Journal of Electrical and Computer Engineering The latest articles from Hindawi © 2017 , Hindawi Limited . All rights reserved. Image Encryption Algorithm Based on a Novel Improper Fractional-Order Attractor and a Wavelet Function Map Wed, 22 Mar 2017 08:26:42 +0000 This paper presents a three-dimensional autonomous chaotic system with high fraction dimension. It is noted that the nonlinear characteristic of the improper fractional-order chaos is interesting. Based on the continuous chaos and the discrete wavelet function map, an image encryption algorithm is put forward. The key space is formed by the initial state variables, parameters, and orders of the system. Every pixel value is included in secret key, so as to improve antiattack capability of the algorithm. The obtained simulation results and extensive security analyses demonstrate the high level of security of the algorithm and show its robustness against various types of attacks. Jian-feng Zhao, Shu-ying Wang, Li-tao Zhang, and Xiao-yan Wang Copyright © 2017 Jian-feng Zhao et al. All rights reserved. Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine Tue, 21 Mar 2017 07:27:09 +0000 The morphology of wear particles reflects the complex properties of wear processes involved in particle formation. Typically, the morphology of wear particles is evaluated qualitatively based on microscopy observations. This procedure relies upon the experts’ knowledge and, thus, is not always objective and cheap. With the rapid development of computer image processing technology, neural network based on traditional gradient training algorithm can be used to recognize them. However, the feedforward neural network based on traditional gradient training algorithms for image segmentation creates many issues, such as needing multiple iterations to converge and easy fall into local minimum, which restrict its development heavily. Recently, extreme learning machine (ELM) for single-hidden-layer feedforward neural networks (SLFN) has been attracting attentions for its faster learning speed and better generalization performance than those of traditional gradient-based learning algorithms. In this paper, we propose to employ ELM for ferrography wear particles image recognition. We extract the shape features, color features, and texture features of five typical kinds of wear particles as the input of the ELM classifier and set five types of wear particles as the output of the ELM classifier. Therefore, the novel ferrography wear particle classifier is founded based on ELM. Qiong Li, Tingting Zhao, Lingchao Zhang, Wenhui Sun, and Xi Zhao Copyright © 2017 Qiong Li et al. All rights reserved. Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm Thu, 16 Mar 2017 00:00:00 +0000 Thousands of flights datasets should be analyzed per day for a moderate sized fleet; therefore, flight datasets are very large. In this paper, an improved kernel principal component analysis (KPCA) method is proposed to search for signatures of anomalies in flight datasets through the squared prediction error statistics, in which the number of principal components and the confidence for the confidence limit are automatically determined by OpenMP-based -fold cross-validation algorithm and the parameter in the radial basis function (RBF) is optimized by GPU-based kernel learning method. Performed on Nvidia GeForce GTX 660, the computation of the proposed GPU-based RBF parameter is 112.9 times (average 82.6 times) faster than that of sequential CPU task execution. The OpenMP-based -fold cross-validation process for training KPCA anomaly detection model becomes 2.4 times (average 1.5 times) faster than that of sequential CPU task execution. Experiments show that the proposed approach can effectively detect the anomalies with the accuracy of 93.57% and false positive alarm rate of 1.11%. Xiaoyu Zhang, Jiusheng Chen, and Quan Gan Copyright © 2017 Xiaoyu Zhang et al. All rights reserved. Enhancing the Cloud Computing Performance by Labeling the Free Node Services as Ready-To-Execute Tasks Thu, 16 Mar 2017 00:00:00 +0000 The huge bandwidth and hardware capacity form a high combination together which leads to a vigorous development in the Internet. On the other hand, different problems will come up during the use of the networks such as delay and node tasks load. These problems lead to degrade the network performance and then affect network service for users. In cloud computing, users are looking to be provided with a high level of services from the service provider. In addition, cloud computing service facilitates the execution of complicated tasks that needed high-storage scale for the computation. In this paper, we have implemented a new technique to retain the service and assign tasks to the best and free available node already labeled by the manager node. The Cloud Computing