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Journal Cover IEEE Aerospace and Electronic Systems Magazine
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   ISSN (Print) 0885-8985
   Published by IEEE Homepage  [191 journals]
  • Channel Characterization of Acoustic Waveguides Consisting of Straight Gas
           and Water Pipelines
    • Authors: Liwen Jing;Zhao Li;Yue Li;Ross D. Murch;
      Pages: 6807 - 6819
      Abstract: Characterizing acoustic waveguide channels is becoming important for the development of communication and signal processing applications across diverse fields ranging from urban water supply systems to oil and gas distribution pipeline networks. These applications include sonar and transmission systems in support of leak detection, blockage location, sensing, monitoring, and signaling for example. In this paper, we provide experimental results and models for the wideband channel characterization of acoustic waveguides formed from gas and water pipelines over the 1–50 kHz frequency band. Experimental results are provided for two straight pipe systems comprising an acrylic pipe filled with air and a steel pipe filled with water. A mode-based analytical model for predicting acoustic wave propagation in rigid and elastic pipes is proposed with deterministic and stochastic characteristics both considered. Good matching is demonstrated between the model predictions and experimental results in terms of dispersion curves, channel spectrograms, and delay spread. A key finding is that acoustic waveguides filled with water should be treated as elastic pipes, and they have significantly different characteristics from those filled with gas, which can usually be treated as rigid pipes. Furthermore for the steel-water waveguide pipeline, link budget calculations and noise power spectral density measurements reveal that a communication range of more than 50 m can be obtained.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Efficient Tracking of Moving Target Based on an Improved Fast Differential
           Evolution Algorithm
    • Authors: Laijie Lin;Min Zhu;
      Pages: 6820 - 6828
      Abstract: Computer vision, which is used to detect and track a specific target in image sequences, has drawn great attention in recent years. The process of tracking can be formulated as a dynamic optimization problem that identifies the optimal position of the target in each image. Differential evolution (DE), who owns the advantages of simplicity, parallel computing, and self-adaptive search for global optimization, is envisioned as a promising algorithm to provide effective target tracking. In this paper, several improvements are made in DE for better adaptability in target tracking. Specifically, we introduce two inferior individuals into the mutation stage, which further enriches the diversity of the population and speeds up the offspring’s evolution. We also proceed several image preprocessing and build an adaptive Gaussian mixture model of the target to deal with the complex tracking scenarios. Experimental results show that the designed tracking algorithm based on our improved DE demonstrates a higher tracking accuracy and faster tracking speed in several challenging tracking scenarios.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Color Transfer Using Adaptive Second-Order Total Generalized Variation
    • Authors: Bin Xie;Chen Xu;Yu Han;Robert K. F. Teng;
      Pages: 6829 - 6839
      Abstract: Color transfer is to generate synthetic images by changing the color of target images with new colors obtained from given source images, while the geometrical structure of the synthetic images remains the same. Classical color transfer models use a total variation (TV) regularizer to preserve the details and suppress the noise of the synthetic images. These models can sometimes cause staircase effect and geometrical structure details over-smoothed. To overcome these problems, we propose a new color transfer model in which an adaptive second-order total generalized variation (TGV) regularizer is designed. Here, the adaptive second-order TGV regularizer is a weighted second-order TGV regularizer. The weight is computed by an adaptive edge indicator function. In addition, an efficient algorithm is developed to program our new model. The algorithm is based on a weighted primal–dual method. Experimental results and comparisons demonstrate that our new color transfer model can generate better results than classical TV regularizer-based models in the aspects of the inhibition of staircase effect and the preservation of image details.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Overlapped Multiplexing Principle and an Improved Capacity on Additive
           White Gaussian Noise Channel
    • Authors: Daoben Li;
      Pages: 6840 - 6848
      Abstract: A new overlapped multiplexing principle is revealed that points out the overlapping between consecutive and adjacent data symbols is never an interference but a beneficial coding constraint relation. The heavier the overlapping the higher the coding gain. The destroy facts coming from outside the system is the only interference. Under such a principle an improved channel capacity is obtained, which points out the channel capacity is linear to SNR rather than logarithm to SNR of the channel.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Remote Sensing Image Fusion Based on Adaptively Weighted Joint Detail
    • Authors: Yong Yang;Lei Wu;Shuying Huang;Weiguo Wan;Yue Que;
      Pages: 6849 - 6864
      Abstract: Remote sensing image fusion based on the detail injection scheme consists of two steps: spatial details extraction and injection. The quality of the extracted spatial details plays an important role in the success of a detail injection scheme. In this paper, a remote sensing image fusion method based on adaptively weighted joint detail injection is presented. In the proposed method, the spatial details are first extracted from the multispectral (MS) and panchromatic (PAN) images through à trous wavelet transform and multiscale guided filter. Different from the traditional detail injection scheme, the extracted details are then sparsely represented to produce the primary joint details by dictionary learning from the subimages themselves. To obtain the refined joint details information, we subsequently design an adaptive weight factor considering the correlation and difference between the previous joint details and PAN image details. Finally, the refined joint details are injected into the MS image using modulation coefficient to achieve the fused image. The proposed method has been tested on QuickBird, IKONOS, and WorldView-2 datasets and compared to several state-of-the-art fusion methods in both subjective and objective evaluations. The experimental results indicate that the proposed method is effective and robust to images from various satellites sensors.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • An Optimized Link Layer Design for Communication-Based Train Control
           Systems Using WLAN
    • Authors: Qisheng Dong;Kazunori Hayashi;Megumi Kaneko;
      Pages: 6865 - 6877
      Abstract: With the advent of machine-to-machine and vehicular-to-everything communication systems, next-generation train control systems known as communication-based train control (CBTC) systems are also gathering increased interests both from academia and industry. Unlike the traditional train control systems based on track circuits, CBTC systems are expected to provide greater transportation capacity while ensuring safety by exploiting wireless communications between trains and wayside access points. However, due to the nature of wireless channels, packet transmission delays between APs and trains can greatly affect the train control performance. Most previous works have adopted an adaptive modulation and coding (AMC) method that minimizes the average delay to improve the control performance taking care of transmission errors due to channel fading. However, medium access control (MAC) layer contention due to multiple competing trains, which can entail significant degradations of the delay and control performance, has not been considered. Therefore, we propose an optimized link layer AMC method for CBTC systems using wireless local area network that encompasses the impacts of fading channels as well as of MAC layer contention. With much reduced required information, the proposed scheme enables to select the transmission mode that minimizes this average delay in each control period. The simulation results show that the proposed method greatly outperforms the conventional schemes over a wide range of parameters and settings.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Review of Non-Invasive Sensory Feedback Methods for Transradial
           Prosthetic Hands
    • Authors: Benjamin Stephens-Fripp;Gursel Alici;Rahim Mutlu;
      Pages: 6878 - 6899
      Abstract: Any implant or prosthesis replacing a function or functions of an organ or group of organs should be biologically and sensorily integrated with the human body in order to increase their acceptance with their user. If this replacement is for a human hand, which is an important interface between humans and their environment, the acceptance issue and developing sensory-motor embodiment will be more challenging. Despite progress in prosthesis technologies, 50–60% of hand amputees wear a prosthetic device. One primary reason for the rejection of the prosthetic hands is that there is no or negligibly small feedback or tactile sensation from the hand to the user, making the hands less functional. In fact, the loss of a hand means interrupting the closed-loop sensory feedback between the brain (motor control) and the hand (sensory feedback through the nerves). The lack of feedback requires significant cognitive efforts from the user in order to do basic gestures and daily activities. To this aim, recently, there has been significant development in the provision of sensory feedback from transradial prosthetic hands, to enable the user take part in the control loop and improve user embodiment. Sensory feedback to the hand users can be provided via invasive and non-invasive methods. The latter includes the use of temperature, vibration, mechanical pressure and skin stretching, electrotactile stimulation, phantom limb stimulation, audio feedback, and augmented reality. This paper provides a comprehensive review of the non-invasive methods, performs their critical evaluation, and presents challenges and opportunities associated with the non-invasive sensory feedback methods.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Survey on the Edge Computing for the Internet of Things
    • Authors: Wei Yu;Fan Liang;Xiaofei He;William Grant Hatcher;Chao Lu;Jie Lin;Xinyu Yang;
      Pages: 6900 - 6919
      Abstract: The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we-compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Event Feedback Supervision for a Class of Petri Nets With Unobservable
    • Authors: Ning Ran;Shouguang Wang;Wenhui Wu;
      Pages: 6920 - 6926
      Abstract: In this paper, we propose a method to design an on-line event feedback supervisor (EFS) for a class of Petri nets whose augmented unobservable subnets are acyclic forward synchronization and backward conflict-free (FSBCF) nets. In more detail, an FSBCF net is an ordinary Petri net in which each place has at most one output transition, and each transition has at most one input place. The designed EFS is able to compute a set of transitions that need to be forbidden based on the current observation of the system. In particular, the EFS is maximally permissive, i.e., it ensures that the controlled system never enters into illegal markings while minimally restricting its behavior. Finally, we use an example to illustrate the effectiveness of the proposed method.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A New Processing Approach for Reducing Computational Complexity in
           Cloud-RAN Mobile Networks
    • Authors: Ali M. Mahmood;Adil Al-Yasiri;Omar Y. K. Alani;
      Pages: 6927 - 6946
      Abstract: Cloud computing is considered as one of the key drivers for the next generation of mobile networks (e.g., 5G). This is combined with the dramatic expansion in mobile networks, involving millions (or even billions) of subscribers with a greater number of current and future mobile applications (e.g., IoT). Cloud Radio Access Network (C-RAN) architecture has been proposed as a novel concept to gain the benefits of cloud computing as an efficient computing resource, to meet the requirements of future cellular networks. However, the computational complexity of obtaining the channel state information in the full-centralized C-RAN increases as the size of the network is scaled up, as a result of enlargement in channel information matrices. To tackle this problem of complexity and latency, MapReduce framework and fast matrix algorithms are proposed. This paper presents two levels of complexity reduction in the process of estimating the channel information in cellular networks. The results illustrate that complexity can be minimized from O(N3) to O((N/k)3), where N is the total number of RRHs and k is the number of RRHs per group, by dividing the processing of RRHs into parallel groups and harnessing the MapReduce parallel algorithm in order to process them. The second approach reduces the computation complexity from O((N/k)3) to O((N/k)2.807) using the algorithms of fast matrix inversion. The reduction in complexity and latency leads to a significant improvement in both the estimation time and in the scalability of C-RAN networks.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Spatial Signal Attenuation Model of Active RFID Tags
    • Authors: Shouzhi Xu;Huan Zhou;Changzhi Wu;Chung-Ming Huang;Sungkon Moon;
      Pages: 6947 - 6960
      Abstract: How to improve localization accuracy is a big challenge for highly dynamic and sparse industrial scenarios with active RFID tags. Since antenna of active tag is anisotropic, its emitting signal propagates damply with transmission distance and emitting orientation. In this paper, we aim at modeling anisotropic signal attenuation of active RFID tags by analyzing measurement data in real environment. As the features of signal attenuation with transmission distance on different signal-emitting orientations are the same, two basic models are regressed using experimental data firstly: 1) directional signal-distribution models for both horizontal and vertical orientation in a certain distance; 2) an attenuation model of RF signal with transmitting distance along one direction. Afterwards, an Anisotropic Signal Attenuation Model of active RFID tag (ASAM) is deduced. Furthermore, a noise filtering model in a tag-grid environment is optimized for the spatial model ASAM. Finally, the experimental results in 400-square-meter experimental field show that the average standard deviation (STD) of the optimized model reduces by 50% when the STD is bigger than 4-dB, and the probability distribution is over 70% when the deviation is less than 2.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Voice Pathology Detection and Classification Using Auto-Correlation and
           Entropy Features in Different Frequency Regions
    • Authors: Ahmed Al-Nasheri;Ghulam Muhammad;Mansour Alsulaiman;Zulfiqar Ali;Khalid H. Malki;Tamer A. Mesallam;Mohamed Farahat Ibrahim;
      Pages: 6961 - 6974
      Abstract: Automatic voice pathology detection and classification systems effectively contribute to the assessment of voice disorders, enabling the early detection of voice pathologies and the diagnosis of the type of pathology from which patients suffer. This paper concentrates on developing an accurate and robust feature extraction for detecting and classifying voice pathologies by investigating different frequency bands using autocorrelation and entropy. We extracted maximum peak values and their corresponding lag values from each frame of a voiced signal by using autocorrelation as features to detect and classify pathological samples. We also extracted the entropy for each frame of the voice signal after we normalized its values to be used as the features. These features were investigated in distinct frequency bands to assess the contribution of each band to the detection and classification processes. Various samples of the sustained vowel /a/ for both normal and pathological voices were extracted from three different databases in English, German, and Arabic. A support vector machine was used as a classifier. We also performed u-tests to investigate if there is a significant difference between the means of the normal and pathological samples. The best achieved accuracies in both detection and classification varied depending on the used band, method, and database. The most contributive bands in both detection and classification were between 1000 and 8000 Hz. The highest obtained accuracies in the case of detection were 99.69%, 92.79%, and 99.79% for Massachusetts eye and ear infirmary (MEEI), Saarbrücken voice database (SVD), and Arabic voice pathology database (AVPD), respectively. However, the highest achieved accuracies for classification were 99.54%, 99.53%, and 96.02% for MEEI, SVD, and AVPD, correspondingly, using the c-mbined feature.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Denial of Service Defence for Resource Availability in Wireless Sensor
    • Authors: Opeyemi A. Osanaiye;Attahiru S. Alfa;Gerhard P. Hancke;
      Pages: 6975 - 7004
      Abstract: Wireless sensor networks (WSN) over the years have become one of the most promising networking solutions with exciting new applications for the near future. Its deployment has been enhanced by its small, inexpensive, and smart sensor nodes, which are easily deployed, depending on its application and coverage area. Common applications include its use for military operations, monitoring environmental conditions (such as volcano detection, agriculture, and management), distributed control systems, healthcare, and the detection of radioactive sources. Notwithstanding its promising attributes, security in WSN is a big challenge and remains an ongoing research trend. Deployed sensor nodes are vulnerable to various security attacks due to its architecture, hostile deployment location, and insecure routing protocol. Furthermore, the sensor nodes in WSNs are characterized by their resource constraints, such as, limited energy, low bandwidth, short communication range, limited processing, and storage capacity, which have made the sensor nodes an easy target. Therefore, in this paper, we present a review of denial of service attacks that affect resource availability in WSN and their countermeasure by presenting a taxonomy. Future research directions and open research issues are also discussed.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Perceptual Enhancement of Low Light Images Based on Two-Step Noise
    • Authors: Haonan Su;Cheolkon Jung;
      Pages: 7005 - 7018
