Journal of Electrical and Computer Engineering The latest articles from Hindawi © 2017 , Hindawi Limited . 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. Data Selective Rake Reception for Underwater Acoustic Communication in Strong Multipath Interference Mon, 22 May 2017 07:23:41 +0000 In underwater acoustic communication (UAC), very long delay waves are caused by reflection from water surfaces and bottoms and obstacles. Their waves interfere with desired waves and induce strong multipath interference. Use of a guard interval (GI) is effective for channel compensation in OFDM. However, a GI tends to be long in shallow-water environment because a guard time is determined by a delay time of multipath. A long GI produces a very long OFDM frame in several seconds, which is disadvantageous to a response speed of communication. This paper presents a method of keeping good communication performance even for a short GI. We discuss influence of intercarrier interference (ICI) in OFDM demodulation and propose a method of data selective rake reception (DSRake). The effectiveness of the proposed method is discussed by received signal distribution and confirmed by simulation results. Shingo Yoshizawa, Hiroshi Tanimoto, and Takashi Saito Copyright © 2017 Shingo Yoshizawa et al. All rights reserved. Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection Tue, 16 May 2017 09:22:26 +0000 In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM) and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD) and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR) algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed. Heng Liu, Caihong Zhang, and Dongdong Huang Copyright © 2017 Heng Liu et al. All rights reserved. Speaker Recognition Using Wavelet Packet Entropy, I-Vector, and Cosine Distance Scoring Sun, 14 May 2017 07:19:48 +0000 Today, more and more people have benefited from the speaker recognition. However, the accuracy of speaker recognition often drops off rapidly because of the low-quality speech and noise. This paper proposed a new speaker recognition model based on wavelet packet entropy (WPE), i-vector, and cosine distance scoring (CDS). In the proposed model, WPE transforms the speeches into short-term spectrum feature vectors (short vectors) and resists the noise. I-vector is generated from those short vectors and characterizes speech to improve the recognition accuracy. CDS fast compares with the difference between two i-vectors to give out the recognition result. The proposed model is evaluated by TIMIT speech database. The results of the experiments show that the proposed model can obtain good performance in clear and noisy environment and be insensitive to the low-quality speech, but the time cost of the model is high. To reduce the time cost, the parallel computation is used. Lei Lei and She Kun Copyright © 2017 Lei Lei and She Kun. All rights reserved. Phase Noise Suppression Algorithm Based on Modified LLR Metric in SC-FDMA System Tue, 09 May 2017 00:00:00 +0000 In single-carrier frequency-division multiple-access (SC-FDMA) receivers in the long-term evolution uplink, phase noise is one of the major radio-frequency front-end impairments that degrade system performance. To address this issue, we propose a new log-likelihood ratio (LLR) computation algorithm. A signal model with residual phase noise is considered in the algorithm. Based on this model, we derive a closed-form expression of the likelihood function of the received symbol and calculate more accurate LLR information. Thus the accuracy of the decoder is increased and the performance of the SC-FDMA system is improved. Simulation results show that our proposed algorithm achieves superior bit error rate (BER) performance compared with the existing LLR calculation algorithms in high-order quadrature amplitude modulations (QAM). Zijie Xu and Guangliang Ren Copyright © 2017 Zijie Xu and Guangliang Ren. All rights reserved. Leveraging Fog Computing for Scalable IoT Datacenter Using Spine-Leaf Network Topology Mon, 24 Apr 2017 00:00:00 +0000 With the Internet of Everything (IoE) paradigm that gathers almost every object online, huge traffic workload, bandwidth, security, and latency issues remain a concern for IoT users in today’s world. Besides, the scalability requirements found in the current IoT data processing (in the cloud) can hardly be used for applications such as assisted living systems, Big Data analytic solutions, and smart embedded applications. This paper proposes an extended cloud IoT model that optimizes bandwidth while allowing edge devices (Internet-connected objects/devices) to smartly process data without relying on a cloud network. Its integration with a massively scaled spine-leaf (SL) network topology is highlighted. This is contrasted with a legacy multitier layered architecture housing network services and routing policies. The perspective offered in this paper explains how low-latency and bandwidth intensive applications can transfer data to the cloud (and then back to the edge application) without impacting QoS performance. Consequently, a spine-leaf Fog computing network (SL-FCN) is presented for reducing latency and network congestion issues in a highly distributed and multilayer virtualized IoT datacenter