Alarm (CCA) technique is working to provide all information about the services node and which one is ready to receive the task from users. According to the simulation results, the CCA technique is making good enhancements on the QoS which will increase the number of users to use the service. Additionally, the results showed that the CCA technique improved the services without any degrading of network performance by completing each task in less time. Radwan S. Abujassar and Moneef Jazzar Copyright © 2017 Radwan S. Abujassar and Moneef Jazzar. All rights reserved. A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning Wed, 15 Mar 2017 00:00:00 +0000 The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR), accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, -Nearest Neighbor (-NN), and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance. Bin Jia, Xiaohong Huang, Rujun Liu, and Yan Ma Copyright © 2017 Bin Jia et al. All rights reserved. Online Behavior Analysis-Based Student Profile for Intelligent E-Learning Mon, 13 Mar 2017 00:00:00 +0000 With the development of mobile platform, such as smart cellphone and pad, the E-Learning model has been rapidly developed. However, due to the low completion rate for E-Learning platform, it is very necessary to analyze the behavior characteristics of online learners to intelligently adjust online education strategy and enhance the quality of learning. In this paper, we analyzed the relation indicators of E-Learning to build the student profile and gave countermeasures. Adopting the similarity computation and Jaccard coefficient algorithm, we designed a system model to clean and dig into the educational data and also the students’ learning attitude and the duration of learning behavior to establish student profile. According to the E-Learning resources and learner behaviors, we also present the intelligent guide model to guide both E-Learning platform and learners to improve learning things. The study on student profile can help the E-Learning platform to meet and guide the students’ learning behavior deeply and also to provide personalized learning situation and promote the optimization of the E-Learning. Kun Liang, Yiying Zhang, Yeshen He, Yilin Zhou, Wei Tan, and Xiaoxia Li Copyright © 2017 Kun Liang et al. All rights reserved. Security Enrichment in Intrusion Detection System Using Classifier Ensemble Sun, 12 Mar 2017 08:06:32 +0000 In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique. Uma R. Salunkhe and Suresh N. Mali Copyright © 2017 Uma R. Salunkhe and Suresh N. Mali. All rights reserved. A Fast and Robust Key Frame Extraction Method for Video Copyright Protection Wed, 08 Mar 2017 08:36:59 +0000 The paper proposes a key frame extraction method for video copyright protection. The fast and robust method is based on frame difference with low level features, including color feature and structure feature. A two-stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Compared with the previous methods, the proposed method has advantage in computation complexity and robustness on several video formats, video resolution, and so on. Yunyu Shi, Haisheng Yang, Ming Gong, Xiang Liu, and Yongxiang Xia Copyright © 2017 Yunyu Shi et al. All rights reserved. Self-Tuning Control Scheme Based on the Robustness σ-Modification Approach Wed, 08 Mar 2017 00:00:00 +0000 This paper deals with the self-tuning control problem of linear systems described by autoregressive exogenous (ARX) mathematical models in the presence of unmodelled dynamics. An explicit scheme of control is described, which we use a recursive algorithm on the basis of the robustness σ-modification approach to estimate the parameters of the system, to solve the problem of regulation tracking of the system. This approach was designed with the assumptions that the norm of the vector of the parameters is well-known. A new quadratic criterion is proposed to develop a modified recursive least squares (M-RLS) algorithm with σ-modification. The stability condition of the proposed estimation scheme is proved using the concepts of the small gain theorem. The effectiveness and reliability of the proposed M-RLS algorithm are shown by an illustrative simulation example. The effectiveness of the described explicit self-tuning control scheme is demonstrated by simulation results of the cruise control system for a vehicle. Nabiha Touijer, Samira Kamoun, Najib Essounbouli, and Abdelaziz Hamzaoui Copyright © 2017 Nabiha Touijer et al. All rights reserved. Health Monitoring System for Nursing Homes with Lightweight Security and Privacy Protection Tue, 07 Mar 2017 07:12:24 +0000 With the rapid growth of aged population in China, it is urgent to design a safe and effective monitoring system for the nursing homes. An optimized scheme and high performance security and privacy