, Lingyin Wang, and Yongliang Lin Copyright © 2017 Xiaoqing Jiang et al. All rights reserved. Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments Wed, 26 Jul 2017 06:55:16 +0000 Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%–100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively. Omar Lopez-Rincon, Oleg Starostenko, Vicente Alarcon-Aquino, and Juan C. Galan-Hernandez Copyright © 2017 Omar Lopez-Rincon et al. All rights reserved. Evolutionary Game Algorithm for Image Segmentation Tue, 25 Jul 2017 15:00:00 +0000 The traditional two-dimensional Otsu algorithm only considers the limitations of the maximum variance of between-cluster variance of the target class and background class; this paper proposes evolutionary game improved algorithm. Algorithm takes full consideration of own pixel cohesion of target and background. It can meet the same of maximum variance of between-cluster variance. To ensure minimum threshold discriminant function within the variance, this kind of evolutionary game algorithm searching space for optimal solution is applied. Experimental results show that the method proposed in this paper makes the detail of segmentation image syllabify and has better antijamming capability; the improved genetic algorithm which used searching optimal solution has faster convergence speed and better global search capability. Jin Zhong and Hao Wu Copyright © 2017 Jin Zhong and Hao Wu. All rights reserved. Signal Processing Based Remote Sensing Data Simulation in Radar System Tue, 25 Jul 2017 10:20:47 +0000 Range cell migration has a serious impact on the precision of image formation, especially for high-resolution and large-scale imaging. To get the full resolution and high quality of the image, the range cell migration correction in the azimuth time domain must be considered. For tackling this problem, this paper presents a novel and efficient range cell migration correction method based on curve fitting and signal processing. By emulating a curve to approximate the range-compressed echo of a strong point, the range location indexes of the strong point along the azimuth direction can be obtained under the least squares criterion. The merits of the proposed method are twofold: (1) the proposed method is robust to the uncertainty of system parameters (strong tolerance) under real flights and (2) the generalization of the proposed method is better and can be easily adapted to different synthetic aperture radar (SAR) modes (e.g., monostatic and bistatic). The experimental results on real remote sensing data from both the monostatic and the bistatic SAR demonstrate the effectiveness. The regressed distance curve is completely coincident with the trajectory of strong points of the echo. Finally, the imaging focus results also validate the efficiency of the proposed method. Renxuan Hao and Tan Guo Copyright © 2017 Renxuan Hao and Tan Guo. All rights reserved. A Decoupling Control Method for Shunt Hybrid Active Power Filter Based on Generalized Inverse System Mon, 24 Jul 2017 00:00:00 +0000 In this paper, a novel decoupling control method based on generalized inverse system is presented to solve the problem of SHAPF (Shunt Hybrid Active Power Filter) possessing the characteristics of 2-input-2-output nonlinearity and strong coupling. Based on the analysis of operation principle, the mathematical model of SHAPF is firstly built, which is verified to be invertible using interactor algorithm; then the generalized inverse system of SHAPF is obtained to connect in series with the original system so that the composite system is decoupled under the generalized inverse system theory. The PI additional controller is finally designed to control the decoupled 1-order pseudolinear system to make it possible to adjust the performance of the subsystem. The simulation results demonstrated by MATLAB show that the presented generalized inverse system strategy can realise the dynamic decoupling of SHAPF. And the control system has fine dynamic and static performance. Xin Li and Bo Li Copyright © 2017 Xin Li and Bo Li. All rights reserved. Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images Mon, 24 Jul 2017 00:00:00 +0000 Change detection (CD) of any surface using multitemporal remote sensing images is an important research topic since up-to-date information about earth surface is of great value. Abrupt changes are occurring in different earth surfaces due to natural disasters or man-made activities which cause damage to that place. Therefore, it is necessary to observe the changes for taking necessary steps to recover the subsequent damage. This paper is concerned with this issue and analyzes statistical similarity measure to perform CD using remote sensing images of the same scene taken at two different dates. A variation of normalized mutual information (NMI) as a similarity measure has been developed here using sliding window of different sizes. In sliding window approach, pixels’ local neighborhood plays a significant role in computing the similarity compared to the whole image. Thus the insignificant global characteristics containing noise and sparse samples can be avoided when evaluating the probability density function. Therefore, NMI with different window sizes is proposed here to identify changes using multitemporal data. Experiments have been carried out using two separate multitemporal remote sensing images captured one year apart and one month apart, respectively. Experimental analysis reveals that the proposed technique can detect up to 97.71% of changes which outperforms the traditional approaches. Mumu Aktar, Md. Al Mamun, and Md. Ali Hossain Copyright © 2017 Mumu Aktar et al. All rights reserved. The High Security Mechanisms Algorithm of Similarity Metrics for Wireless and Mobile Networking Thu, 20 Jul 2017 09:54:09 +0000 With the development of human society and the development of Internet of things, wireless and mobile networking have been applied to every field of scientific research and social production. In this scenario, security and privacy have become the decisive factors. The traditional safety mechanisms give criminals an opportunity to exploit. Association rules are an important topic in data mining, and they have a broad application prospect in wireless and mobile networking as they can discover interesting correlations between items hidden in a large number of data. Apriori, the most influential algorithm of association rules mining, needs to scan a database many times, and the efficiency is low when the database is huge. To solve the security mechanisms problem and improve the efficiency, this paper proposes a new algorithm. The new algorithm scans the database only one time and the scale of data to deal with is getting smaller and smaller with the algorithm running. Experiment results show that the new algorithm can efficiently discover useful association rules when applied to data. Xingwang Wang Copyright © 2017 Xingwang Wang. All rights reserved. Signal Processing Platforms and Algorithms for Real-Life Communications and Listening to Digital Audio Wed, 19 Jul 2017 09:10:34 +0000 Alexander Petrovsky, Wanggen Wan, Manuel Rosa-Zurera, and Alexey Karpov Copyright © 2017 Alexander Petrovsky et al. All rights reserved.