environment. This approach is cost-effective as it maximizes bandwidth while maintaining redundancy and resiliency against failures in mission critical applications. K. C. Okafor, Ifeyinwa E. Achumba, Gloria A. Chukwudebe, and Gordon C. Ononiwu Copyright © 2017 K. C. Okafor et al. All rights reserved. The Channel Compressive Sensing Estimation for Power Line Based on OMP Algorithm Sun, 23 Apr 2017 00:00:00 +0000 Power line communication (PLC) can collect information by power line which increases the coverage and connectivity of the smart grid. In this paper, we analyze the transmission characteristics of the power line channel and model it with mathematics channel. The multipath effect of the power line channel is studied with a novel technology named compressive sensing herein. We also proposed a new method to the power line channel estimation based on compressive sensing. We can collect and extract the effective parameters of the power line channel to storage, which only take very little storage space. The simulation results show that the proposed approach can reduce the amount of processing data in the digital signal processing module and decrease the requirement for the hardware. Yiying Zhang, Kun Liang, Yeshen He, Yannian Wu, Xin Hu, and Lili Sun Copyright © 2017 Yiying Zhang et al. All rights reserved. Study of SAW Based on a Micro Force Sensor in Wireless Sensor Network Wed, 19 Apr 2017 00:00:00 +0000 Wireless sensor network (WSN) technology has increasingly assumed an active role in detection, identification, location, and tracking applications after more than ten years of development. However, its application still suffers from technology bottlenecks, which must be solved and perfected to eliminate the key problems of the technology. This article investigates WSN acquisition nodes and analyzes the relationship between the frequency and actual pressure values of sensor nodes. The sensitive mechanism of the surface acoustic wave (SAW) based on a micro force sensor is researched, and the principle of least squares method is used to establish a transformation model of frequency and pressure for the SAW sensor. According to the model, polyfit function and matrix calculation are selected to solve and calculate the estimate of the polynomial coefficients, which simulate the data acquisition of WSN nodes and draw a polynomial curve fitting. The actual SAW sensor is tested to demonstrate the reasonableness of the device stability in WSNs. Jun Wang, Yuanyuan Li, Ke Chen, Wenke Lu, Qinghong Liu, Haoxin Zhang, and Huashan Yan Copyright © 2017 Jun Wang et al. All rights reserved. Development of an Integrated Cooling System Controller for Hybrid Electric Vehicles Mon, 10 Apr 2017 00:00:00 +0000 A hybrid electrical bus employs both a turbo diesel engine and an electric motor to drive the vehicle in different speed-torque scenarios. The cooling system for such a vehicle is particularly power costing because it needs to dissipate heat from not only the engine, but also the intercooler and the motor. An electronic control unit (ECU) has been designed with a single chip computer, temperature sensors, DC motor drive circuit, and optimized control algorithm to manage the speeds of several fans for efficient cooling using a nonlinear fan speed adjustment strategy. Experiments suggested that the continuous operating performance of the ECU is robust and capable of saving 15% of the total electricity comparing with ordinary fan speed control method. Chong Wang, Qun Sun, and Limin Xu Copyright © 2017 Chong Wang et al. All rights reserved. Abnormal Event Detection in Wireless Sensor Networks Based on Multiattribute Correlation Thu, 06 Apr 2017 00:00:00 +0000 Abnormal event detection is one of the vital tasks in wireless sensor networks. However, the faults of nodes and the poor deployment environment have brought great challenges to abnormal event detection. In a typical event detection technique, spatiotemporal correlations are collected to detect an event, which is susceptible to noises and errors. To improve the quality of detection results, we propose a novel approach for abnormal event detection in wireless sensor networks. This approach considers not only spatiotemporal correlations but also the correlations among observed attributes. A dependency model of observed attributes is constructed based on Bayesian network. In this model, the dependency structure of observed attributes is obtained by structure learning, and the conditional probability table of each node is calculated by parameter learning. We propose a new concept named attribute correlation confidence to evaluate the fitting degree between the sensor reading and the abnormal event pattern. On the basis of time correlation detection and space correlation detection, the abnormal events are identified. Experimental results show that the proposed algorithm can reduce the impact of interference factors and the rate of the false alarm effectively; it can also improve the accuracy of event detection. Mengdi Wang, Anrong Xue, and Huanhuan Xia Copyright © 2017 Mengdi Wang et al. All rights reserved. Energy Efficient Partial Permutation Encryption on Network Coded MANETs Thu, 06 Apr 2017 00:00:00 +0000 Mobile Ad Hoc Networks (MANETs) are composed of a large number