protection for monitoring system have already become the focus studied especially. So this paper proposed a health monitoring system with lightweight security and privacy protection for nursing homes. Dual-band RFID, virtual routing location algorithm, and diet and exercise data collection based on RFID were adopted to obtain the location and health information. And that fused a mobile authentication protocol based on Hash function to realize security access and privacy protection, which can improve security and reduce the complexity of calculation and the implementation cost compared with the typical authentication protocols. The experiment results show that the ratio of relative network delay is below 35%. The system has strong real-time, high security, more comprehensive data, and lower cost of computation and communication. It can satisfy the requirements of health monitoring for nursing homes. Yu’e Jiang and Jiaxiang Liu Copyright © 2017 Yu’e Jiang and Jiaxiang Liu. All rights reserved. Dynamically Predicting the Quality of Service: Batch, Online, and Hybrid Algorithms Mon, 06 Mar 2017 00:00:00 +0000 This paper studies the problem of dynamically modeling the quality of web service. The philosophy of designing practical web service recommender systems is delivered in this paper. A general system architecture for such systems continuously collects the user-service invocation records and includes both an online training module and an offline training module for quality prediction. In addition, we introduce matrix factorization-based online and offline training algorithms based on the gradient descent algorithms and demonstrate the fitness of this online/offline algorithm framework to the proposed architecture. The superiority of the proposed model is confirmed by empirical studies on a real-life quality of web service data set and comparisons with existing web service recommendation algorithms. Ya Chen and Zhong-an Jiang Copyright © 2017 Ya Chen and Zhong-an Jiang. All rights reserved. Critical Gates Identification for Fault-Tolerant Design in Math Circuits Thu, 02 Mar 2017 09:42:19 +0000 Hardware redundancy at different levels of design is a common fault mitigation technique, which is well known for its efficiency to the detriment of area overhead. In order to reduce this drawback, several fault-tolerant techniques have been proposed in literature to find a good trade-off. In this paper, critical constituent gates in math circuits are detected and graded based on the impact of an error in the output of a circuit. These critical gates should be hardened first under the area constraint of design criteria. Indeed, output bits considered crucial to a system receive higher priorities to be protected, reducing the occurrence of critical errors. The 74283 fast adder is used as an example to illustrate the feasibility and efficiency of the proposed approach. Tian Ban and Gutemberg G. S. Junior Copyright © 2017 Tian Ban and Gutemberg G. S. Junior. All rights reserved. Algebraic Cryptanalysis Scheme of AES-256 Using Gröbner Basis Thu, 23 Feb 2017 11:14:14 +0000 The zero-dimensional Gröbner basis construction is a crucial step in Gröbner basis cryptanalysis on AES-256. In this paper, after performing an in-depth study on the linear transformation and the system of multivariate polynomial equations of AES-256, the zero-dimensional Gröbner basis construction method is proposed by choosing suitable term order and variable order. After giving a detailed construction process of the zero-dimensional Gröbner basis, the necessary theoretical proof is presented. Based on this, an algebraic cryptanalysis scheme of AES-256 using Gröbner basis is proposed. Analysis shows that the complexity of our scheme is lower than that of the exhaustive attack. Kaixin Zhao, Jie Cui, and Zhiqiang Xie Copyright © 2017 Kaixin Zhao et al. All rights reserved. Autofocus on Depth of Interest for 3D Image Coding Wed, 22 Feb 2017 00:00:00 +0000 For some 3D applications, one may want to focus on a specific depth zone representing a region of interest in the scene. In this context, we introduce a new functionality called “autofocus” for 3D image coding, exploiting the depth map as an additional semantic information provided by the 3D sequence. The method is based on a joint “Depth of Interest” (DoI) extraction and coding scheme. First, the DoI extraction scheme consists of a precise extraction of objects located within a DoI zone, given by the viewer or deduced from an analysis process. Then, the DoI coding scheme provides a higher quality for the objects in the DoI at the expense of other depth zones. The local quality enhancement supports both higher SNR and finer resolution. The proposed scheme embeds the Locally Adaptive Resolution (LAR) codec, initially designed for 2D images. The proposed DoI scheme is developed