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  • Open Access

    ARTICLE

    An Application-Oriented Buffer Management Strategy in Opportunistic Networks

    Meihua Liu1, Xinchen Zhang2,*, Shuangkui Ge3, Xiaoli Chen1, Jianbin Wu2, Mao Tian1

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 559-574, 2019, DOI:10.32604/cmc.2019.04843

    Abstract In Opportunistic networks (ONs), buffer management is critical to improve the message exchanging efficiency due to the limited storage space and transmission bandwidth at the wireless edge. Current solutions make message scheduling and drop policy based on assumptions that messages can always been forwarded in a single contact, and all node pairs have the same contact rates. However, such ideal assumptions are invalid for realistic mobility traces of hand-held. Recent studies show that the single contact duration is limited and the mobility of nodes is heterogeneous in reality. In this paper, a buffer management strategy based on contact duration and… More >

  • Open Access

    ARTICLE

    Binaural Sound Source Localization Based on Convolutional Neural Network

    Lin Zhou1,*, Kangyu Ma1, Lijie Wang1, Ying Chen1,2, Yibin Tang3

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 545-557, 2019, DOI:10.32604/cmc.2019.05969

    Abstract Binaural sound source localization (BSSL) in low signal-to-noise ratio (SNR) and high reverberation environment is still a challenging task. In this paper, a novel BSSL algorithm is proposed by introducing convolutional neural network (CNN). The proposed algorithm first extracts the spatial feature of each sub-band from binaural sound signal, and then combines the features of all sub-bands within one frame to assemble a two-dimensional feature matrix as a grey image. To fully exploit the advantage of the CNN in extracting high-level features from the grey image, the spatial feature matrix of each frame is used as input to train the… More >

  • Open Access

    ARTICLE

    Directional Antenna Intelligent Coverage Method Based on Traversal Optimization Algorithm

    Jialuan He1,2, Zirui Xing2, Rong Hu2, Jing Qiu3,*, Shen Su3,*, Yuhan Chai3, Yue Wu4

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 527-544, 2019, DOI:10.32604/cmc.2019.05586

    Abstract Wireless broadband communication is widely used in maneuver command communications systems in many fields, such as military operations, counter-terrorism and disaster relief. How to reasonably formulate the directional antenna coverage strategy according to the mobile terminal dynamic distribution and guide the directional antenna dynamic coverage becomes a practical research topic. In many applications, a temporary wireless boardband base station is required to support wireless signal communications between many terminals from nearby vehicles and staffs. It is therefore important to efficiently set directional antenna while ensuring large enough coverage over dynamically distributed terminals. The wireless broadband base station mostly uses two… More >

  • Open Access

    ARTICLE

    Rigid Medical Image Registration Using Learning-Based Interest Points and Features

    Maoyang Zou1,2, Jinrong Hu2, Huan Zhang2, Xi Wu2, Jia He2, Zhijie Xu3, Yong Zhong1,*

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 511-525, 2019, DOI:10.32604/cmc.2019.05912

    Abstract For image-guided radiation therapy, radiosurgery, minimally invasive surgery, endoscopy and interventional radiology, one of the important techniques is medical image registration. In our study, we propose a learning-based approach named “FIP-CNNF” for rigid registration of medical image. Firstly, the pixel-level interest points are computed by the full convolution network (FCN) with self-supervise. Secondly, feature detection, descriptor and matching are trained by convolution neural network (CNN). Thirdly, random sample consensus (Ransac) is used to filter outliers, and the transformation parameters are found with the most inliers by iteratively fitting transforms. In addition, we propose “TrFIP-CNNF” which uses transfer learning and fine-tuning… More >

  • Open Access

    RETRACTION

    RETRACTED: Automatic Arrhythmia Detection Based on Convolutional Neural Networks

    Zhong Liu1,2, Xinan Wang1,*, Kuntao Lu1, David Su3

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 497-509, 2019, DOI:10.32604/cmc.2019.04882