      Abstract: Low-light images are seriously corrupted by noise due to the low signal-to-noise ratio. In low intensity, just-noticeable-difference (JND) is high, and thus the noise is not perceived well by human eyes. However, after contrast enhancement, the noise becomes obvious and severe, because JND decreases as intensity increases. Thus, contrast enhancement without considering human visual perception causes serious noise amplification in low-light images. In this paper, we propose perceptual enhancement of low-light images based on two-step noise suppression. We adopt two-step noise suppression based on noise characteristics corresponding to human visual perception. First, we perform noise aware contrast enhancement using a noise-level function. However, the increase of the intensity caused by contrast enhancement reduces JND in low intensity, which makes noise much more visible by human eyes. Second, we perceptually reduce noise in images while preserving details using a JND model, which represents noise visibility in contrast enhancement. We estimate the noise visibility based on the intensity change using luminance adaptation. Also, we extract image details by contrast masking and visual regularity, because textural regions have higher visibility thresholds than the smooth ones. Based on the human visual characteristics, we perform perceptual noise suppression using the JND model. Experimental results show that the proposed method perceptually enhances contrast in low-light images while successfully minimizing distortions and preserving details.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Real-Time Continuous Detection and Recognition of Subject-Specific Smart
           TV Gestures via Fusion of Depth and Inertial Sensing
    • Authors: Neha Dawar;Nasser Kehtarnavaz;
      Pages: 7019 - 7028
      Abstract: This paper presents a real-time detection and recognition approach to identify actions of interest involved in the smart TV application from continuous action streams via simultaneous utilization of a depth camera and a wearable inertial sensor. Continuous action streams mean when actions of interest are performed continuously and randomly among arbitrary actions of non-interest. The developed approach consists of a detection part and a recognition part. In the detection part, two support vector data descriptor classifiers corresponding to the two sensing modalities are used to separate actions of interest from actions of non-interest in continuous action streams. The actions detected as actions of interest by both of the sensing modalities are then passed to the recognition part. In this part, actions of interest are classified by fusing the decisions from two collaborative representation classifiers, one classifier using skeleton joint positions and the other classifier using inertial signals. The developed approach is applied to the hand gestures in the smart TV application. The experimental results obtained indicate the effectiveness of the developed approach to detect and recognize smart TV gestures in continuous action streams.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Decoupling Analysis of a Six-Dimensional Force Sensor Bridge Fault
    • Authors: Guangyu Hu;Qing Gao;Huibin Cao;Hongqing Pan;Feng Shuang;
      Pages: 7029 - 7036
      Abstract: This paper introduces the structural characteristics of a 6-D force sensor based on an E-type membrane, analyzes the calibration results of each bridge, and determines the coupling relationship between bridges of a sensor. In the case that a sensor has no fault in its bridges, a decoupling matrix is calculated by identifying a linear decoupling model. In the case that the fault happens, a linear neural network method is used to discard the faulty bridge to calculate the reduced-dimensional decoupling matrix, and the BP neural network nonlinear method is used to compensate the faulty bridge signal for fault-tolerant decoupling. Simulation results indicate that the reliability of the sensor under the fault condition has been significantly improved.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • The Effects of Comparator Dynamic Capacitor Mismatch in SAR ADC and
    • Authors: Jian Luo;Jing Li;Ning Ning;Yang Liu;Qi Yu;
      Pages: 7037 - 7043
      Abstract: This paper proposes a method of correcting the nonlinear parasitic capacitor of the input pair of comparator in successive approximations analog-to-digital converters (SAR ADCs). The correction method is proposed for the conventional binary-weighted capacitor array topology used in most of high resolution and high speed SAR ADCs. The effects of dynamic capacitor mismatch are first analyzed and then two feasible correction schemes are proposed to mitigate the impact of the nonlinear parasitic capacitor of the comparator. To verify the effectiveness of the proposed method, we designed a SAR ADC in a CMOS 40 nm process and characterized the design by intensive post-simulations. With the proposed correction schemes, the SFDR and SNDR of the SAR ADC increase about 7 and 4 dB, respectively, the differential nonlinearity and integral nonlinearity after calibration are improved from 1.00 and 3.81 to 0.67 LSB/0.57 LSB and 1.46 LSB/0.77 LSB, respectively.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Modeling and Self-Learning Soft-Grasp Control for Free-Floating Space
           Manipulator During Target Capturing Using Variable Stiffness Method
    • Authors: Ming Chu;Xingyu Wu;
      Pages: 7044 - 7054
      Abstract: During target capturing operation, the changes in the dynamics parameters of a free-floating space manipulator degrade the performance of the base attitude stabilization. This paper presents a new self-learning soft-grasp control algorithm based on the variable stiffness technology. First, the dynamic model of variable stiffness joint space manipulator system is established. Simultaneously, the detailed dynamic analysis of pre-impact and post-impact stages is carried out. Second, a new soft-grasp control strategy utilizing cellular differential evolution algorithm combined opposition-based learning with orthogonal crossover is employed to minimize the base angular momentum. Its principle is to solve the optimal stiffness value of the variable stiffness joint to realize desired buffering. Thereafter, we put forward an adaptive backstepping sliding mode control method to track the actual joint stiffness. Finally, the proposed method is applied to a two-degree of freedom planar free-floating space manipulator and the simulation results verify the effectiveness.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Physical Layer Security in Cognitive Untrusted Relay Networks
    • Authors: Dechuan Chen;Yunpeng Cheng;Weiwei Yang;Jianwei Hu;Yueming Cai;
      Pages: 7055 - 7065
      Abstract: In this paper, we consider the secure communications in a cognitive untrusted relay network, where the secondary source intends to communicate with a secondary destination through an untrusted relay in the presence of the direct source–destination link. Specifically, we first examine the connection outage probability (COP) and secrecy outage probability (SOP) to investigate the reliability and security performance in two cases, where the secondary destination exploits the maximum ratio combining (MRC) scheme or the selection combining (SC) scheme. To characterize the tradeoff between reliability and security, we then investigate the effective secrecy throughput (EST) performance by including the COP and SOP in a unified manner. In order to gain additional insights from the performance evaluation, we also provide the asymptotic expressions for the COP, SOP, and EST in high signal-to-noise ratio region. It is demonstrated that the interference temperature constraint incurred from the primary network enables a tradeoff between reliability and security of the secondary network. Moreover, the resulting analysis shows that using the untrusted relay to forward the transmitted message is unnecessary when the secondary destination employs the SC scheme and the untrusted relay operates in half-duplex mode.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A UI-DSPL Approach for the Development of Context-Adaptable User
    • Authors: Thouraya Sboui;Mounir Ben Ayed;Adel M. Alimi;
      Pages: 7066 - 7081
      Abstract: Unlike adaptive interfaces which use sensors to adapt themselves, adaptable interfaces need the intervention of end users to adapt their different aspects according to user requirements. These requirements are commonly expressed according to the context of use. This latter was defined by the triplet where the platform refers to the physical device and the device software, the environment refers to the physical environment in which the application is used and the user element refers to the user preferences and user profile. In this paper, we define a dynamic software product line (DSPL) approach for the development of a family of context-adaptable user interfaces. The DSPL paradigm exploits the knowledge acquired in software product line engineering to develop systems that can be context-aware, or runtime adaptable. Our approach satisfies a set of contributions which will be validated by implementing and evaluating them according to an illustrative case study.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Adaptive Fuzzy Control for Synchronization of Coronary Artery System With
           Input Nonlinearity
    • Authors: Zhanshan Zhao;Haoliang Cui;Jing Zhang;Jie Sun;
      Pages: 7082 - 7087
      Abstract: In this paper, we propose a parametric adaptive control strategy for synchronization of Takagi–Sugeno (T-S) fuzzy coronary artery system. We use the T-S fuzzy model to represent the coronary artery system, because the coronary artery system has complicated nonlinear characteristic in reality. Based on the new model, a fuzzy parametric adaptive output feedback controller is designed to achieve the $H_{infty }$ synchronization of coronary artery system with input nonlinearity and parameter perturbations. Some simulation results are given to illustrate the effectiveness of our control strategy.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Handmade Trileaflet Valve Design and Validation for Pulmonary Valved
           Conduit Reconstruction Using Taguchi Method and Cascade Correlation
           Machine Learning Model
    • Authors: Chung-Dann Kan;Wei-Ling Chen;Chia-Hung Lin;Jieh-Neng Wang;Pong-Jeu Lu;Ming-Yao Chan;Jui-Te Wu;
      Pages: 7088 - 7099
      Abstract: Pulmonary valve diseases in children and adults include different degrees of stenosis, regurgitation, or congenital defects. Valve repair or replacement surgery is used to treat valvular dysfunction and to improve regurgitations flow for pulmonary valve pathologies. Handmade trileaflet valve designs with different ranges of diameters have been used for pulmonary valved conduit reconstruction among children or adult patients with available conditions. In this paper, we propose a multiple regression model as a cascade-correlation-network-based estimator to determine optimal trileaflet parameters, including width, length, and upper/lower curved structures, for trileaflet valve reconstruction. The diameter of the main pulmonary artery is determined via computed tomography pulmonary angiography, and a trileaflet valve template is rapidly sketched. The actual valve is constructed using an expanded polytetrafluoroethylene material. Using an experimental pulmonary circulation loop system, design parameters and valve efficacy can be validated by the Taguchi method through calculation of signal-to-noise ratios. Experimental results indicate that in contrast to commercial valve stents, the handmade trileaflet valve exhibits good performance and is a valuable option in treatment of severe pulmonary regurgitation.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Distributed Optimal Reactive Power Control of Power Systems
    • Authors: Irfan Khan;Yinliang Xu;Hongbin Sun;Vikram Bhattacharjee;
      Pages: 7100 - 7111
      Abstract: To accommodate the increasing penetration level of distributed generators (DGs) in the electrical energy power system, appropriate reactive power control of DGs, which can lead to the voltage profile improvement and power loss minimization, should be addressed. This paper proposed a consensus-based distributed algorithm for the reactive power control of DGs in the power system to optimize the multi-objective function, which includes power loss, voltage deviation, and cost of the reactive power generation of DGs. The formulated problem is proved to be convex. The proposed algorithm is tested on 6- and 34-bus systems to validate its effectiveness and scalability. The proposed algorithm is also compared with the centralized technique particle swarm optimization (PSO), which demonstrates the effectiveness of the proposed distributed algorithm.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Who Used My Smart Object' A Flexible Approach for the Recognition of
    • Authors: Hamdi Amroun;Mehdi Ammi;
      Pages: 7112 - 7122
      Abstract: This paper deals with the authentication of the user of a connected object. We propose a flexible and nonintrusive method based on the use of two categories of everyday connected objects (i.e., smart watch and remote control). Data were collected during participants’ interactions with a smart TV. The discrete cosine transform algorithm was used to extract the most informative features. Based on these features, four classification algorithms (deep neural network, support vector machine, Naïve Bayes classifier, and C45) were applied to the data in order to detect the user’s identity. The classification was performed based on the recognition of four types of human activities (sitting, standing, walking, and lying down) through building four databases. Following this, a second classification was made for each data set activity type in order to identify the users. The results show that it is possible to discriminate between users according to their activities. The accuracy of recognition reached 91% for some participants within a certain activity configuration.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • TDMA-Based Cooperative NC MAC Scheme for Two-Way Relaying Networks
    • Authors: Qing F. Zhou;Lin Zhao;Min Peng;Xinyu Liu;Lisheng Fan;
      Pages: 7123 - 7133
      Abstract: In this paper, we study a cooperative network-coding (NC) media-access control for the two-way two-hop relay network specified with several helpers besides a default relay node. The idle helpers can help forward relay packets. The considered protocol focuses on the channel-based time-division multiple access (TDMA) principle and is called a two-way network-coding-based cooperative relaying TDMA (TW-NCCR) protocol. As the main contribution, we analytically derive the throughput performance of TW-NCCR, and further verify it by simulation. Then, we compare it with a two-way cooperative relaying TDMA (TW-CR) protocol, which is the counterpart of TW-NCCR but applies no network coding. Both the theoretical analysis and simulation results demonstrate that TW-NCCR provides superior throughput performance compared with TW-CR, especially when the two-way relay network is symmetric. On the other hand, no matter how weak they are, helpers are able to improve the performance of asymmetric networks using TW-NCCR, especially when the non-reciprocation packet generation rate of the relay node is small.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Multi-Layer FFR-Aided OFDMA-Based Networks Using Channel-Aware Schedulers
    • Authors: Jan García-Morales;Guillem Femenias;Felip Riera-Palou;John S. Thompson;
      Pages: 7134 - 7147
      Abstract: In orthogonal frequency-division multiple access (OFDMA) networks, the use of universal frequency reuse improves overall cell capacity at the cost of very high levels of inter-cell interference particularly affecting the users located in the cell-edge regions. In order to provide a better quality of experience to cell-edge users while still achieving high spectral efficiencies, conventional fractional frequency reuse (FFR) schemes split the cells into inner and outer regions (or layers) and allocate disjoint frequency resources to each of these regions by applying higher frequency reuse factors to the outer regions. Recently, multi-layer FFR-aided OFDMA-based designs, splitting the cell into inner, middle, and outer layers, have been proposed with the aim of further improving the throughput fairness among users. This paper presents an analytical framework allowing the performance evaluation and optimization of multi-layer FFR-aided OFDMA-based networks. Tractable mathematical expressions of the average spectral efficiency are derived and used to pose optimization problems allowing network designers to achieve the optimal trade-off between spectral efficiency and fairness. Analytical and simulation results clearly show that, irrespective of the channel-aware scheduler in use, multi-layer FFR-schemes can outperform the conventional two-layer FFR architectures.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Big Data Modeling and Analysis for Power Transmission Equipment: A Novel
           Random Matrix Theoretical Approach
    • Authors: Yingjie Yan;Gehao Sheng;Robert Caiming Qiu;Xiuchen Jiang;
      Pages: 7148 - 7156
      Abstract: This paper explores a novel idea for power equipment monitoring and finds that random matrix theory is suitable for modeling the massive data sets in this situation. Big data analytics are mined from those data. We extract the statistical correlation between key states and those parameters. In particular, the (empirical) eigenvalue spectrum distribution and the (theoretical) single ring law are derived from large-dimensional random matrices whose entries are modeled as time series. The radii of the single ring law are used as statistical analytics to characterize the measured data. The evaluation of key state and anomaly detection are accomplished through the comparison of those statistical analytics.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Underwater Positioning Algorithm Based on SINS/LBL Integrated System
    • Authors: Tao Zhang;Liping Chen;Yaxiong Yan;
      Pages: 7157 - 7163
      Abstract: The interactive assistance of a strap-down inertial navigation system (SINS) and a long baseline (LBL) underwater positioning algorithm based on time of arrival is studied, and this algorithm mainly includes a solution algorithm of equivalent sound velocity, 3-D LBL underwater positioning algorithm with aided of SINS. The proposed method can quickly get the ideal equivalent sound velocity to calculate the distance from sound source to hydrophones, and effectively reduce the positioning errors caused by the uneven distribution of underwater sound velocity and sound ray bending, with high flexibility and adaptability. The results of simulation indicate that compared with the traditional algorithm, the improved can correct the cumulative error more directly and effectively, and extend the working hours of autonomous underwater vehicles.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to
           Medical Diagnosis
    • Authors: Alfonso Rojas-Domínguez;Luis Carlos Padierna;Juan Martín Carpio Valadez;Hector J. Puga-Soberanes;Héctor J. Fraire;
      Pages: 7164 - 7176