without modifying the global coder framework, and the DoI mask is not transmitted, but it is deduced at the decoder. Results showed that our proposed joint DoI extraction and coding scheme provide a high correlation between texture objects and depth. This consistency avoids the distortion along objects contours in depth maps and those of texture images and synthesized views. Khouloud Samrouth, Olivier Deforges, Yi Liu, Mohamad Khalil, and Wassim EL Falou Copyright © 2017 Khouloud Samrouth et al. All rights reserved. Acoustic Log Prediction on the Basis of Kernel Extreme Learning Machine for Wells in GJH Survey, Erdos Basin Wed, 22 Feb 2017 00:00:00 +0000 In petroleum exploration, the acoustic log (DT) is popularly used as an estimator to calculate formation porosity, to carry out petrophysical studies, or to participate in geological analysis and research (e.g., to map abnormal pore-fluid pressure). But sometime it does not exist in those old wells drilled 20 years ago, either because of data loss or because of just being not recorded at that time. Thus synthesizing the DT log becomes the necessary task for the researchers. In this paper we propose using kernel extreme learning machine (KELM) to predict missing sonic (DT) logs when only common logs (e.g., natural gamma ray: GR, deep resistivity: REID, and bulk density: DEN) are available. The common logs are set as predictors and the DT log is the target. By using KELM, a prediction model is firstly created based on the experimental data and then confirmed and validated by blind-testing the results in wells containing both the predictors and the target (DT) values used in the supervised training. Finally the optimal model is set up as a predictor. A case study for wells in GJH survey from the Erdos Basin, about velocity inversion using the KELM-estimated DT values, is presented. The results are promising and encouraging. Jianhua Cao, Yancui Shi, Dan Wang, and Xiankun Zhang Copyright © 2017 Jianhua Cao et al. All rights reserved. Chattering-Free Sliding-Mode Control for Electromechanical Actuator with Backlash Nonlinearity Mon, 13 Feb 2017 13:08:57 +0000 Considering the backlash nonlinearity and parameter time-varying characteristics in electromechanical actuators, a chattering-free sliding-mode control strategy is proposed in this paper to regulate the rudder angle and suppress unknown external disturbances. Different from most existing backlash compensation methods, a special continuous function is addressed to approximate the backlash nonlinear dead-zone model. Regarding the approximation error, unmodeled dynamics, and unknown external disturbances as a disturbance-like term, a strict feedback nonlinear model is established. Based on this nonlinear model, a chattering-free nonsingular terminal sliding-mode controller is proposed to achieve the rudder angle tracking with a chattering elimination and tracking dynamic performance improvement. A Lyapunov-based proof ensures the asymptotic stability and finite-time convergence of the closed-loop system. Experimental results have verified the effectiveness of the proposed method. Dongqi Ma, Hui Lin, and Bingqiang Li Copyright © 2017 Dongqi Ma et al. All rights reserved. 3D Curvelet-Based Segmentation and Quantification of Drusen in Optical Coherence Tomography Images Tue, 31 Jan 2017 14:44:23 +0000 Spectral-Domain Optical Coherence Tomography (SD-OCT) is a widely used interferometric diagnostic technique in ophthalmology that provides novel in vivo information of depth-resolved inner and outer retinal structures. This imaging modality can assist clinicians in monitoring the progression of Age-related Macular Degeneration (AMD) by providing high-resolution visualization of drusen. Quantitative tools for assessing drusen volume that are indicative of AMD progression may lead to appropriate metrics for selecting treatment protocols. To address this need, a fully automated algorithm was developed to segment drusen area and volume from SD-OCT images. The proposed algorithm consists of three parts: (1) preprocessing, which includes creating binary mask and removing possible highly reflective posterior hyaloid that is used in accurate detection of inner segment/outer segment (IS/OS) junction layer and Bruch’s membrane (BM) retinal layers; (2) coarse segmentation, in which 3D curvelet transform and graph theory are employed to get the possible candidate drusenoid regions; (3) fine segmentation, in which morphological operators are used to remove falsely extracted elongated structures and get the refined segmentation results. The proposed method was evaluated in 20 publically available volumetric scans acquired by using Bioptigen spectral-domain ophthalmic imaging system. The average true positive and false positive volume fractions (TPVF and FPVF) for the segmentation of drusenoid regions were found to be 89.15% ± 3.76 and 0.17% ± .18%, respectively. M. Esmaeili, A. M. Dehnavi, and H. Rabbani Copyright © 2017 M. Esmaeili et al. All rights reserved. Internet of Things: Architectures, Protocols, and Applications Thu, 26 Jan 2017 06:11:48 +0000 The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications. Pallavi Sethi and Smruti R. Sarangi Copyright © 2017 Pallavi Sethi and Smruti R. Sarangi. All rights reserved. Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features Tue, 24 Jan 2017 00:00:00 +0000 According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement. Hui Huang, Xi’an Feng, and Jionghui Jiang Copyright © 2017 Hui Huang et al. All rights reserved. A Comparative Study of Symmetrical Cockcroft-Walton Voltage Multipliers Mon, 16 Jan 2017 00:00:00 +0000 Decades after invention of the Cockcroft-Walton voltage multiplier, it is still being used in broad range of high voltage and ac to dc applications. High voltage ratio, low voltage stress on components, compactness, and high efficiency are its main features. Due to the problems of original circuit, reduction of output ripple and increase of accessible voltage level were the motivations for scientist to propose new topologies. In this article a comparative study on these voltage multipliers was presented. By simulations and experimental prototypes, characteristics of the topologies have been compared. In addition to the performances, components count, voltage stress on the components, and the difficulty and cost of construction are other factors which have been considered in this comparison. An easy to use table which summarized the characteristics of VMs was developed, which can be used as a decision mean for selecting of a topology based on the requirements. It is shown that, due to the application, sometimes a simple and not very famous topology is more effective than a famous one. Mohsen Ruzbehani Copyright © 2017 Mohsen Ruzbehani. All rights reserved. Modified Tang and Pun’s Current Comparator and Its Application to Full Flash and Two-Step Flash Current Mode ADCs Wed, 11 Jan 2017 06:16:55 +0000 A modification to an existing current comparator proposed by Tang and Pun has been presented. The circuit introduces a flipped voltage follower (FVF) which replaces the source follower input stage of the existing current comparator of Tang and Pun. This modification culminates into higher speed especially at lower currents and lower power dissipation. The application of the proposed current comparator has also been put forth by implementing a 3-bit current mode (CM) ADC and a two-step 3-bit CM ADC. The theoretical propositions are verified through spice simulation using 0.18 μm TSMC CMOS technology at a power supply of 1.8 V. Propagation delay, power dissipation, and power delay product (PDP) have been calculated for the proposed current comparator and process parameter variation has been studied. For both the implementations of ADCs, performance parameters, namely, DNL, INL, missing codes, monotonicity, offset, and gain errors, have been evaluated. Veepsa Bhatia and Neeta Pandey Copyright © 2017 Veepsa Bhatia and Neeta Pandey. All rights reserved. A New Digital to Analog Converter Based on Low-Offset Bandgap Reference Mon, 02 Jan 2017 14:08:02 +0000 This paper presents a new 12-bit digital to analog converter (DAC) circuit based on a low-offset bandgap reference (BGR) circuit with two cascade transistor structure and two self-contained feedback low-offset operational amplifiers to reduce the effects of offset operational amplifier voltage effect on the reference voltage, PMOS current-mirror mismatch, and its channel modulation. A Start-Up circuit with self-bias current architecture and multipoint voltage monitoring is employed to keep the BGR circuit working properly. Finally, a dual-resistor ladder DAC-Core circuit is used to generate an accuracy DAC output signal to the buffer operational amplifier. The proposed circuit was fabricated in CSMC 0.5 μm 5 V 1P4M process. The measured differential nonlinearity (DNL) of the output voltages is less than 0.45 LSB and integral nonlinearity (INL) less than 1.5 LSB at room temperature, consuming only 3.5 mW from a 5 V supply voltage. The DNL and INL at −55°C and 125°C are presented as well together with the discussion of possibility of improving the DNL and INL accuracy in future design. Jinpeng Qiu, Tong Liu, Xubin Chen, Yongheng Shang, Jiongjiong Mo, Zhiyu Wang, Hua Chen, Jiarui Liu, Jingjing Lv, and Faxin Yu Copyright © 2017 Jinpeng Qiu et al. All rights reserved. A Novel DBN Feature Fusion Model for Cross-Corpus Speech Emotion Recognition Wed, 28 Dec 2016 07:10:58 +0000 The feature fusion from separate source is the current technical difficulties of cross-corpus speech emotion recognition. The purpose of this paper is to, based on