of devices that act as dynamic nodes with limited processing capabilities that can share data among each other. Energy efficient security is the major issue in MANETs where data encryption and decryption operations should be optimized to consume less energy. In this regard, we have focused on network coding which is a lightweight mechanism that can also be used for data confidentiality. In this paper, we have further reduced the cost of network coding mechanism by reducing the size of data used for permutation. The basic idea is that source permutes only global encoding vectors (GEVs) without permuting the whole message symbols which significantly reduces the complexity and transmission cost over the network. We have also proposed an algorithm for key generation and random permutation confusion key calculation. The proposed scheme achieves better performance in throughput, encryption time, and energy consumption as compared to previous schemes. Ali Khan, Qifu Tyler Sun, Zahid Mahmood, and Ata Ullah Ghafoor Copyright © 2017 Ali Khan et al. All rights reserved. On Improving the Performance of Dynamic DCVSL Circuits Tue, 04 Apr 2017 00:00:00 +0000 This contribution aims at improving the performance of Dynamic Differential Cascode Voltage Switch Logic (Dy-DCVSL) and Enhanced Dynamic Differential Cascode Voltage Switch Logic (EDCVSL) and suggests three architectures for the same. The first architecture uses transmission gates (TG) to reduce the logic tree depth and width, which results in speed improvement. As leakage is a dominant issue in lower technology nodes, the second architecture is proposed by adapting the leakage control technique (LECTOR) in Dy-DCVSL and EDCVSL. The third proposed architecture combines features of both the first and the second architectures. The operation of the proposed architectures has been verified through extensive simulations with different CMOS submicron technology nodes (90 nm, 65 nm, and 45 nm). The delay of the gates based on the first architecture remains almost the same for different functionalities. It is also observed that Dy-DCVSL gates are 1.6 to 1.4 times faster than their conventional counterpart. The gates based on the second architecture show a maximum of 74.3% leakage power reduction. Also, it is observed that the percentage of reduction in leakage power increases with technology scaling. Lastly, the gates based on the third architecture achieve similar leakage power reduction values to the second one but are not able to exhibit the same speed advantage as achieved with the first architecture. Pratibha Bajpai, Neeta Pandey, Kirti Gupta, Shrey Bagga, and Jeebananda Panda Copyright © 2017 Pratibha Bajpai et al. All rights reserved. Estimation of Sideslip Angle Based on Extended Kalman Filter Tue, 28 Mar 2017 00:00:00 +0000 The sideslip angle plays an extremely important role in vehicle stability control, but the sideslip angle in production car cannot be obtained from sensor directly in consideration of the cost of the sensor; it is essential to estimate the sideslip angle indirectly by means of other vehicle motion parameters; therefore, an estimation algorithm with real-time performance and accuracy is critical. Traditional estimation method based on Kalman filter algorithm is correct in vehicle linear control area; however, on low adhesion road, vehicles have obvious nonlinear characteristics. In this paper, extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change. To test and verify the effect of extended Kalman filtering estimation algorithm, the real vehicle test was carried out on the limit test field. The experimental results show that the accuracy of vehicle sideslip angle acquired by extended Kalman filtering algorithm is obviously higher than that acquired by Kalman filtering in the area of the nonlinearity. Yupeng Huang, Chunjiang Bao, Jian Wu, and Yan Ma Copyright © 2017 Yupeng Huang et al. All rights reserved. Dynamic Search Mechanism with Threat Prediction in a GNSS Receiver Thu, 23 Mar 2017 09:50:53 +0000 With the development of GNSS and the application of multimode signals, efficient GNSS receiver research has become very important. However, threat signals in the received signal are inevitable, which will represent important threats to navigation applications and will lead to leakage and fault detection for the receiver. Therefore, the searches with threat prediction in GNSS signals have been regarded as the important problem for GNSS receivers. In view of the limitations and nonadaptability of the current search technologies as well as on the basis of the proportionality peak judgment mechanism, a dynamic search mechanism with threat prediction (DSM-TP) is proposed in this paper, in which we define a series of preset coefficients and a threat index to optimize the decision mechanism. The simulation results demonstrate that the DSM-TP method can predict the threat situation and can adapt to lower SNR environments compared to the traditional method. In addition, the DSM-TP method can avoid the impact of threats, and the detection capability of the new method is better than the detection capability of the traditional method. Fang Liu and Meng Liu Copyright © 2017 Fang Liu and Meng Liu. 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.