      Abstract: Proper tuning of hyper-parameters is essential to the successful application of SVM-classifiers. Several methods have been used for this problem: grid search, random search, estimation of distribution Algorithms (EDAs), bio-inspired metaheuristics, among others. The objective of this paper is to determine the optimal method among those that recently reported good results: Bat algorithm, Firefly algorithm, Fruit-fly optimization algorithm, particle Swarm optimization, Univariate Marginal Distribution Algorithm (UMDA), and Boltzmann-UMDA. The criteria for optimality include measures of effectiveness, generalization, efficiency, and complexity. Experimental results on 15 medical diagnosis problems reveal that EDAs are the optimal strategy under such criteria. Finally, a novel performance index to guide the optimization process, that improves the generalization of the solutions while maintaining their effectiveness, is presented.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A GO-FLOW and Dynamic Bayesian Network Combination Approach for
           Reliability Evaluation With Uncertainty: A Case Study on a Nuclear Power
    • Authors: Yi Ren;Dongming Fan;Xinrui Ma;Zili Wang;Qiang Feng;Dezhen Yang;
      Pages: 7177 - 7189
      Abstract: Uncertainty analyses have been considered critical analysis methods for identifying the risks in reliability evaluations. However, with multi-phase, multi-state, and repairable features, this method cannot effectively and precisely display the reliability evaluation results with uncertainty for dynamic and complex systems. In this paper, uncertainty analysis has been conducted in the evaluation of safety-related risk analysis for a nuclear power plant (NPP). A GO-FLOW and dynamic Bayesian network (DBN) combination approach for the reliability evaluation with uncertainty is proposed in this paper. Based on the unified rules, the various operators can be mapped into the DBN even with the multi-phase, multi-state, and repairable characteristics. As the framework of the DBN, utilizing sensitivity analysis, this approach can provide information on those inputs that are contributing the most to the uncertainty. Next, the DBN algorithm and the Monte Carlo simulation are used to quantify the uncertainty in terms of appropriate estimates for the analysis results. Finally, the auxiliary power supply system of the pressurized water reactor in the NPP is analyzed as an example to illustrate the approach. The results of this paper show that uncertainty analysis makes the reliability evaluation more accurate compared with the results without the uncertainty analysis. Moreover, the GO-FLOW methodology can be applied easily for uncertainty analysis with its modified functions and algorithms.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Markov Prediction Model for Host Load Detection and VM Placement in Live
    • Authors: Suhib Bani Melhem;Anjali Agarwal;Nishith Goel;Marzia Zaman;
      Pages: 7190 - 7205
      Abstract: The design of good host overload/underload detection and virtual machine (VM) placement algorithms plays a vital role in assuring the smoothness of VM live migration. The presence of the dynamic environment that leads to a changing load on the VMs motivates us to propose a Markov prediction model to forecast the future load state of the host. We propose a host load detection algorithm to find the future overutilized/underutilized hosts state to avoid immediate VMs migration. Moreover, we propose a VM placement algorithm to determine the set of candidates hosts to receive the migrated VMs in a way to reduce their VM migrations in near future. We evaluate our proposed algorithms through CloudSim simulation on different types of PlanetLab real and random workloads. The experimental results show that our proposed algorithms have a significant reduction in terms of service-level agreement violation, the number of VM migrations, and other metrics than the other competitive algorithms.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Robotic Arm Based 3D Reconstruction Test Automation
    • Authors: Debdeep Banerjee;Kevin Yu;Garima Aggarwal;
      Pages: 7206 - 7213
      Abstract: The 3-D reconstruction involves the construction of a 3-D model from a set of images. The 3-D reconstruction has varied uses that include 3-D printing, the generation of 3-D models that can be shared through social media, and more. The 3-D reconstruction involves complex computations in mobile phones that must determine the pose estimation. The pose estimation involves the process of transforming a 2-D object into 3-D space. Once the pose estimation is done, then the mesh generation is performed using the graphics processing unit. This helps render the 3-D object. The competitive advantages of using hardware processors are to accelerate the intensive computation using graphics processors and digital signal processors. The stated problem that this technical paper addresses is the need for a reliable automated test for the 3-D reconstruction feature. The solution to this problem involved the design and development of an automated test system using a programmable robotic arm and rotor for precisely testing the quality of 3-D reconstruction features. The 3-D reconstruction testing involves using a robotic arm lab to accurately test the algorithmic integrity and end-to-end validation of the generated 3-D models. The robotic arm can move the hardware at different panning speeds, specific angles, fixed distances from the object, and more. The ability to reproduce the scanning at a fixed distance and the same panning speed helps to generate test results that can be benchmarked by different software builds. The 3-D reconstruction also requires a depth sensor to be mounted onto the device under examination. We use this robotic arm lab for functional, high performance, and stable validation of the 3-D reconstruction feature. This paper addresses the computer vision use case testing for 3-D reconstruction features and how we have used the robotic arm lab for automating these use cases.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Weighted Greedy Dual Size Frequency Based Caching Replacement Algorithm
    • Authors: Tinghuai Ma;Jingjing Qu;Wenhai Shen;Yuan Tian;Abdullah Al-Dhelaan;Mznah Al-Rodhaan;
      Pages: 7214 - 7223
      Abstract: Caches are used to improve the performance of the internet, and to reduce the latency of data access time and the low speed of repeated computing processes. Cache replacement is one of the most important issues in a caching system; therefore, it must be coordinated with the caching system to minimize the access latency and maximize the hit rate or byte hit rate. In this paper, we presented a novel caching replacement algorithm named Weighted Greedy Dual Size Frequency (WGDSF) algorithm, which is an improvement on the Greedy Dual Size Frequency (GDSF) algorithm. The WGDSF algorithm mainly adds weighted frequency-based time and weighted document type to GDSF. By increasing the above two weighted parameters, WGDSF performs fairly well at keeping popular objects in the cache and replacing rarely used ones. Our experiment shows that this algorithm has a better hit rate, byte hit rate and access latency than state-of-the-art algorithms, such least Recently Used, least Frequently Used, and GDSF.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Evaluation of Presence in Virtual Environments: Haptic Vest and
           User’s Haptic Skills
    • Authors: Gonzalo García-Valle;Manuel Ferre;Jose Breñosa;David Vargas;
      Pages: 7224 - 7233
      Abstract: This paper presents the integration of a haptic vest with a multimodal virtual environment, consisting of video, audio, and haptic feedback, with the main objective of determining how users, who interact with the virtual environment, benefit from tactile and thermal stimuli provided by the haptic vest. Some experiments are performed using a game application of a train station after an explosion. The participants of this experiment have to move inside the environment, while receiving several stimuli to check if any improvement in presence or realism in that environment is reflected on the vest. This is done by comparing the experimental results with those similar scenarios, obtained without haptic feedback. These experiments are carried out by three groups of participants who are classified on the basis of their experience in haptics and virtual reality devices. Some differences among the groups have been found, which can be related to the levels of realism and synchronization of all the elements in the multimodal environment that fulfill the expectations and maximum satisfaction level. According to the participants in the experiment, two different levels of requirements are to be defined by the system to comply with the expectations of professional and conventional users.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Enhancing Trust Management for Wireless Intrusion Detection via Traffic
           Sampling in the Era of Big Data
    • Authors: Weizhi Meng;Wenjuan Li;Chunhua Su;Jianying Zhou;Rongxing Lu;
      Pages: 7234 - 7243
      Abstract: Internet of Things (IoT) has been widely used in our daily life, which enables various objects to be interconnected for data exchange, including physical devices, vehicles, and other items embedded with network connectivity. Wireless sensor network (WSN) is a vital application of IoT, providing many kinds of information among sensors, whereas such network is vulnerable to a wide range of attacks, especially insider attacks, due to its natural environment and inherent unreliable transmission. To safeguard its security, intrusion detection systems (IDSs) are widely adopted in a WSN to defend against insider attacks through implementing proper trust-based mechanisms. However, in the era of big data, sensors may generate excessive information and data, which could degrade the effectiveness of trust computation. In this paper, we focus on this challenge and propose a way of combining Bayesian-based trust management with traffic sampling for wireless intrusion detection under a hierarchical structure. In the evaluation, we investigate the performance of our approach in both a simulated and a real network environment. Experimental results demonstrate that packet-based trust management would become ineffective in a heavy traffic environment, and that our approach can help lighten the burden of IDSs in handling traffic, while maintaining the detection of insider attacks.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Predictive Modeling of Hospital Mortality for Patients With Heart Failure
           by Using an Improved Random Survival Forest
    • Authors: Fen Miao;Yun-Peng Cai;Yu-Xiao Zhang;Xiao-Mao Fan;Ye Li;
      Pages: 7244 - 7253
      Abstract: Identification of different risk factors and early prediction of mortality for patients with heart failure are crucial for guiding clinical decision-making in Intensive care unit cohorts. In this paper, we developed a comprehensive risk model for predicting heart failure mortality with a high level of accuracy using an improved random survival forest (iRSF). Utilizing a novel split rule and stopping criterion, the proposed iRSF was able to identify more accurate predictors to separate survivors and nonsurvivors and thus improve discrimination ability. Based on the public MIMIC II clinical database with 8059 patients, 32 risk factors, including demographics, clinical, laboratory information, and medications, were analyzed and used to develop the risk model for patients with heart failure. Compared with previous studies, more critical laboratory predictors were identified that could reveal difficult-to-manage comorbidities, including aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum creatine, blood urea nitrogen, and their inherent effects on events; these were determined to be critical indicators for predicting heart failure mortality with the proposed iRSF. The experimental results showed that the developed risk model was superior to those used in previous studies and the conventional random survival forest-based model with an out-of-bag C-statistic value of 0.821. Therefore, the developed iRSF-based risk model could serve as a valuable tool for clinicians in heart failure mortality prediction.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Simplified Modulation Strategy of Nine-Switch Inverter to Cut Off Half
           of Switching Modes
    • Authors: Xiangfeng Li;Lili Qu;Bo Zhang;Guidong Zhang;Hui Liao;
      Pages: 7254 - 7261
      Abstract: Nine-switch inverters were designed by sharing three-switches to realize dual-outputs, which are normally realized by two three phase inverters with 12 power semiconductors. Therefore, its modulation strategy is very complex and hard to implement. For this reason, a simplified modulations strategy for the nine-switch inverter is proposed. Based on the switched system model, m-modes controllability of the nine-switch inverter is first proposed to design a simplified space vector pulse width modulation (SVPWM) strategy. Compared with the conventional SVPWM strategies, the newly proposed switching scheme cannot only reduce half of the operating modes but also reduce the switching frequencies. It is significant since it can simplify the control, increase power efficiency and reduce economy cost. Finally, a prototype is designed to verify the proposed modulation strategy.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Mobile Cloud Based Scheduling Strategy for Industrial Internet of Things
    • Authors: Chaogang Tang;Xianglin Wei;Shuo Xiao;Wei Chen;Weidong Fang;Wuxiong Zhang;Mingyang Hao;
      Pages: 7262 - 7275
      Abstract: The Industrial Internet of Things is cited as the latest means for making manufacturing more flexible, cost effective, and responsive to changes in customer demands. In this paper, we present a mobile cloud based scheduling strategy for the industrial Internet of Things. Several computing paradigms, such as mobile cloud computing, fog computing, and edge computing can be integrated to the industrial Internet of Things, which allow to offload tasks to the cloud for execution. We model the task scheduling problem as an energy consumption optimization problem, while taking into account task dependency, data transmission, and some constraint conditions, such as response time deadline and cost, and further solve it by genetic algorithms. A series of simulation experiments are conducted to evaluate the performance of the algorithm and the results have shown that our proposal is more efficient than the baseline approach.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Throughput and Economics of DSRC-Based Internet of Vehicles
    • Authors: Alexandre K. Ligo;Jon M. Peha;Pedro Ferreira;João Barros;
      Pages: 7276 - 7290
      Abstract: Vehicular mesh networks could be an important new way to provide Internet access in urban areas using dedicated short range communications (DSRC). In some circumstances, DSRC technology is more cost-effective than expanding the capacity of cellular networks. We determine what those circumstances are by combining our simulation model with data collected from an actual vehicular network that is operating in Portugal. We use the model to estimate how much Internet traffic can be offloaded to vehicular networks that would otherwise be carried by cellular networks, under a variety of conditions. We use offloaded traffic to estimate the benefits of cost savings of reduced cellular infrastructure due to offload, and the cost of the DSRC vehicular network to carry that traffic. Then, we determine when benefit exceeds cost. We find that the benefits from the Internet traffic alone are not enough to justify a universal mandate to deploy DSRC in all vehicles, i.e., the benefits of Internet access alone are less than total costs. However, the majority of DSRC-related costs must be incurred anyway if safety is to be enhanced. Thus, soon after a mandate to put DSRC in new vehicles becomes effective, the benefits of Internet access through vehicular networks in densely populated areas would be significantly greater than the remaining costs, which are the costs of roadside infrastructure that can serve as a gateway to the Internet. Moreover, the benefit of Internet access would exceed DSRC infrastructure cost in regions with lower and lower population densities over time.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Research on Distribution Network Fault Recognition Method Based on
           Time-Frequency Characteristics of Fault Waveforms
    • Authors: Xue Qin;Peng Wang;Yadong Liu;Linhui Guo;Gehao Sheng;Xiuchen Jiang;
      Pages: 7291 - 7300
      Abstract: Accurate recognition of distribution line fault types can provide directional guidance for line operation and maintenance personnel. Based on the analysis of time-frequency features of fault waveform, a recognized method of distribution line fault type was proposed in this paper. Through modeling and theoretical analysis of waveforms of different fault types, characteristic parameters, which could characterize waveforms of different fault types from three aspects, time domain, frequency domain, and electric arc, were put forward. Calculation formula for extracting characteristic parameters according to fault waveform data was proposed, recognition logic was established by taking multi-parameter fusion as a basis, and then,automatic recognition of distribution line fault types caused by different factors was realized through detection and classification of characteristic parameters of input waveform data. Finally, 136 groups of field fault waveform data provided by the Electric Power Research Institute were used to do closed-loop control and verification of the algorithm, and results indicated that recognition success rate reached 90%, which verified the feasibility of using time-frequency characteristics of fault waveform to realize recognition of distribution line fault types.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • The Modeling and Analysis of the Extensible Network Service Model
    • Authors: Zuqin Ji;Jun Shen;Delin Ding;Xiaowei Cui;
      Pages: 7301 - 7309
      Abstract: With the increasing number of new application requirements in network, the capability of providing services in traditional network is being challenged constantly. The network service model, which is the core of the network architecture, directly determines the capability of providing network services. However, there lacks the explicit service model about the research of the next generation network architectures. We propose the extensible network service model (ENSM). In this paper, the basic principle of the ENSM is summarized. Then the mathematical modeling of the ENSM is provided and, based on the mathematical modeling, we analyze the functional description and the performance calculation for composite services when a service composition instance is implemented. Finally, through the experimental modeling and the simulation, the performances of the ENSM are compared with those of the traditional network when providing the same service. The experimental results show that the services can be customized or composed flxibly without sacrificing the performances in the extensible network service model. It can also be seen that the model can implement service extension better and the model idea is correct.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Coordinated Frequency Regulation Framework Based on Hybrid
