Journal of Electrical and Computer Engineering The latest articles from Hindawi © 2017 , Hindawi Limited . 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. Crosslinguistic Intelligibility of Russian and German Speech in Noisy Environment Thu, 13 Jul 2017 08:39:48 +0000 This paper discusses the results of the pilot experimental research dedicated to speech recognition and perception of the semantic content of the utterances in noisy environment. The experiment included perceptual-auditory analysis of words and phrases in Russian and German (in comparison) in the same noisy environment: various (pink and white) types of noise with various levels of signal-to-noise ratio. The statistical analysis showed that intelligibility and perception of the speech in noisy environment are influenced not only by noise type and its signal-to-noise ratio, but also by some linguistic and extralinguistic factors, such as the existing redundancy of a particular language at various levels of linguistic structure, changes in the acoustic characteristics of the speaker while switching from one language to another one, the level of speaker and listener’s proficiency in a specific language, and acoustic characteristics of the speaker’s voice. Rodmonga Potapova and Maria Grigorieva Copyright © 2017 Rodmonga Potapova and Maria Grigorieva. All rights reserved. An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach Thu, 22 Jun 2017 08:22:35 +0000 We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios. Ibrahim Delibalta, Lemi Baruh, and Suleyman Serdar Kozat Copyright © 2017 Ibrahim Delibalta et al. All rights reserved. Electronically Tunable Quadrature Sinusoidal Oscillator with Equal Output Amplitudes during Frequency Tuning Process Wed, 21 Jun 2017 00:00:00 +0000 A new configuration of voltage-mode quadrature sinusoidal oscillator is proposed. The proposed oscillator employs two voltage differencing current conveyors (VDCCs), two resistors, and two grounded capacitors. In this design, the use of multiple/dual output terminal active building block is not required. The tuning of frequency of oscillation (FO) can be done electronically by adjusting the bias current of active device without affecting condition of oscillation (CO). The electronic tuning can be done by controlling the bias current using a digital circuit. The amplitude of two sinusoidal outputs is equal when the frequency of oscillation is tuned. This makes the sinusoidal output voltages meet good total harmonic distortions (THD). Moreover, the proposed circuit can provide the sinusoidal output current with high impedance which is connected to external load or to another circuit without the use of buffer device. To confirm that the oscillator can generate the quadrature sinusoidal output signal, the experimental results using VDCC constructed from commercially available ICs are also included. The experimental results agree well with theoretical anticipation. Den Satipar, Pattana Intani, and Winai Jaikla Copyright © 2017 Den Satipar et al. All rights reserved. Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters Sun, 18 Jun 2017 09:02:46 +0000 Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (<10 µg/m3) versus high (>25 µg/m3) and low (<10 µg/m3) versus moderate (10–25 µg/m3) concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data. Jan Kleine Deters, Rasa Zalakeviciute, Mario Gonzalez, and Yves Rybarczyk Copyright © 2017 Jan Kleine Deters et al. All rights reserved. The Anonymization Protection Algorithm Based on Fuzzy Clustering for the Ego of Data in the Internet of Things Thu, 08 Jun 2017 09:26:05 +0000 In order to enhance the enthusiasm of the data provider in the process of data interaction and improve the adequacy of data interaction, we put forward the concept of the ego of data and then analyzed the characteristics of the ego of data in the Internet of Things (IOT) in this paper. We implement two steps of data clustering for the Internet of things; the first step is the spatial location of adjacent fuzzy clustering, and the second step is the sampling time fuzzy clustering. Equivalent classes can be obtained through the two steps. In this way we can make the data with layout characteristics to be classified into different equivalent classes, so that the specific location information of the data can be obscured, the layout characteristics of tags are eliminated, and ultimately anonymization protection would be achieved. The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss. The anonymization data set generated by this method has better data availability, and this algorithm can effectively improve the security of data exchange. Mingshan Xie, Mengxing Huang, Yong Bai, and Zhuhua Hu Copyright © 2017 Mingshan Xie et al. All rights reserved. Secure-Network-Coding-Based File Sharing via Device-to-Device Communication Thu, 08 Jun 2017 00:00:00 +0000 In order to increase the efficiency and security of file sharing in the next-generation networks, this paper proposes a large scale file sharing scheme based on secure network coding via device-to-device (D2D) communication. In our scheme, when a user needs to share data with others in the same area, the source node and all the intermediate nodes need to perform secure network coding operation before forwarding the received data. This process continues until all the mobile devices in the networks successfully recover the original file. The experimental results show that secure network coding is very feasible and suitable for such file sharing. Moreover, the sharing efficiency and security outperform traditional replication-based sharing scheme. Lei Wang and Qing Wang Copyright © 2017 Lei Wang and Qing Wang. All rights reserved. Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning Thu, 08 Jun 2017 00:00:00 +0000 Estimating the crowd density of public territories, such as scenic spots, is of great importance for ensuring population safety and social stability. Due to problems in scenic spots such as illumination change, camera angle change, and pedestrian occlusion, current methods are unable to make accurate estimations. To deal with these problems, an ensemble learning (EL) method using support vector regression (SVR) is proposed in this study for crowd density estimation (CDE). The method first uses human head width as a reference to separate the foreground into multiple levels of blocks. Then it adopts the first-level SVR model to roughly predict the three features extracted from image blocks, including D-SIFT, ULBP, and GIST, and the prediction results are used as new features for the second-level SVR model for fine prediction. The prediction results of all image blocks are added for density estimation according to the crowd levels predefined for different scenes of scenic spots. Experimental results demonstrate that the proposed method can achieve a classification rate over 85% for multiple scenes of scenic spots, and it is an effective CDE method with strong adaptability. Xiaohang Xu, Dongming Zhang, and Hong Zheng Copyright © 2017 Xiaohang Xu et al. All rights reserved. Random Harmonic Detection and Compensation Based on Synchronous Reference Frame Tue, 06 Jun 2017 08:36:45 +0000 Algorithms for harmonic detection and compensation are important guarantees for an active power filter (APF) to achieve the harmonic control function and directly determine the overall performance. Existing algorithms usually need a large amount of computation, and the compensation effect of specified order harmonic is also limited. DC side capacitor voltage at sudden change of load is affected by the algorithm as well. This paper proposes a new algorithm for harmonic detection and compensation based on synchronous reference frame (SRF), in which a band-pass filter with center frequency of th harmonic is designed in fundamental frequency SRF to extract random harmonic current with two different frequencies of ()th harmonic in stationary reference frame. This new algorithm can rapidly detect any specified harmonic, and it can adjust the power factor to compensate reactive power. Meanwhile, it has few impacts on DC side capacitor voltage under complicated operating conditions such as sudden change of load. The correctness and effectiveness of this new algorithm are verified by simulation and experiment. Yanbo Che, Zhaojing Yin, Shuyan Yu, and Qiang Sun Copyright © 2017 Yanbo Che et al. All rights reserved. A Variable Weight Privacy-Preserving Algorithm for the Mobile Crowd Sensing Network Mon, 05 Jun 2017 08:34:07 +0000 Mobile crowd sensing (MCS) network collects scenario, environmental, and individual data within a specific range via the intelligent sensing equipment carried by the mobile users, thus providing social decision-making services. MCS is emerging as a most important sensing paradigm. However, the person-centered sensing itself carries the risk of divulging users’ privacy. To address this problem, we proposed a variable weight privacy-preserving algorithm of secure multiparty computation. This algorithm is based on privacy-preserving utility and its effectiveness and feasibility are demonstrated through experiment. Jiezhuo Zhong, Wei Wu, Chunjie Cao, and Wenlong Feng Copyright © 2017 Jiezhuo Zhong et al. All rights reserved. Predicting Harmonic Distortion of Multiple Converters in a Power System Mon, 05 Jun 2017 00:00:00 +0000 Various uncertainties arise in the operation and management of power systems containing Renewable Energy Sources (RES) that affect the systems power quality. These uncertainties may arise due to system parameter changes or design parameter choice. In this work, the impact of uncertainties on the prediction of harmonics in a power system containing multiple Voltage Source Converters (VSCs) is investigated. The study focuses on the prediction of harmonic distortion level in multiple VSCs when some system or design parameters are only known within certain constraints. The Univariate Dimension Reduction (UDR) method was utilized in this study as an efficient predictive tool for the level of harmonic distortion of the VSCs measured at the Point of Common Coupling (PCC) to the grid. Two case studies were considered and the UDR technique was also experimentally validated. The obtained results were compared with that of the Monte Carlo Simulation (MCS) results. P. M. Ivry, O. A. Oke, D. W. P. Thomas, and M. Sumner Copyright © 2017 P. M. Ivry et al. All rights reserved. Advanced Information Technology Convergence 2017 Mon, 29 May 2017 00:00:00 +0000 Jucheng Yang, Anthony T. S. Ho, Hui Cheng, Sook Yoon, and Lu Liu Copyright © 2017 Jucheng Yang et al. All rights reserved.