Deep Belief Nets (DBN) in Deep Learning, use the emotional information hiding in speech spectrum diagram (spectrogram) as image features and then implement feature fusion with the traditional emotion features. First, based on the spectrogram analysis by STB/Itti model, the new spectrogram features are extracted from the color, the brightness, and the orientation, respectively; then using two alternative DBN models they fuse the traditional and the spectrogram features, which increase the scale of the feature subset and the characterization ability of emotion. Through the experiment on ABC database and Chinese corpora, the new feature subset compared with traditional speech emotion features, the recognition result on cross-corpus, distinctly advances by 8.8%. The method proposed provides a new idea for feature fusion of emotion recognition. Zou Cairong, Zhang Xinran, Zha Cheng, and Zhao Li Copyright © 2016 Zou Cairong et al. All rights reserved. Hardware Efficient Architecture with Variable Block Size for Motion Estimation Mon, 26 Dec 2016 08:05:52 +0000 Video coding standards such as MPEG-x and H.26x incorporate variable block size motion estimation (VBSME) which is highly time consuming and extremely complex from hardware implementation perspective due to huge computation. In this paper, we have discussed basic aspects of video coding and studied and compared existing architectures for VBSME. Various architectures with different pixel scanning pattern give a variety of performance results for motion vector (MV) generation, showing tradeoff between macroblock processed per second and resource requirement for computation. Aim of this paper is to design VBSME architecture which utilizes optimal resources to minimize chip area and offer adequate frame processing rate for real time implementation. Speed of computation can be improved by accessing 16 pixels of base macroblock of size 4 × 4 in single clock cycle using z scanning pattern. Widely adopted cost function for hardware implementation known as sum of absolute differences (SAD) is used for VBSME architecture with multiplexer based absolute difference calculator and partial summation term reduction (PSTR) based multioperand adders. Device utilization of proposed implementation is only 22k gates and it can process 179 HD (1920 × 1080) resolution frames in best case and 47 HD resolution frames in worst case per second. Due to such higher throughput design is well suitable for real time implementation. Nehal N. Shah, Harikrishna Singapuri, and Upena D. Dalal Copyright © 2016 Nehal N. Shah et al. All rights reserved. Fetal Heart Rate Monitoring from Phonocardiograph Signal Using Repetition Frequency of Heart Sounds Sun, 25 Dec 2016 14:33:57 +0000 As a passive, harmless, and low-cost diagnosis tool, fetal heart rate (FHR) monitoring based on fetal phonocardiography (fPCG) signal is alternative to ultrasonographic cardiotocography. Previous fPCG-based methods commonly relied on the time difference of detected heart sound bursts. However, the performance is unavoidable to degrade due to missed heart sounds in very low signal-to-noise ratio environments. This paper proposes a FHR monitoring method using repetition frequency of heart sounds. The proposed method can track time-varying heart rate without both heart sound burst identification and denoising. The average accuracy rate comparison to benchmark is 88.3% as the SNR ranges from −4.4 dB to −26.7 dB. Hong Tang, Ting Li, Tianshuang Qiu, and Yongwan Park Copyright © 2016 Hong Tang et al. All rights reserved. Design of CPW-Fed Antenna with Defected Substrate for Wideband Applications Tue, 20 Dec 2016 09:28:04 +0000 A CPW-fed defected substrate microstrip antenna is proposed. The proposed antenna shows wideband applications by choosing suitable defected crown shaped substrate. Defected substrate also reduces the size of an antenna. The radiating patch of proposed antenna is taken in the form of extended U-shape. The space around the radiator is utilized by extending the ground plane on both sides of radiator. Simulation of proposed antenna is done on Ansoft’s High Frequency Structure Simulator (HFSS v. 14). Measured results are in good agreement with simulated results. The prototype is taken with dimensions 36 mm × 42 mm × 1.6 mm that achieves good return loss, constant group delay, and good radiation characteristics within the entire operating band from 4.5 to 13.5 GHz (9.0 GHz) with 100% impedance bandwidth at 9.0 GHz centre frequency. Thus, the proposed antenna is applicable for C and X band applications. Amar Sharma, Puneet Khanna, Kshitij Shinghal, and Arun Kumar Copyright © 2016 Amar Sharma et al. All rights reserved. Experiments on Detection of Voiced Hesitations in Russian Spontaneous Speech Sun, 18 Dec 2016 14:34:43 +0000 The development and popularity of voice-user