           Battery-Ultracapacitor Energy Storage Technologies
    • Authors: Umer Akram;Muhammad Khalid;
      Pages: 7310 - 7320
      Abstract: The replacement of conventional electricity generators by wind turbines and solar photovoltaic panels results in reduced system inertia, which jeopardizes the electric power system frequency. Frequency variation is critical as it may cause equipment damage and blackouts. Frequency regulation (FR) plays a crucial role in sustaining the stability of electric power grids by minimizing the instantaneous mismatches between electric power generation and load demand. Regulation service (RS) providers dynamically inject/absorb electric power to/from the grid, in response to regulation signals provided by independent system operators (ISOs), in order to keep the frequency within the permissible limits. The regulation signals are highly transient and hence require quick responding resources in order to provide FR effectively. This paper proposes innovative design and operation frameworks for state-of-the-art battery-energy storage system (BESS) and ultracapacitor (UC)-based hybrid energy storage system (HESS) employed for FR in electricity market. The proposed system design framework is based upon the initial investment cost, replacement cost, maintenance cost, and financial penalty imposed by ISO on RS provider for not supplying the required RS. The proposed system operation framework allocates power to both BESS and UC based upon their maximum power ratings while fulfilling their constraints at the same time. The frequent partial charge–discharge transitions, which are detrimental for BESS, are reduced by using two battery banks instead of one large battery bank. The charging and discharging of two battery banks are controlled innovatively to reduce the transitions between the partial charge–discharge transitions. Moreover, a comparison based upon cost per unit between two cases, that is: 1) HESS employed for FR and 2) BESS employed for FR, is presented, which shows that the HESS is more economical.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Peer Assessment and Self-Assessment in Social Learning Environments
           Through a New Crowd-Sourced Mechanism
    • Authors: Fatemeh Orooji;Fattaneh Taghiyareh;
      Pages: 7321 - 7339
      Abstract: Social learning environments generally provide learners with the grounds to collaboratively create and share different learning contents. The variety and considerably large amount of created contents makes them infeasible for students to read through and often results in a continuous reduction in students’ contribution. Therefore, social learning environments should be equipped with effective mechanisms to evaluate and accredit learner-created content relying on students’ participation. In order to suggest a voluntary mechanism for peer assessment with the least overhead, the current study proposed a new crowd sourced approach. The approach called content-dependent multi-label voting (COMVO) offers various assessment options for each type of learning content consisting of resource, assignment, forum, discussion, reply, and comment. COMVO was implemented in a social learning environment and was utilized by students and experts during educational activities in a university course. Peer voting, self-voting, voting to experts, and expert voting were qualitatively analyzed. The results indicated that in contrast to peer voting, which mostly consists of positively describing labels, self-voting labels match those given by experts. Analysis implied that peer voting is reliable and expert-independent. This paper also provided insights about student behaviors and reciprocal effects in identified voting, investigating the role of students’ extrinsic and intrinsic motivational orientation in their voting behavior. Results of a subjective evaluation indicated that the majority of respondents found COMVO an enthusiastic and efficient tool with the potential to complete other similar crowd sourced peer assessment mechanisms.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Joint Source-Channel Polarization With Side Information
    • Authors: Liqiang Jin;Hongwen Yang;
      Pages: 7340 - 7349
      Abstract: As an extension of source polarization and channel polarization, this paper considers joint source-channel polarization, which results in a joint source-channel coding (JSCC) scheme using a quasi-uniform systematic polar code (SPC). In this JSCC scheme, the source with side information is encoded as a systematic polar codeword and only parity bits are transmitted through the channel. The indices of systematic bits are quasi-uniform, which enable the source and the channel to be jointly polarized to either a high entropy part or a low entropy part. The analysis reveals that the quasi-uniform SPC cannot be constructed via original polar coding. To solve this problem, additional bit-swap coding is introduced to modify original polar coding and construct this kind of SPCs. The proposed JSCC scheme can asymptotically approach the information-theoretical limit. For the noiseless channel, the proposed scheme is degraded into classic Slepain-Wolf coding or lossless source coding based on parity approach.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Textile Antenna With Simultaneous Frequency and Polarization
           Reconfiguration for WBAN
    • Authors: Shakhirul Mat Salleh;Muzammil Jusoh;Abdul Hafiizh Ismail;Muhammad Ramlee Kamarudin;Philip Nobles;Mohamad Kamal A. Rahim;Thennarasan Sabapathy;Mohamed Nasrun Osman;Mohd Ilman Jais;Ping Jack Soh;
      Pages: 7350 - 7358
      Abstract: This paper proposes the design of a reconfigurable circularly polarized textile antenna. The circular polarization feature in the proposed antenna is generated by the edge truncation of a rectangular patch and the incorporation of a slotted ground plane, whilst the frequency reconfigurability feature is realized by slot size modification via the use of three embedded RF p-i-n diode switches. Consequently, the antenna operation can be switched between six frequencies (1.57, 1.67, 1.68, 2.43, 2.50, and 2.55 GHz) depending on the seven switch configurations. The proposed antenna is validated experimentally to be operable within the WBAN, WLAN, and GPS range in a compact and wearable format, with gains of up to 4.8 dBi.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Delivering Fairness and QoS Guarantees for LTE/Wi-Fi Coexistence Under LAA
    • Authors: Massimiliano Maule;Dmitri Moltchanov;Pavel Kustarev;Mikhail Komarov;Sergey Andreev;Yevgeni Koucheryavy;
      Pages: 7359 - 7373
      Abstract: Licensed assisted access (LAA) enables the coexistence of long-term evolution (LTE) and Wi-Fi in unlicensed bands, while potentially offering improved coverage and data rates. However, cooperation with the conventional random-access protocols that employ listen-before-talk (LBT) considerations makes meeting the LTE performance requirements difficult, since delay and throughput guarantees should be delivered. In this paper, we propose a novel channel sharing mechanism for the LAA system that is capable of simultaneously providing the fairness of resource allocation across the competing LTE and Wi-Fi sessions as well as satisfying the quality-of-service guarantees of the LTE sessions in terms of their upper delay bound and throughput. Our proposal is based on two key mechanisms: 1) LAA connection admission control for the LTE sessions and 2) adaptive duty cycle resource division. The only external information necessary for the intended operation is the current number of active Wi-Fi sessions inferred by monitoring the shared channel. In the proposed scheme, LAA-enabled LTE base station fully controls the shared environment by dynamically adjusting the time allocations for both Wi-Fi and LTE technologies, while only admitting those LTE connections that should not interfere with Wi-Fi more than another Wi-Fi access point operating on the same channel would. To characterize the key performance trade-offs pertaining to the proposed operation, we develop a new analytical model. We then comprehensively investigate the performance of the developed channel sharing mechanism by confirming that it allows to achieve a high degree of fairness between the LTE and Wi-Fi connections as well as provides guarantees in terms of upper delay bound and throughput for the admitted LTE sessions. We also demonstrate that our scheme outperforms a typical LBT-based LAA implementation.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Pattern Based Comprehensive Urdu Stemmer and Short Text Classification
    • Authors: Mubashir Ali;Shehzad Khalid;Muhammad Haseeb Aslam;
      Pages: 7374 - 7389
      Abstract: Urdu language is used by approximately 200 million people for spoken and written communications. The bulk of unstructured Urdu textual data is available in the world. We can employ data mining techniques to extract useful information from such a large, potentially informative base data. There are many text processing systems available to process unstructured textual data. However, these systems are mostly language specific with the large proportion of systems applicable to English text. This is primarily due to language-dependent preprocessing systems, mainly the stemming requirement. Stemming is a vital preprocessing step in the text mining process and its primary aim is to reduce grammatical words form, e.g., parts of speech, gender, tense, and so on, to their root form. In the proposed work, we have developed a rule-based comprehensive stemming method for Urdu text. This proposed Urdu stemmer has the ability to generate the stem of Urdu words as well as loan words that belong to borrowed languages, such as Arabic, Persian, and Turkish, by removing prefix, infix, and suffix from the words. In the proposed stemming technique, we introduced six novel Urdu infix words classes and a minimum word length rule to generate the stem of Urdu text. In order to cope with the challenge of Urdu infix stemming, we have developed infix stripping rules for introduced infix words classes and generic stemming rules for prefix and suffix stemming. We also present a probabilistic classification approach to classify Urdu short text. Different experiments are performed to demonstrate the effectiveness and efficacy of the proposed approach. Comparison with existing state-of-the art approaches is also made. Stemming accuracy results demonstrate the adoptability of the proposed stemming approach for a variety text processing applications.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Identifying Influential Nodes Based on Community Structure to Speed up the
           Dissemination of Information in Complex Network
    • Authors: Muluneh Mekonnen Tulu;Ronghui Hou;Talha Younas;
      Pages: 7390 - 7401
      Abstract: Applying effective methods to identify important nodes in a complex network is highly invaluable. Recently, in a complex network, finding a powerful leader of the community to spread information quickly throughout the network is the concern of many researchers. In this paper, to identify influential nodes in a large and complex network, community-based mediator (CbM), which considers the entropy of a random walk from a node to each community is proposed as a metrics. CbM describes how the node is essential to connect two or more than two communities of the network. Correlations between CbM and other classical methods used to identify influential nodes are discussed. The performance of CbM is evaluated by susceptible-infected-recovered (SIR) model. In SIR model, the node is the most powerful node in the network, if the percentage of infected node is more while the node is used as the source of infection. Simulation results show that the proposed method performs better than the existing methods to spread information quickly and it can also introduce new influential nodes that other methods failed to identify.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Secure Communication for MISO Secrecy Channel With Multiple Multiantenna
           Eavesdroppers Having Finite Alphabet Inputs
    • Authors: Kuo Cao;Yueming Cai;Yongpeng Wu;Weiwei Yang;Xinrong Guan;
      Pages: 7402 - 7411
      Abstract: This paper considers secure transmission design for multiple-input-single-output multiple multi-antenna eavesdroppers networks with finite alphabet inputs. To improve the achievable secrecy rate, the joint optimization of beamforming vector and transmitting power is studied. We transform the multi-variable problem into a single-variable problem, and then derive the beamforming vector and the transmitting power using one-dimensional search method. Specially, the secure transmission designs in the extremely signal-to-noise ratio (SNR) regime are investigated. In the low SNR regime, the problem of beamforming design is transformed into a semi-definite programming problem, which can be handled by interior-point-based methods, and the optimal transmitting power is obtained. Besides, two different secure schemes, which are zero-force beamforming scheme and joint beamforming and jamming scheme, are proposed to increase the achievable secrecy rate at high SNR. Numerical results demonstrate that the proposed schemes significantly improve the secrecy performance of the system.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Measuring Short Text Reuse for the Urdu Language
    • Authors: Sara Sameen;Muhammad Sharjeel;Rao Muhammad Adeel Nawab;Paul Rayson;Iqra Muneer;
      Pages: 7412 - 7421
      Abstract: Text reuse occurs when one borrows the text (either verbatim or paraphrased) from an earlier written text. A large and increasing amount of digital text is easily and readily available, making it simpler to reuse but difficult to detect. As a result, automatic detection of text reuse has attracted the attention of the research community due to the wide variety of applications associated with it. To develop and evaluate automatic methods for text reuse detection, standard evaluation resources are required. In this paper, we propose one such resource for a significantly under-resourced language—Urdu, which is widely used in day to day communication and has a large digital footprint particularly in the Indian subcontinent. Our proposed Urdu short text reuse corpus contains 2684 short Urdu text pairs, manually labeled as verbatim (496), paraphrased (1329), and independently written (859). In addition, we describe an evaluation of the corpus using various state-of-the-art text reuse detection methods with binary and multi-classification settings and a set of four classifiers. Output results show that character n-gram overlap using J48 classifier outperform other methods for the Urdu short text reuse detection task.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Multi-Device Fusion for Enhanced Contextual Awareness of Localization in
           Indoor Environments
    • Authors: Yali Yuan;Christian Melching;Yachao Yuan;Dieter Hogrefe;
      Pages: 7422 - 7431
      Abstract: Recently, with various developing sensors, mobile devices have become interesting in the research community for indoor localization. In this paper, we propose Twi-Adaboost, a collaborative indoor localization algorithm with the fusion of internal sensors, such as the accelerometer, gyroscope, and magnetometer from multiple devices. Specifically, the data sets are collected first by one person wearing two devices simultaneously: a smartphone and a smartwatch, each collecting multivariate data represented by their internal parameters in a real environment. Then, we evaluate the data sets from these two devices for their strengths and weaknesses in recognizing the indoor position. Based on that, the Twi-AdaBoost algorithm, an interactive ensemble learning method, is proposed to improve the indoor localization accuracy by fusing the co-occurrence information. The performance of the proposed algorithm is assessed on a real-world dataset. The experiment results demonstrate that Twi-AdaBoost achieves a localization error about 0.39 m on average with a low deployment cost, which outperforms the state-of-the-art indoor localization algorithms.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Benefits of Beamforming Training Scheme in Distributed Large-Scale MIMO
    • Authors: Jiamin Li;Dongming Wang;Pengcheng Zhu;Xiaohu You;
      Pages: 7432 - 7444
      Abstract: The downlink training yields rather modest performance gains due to the high degree of channel hardening effect in co-located large-scale multi-input multi-output (MIMO) systems. However, in distributed large-scale MIMO systems, the potential benefits of the downlink training become larger, since there is a high probability that each user is very close to only a part of remote antenna units and is effectively served by them which results in less channel hardening effect. In this paper, we study the benefits of the beamforming training (BT) scheme (users estimate the channel state information by using the received training symbols, which are processed by linear beamforming in advance) in distributed large-scale MIMO systems. We derive accurate and tractable closed-form analytical expressions for the spectral efficiency (SE) of the BT scheme in distributed large-scale MIMO systems with maximum ratio transmission and zero-forcing (ZF) beamforming. Based on these expressions, we analyze the benefits of the BT scheme and investigate how the number of transmit antennas and the length of coherence interval affect the SE of the BT scheme in the co-located and distributed large-scale MIMO systems. The analytical results show that the BT scheme is more suitable for distributed large-scale MIMO systems with ZF beamforming, and a less number of transmit antennas and longer coherence interval can improve the performance gain of BT scheme.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Nuclear Norm Based Matrix Regression Based Projections Method for
           Feature Extraction
    • Authors: Wankou Yang;Jun Li;Hao Zheng;Richard Yi Da Xu;
      Pages: 7445 - 7451
      Abstract: In the traditional graph embedding framework, the graph is usually built by k-NN or r-ball. Since it is difficult to manually set the parameters k and r in the high-dimensional space, sparse representation-based methods are usually introduced to automatically build the graphs. In recent years, nuclear norm-based matrix regression (NMR) has been proposed for face recognition using the low rank structural information (i.e., the image matrix-based error model). Inspired by NMR, we give a NMR-based projections (NMRP) method for feature extraction and recognition. The experiments on FERET and extended Yale B face databases show that NMR can be used to build the graph while NMRP is an effective feature extraction method.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Robust Mutual Authentication Scheme Based on Elliptic Curve Cryptography
           for Telecare Medical Information Systems
    • Authors: Shuming Qiu;Guoai Xu;Haseeb Ahmad;Licheng Wang;
      Pages: 7452 - 7463
      Abstract: The telecare medical information systems (TMISs) provide the convenience to the patients/users to be served at home. Along with such ease, it is essential to preserve the privacy and to provide the security to the patients/users in TMIS. Often, authentication protocols are adopted to guarantee privacy and secure interaction between the patients/users and remote server. Recently, Chaudhry et al. pointed out that Islam et al.’s scheme based on smart card is prone to user impersonation and server impersonation attacks. Chaudhry et al. later presented an enhanced scheme based on elliptic curve cryptography to remedy the weaknesses of Islam et al.’s scheme. Unfortunately, we find some important limitations in both schemes. We remark that their scheme is prone to off-line password guessing attack, user/server impersonation attack, and man-in-middle attack. To overcome these limitations, we present an improved authentication scheme keeping apart the threats encountered in the design of Chaudhry et al.’s scheme. Moreover, the presented scheme can also resist all known attacks. We prove the security of the proposed scheme with the help of widespread Burrows–Abadi–Needham logic. A brief comparison with the previous works provides that the presented protocol is more efficient and more secure than other related schemes.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Robust Waveform Design for Multistatic Cognitive Radars
    • Authors: Gaia Rossetti;Sangarapillai Lambotharan;
      Pages: 7464 - 7475
      Abstract: In this paper, we propose robust waveform techniques for multistatic cognitive radars in a signal-dependent clutter environment. In cognitive radar design, certain second order statistics such as the covariance matrix of the clutter, are assumed to be known. However, exact knowledge of the clutter parameters is difficult to obtain in practical scenarios. Hence, we consider the case of waveform design in the presence of uncertainty on the knowledge of the clutter environment and propose both worst-case and probabilistic robust waveform design techniques. Initially, we tested our multistatic, signal-dependent model against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered scenario. We therefore derived a new approach where we assume uncertainty directly on the radar cross-section and Doppler parameters of the clutters. Accordingly, we propose a clutter-specific stochastic optimization that, by using Taylor series approximations, is able to determine robust waveforms with specific signal to interference and noise ratio outage constraints.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Air Core Transformer Winding Disk Deformation: A Precise Study on Mutual
           Inductance Variation and Its Influence on Frequency Response Spectrum
    • Authors: Mehdi Bagheri;Svyatoslav Nezhivenko;B. T. Phung;Trevor Blackburn;
      Pages: 7476 - 7488
      Abstract: It is well-known that the deformation of transformer winding can produce detectable changes to the frequency response spectrum compared with a referenced past measurement. To interpret such changes for diagnostic purposes, main causes of the trace deviation need to be recognized precisely. In addition, it is useful that the interpretation of transformer frequency response is classified in a way that IoT-based techniques can be developed in the near future to analyze the transformer mechanical integrity. This paper has specifically concentrated on the inductance and capacitance variation due to the axial and radial disk deformation of transformer winding. Analytical analyses on self- and mutual-inductance variations are discussed and capacitance variation is studied in detail for symmetrical and asymmetrical transformer disk deformations. A numerical example is provided to establish the analytical approach and compare inductance and capacitance variation. The analytical approach is finally examined through the experimental study of disk deformation in a 66 kV transformer winding.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • On the Deployment of Distributed Antennas of Power Beacon in Wireless
           Power Transfer
    • Authors: Chao Zhang;Guanghe Zhao;
      Pages: 7489 - 7502
      Abstract: Wireless power transfer (WPT) has drawn significant attention in the last decade and more and more literatures are arising on related research topics such as microwave power transfer and wireless powered communications network. The extremely low efficiency of WPT is regarded as the major bottleneck thus becoming the main task that researchers need to tackle. In this paper, we propose a novel distributed antenna power beacon (DA-PB) whose antennas are uniformly distributed on a circle with the same height when performing WPT. Closed-form expression of antenna height for DA-PB is derived to make the radio frequency (RF) electromagnetic radiation power density at any location of the charging cell lower than the safety level given by federal communications commission. In addition, we get the closed-form expression of average harvested direct current (DC) power per user in the charging cell for path-loss exponent 2 and 4. In order to maximize the average efficiency of WPT, the optimal radius for distributed antennas elements is derived when the path-loss exponent takes the typical value 2 and 4, which could be lower bound and upper bound for common path-loss exponent. Conventional colocated antenna (CA) PB is also analyzed as a benchmark for our proposed DA-PB. Simulation results verify our derived theoretical results. And it is shown that the proposed DA-PB indeed achieves larger average harvested dc power per user and efficiency of WPT than conventional CA-PB.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • V2V Radio Channel Performance Based on Measurements in Ramp Scenarios at
           5.9 GHz
    • Authors: Changzhen Li;Kun Yang;Junyi Yu;Fang Li;Yishui Shui;Fuxing Chang;Wei Chen;
      Pages: 7503 - 7514
      Abstract: This paper focuses on vehicle-to-vehicle (V2V) radio channel properties under ramp scenarios with different structures. Ramps are categorized according to different construction structures into: 1) viaduct ramp with soundproof walls in an urban area and 2) a general ramp without soundproof walls in a suburban region. Furthermore, considering whether the line of sight is available, the entire propagation process of the radio signal is divided into various propagation zones. Propagation characteristics, including the distribution of fading, fading depth (FD), level crossing rate, average fade duration, Root-Mean-Square (rms) delay spread, propagation path loss, and shadow fading, have been estimated and extracted. In particular, the radio channel properties in different types of ramp scenarios are compared and some interesting findings are obtained: 1) an abrupt fluctuation of the received signal level (RSL) in the urban viaduct ramp scenario indicates the nonignorable impact of soundproof walls on V2V radio channel and 2) continuous changes of RSL and different FD values in various propagation zones can be observed in suburban ramp scenarios. Furthermore, the statistical characteristics of RMS delay spread are fitted using a generalized extreme value model with a good fit. Furthermore, propagation path loss is modeled, demonstrating the difference of path loss values in the transition region owing to the impact of soundproof walls. Overall, the research results emphasize the significance of the V2V radio channel modeling under ramp scenarios.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • An Adaptive Policy Evaluation Network Based on Recursive Least Squares
           Temporal Difference With Gradient Correction
    • Authors: Dazi Li;Yuting Wang;Tianheng Song;Qibing Jin;
      Pages: 7515 - 7525
      Abstract: Reinforcement learning (RL) is an important machine learning paradigm that can be used for learning from the data obtained by the human–computer interface and the interaction in human-centered smart systems. One of the essential problems in RL algorithms is the value functions. Value functions are usually estimated via linearly parameterized value functions. Prior RL algorithms that generalize in this way required learning times tuning the linear weights leaving out the basis function. In fact, basis functions in value function approximation also have a significant influence on the performance. In this paper, a new adaptive policy evaluation network based on recursive least squares temporal difference (TD) with gradient correction (adaptive RC network) is proposed. Basis functions in the proposed algorithm were adaptive optimized, mainly aiming at the widths. In the proposed algorithm, TD error and value function were estimated by RC algorithm and value function approximation. The gradient derived from the squares of TD error was used to update the widths of basis functions. Therefore, the RC network can adjust its network parameters in an adaptive way with a self-organizing approach according to the progress in learning. Empirical results based on the three RL benchmarks show the performance and applicability of the proposed adaptive RC network.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Multi-Bank Memory Aware Force Directed Scheduling for High-Level Synthesis
    • Authors: Shouyi Yin;Tianyi Lu;Xianqing Yao;Zhicong Xie;Leibo Liu;Shaojun Wei;
      Pages: 7526 - 7540
      Abstract: High-level synthesis has been widely recognized and accepted as an efficient compilation process targeting field-programmable gate arrays for algorithm evaluation and product prototyping. However, the massively parallel memory access demands and the extremely expensive cost of single-bank memory with multi-port have impeded loop pipelining performance. Thus, based on an alternative multi-bank memory architecture, a joint approach that employs memory-aware force directed scheduling and multi-cycle memory partitioning is formally proposed to achieve legitimate pipelining kernel and valid bank mapping with less resource consumption and optimal pipelining performance. The experimental results over a variety of benchmarks show that our approach can achieve the optimal pipelining performance and meanwhile reduce the number of multiple independent memory banks by 49.2% on average, compared with the state-of-the-art approaches.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Fault Diagnosis in Partially Observed Petri Nets Using Redundancies
    • Authors: Li Yin;Zhiwu Li;Naiqi Wu;Shouguang Wang;Ting Qu;
      Pages: 7541 - 7556
      Abstract: This paper is devoted to the development of an approach to the diagnosability of a system described in the framework of partially observed Petri nets (POPNs) such that the developed fault diagnosis technique can be widely applicable to systems with mutable initial states and partial observations. Existing studies show that the diagnosability of a discrete event system (DES) can be improved by suitable sensor selections or redundancies. This paper proposes a redundancy-building method for a POPN with a certain sensor selection such that no matter how the POPN is initially marked, it achieves maximally structural diagnosability, i.e., the diagnosability of a system cannot be further improved based on the given sensor selection and knowledge of the plant model, which is critical and fundamental in fault recovery capabilities for operating large and complex DESs. To make the proposed method practically applicable, we do not require prior knowledge of faults or special structure of a system, instead we model faults as abnormal events occurring on transitions or places in the plant but not special transitions. Necessary and sufficient conditions for maximally structural diagnosability of a system are established. Redundancies (externally observable places) that guarantee behavior permissiveness and maximally structural diagnosability are built by solving integer linear programming problems.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • PDOA Based Indoor Positioning Using Visible Light Communication
    • Authors: Ayesha Naz;Hafiz M. Asif;Tariq Umer;Byung-Seo Kim;
      Pages: 7557 - 7564
      Abstract: A novel indoor positioning algorithm with improved location accuracy is proposed for visible light communication system. The novelty of the positioning algorithm lies in a hybrid approach of making use of both frequency and variable phase of the transmitted signal. The algorithm has the capability of estimating the position of an object with localization error in millimeters when the signal passes through an optical channel. The LEDs are modulated at very high frequencies. The simulation results demonstrate that the positioning error is reasonably reduced compared with other existing approaches (algorithms). Unlike existing work, the current work also evaluates the performance of the positioning algorithm in the presence of different noise distributions and shows that 1 – 2 cm positioning accuracy can be achieved in the presence of the noise. Finally, multiple potential applications are discussed in which the proposed positioning scheme can be deployed.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Topology-Aware Space-Time Network Coding in Cellular Networks
    • Authors: Rodolfo Torrea-Duran;Máximo Morales Céspedes;Jorge Plata-Chaves;Luc Vandendorpe;Marc Moonen;
      Pages: 7565 - 7578
      Abstract: Space-time network coding (STNC) is a time-division multiple access (TDMA)-based scheme that combines network coding and space-time coding by allowing relay nodes to combine the information received from different source nodes during the transmission phase and to forward the combined signal to a destination node in the relaying phase. However, STNC schemes require all the relay nodes to overhear the signals transmitted from all the source nodes in the network. They also require a large number of time-slots to achieve full diversity in a multipoint-to-multipoint transmission. Both conditions are particularly challenging for large cellular networks where, assuming a downlink transmission, base stations (BSs) and users only overhear a subset of all the BSs. In this paper, we exploit basic knowledge of the network topology in order to reduce the number of time-slots by allowing simultaneous transmissions from those BSs that do not overhear each other. Our results show that these topology-aware schemes are able to increase the spectral efficiency per time-slot and bit error rate with unequal transmit power and channel conditions.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • D-GRACE: Discounted Spectrum Price Game-Based Resource Allocation in a
           Competitive Environment for TVWS Networks
    • Authors: Anabi Hilary Kelechi;Nor Fadzilah Abdullah;Rosdiadee Nordin;Mahamod Ismail;
      Pages: 7579 - 7592
      Abstract: The IEEE 802.22 standard targets rural and sparsely populated regions exploiting television white space (TVWS) technology. In these regions, there are fewer mobile users per density and end-user traffic is light. Hence, there is a need to adopt traffic aware algorithm leveraging on the end-user non-uniform traffic attributes and in essence, promote spectrum efficiency in the TVWS spectrum-management regime. This paper investigates a mechanism to encourage spectrum sharing during low end-user traffic regime motivated by financial inducement. Since incumbent coexistence has been achieved using market models, it is tractable to apply market-assisted spectrum sharing models to address self-coexistence issues in TVWS networks. The purpose of this paper is to use the market model to promote self-coexistence in TVWS networks in the uplink self-frequency reuse. Toward this goal, this paper proposes discounted spectrum price game-based resource allocation in a competitive environment (D-GRACE). Specifically, D-GRACE is a transmit power reduction strategy motivated by financial incentives during light TVWS end-user traffic. When compared with an existing non-market-inspired TVWS self-coexistence resource allocation algorithm under the same scenario, D-GRACE exhibited superior power savings of about 20% and converged after five iterations.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • MEMU: More Efficient Algorithm to Mine High Average-Utility Patterns With
           Multiple Minimum Average-Utility Thresholds
    • Authors: Jerry Chun-Wei Lin;Shifeng Ren;Philippe Fournier-Viger;
      Pages: 7593 - 7609
      Abstract: High average-utility itemsets mining (HAUIM) is an emerging topic in data mining. Compared to traditional high utility itemset mining, HAUIM more fairly measures the utility of itemsets by considering their lengths (number of items). Many previous studies have presented algorithms to efficiently mine high average-utility itemsets (HAUIs). Most of these algorithms, however, only mine HAUIs using a single minimum high average-utility threshold, which limits their usefulness to analyze real data. This is a problem because different items are not equally important to the user. The importance of an item can be expressed for example in terms of weights, interestingness or unit profit. In the past, a baseline algorithm called HAUIM-MMAU was presented to mine HAUIs using multiple minimum high average-utility thresholds. However, it uses a generate-and-test approach to mine HAUIs using a level-wise approach, which is time consuming. In this paper, we propose an efficient algorithm to discover HAUIs based on the average-utility list structure. A tighter upper-bound model is used to reduce the search space instead of the one used in traditional HAUIM, which is called the auub model. Three pruning strategies are also respectively developed to increase the performance HAUIs. Experiments show that the proposed algorithm outperforms the state-of-the-art HAUIM-MMAU algorithm in terms of runtime, memory usage, number of candidates, and scalability.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Affective Algorithm for Controlling Emotional Fluctuation of Artificial
           Investors in Stock Markets
    • Authors: Daniel Cabrera;Claudio Cubillos;Alonso Cubillos;Enrique Urra;Rafael Mellado;
      Pages: 7610 - 7624
      Abstract: This paper presents the design of an affective algorithm for implementing autonomous decision-making systems that incorporate an emotional stabilizer mechanism for the use in the stock market domain. Emotions have a direct influence on human decision-making processes. Non-deterministic behavior in humans can be partially explained by emotions. In this sense, an artificial emotion can be implemented as a synthetic abstraction derived from the observation of human emotions. This paper presents studies related to emotional stability and emotional regulation. However, to the best of our knowledge, it is not possible to identify studies that define a relationship between the regulation of artificial emotions and the decision effectiveness of autonomous decision-making systems, specifically for the stock market domain. With the aim to improve investment results in the stock market domain, a mechanism based on artificial emotions is presented that was designed as a single layer of decision criteria defined by both rational and emotional perspectives. Along with the proposal of an emotional stabilizer mechanism, different values of emotional bandwidths and emotional update rates were tested, aiming to explore the degree of influence of these parameters on the effectiveness of investment decisions made by artificial investors. Our proposal considers the definition of an experimental scenario based on official data from the New York Stock Exchange. The results are promising and include a linear regression analysis. The test results suggest that the use of autonomous affective decision-making systems with emotional stabilization can improve the effectiveness of the decision made.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Pre-Distortion Scheme to Enhance the Transmission Performance of Organic