interfaces made spontaneous speech processing an important research field. One of the main focus areas in this field is automatic speech recognition (ASR) that enables the recognition and translation of spoken language into text by computers. However, ASR systems often work less efficiently for spontaneous than for read speech, since the former differs from any other type of speech in many ways. And the presence of speech disfluencies is its prominent characteristic. These phenomena are an important feature in human-human communication and at the same time they are a challenging obstacle for the speech processing tasks. In this paper we address an issue of voiced hesitations (filled pauses and sound lengthenings) detection in Russian spontaneous speech by utilizing different machine learning techniques, from grid search and gradient descent in rule-based approaches to such data-driven ones as ELM and SVM based on the automatically extracted acoustic features. Experimental results on the mixed and quality diverse corpus of spontaneous Russian speech indicate the efficiency of the techniques for the task in question, with SVM outperforming other methods. Vasilisa Verkhodanova and Vladimir Shapranov Copyright © 2016 Vasilisa Verkhodanova and Vladimir Shapranov. All rights reserved. Parameter Extraction of Solar Photovoltaic Modules Using Gravitational Search Algorithm Thu, 15 Dec 2016 14:22:48 +0000 Parameter extraction of a solar photovoltaic system is a nonlinear problem. Many optimization algorithms are implemented for this purpose, which failed in giving better results at low irradiance levels. This article presents a novel method for parameter extraction using gravitational search algorithm. The proposed method evaluates the parameters of different PV panels at various irradiance levels. A critical evaluation and comparison of gravitational search algorithm with other optimization techniques such as genetic algorithm are given. Extensive simulation analyses are carried out on the proposed method and show that GSA is much suitable for parameter extraction problem. R. Sarjila, K. Ravi, J. Belwin Edward, K. Sathish Kumar, and Avagaddi Prasad Copyright © 2016 R. Sarjila et al. All rights reserved. The Comprehensive Study of Electrical Faults in PV Arrays Tue, 06 Dec 2016 13:34:13 +0000 The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. Fault analysis in solar photovoltaic (PV) arrays is a fundamental task to increase reliability, efficiency, and safety in PV systems and, if not detected, may not only reduce power generation and accelerated system aging but also threaten the availability of the whole system. Due to the current-limiting nature and nonlinear output characteristics of PV arrays, faults in PV arrays may not be detected. In this paper, all possible faults that happen in the PV system have been classified and six common faults (shading condition, open-circuit fault, degradation fault, line-to-line fault, bypass diode fault, and bridging fault) have been implemented in 7.5 KW PV farm. Based on the simulation results, both normal operational curves and fault curves have been compared. M. Sabbaghpur Arani and M. A. Hejazi Copyright © 2016 M. Sabbaghpur Arani and M. A. Hejazi. All rights reserved. Score-Informed Source Separation for Multichannel Orchestral Recordings Mon, 05 Dec 2016 09:34:13 +0000 This paper proposes a system for score-informed audio source separation for multichannel orchestral recordings. The orchestral music repertoire relies on the existence of scores. Thus, a reliable separation requires a good alignment of the score with the audio of the performance. To that extent, automatic score alignment methods are reliable when allowing a tolerance window around the actual onset and offset. Moreover, several factors increase the difficulty of our task: a high reverberant image, large ensembles having rich polyphony, and a large variety of instruments recorded within a distant-microphone setup. To solve these problems, we design context-specific methods such as the refinement of score-following output in order to obtain a more precise alignment. Moreover, we extend a close-microphone separation framework to deal with the distant-microphone orchestral recordings. Then, we propose the first open evaluation dataset in this musical context, including annotations of the notes played by multiple instruments from an orchestral ensemble. The evaluation aims at analyzing the interactions of important parts of the separation framework on the quality of separation. Results show that we are able to align the original score with the audio of the performance and separate the sources corresponding to the instrument sections. Marius Miron, Julio J. Carabias-Orti, Juan J. Bosch, Emilia Gómez, and Jordi Janer Copyright © 2016 Marius Miron et al. All rights reserved.