           Photo-Detector (OPD) Based Visible Light Communication (VLC)
    • Authors: Chi-Wai Chow;Hao-Yu Wang;Chao-Hsuan Chen;Hsiao-Wen Zan;Chien-Hung Yeh;Hsin-Fei Meng;
      Pages: 7625 - 7630
      Abstract: Organic photo-detector (OPD) is lightweight and physically flexible. It can transform plastic or glass into smart surfaces. Visible light communication (VLC) is considered as one of the promising technologies for the future wireless communications. VLC combines the advantages of lighting and communication simultaneously. Besides, it can be deployed using existing lighting infrastructure; hence little extra cost is needed to provide this wireless communication. As the OPD has a large optical detection area, it would be interesting to employ the OPD as VLC receiver (Rx). If the OPD can be used in the VLC systems, it can benefit the Internet-of-Thing (IoT) networks by providing efficient human-to-machine and machine-to-machine communications. However, implementing VLC using OPD Rx is challenging. Due to the slow charging and discharging processes of the OPD, the optical-to-electrical response speed is limited. Besides, the OPD requires external bias to be operated. Here, we propose and demonstrate using pre-distortion scheme for pulse-amplitude-modulation (PAM) signal to a self-powered PCBM:P3HT OPD to enhance the VLC transmission performance. The operation of the self-powered PCBM:P3HT OPD is discussed. Both experiments using PAM-2 and PAM-4 (with and without pre-distortion) are evaluated. The performance, requirement and capability limitations of the pre-distortion schemes are also analyzed and discussed by using numerical simulations.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • No-Reference Stereoimage Quality Assessment for Multimedia Analysis
           Towards Internet-of-Things
    • Authors: Jiachen Yang;Bin Jiang;Houbing Song;Xiahan Yang;Wen Lu;Hehan Liu;
      Pages: 7631 - 7640
      Abstract: With continuous progress of Internet of Things, multimedia analysis in it has attracted more and more attention. Specially, stereoscopic display technology plays an important role in the multimedia analysis processing. In the Internet of Things system, the quality of stereoscopic image will be reduced in the transmission process. In this mode, it will have a great impact on multimedia analysis to judge whether the quality of stereoscopic image meets the requirements. In this paper, a new no-reference stereoscopic image quality assessment model for multimedia analysis towards Internet of Things is built, which is based on a deep learning model to learn from the class labels and image representations. In our framework, images are represented by natural scene statistics features that are extracted from discrete cosine transform domain, and a regression model is employed to shine upon the quality from the feature vector. The training process of the proposed model contains an unsupervised pretraining phase and a supervised fine-tuning phase, enabling it to generalize over the whole distortion types and severity. The proposed model greatly shows the correlation with subjective assessment as demonstrated by experiments on the LIVE 3-D Image Quality Database and IVC 3-D Image Quality Database.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • On the Design of OFDMA-Based FFR-Aided Irregular Cellular Networks With
    • Authors: Jan García-Morales;Guillem Femenias;Felip Riera-Palou;
      Pages: 7641 - 7653
      Abstract: Owing to its high capabilities in terms of spectral efficiency and flexibility, orthogonal frequency division multiple access (OFDMA) has played a crucial role towards the success of 4G cellular systems and an increasing number of actors in the 5G arena strongly advocate for its continuation. OFDMA-based architectures do not introduce intracell interference but, due to the use of very aggressive frequency reuse plans, they must implement some form of inter-cell interference (ICI) control to warrant prescribed levels of quality of service, specially to users located near the cell edge. An efficient technique for mitigating ICI in OFDMA networks is the well-known fractional frequency reuse (FFR) scheme. In FFR, a signal-to-interference-plus-noise ratio threshold is used to categorize mobile stations (MSs) as cell-center or cell-edge MSs. Furthermore, a different number of frequency resources are allocated to cell-center and cell-edge areas according to a prescribed frequency reuse plan. This paper presents an analytical characterization of FFR-aided OFDMA-based multi-cellular networks that, unlike most previous studies, incorporates shadowing effects and, furthermore, considers that base stations are irregularly deployed. This analytical approach can incorporate different scheduling rules and can underpin different designs for which the optimal FFR parameters can be derived. The proposed framework allows the performance evaluation and optimization of any cell in the system by considering the specific network topology, the user association and categorization processes, the spatial density of users and the characteristics of both the fast multipath fading and the spatially correlated slow shadow fading.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Social-Network-Based Cryptocurrency Wallet-Management Scheme
    • Authors: Shuangyu He;Qianhong Wu;Xizhao Luo;Zhi Liang;Dawei Li;Hanwen Feng;Haibin Zheng;Yanan Li;
      Pages: 7654 - 7663
      Abstract: Effective cryptocurrency key management has become an urgent requirement for modern cryptocurrency. Although a large body of cryptocurrency wallet-management schemes has been proposed, they are mostly constructed for specific application scenarios and often suffer from weak security. In this paper, we propose a more effective, usable, and secure cryptocurrency wallet-management system based on semi-trusted social networks, therein allowing users to collaborate with involved parties to achieve some powerful functions and recovery under certain circumstances. Furthermore, we employ an identity-based hierarchical key-insulated encryption scheme to achieve time-sharing authorization and present a semi-trusted portable social-network-based wallet-management scheme that provides the features of security-enhanced storage, portable login on different devices, no-password authentication, flexible key delegation, and so on. The performance analysis shows that our proposed schemes require minimal additional overhead and have low time delays, making them sufficiently efficient for real-world deployment.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Estimation of Modulation Index for Partial Response CPM Signal
    • Authors: Tayyaba Munawar;Sajid Saleem;Syed Ali Hassan;Syed Mohammad Hassan Zaidi;
      Pages: 7664 - 7674
      Abstract: Continuous phase modulation (CPM) is a power and bandwidth efficient modulation scheme used in cellular, personal, and satellite communications among other applications. If the modulation index of the CPM waveform used by the transmitter is unavailable at the receiver, serious performance degradation may occur. This paper investigates the problem of modulation index estimation for partial response continuous phase modulation schemes. Specifically, we propose two novel estimators for the CPM signal observed in an additive white Gaussian noise channel. A non-data aided method of moments (MoM) estimator based upon the fourth-order cumulants and a data-aided best linear unbiased estimator (BLUE) based upon an approximate linear phase model of the received CPM signal have been proposed. Modified Cramer–Rao lower bound is derived for the partial response CPM schemes to assess the performance of the estimators. We also perform numerical simulations to compare the mean-squared error (MSE) performance of the proposed MoM and BLUE estimators with previously proposed estimators in the literature. It is shown that the MoM estimator exhibits good MSE performance at low signal-to-noise ratio (SNR) while the BLUE estimator performs very well at high SNRs. Moreover, the proposed estimators have lower complexity than existing methods and are based upon non-iterative algorithms.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks
           and SVM
    • Authors: Senlin Zhang;Yixing Wang;Meiqin Liu;Zhejing Bao;
      Pages: 7675 - 7686
      Abstract: Power system faults are significant problems in power transmission and distribution. Methods based on relay protection actions and electrical component actions have been put forward in recent years. However, they have deficiencies dealing with power system fault. In this paper, a method for data-based line trip fault prediction in power systems using long short-term memory (LSTM) networks and support vector machine (SVM) is proposed. The temporal features of multisourced data are captured with LSTM networks, which perform well in extracting the features of time series for a long-time span. The strong learning and mining ability of LSTM networks is suitable for a large quantity of time series in power transmission and distribution. SVM, with a strong generalization ability and robustness, is introduced for classification to get the final prediction results. Considering the overfitting problem in fault prediction, layer of dropout and batch normalization are added into the network. The complete network architecture is shown in this paper in detail. The parameters are adjusted to fit the specific situation of the actual power system. The data for experiments are obtained from the Wanjiang substation in the China Southern Power Grid. The real experiments prove the proposed method’s improvements compared with current data mining methods. Concrete analyses of results are elaborated in this paper. A discussion of practical applications is presented to demonstrate the feasibility in real scenarios.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Energy-Efficient Wireless Transmissions for Battery-Less Vehicle Tire
           Pressure Monitoring System
    • Authors: Qinmiao Kang;Xudong Huang;Yong Li;Zhifeng Xie;Yongquan Liu;Ming Zhou;
      Pages: 7687 - 7699
      Abstract: This paper presents an energy-efficient wireless transmission scheme for battery-less vehicle tire pressure monitoring system (TPMS). Our proposed transmission scheme includes a wake-up communication link with 125-kHz carrier frequency and a data communication link with 433-MHz carrier frequency. Considering the TPMS application requirements and the special nature of the vehicle environment, we derive the relevant circuit parameters. In order to verify the reliability of the wireless communication system under the derived circuit parameters, we design an in-tire data transmitter and wake-up receiver. The 125-kHz wake-up receiver adopts dual-channel to improve the communication reliability and logarithmic amplifiers to achieve ASK demodulation and dynamic range compression. The receiver is implemented in 0.35- $mu text{m}$ high voltage (HV) BCD process. Experiment results show that typical power consumption of the receiver is no more than $5~mu text{A}$ under 3.3 V supply voltage; the maximum data rate is 35 kb/s with 0.5 mVpp sensitivity. On the other hand, the data transmitter is implemented in 0.18- $mu text{m}$ MMRF process. Experiment results show that the typical power consumption is 7.3 mA under 1.8 V supply voltage, and the emission power is −10 dBm@433.92 MHz with a phase noise of −103 dBc/Hz@300 kHz. System level experiments demonstrate that the proposed wireless transmission scheme fulfills the vehicle TPMS requirements. The data transmitter and wake-up receiver can communicate with the commercial data receiver and wake-up transmitter in the range of 20 m, which meets the requirements of most vehicles.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G
    • Authors: Lorenzo Fernández Maimó;Ángel Luis Perales Gómez;Félix J. García Clemente;Manuel Gil Pérez;Gregorio Martínez Pérez;
      Pages: 7700 - 7712
      Abstract: The upcoming fifth-generation (5G) mobile technology, which includes advanced communication features, is posing new challenges on cybersecurity defense systems. Although innovative approaches have evolved in the last few years, 5G will make existing intrusion detection and defense procedures become obsolete, in case they are not adapted accordingly. In this sense, this paper proposes a novel 5G-oriented cyberdefense architecture to identify cyberthreats in 5G mobile networks efficient and quickly enough. For this, our architecture uses deep learning techniques to analyze network traffic by extracting features from network flows. Moreover, our proposal allows adapting, automatically, the configuration of the cyberdefense architecture in order to manage traffic fluctuation, aiming both to optimize the computing resources needed in each particular moment and to fine tune the behavior and the performance of analysis and detection processes. Experiments using a well-known botnet data set depict how a neural network model reaches a sufficient classification accuracy in our anomaly detection system. Extended experiments using diverse deep learning solutions analyze and determine their suitability and performance for different network traffic loads. The experimental results show how our architecture can self-adapt the anomaly detection system based on the volume of network flows gathered from 5G subscribers’ user equipments in real-time and optimizing the resource consumption.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • In Vivo Fascicle Bifurcation Imaging of Rat Sciatic Nerve Using
           Swept-Source Optical Coherence Tomography
    • Authors: Daeyoung Choi;Jaeyul Lee;Mansik Jeon;Jeehyun Kim;
      Pages: 7713 - 7718
      Abstract: The sciatic nerve is the longest and widest single nerve in the human body and is responsible for the signal transduction of the entire hind limb region. Its wide nerve dynamic range and size makes it sensitive to injury. The branching and location of the sciatic nerve are important, and unlike histology, optical coherence tomography (OCT) can provide rapid non-destructive cross-sectional images. The sciatic nerves of ten rats were analyzed using swept-source (SS)-OCT. The sufficient depth penetration of the SS-OCT system allowed clear identification of the internal bifurcation point of the external branching and the internal route for the three terminal nerves in cross-sectional images. Internal bifurcation is observed through interfascicular epineurium resulting from epineurium division. Two bifurcations occur at the bottom of the sciatic nerve. The first and second bifurcations occur approximately 7 and 5 mm, respectively, above the external branching. SS-OCT enabled visualization of surgical needle positioning during direct injections into the sciatic nerve, which is beneficial for drug injection or microelectrode placement for electrical signal processing as a nerve detection guide. Therefore, analysis of the internal structure obtained in real time and needle position information inside the nerve are expected to act as a guide for neurosurgery.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Dataset Optimization for Real-Time Pedestrian Detection
    • Authors: Remi Trichet;Francois Bremond;
      Pages: 7719 - 7727
      Abstract: This paper tackles the problem of data selection for training set generation in the context of near real-time pedestrian detection through the introduction of a training methodology: FairTrain. After highlighting the impact of poorly chosen data on detector performance, we introduce a new data selection technique utilizing the expectation-maximization algorithm for data weighting. FairTrain also features a version of the cascade-of-rejectors enhanced with data selection principles. Experiments on the INRIA and CALTECH data sets prove that, when finely trained, a simple HoG-based detector can outperform most of its near real-time competitors.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Adaptive Fault Tolerant Control for a Class of Nonlinear Switched Systems
    • Authors: Shuni Song;Jingyi Liu;Heng Wang;
      Pages: 7728 - 7738
      Abstract: This paper studies the adaptive fault-tolerant control problem for a class of switched systems under arbitrary switchings between two uncertain nonlinear strict-feedback subsystems. Through using backstepping techniques, two adaptive state feedback control approaches are presented, where unknown switching parameters are directly estimated via switched adaptive laws. A new trajectory initialization method is proposed, where the adaptive parameters are reset by exploiting the previous estimation information. By constructing a special Lyapunov function, the global stability and asymptotic tracking of the closed-loop system are achieved in the presence of actuator faults. Furthermore, the prescribed performance method is introduced in the second control approach to improve the transient performance of the first approach. Finally, two examples are presented to show the effectiveness of the proposed approaches.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Entropy Coding Aided Adaptive Subcarrier-Index Modulated OFDM
    • Authors: Mohammad Ismat Kadir;Hongming Zhang;Sheng Chen;Lajos Hanzo;
      Pages: 7739 - 7752
      Abstract: We propose entropy coding-aided adaptive subcarrier-index-modulated orthogonal frequency division multiplexing (SIM-OFDM). In conventional SIM-OFDM, the indices of the subcarriers activated are capable of conveying extra information. We propose the novel concept of compressing the index information bits by employing the Huffman coding. The probabilities of the different subcarrier activation patterns are obtained from an optimization procedure, which improves the performance of the scheme. Both the maximum-likelihood as well as the logarithmic-likelihood ratio-based soft detector may be employed for detecting the subcarriers activated as well as the information mapped to the classic constellation symbols. As an additional advantage of employing the variable-length Huffman codebook, all the legitimate subcarrier activation patterns may be employed, whereas the conventional SIM-OFDM is capable of using only a subset of the patterns. Our simulation results show that an improved performance is attainable by the proposed system.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Improved Stability Method for Linear Time-Varying Delay Systems
    • Authors: Zhanshan Zhao;Meili He;Jing Zhang;Jie Sun;
      Pages: 7753 - 7758
      Abstract: In this paper, we consider the stability of the system with time-varying delay. By partitioning the delay interval and taking account of the triple integral term, a new augmented Lyapunov–Krasovskii function (LKF) is proposed. The Wirtinger-based inequality and improved convex combination method are applied to estimate the upper bound of the derivative produced by LKF. For the quadratic functions, we make full use of the convex approaches. The Moon inequality mixes the convex approach is applied to tackle with the integral term made by the Wirtinger-based inequality. Then according to the result, our method can obtain less conservative stability condition. Finally, examples are provided to demonstrate our method is efficiency.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Smooth Second Order Sliding Mode Control of a Class of Underactuated
           Mechanical Systems
    • Authors: Ibrahim Shah;Fazal Ur Rehman;
      Pages: 7759 - 7771
      Abstract: This paper investigates a framework for the application of robust and smooth second order sliding mode control to a class of underactuated mechanical systems for the realization of high performance control applications. First, using input and state transformations, the dynamics of the class are transformed into a normal form which consists of a set of reduced order nonlinear subsystems and a set of reduced order linear subsystems. Then we present nonlinear sliding manifold and sliding mode control for the reduced order nonlinear subsystem known as the Lagrangian zero dynamics such that stability of the overall system is guaranteed. The control design procedure is illustrated for the Furuta Pendulum, the Overhead Crane, and the Beam-and-Ball system. Numerical simulations verify the effectiveness of the proposed framework. Additionally, we design swingup control law for the Furuta Pendulum to overcome the limitation of the sliding mode control law and achieve global stabilization in the presence of external disturbance.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Decorrelating Receiver of Interference Mitigation in MMwave Small Cells
    • Authors: Yinghui Zhang;Yang Liu;Jing Gao;
      Pages: 7772 - 7779
      Abstract: Heterogeneous networks (HetNets) that incorporate small cells save energy effectively because the small cells have lower transmission and operational power consumptions. However, the performance of HetNets depends heavily on reducing the interference. In this paper, we investigated interference cancellation (IC) with the aim of developing a decorrelating receiver. We focused on millimeter wave small cells systems in which multiuser multiple-input multiple-output transmission was utilized. We began by suppressing the interference in the multiple antennas mode by means of well-known zero-forcing and a minimum mean square error algorithm. Then, we investigated more active IC. To further reduce the impact of interference in HetNets, we developed a hybrid HetNets paradigm. Finally, we used simulations to achieve performance evaluation and comprehensive comparative analysis. Extensive numerical results verified the effectiveness of the proposed schemes. This research provides an efficient solution for suppressing multiple access interference and improving the throughput in hybrid HetNets.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Joint Reconstruction and Segmentation Method for Limited-Angle X-Ray
    • Authors: Zenghui Wei;Baodong Liu;Bin Dong;Long Wei;
      Pages: 7780 - 7791
      Abstract: Limited-angle computed tomography (CT) is common in industrial applications, where incomplete projection data can cause artifacts. For objects made from homogeneous materials, we propose a joint reconstruction and segmentation method that performs joint image reconstruction and segmentation directly on the projection data. We describe an alternating minimization algorithm to solve the resulting optimization problem, and we modify the primal-dual hybrid gradient algorithm for the non-convex piecewise constant Mumford-Shah model, which is a popular approximation model in biomedical image segmentation. The effectiveness of the proposed approach is validated by simulation and by application to actual micro-CT data sets.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • An Energy-Aware and Void-Avoidable Routing Protocol for Underwater Sensor
    • Authors: Zhuo Wang;Guangjie Han;Hongde Qin;Suping Zhang;Yancheng Sui;
      Pages: 7792 - 7801
      Abstract: Underwater sensor networks are facing a great challenge in designing a routing protocol with longer network lifetime and higher packet delivery rate under the complex underwater environment. In this paper, we propose an energy-aware and void-avoidable routing protocol (EAVARP). EAVARP includes layering phase and data collection phase. During the layering phase, concentric shells are built around sink node, and sensor nodes are distributed on different shells. Sink node performs hierarchical tasks periodically to ensure the validity and real-time of the topology. It makes EAVARP apply to dynamic network environment. During the data collection phase, data packets are forwarded based on different concentric shells through opportunistic directional forwarding strategy (ODFS), even if there are voids. The ODFS takes into account the remaining energy and data transmission of nodes in the same shell, and avoids cyclic transmission, flooding, and voids. The verification and analysis of simulation results show the effectiveness of our proposed EAVARP in terms of selecting performance matrics in comparison to existing routing protocols.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Expected Value of Partial Perfect Information in Hybrid Models Using
           Dynamic Discretization
    • Authors: Barbaros Yet;Anthony Constantinou;Norman Fenton;Martin Neil;
      Pages: 7802 - 7817
      Abstract: In decision theory models, expected value of partial perfect information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert knowledge is most beneficial. Calculating EVPPI of continuous variables is challenging, and several sampling and approximation techniques have been proposed. This paper proposes a novel approach for calculating EVPPI in hybrid influence diagram (HID) models (these are influence diagrams (IDs) containing both discrete and continuous nodes). The proposed approach transforms the HID into a hybrid Bayesian network and makes use of the dynamic discretization and the junction tree algorithms to calculate the EVPPI. This is an approximate solution (no feasible exact solution is possible generally for HIDs) but we demonstrate it accurately calculates the EVPPI values. Moreover, unlike the previously proposed simulation-based EVPPI methods, our approach eliminates the requirement of manually determining the sample size and assessing convergence. Hence, it can be used by decision-makers who do not have deep understanding of programming languages and sampling techniques. We compare our approach to the previously proposed techniques based on two case studies.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Novel Cooperative Hunting Algorithm for Inhomogeneous Multiple
           Autonomous Underwater Vehicles
    • Authors: Mingzhi Chen;Daqi Zhu;
      Pages: 7818 - 7828
      Abstract: Cooperative hunting of multi-autonomous underwater vehicle (AUV) is an important research topic. Current studies concentrate on AUVs with the same speed abilities and mostly do not consider their speed differences. In fact, AUVs in a hunting group are often of different types and possess different maximum sailing speeds. For inhomogeneous multi-AUV, a novel time competition mechanism is proposed to construct an efficient dynamic hunting alliance. Hunting team with AUVs possessing higher speed abilities is more suitable for the vast underwater environment. In the pursuing stage, AUV needs to act fast enough to avoid the escape of evader. To achieve a quick and accurate pursuit, a combined path planning approach is presented, which combines a Glasius bio-inspired neural network model and a belief function. Simulation experiments demonstrate the feasibility and efficiency of the proposed algorithm in the cooperative hunting of inhomogeneous multi-AUV under dynamic underwater environment with intelligent evaders and multi-obstacle.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Virtual Drive Testing of Adaptive Antenna Systems in Dynamic Propagation
           Scenarios for Vehicle Communications
    • Authors: Wei Fan;Lassi Hentilä;Fengchun Zhang;Pekka Kyösti;Gert Frølund Pedersen;
      Pages: 7829 - 7838
      Abstract: Virtual drive testing (VDT) has gained great interest from both industry and academia, owing to its promise to replay field trials in a controllable laboratory condition. VDT is especially appealing for vehicle communication scenarios, where actual field trials can be difficult to carry out in practice. Research work on VDT of adaptive antenna systems in dynamic propagation scenarios has been limited, mainly due to high VDT setup cost and lack of efficient dynamic propagation channel models. In this paper, we propose to jointly emulate adaptive antennas (i.e. beamformers) and radio channels in the radio channel emulator to reduce the VDT setup cost. A simple dynamic propagation channel framework, which is based on linear interpolation of propagation parameters of the stationary channel models, is also presented. We further experimentally evaluate the beamformer capability (i.e. beam tracking and null steering) in dynamic line-of-sight (LOS) and non-LOS scenarios in the proposed cost-effective conducted VDT setups. The simulation and measurement results have demonstrated the effectiveness of the beam tracking and nulling steering algorithms in dynamic propagation conditions in the presence of interfering signal. The proposed setup and dynamic channel modeling framework are valuable for the VDT of adaptive antenna systems.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Relay Selection for Underwater Acoustic Sensor Networks: A Multi-User
           Multi-Armed Bandit Formulation
    • Authors: Xinbin Li;Jiajia Liu;Lei Yan;Song Han;Xinping Guan;
      Pages: 7839 - 7853
      Abstract: Multi-user cooperative transmission is an attractive architecture for underwater acoustic sensor networks (UASNs). Cooperative transmission depends on careful allocations of resources such as relay selection, but traditional relay selection requires precise measurements of channel state information, which is infeasible for multi-user cooperative transmission due to the unique features and hardware restrictions of UASNs. In this paper, we model multi-user relay selection under a multiuser multi-armed bandit (MU-MAB) framework, whereby users are not provided any prior knowledge about underwater acoustic channel conditions. We first exploit a novel MU-MAB algorithm, DSMU-MAB, for relay selection, assuming that the reward distributions are initially unknown but remain constant. Second, we consider an evolving environment in which the reward distributions undergo changes in time, and DSMU-rMAB, a derivative of DSMU-MAB, is proposed, which can be robust to abrupt changes in underwater communication environments. The proposed algorithms not only help sources find the suitable relays to achieve a high quality transmission and avoid collisions among users but also reduce the mass of information exchanged among users. We established the effectiveness of our proposed algorithms using theoretical and numerical analyses.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Mutual Heterogeneous Signcryption Schemes for 5G Network Slicings
    • Authors: Jingwei Liu;Lihuan Zhang;Rong Sun;Xiaojiang Du;Mohsen Guizani;
      Pages: 7854 - 7863
      Abstract: With the emergence of mobile communication technologies, we are entering the fifth generation (5G) mobile communication system era. Various application scenarios will arise in the 5G era to meet the different service requirements. Different 5G network slicings may deploy different public key cryptosystems. The security issues among the heterogeneous systems should be considered. In order to ensure the secure communications between 5G network slicings, in different public cryptosystems, we propose two heterogeneous signcryption schemes which can achieve mutual communications between the public key infrastructure and the certificateless public key cryptography environment. We prove that our schemes have the indistinguishability against adaptive chosen ciphertext attack under the computational Diffie–Hellman problem and the existential unforgeability against adaptive chosen message attack under the discrete logarithm problem in the random oracle model. We also set up two heterogeneous cryptosystems on Raspberry Pi to simulate the interprocess communication between different public key environments. Furthermore, we quantify and analyze the performance of each scheme. Compared with the existing schemes, our schemes achieve greater efficiency and security.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Tensor 2-D DOA Estimation for a Cylindrical Conformal Antenna Array in a
           Massive MIMO System Under Unknown Mutual Coupling
    • Authors: Xiaoyu Lan;Lening Wang;Yupeng Wang;Chang Choi;Dongmin Choi;
      Pages: 7864 - 7871
      Abstract: In this paper, a novel 2-D direction of arrival (DOA) estimation approach based on the tensor technique is proposed for a conformal array in a massive multiple-input-multiple-output system under unknown mutual coupling. By placing sensors uniformly on the cylindrical surface, the received signal expression is formulated with the Khatri–Rao product. An unknown mutual coupling auto-suppression method based on the conformal array is investigated. Then, to utilize the multidimensional information of the received data, a third-order tensor is constructed based on the conformal array signal model, and the signal subspace is provided by higher order singular value decomposition. Finally, the DOAs are estimated by conventional subspace-based algorithms. This approach provides improved DOA estimation performance owing to the utilization of the multidimensional information and the covariance tensor method, with lower SNR and fewer snapshots. The simulation results confirm that the proposed method outperforms the conventional method based on the vector model.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Finding Top- $k$ Dominance on Incomplete Big Data Using MapReduce
    • Authors: Payam Ezatpoor;Justin Zhan;Jimmy Ming-Tai Wu;Carter Chiu;
      Pages: 7872 - 7887
      Abstract: Incomplete data is one major kind of multi-dimensional dataset that has random-distributed missing nodes in its dimensions. It is very difficult to retrieve information from this type of dataset when it becomes large. Finding top- $k$ dominant values in this type of dataset is a challenging procedure. Some algorithms are present to enhance this process, but most are efficient only when dealing with small incomplete data. One of the algorithms that make the application of top- $k$ dominating (TKD) query possible is the Bitmap Index Guided (BIG) algorithm. This algorithm greatly improves the performance for incomplete data, but it is not designed to find top- $k$ dominant values in incomplete big data. Several other algorithms have been proposed to find the TKD query, such as Skyband Based and Upper Bound Based algorithms, but their performance is also questionable. Algorithms developed previously were among the first attempts to apply TKD query on incomplete data; however, these algorithms suffered from weak performance. This paper proposes MapReduced Enhanced Bitmap Index Guided Algorithm (MRBIG) for dealing with the aforementioned issues. MRBIG uses the MapReduce framework to enhance the performance of applying top- $k$ dominance queries on large incomplete datasets. The proposed approach uses the MapReduce parallel computing approach involving multiple computing nodes. The framework separates the tasks between several computing nodes to independently and simultaneously work to find the result. This method has achieved up to two times faster processing time in finding the TKD query result when compared to previously proposed algorithms.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Robust Secure Precoding Design for MIMO SWIPT System With Bounded Channel
    • Authors: Hehao Niu;Bangning Zhang;Yuzhen Huang;Zheng Chu;Zhengyu Zhu;Daoxing Guo;
      Pages: 7888 - 7896
      Abstract: In this paper, we investigate the worst-case robust secure precoding design for simultaneous wireless information and power transfer in multiple-input-multiple-output wiretap system, which consists of one transmitter, one information receiver, and one energy receiver. By treating the ER as potential eavesdropper, we aim to maximize the worst-case secrecy rate by designing the transmit precoding matrix. This is a typical non-convex optimal design while the channel state information uncertainties make this problem harder to handle, thus we propose a new method to obtain the solution. Specifically, instead of approximating the logarithmic determinant as a trace, we propose to linearize the two log-det terms. After linearization, epigraph reformulation is used to deal with the bounded channel uncertainties. Then, an alternating optimization method is utilized to solve the reformulated problem. After obtaining the precoding matrix, we derive the worst-case secrecy rate by solving another optimal problem with respect to the channel uncertainty. Numerical results validate the performance of our proposed design.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • IPv6-Based Architecture of Community Medical Internet of Things
    • Authors: Chao Liu;Fulong Chen;Chuanxin Zhao;Taochun Wang;Cheng Zhang;Ziyang Zhang;
      Pages: 7897 - 7910
      Abstract: The effective integration of Community Medical Internet of Things (CMIoT) and the core technology IPv6 about the next generation of Internet will create a new pattern for the medical field. However, as a heterogeneous network system, the interconnection of different CMIoT components is the primary problem, which needs to be solved. Traditional protocol conversion gateway in the Internet of Things only implements the packet format conversion. When the network environment changes, it is difficult to effectively implement the data path. We propose a CMIoT architecture with the assist of the communication auxiliary gateway, and design the simplified protocol message format under the premise of satisfying the functional requirements. At the same time, the CMIoT architecture model is built based on the Ptolemy II modeling environment and implemented in a community, and it is proved that the interconnection between the IPv6 network and the physical network can be realized more effectively.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Mitigating Cyber Privacy Leakage for Distributed DC Optimal Power Flow in
           Smart Grid With Radial Topology
    • Authors: Endong Liu;Peng Cheng;
      Pages: 7911 - 7920
      Abstract: This paper investigates the privacy preservation issue of distributed dc optimal power flow (OPF) methods while considering the line capacity constraints in smart grids with radial topology. We show that private information, such as the local energy consumption, local power generation, and parameters in the local cost function can be possibly exposed in traditional OPF methods without proper privacy preserving mechanism. Then we design a privacy-preserving alternating direction method of multipliers based distributed OPF algorithm by adding stochastic noise into the communication messages between neighbor buses, and the analysis on the convergence and optimality of the proposed algorithm is provided. Extensive simulations on a nine-bus power system validate the theoretical results and demonstrate the feasibility of the proposed algorithm.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Secrecy Capacity of Artificial Noise Aided Secure Communication in MIMO
           Rician Channels
    • Authors: Mansoor Ahmed;Lin Bai;
      Pages: 7921 - 7929
      Abstract: Recent research on the physical layer security of wireless systems focuses on artificial noise (AN) aided security. The main metric for analysis of such systems is the secrecy capacity of the system. Most of the AN schemes proposed in recent research are based on a hypothesis that the number of transmit antennas is larger than that of the receive antennas. Under this assumption, the system can utilize all eigen-subchannels, equal to the number of the receive antennas, to send secret messages. The remaining null spaces are used for transmitting AN signals. These AN signals null out at the legitimate receivers and degrade illegitimate receiver’s channels. However, this strategy can significantly impair the secrecy capacity of the system if the number of transmit antennas is constrained or even smaller than the number of receive antennas. Recently, a new strategy has been proposed, where messages are encoded in $s$ (which is a variable) strongest eigen-subchannels based on ordered eigenvalues of Wishart matrices, while AN signals are generated in remaining spaces. This paper extends this strategy to Rician channels using the complex non-central Wishart distribution. A closed form expression for secrecy capacity of such system is computed using majorization theory. Performance of a MIMO communication system in Rician fading environment is simulated and effect of the Rician factor on secrecy capacity is studied. The same approach is then extended to decode and forward relay network. Secrecy capacity of the said network is computed, and the effect of AN is studied using the same methodology.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • New Zero-Watermarking Algorithm Using Hurst Exponent for Protection of
           Privacy in Telemedicine
    • Authors: Zulfiqar Ali;M. Shamim Hossain;Ghulam Muhammad;Muhammad Aslam;
      Pages: 7930 - 7940
      Abstract: Telemedicine has numerous potential applications in the medical field due to the significant progress of telecommunication and information technology in recent years. In any category of telemedicine, such as offline, remote monitoring, and interactive, medical data and personal information of an individual must be transmitted to the healthcare center. An unauthorized access to the data and information is unacceptable in a telemedicine application, because it may create unavoidable circumstances for a person’s private and professional life. To avoid any potential threat of identity exposure in telemedicine, a zero-watermarking algorithm to protect the privacy of an individual is proposed in this paper. The proposed algorithm embeds the identity of a person without introducing any distortion in medical speech signals. Two measures, namely, Hurst exponent and zero-crossing, are computed to determine the suitable locations in the signal for insertion of identity. An analysis of the signals indicates that unvoiced speech frames are reliable in insertion and extraction of identity, as well as robust against a noise attack. In the proposed zero-watermarking algorithm, identity is inserted in a secret key instead of a signal by using a 1-D local binary operator. Therefore, imperceptibility is naturally achieved. Experiments are performed by using a publicly available voice disorder database, and experimental results are satisfactory and show that the proposed algorithm can be reliably used in telemedicine applications.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • An IoT-Aware Approach for Elderly-Friendly Cities
    • Authors: Rubén Mulero;Aitor Almeida;Gorka Azkune;Patricia Abril-Jiménez;Maria Teresa Arredondo Waldmeyer;Miguel Páramo Castrillo;Luigi Patrono;Piercosimo Rametta;Ilaria Sergi;
      Pages: 7941 - 7957
      Abstract: The ever-growing life expectancy of people requires the adoption of proper solutions for addressing the particular needs of elderly people in a sustainable way, both from service provision and economic point of view. Mild cognitive impairments and frailty are typical examples of elderly conditions which, if not timely addressed, can turn out into more complex diseases that are harder and costlier to treat. Information and communication technologies, and in particular Internet of Things technologies, can foster the creation of monitoring and intervention systems, both on an ambient-assisted living and smart city scope, for early detecting behavioral changes in elderly people. This allows to timely detect any potential risky situation and properly intervene, with benefits in terms of treatment’s costs. In this context, as part of the H2020-funded City4Age project, this paper presents the data capturing and data management layers of the whole City4Age platform. In particular, this paper deals with an unobtrusive data gathering system implementation to collect data about daily activities of elderly people, and with the implementation of the related linked open data (LOD)-based data management system. The collected data are then used by other layers of the platform to perform risk detection algorithms and generate the proper customized interventions. Through the validation of some use-cases, it is demonstrated how this scalable approach, also characterized by unobtrusive and low-cost sensing technologies, can produce data with a high level of abstraction useful to define a risk profile of each elderly person.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Self-Adaptive Scheduling of Base Transceiver Stations in Green 5G Networks
    • Authors: Uzzal Kumar Dutta;Md Abdur Razzaque;M. Abdullah Al-Wadud;Md Saiful Islam;M. Shamim Hossain;B. B. Gupta;
      Pages: 7958 - 7969
      Abstract: In this paper, we design self-adaptive scheduling (SAS) algorithms for base transceiver stations (BTSs) of 5G networks to improve energy efficiency, reduce carbon footprint, and develop a self-sustainable green cellular network. In the SAS algorithm, a BTS switches among its operating states (active, turned-off, and sleep), thereby exploiting the traffic loads of the BTS and the single-hop neighbor BTSs thereof. The dynamic settings of traffic thresholds help the SAS system in achieving a high degree of cooperation among the neighborhood BTSs, which in turn increases the energy savings of the network. Each active SAS BTS independently and dynamically decides in determining its operation state, thus make our proposed SAS algorithms fully distributed. Results from a simulation conducted in network simulator version 3 show that BTS scheduling significantly influences cellular networks, and the proposed SAS algorithm can significantly increase the energy savings compared with state-of-the-art protocols.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Framework for Intracranial Saccular Aneurysm Detection and
           Quantification using Morphological Analysis of Cerebral Angiograms
    • Authors: Khalid Mahmood Malik;Shakeel M. Anjum;Hamid Soltanian-Zadeh;Hafiz Malik;Ghaus M. Malik;
      Pages: 7970 - 7986
      Abstract: Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Accurate Star Centroid Detection for the Advanced Geosynchronous Radiation
           Imager of Fengyun-4A
    • Authors: Haopeng Zhang;Yi Su;Jian Shang;Lei Yang;Bowen Cai;Chengbao Liu;Jing Wang;Shengxiong Zhou;Zhiqing Zhang;
      Pages: 7987 - 7999
      Abstract: Star observation can be used for the image navigation of certain instruments aboard the three-axis stabilized geostationary satellites. How to extract the accurate star centroids in the observed star images is one of the key problems. In this paper, a high precision star centroid detection method is proposed for the advanced geosynchronous radiation imager (AGRI) of Fengyun-4A (FY-4A), the first experimental satellite of the new generation of Chinese geostationary meteorological satellites FY-4 series. Different from star sensors which are deliberately defocused with relatively large star spot, AGRI is focused for the purpose of earth observation, making it challenging to extract accurate star centroids. To solve the problem, we take the advantage of continuous observing and improve the precision of star centroiding by trajectory fitting and energy response curve fitting. Extensive experiments have been performed on simulated star images and the actual observation data of AGRI aboard FY-4A over ten months in-orbit tests. Experimental results show that the proposed method can accurately extract star centroids with the error less than 0.3 pixels, laying a solid foundation for image navigation of FY-4A.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Enhancement of Data Rate and Packet Size in Image Sensor Communications by
           Employing Constant Power 4-PAM
    • Authors: Duy Thong Nguyen;Yoonsung Chae;Youngil Park;
      Pages: 8000 - 8010
      Abstract: Image sensor communications (ISC) is a scheme to make the LED lamp replace access points or beacons in indoor environments. Its application has been limited, however, due to its low data rate and small packet size. To resolve these problems, we propose a new ISC scheme using 4-PAM modulation and a parallel packet structure. The constant power 4-PAM signal was designed to have constant optical power in each symbol to eliminate the flickering effect. The experiments show that a data rate more than 2.6 times higher than the existing on-off keying schemes was achieved. In addition, it is demonstrated that the packet length can reach more than several frames while it is limited to less than a frame using the existing schemes. The limiting factors of N-PAM levels and packet size are analyzed in this paper to further improvement. All these results are demonstrated through simulations and experiments.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • A Review of Text Watermarking: Theory, Methods, and Applications
    • Authors: Nurul Shamimi Kamaruddin;Amirrudin Kamsin;Lip Yee Por;Hameedur Rahman;
      Pages: 8011 - 8028
      Abstract: During the recent years, the issue of preserving the integrity of digital text has become a focus of interest in the transmission of online content on the Internet. Watermarking has a useful tool in the protection of digital text content as it solves the problem of tampering, duplicating, unauthorized access, and security breaches. The rapid development currently observable in information transfer and access is the consequences of the widespread usage of the Internet. When it comes to the different types of digital data, text constitutes the most complex and challenging type to which the method of text watermarking can be applied. Text watermarking constitutes a highly complex task, most of all, since only limited research has been done in this field. In order to ensure the successful evaluation, analysis, and implementation, a comprehensive research needs to be performed. This paper studies the theory, methods, and applications of text watermarking, which includes the discussion on the definition, embedding and extracting processes, requirements, approaches, and language applications of the established text watermarking methods. This paper reviews in detail the new classification of text watermarking, which is through embedding process and its related issues of attacks and language applicability. Open research challenges and future directions are also investigated, with a focus on its information integrity, information availability, originality preservation, information confidentiality, protection of sensitive information, document transformation, cryptography application, and language flexibility.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Cognitive Privacy Middleware for Deep Learning Mashup in Environmental IoT
    • Authors: Ahmed M. Elmisery;Mirela Sertovic;Brij B. Gupta;
      Pages: 8029 - 8041
      Abstract: Data mashup is a Web technology that combines information from multiple sources into a single Web application. Mashup applications support new services, such as environmental monitoring. The different organizations utilize data mashup services to merge data sets from the different Internet of Multimedia Things (IoMT) context-based services in order to leverage the performance of their data analytics. However, mashup, different data sets from multiple sources, is a privacy hazard as it might reveal citizens specific behaviors in different regions. In this paper, we present our efforts to build a cognitive-based middleware for private data mashup (CMPM) to serve a centralized environmental monitoring service. The proposed middleware is equipped with concealment mechanisms to preserve the privacy of the merged data sets from multiple IoMT networks involved in the mashup application. In addition, we presented an IoT-enabled data mashup service, where the multimedia data are collected from the various IoMT platforms, and then fed into an environmental deep learning service in order to detect interesting patterns in hazardous areas. The viable features within each region were extracted using a multiresolution wavelet transform, and then fed into a discriminative classifier to extract various patterns. We also provide a scenario for IoMT-enabled data mashup service and experimentation results.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Versatile Locomotion Control of a Hexapod Robot Using a Hierarchical
           Network of Nonlinear Oscillator Circuits
    • Authors: Ludovico Minati;Mattia Frasca;Natsue Yoshimura;Yasuharu Koike;
      Pages: 8042 - 8065
      Abstract: A novel hierarchical network based on coupled nonlinear oscillators is proposed for motor pattern generation in hexapod robots. Its architecture consists of a central pattern generator (CPG), producing the global leg coordination pattern, coupled with six local pattern generators, each devoted to generating the trajectory of one leg. Every node comprises a simple nonlinear oscillator and is well-suited for implementation in a standard field-programmable analog array device. The network enables versatile locomotion control based on five high-level parameters which determine the inter-oscillator coupling pattern via simple rules. The controller was realized on dedicated hardware, deployed to control an ant-like hexapod robot, and multi-sensory telemetry was performed. As a function of a single parameter, it was able to stably reproduce the canonical gaits observed in six-legged insects, namely the wave, tetrapod, and tripod gaits. A second parameter enabled driving the robot in ant-like and cockroach-like postures. Three further parameters enabled inhibiting and resuming walking, steering, and producing uncoordinated movement. Emergent phenomena were observed in the form of a multitude of intermediate gaits and of hysteresis and metastability close to a point of gait transition. The primary contributions of this paper reside in the hierarchical controller architecture and associated approach for collapsing a large set of low-level parameters, stemming from the complex hexapod kinematics, into only five high-level parameters. Such parameters can be changed dynamically, an aspect of broad practical relevance opening new avenues for driving hexapod robots via afferent signals from other circuits representing higher brain areas, or by means of suitable brain-computer interfaces. An additional contribution is the detailed characterization via telemetry of the physical robot, involving the definition of parameters which may aid future comparison with other controllers. The-present results renew interest into analog CPG architectures and reinforce the generality of the connectionist approach.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Statistical Analysis of the Effects of Heavyweight and Lightweight
           Methodologies on the Six-Pointed Star Model
    • Authors: Muhammad Azeem Akbar;Jun Sang;Arif Ali Khan;Fazal-E Amin; Nasrullah;Shahid Hussain;Mohammad Khalid Sohail;Hong Xiang;Bin Cai;
      Pages: 8066 - 8079
      Abstract: Traditionally, software development organizations relied on heavyweight development methodologies, such as waterfall, V-model, and others. Later, agile development methodologies known as lightweight methodologies were introduced. Many considered these to be more flexible and more effective than heavyweight methodologies. Both methodologies are equally important for a software development life cycle. The purpose of adopting software development methodologies is to optimize the process model to achieve milestones while concurrently and effectively managing time, budget, and quality. The literature review reveals that there is a lack of statistical evidence for determining the effect of both methodologies on the six-pointed star model (schedule, scope, budget, risk, resource, and quality). In this paper, statistical comparisons were performed for the effects of both methodologies on each factor of the six-pointed star model and the interdependency among factors. Numerical analyses were conducted based on survey responses collected from the experienced users of both methodologies. After examining the results of all the factors of both methodologies, it was determined that lightweight methodologies are suitable for small-scale projects and that heavyweight methodologies perform better for medium- and large-scale projects.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • S-Transform Based FFNN Approach for Distribution Grids Fault Detection and
    • Authors: Md Shafiullah;M. A. Abido;
      Pages: 8080 - 8088
      Abstract: Detection and classification of any anomaly at its commencement are very crucial for optimal management of assets in power system grids. This paper presents a novel hybrid approach that combines S-transform (ST) and feedforward neural network (FFNN) for the detection and classification of distribution grid faults. In this proposed strategy, the measured three-phase current signals are processed through ST with a view to extracting useful statistical features. The extracted features are then fetched to FFNN in order to detect and classify different types of faults. The proposed approach is implemented in two different test distribution grids modeled and simulated in real-time digital simulator and MATLAB/SIMULINK. The obtained results justify the efficacy of the presented technique for both noise-free and noisy data. In addition, the developed technique is independent of fault resistance, inception angle, distance, and prefault loading condition. Besides, the comparative results confirm the superiority and competitiveness of the developed technique over the available techniques reported in the literature.
      PubDate: 2018
      Issue No: Vol. 6 (2018)
  • Microwave Photonic Downconversion With Improved Conversion Efficiency and
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