    Abstract ECG signal is of great importance in the clinical diagnosis of various heart diseases. The abnormal origin or conduction of excitation is the electrophysiological mechanism leading to arrhythmia, but the type and frequency of arrhythmia is an important indicator reflecting the stability of cardiac electrical activity. In clinical practice, arrhythmic signals can be classified according to the origin of excitation, the frequency of excitation, or the transmission of excitation. Traditional heart disease diagnosis depends on doctors, and it is influenced by doctors' professional skills and the department's specialty. ECG signal has the characteristics of weak signal, low frequency, large variation,… More >

  • Open Access

    ARTICLE

    Drug Side-Effect Prediction Using Heterogeneous Features and Bipartite Local Models

    Yi Zheng1,2, Wentao Zhao2,*, Chengcheng Sun2, Qian Li1

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 481-496, 2019, DOI:10.32604/cmc.2019.05536

    Abstract Drug side-effects impose massive costs on society, leading to almost one-third drug failure in the drug discovery process. Therefore, early identification of potential side-effects becomes vital to avoid risks and reduce costs. Existing computational methods employ few drug features and predict drug side-effects from either drug side or side-effect side separately. In this work, we explore to predict drug side-effects by combining heterogeneous drug features and employing the bipartite local models (BLMs) which fuse predictions from both the drug side and side-effect side. Specifically, we integrate drug chemical structures, drug interacted proteins and drug associated genes into a unified framework… More >

  • Open Access

    ARTICLE

    Heterogeneous Memristive Models Design and Its Application in Information Security

    Shaojiang Zhong1, *

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 465-479, 2019, DOI:10.32604/cmc.2019.05853

    Abstract Based on the three-dimensional classic Chua circuit, a nonlinear circuit containing two flux-control memristors is designed. Due to the difference in the design of the characteristic equation of the two magnetron memristors, their position form a symmetrical structure with respect to the capacitor. The existence of chaotic properties is proved by analyzing the stability of the system, including Lyapunov exponent, equilibrium point, eigenvalue, Poincare map, power spectrum, bifurcation diagram et al. Theoretical analysis and numerical calculation show that this heterogeneous memristive model is a hyperchaotic five-dimensional nonlinear dynamical system and has a strong chaotic behavior. Then, the memristive system is… More >

  • Open Access

    ARTICLE

    Analysis of Bus Ride Comfort Using Smartphone Sensor Data

    Hoong-Chor Chin1, Xingting Pang1, Zhaoxia Wang2,3,*

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 455-463, 2019, DOI:10.32604/cmc.2019.05664

    Abstract Passenger comfort is an important indicator that is often used to measure the quality of public transport services. It may also be a crucial factor in the passenger’s choice of transport mode. The typical method of assessing passenger comfort is through a passenger interview survey which can be tedious. This study aims to investigate the relationship between bus ride comfort based on ride smoothness and the vehicle’s motion detected by the smartphone sensors. An experiment was carried out on a bus fixed route within the University campus where comfort levels were rated on a 3-point scale and recorded at 5-second… More >

  • Open Access

    ARTICLE

    A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

    Maha Al-Yahya1, *

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 421-433, 2019, DOI:10.32604/cmc.2019.06209

    Abstract The purpose of this study is to evaluate the performance of five supervised machine learning methods for the task of automated genre identification of classical Arabic texts using text most frequent words as features. We design an experiment for comparing five machine-learning methods for the genre identification task for classical Arabic text. We set the data and the stylometric features and vary the classification method to evaluate the performance of each method. Of the five machine learning methods tested, we can conclude that Support Vector Machine (SVM) are generally the most effective. The contribution of this work lies in the… More >

  • Open Access

    ARTICLE

    Design and Performance Comparison of Rotated Y-Shaped Antenna Using Different Metamaterial Surfaces for 5G Mobile Devices

    Jalal Khan1, Daniyal Ali Sehrai1, Mushtaq Ahmad Khan1, Haseeb Ahmad Khan2, Salman Ahmad3, Arslan Ali4, Arslan Arif5, Anwer Ahmad Memon6, Sahib Khan1,4,*

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 409-420, 2019, DOI:10.32604/cmc.2019.06883

    Abstract In this paper, a rotated Y-shaped antenna is designed and compared in terms of performance using a conventional and EBG ground planes for future Fifth Generation (5G) cellular communication system. The rotated Y-shaped antenna is designed to transmit at 38 GHz which is one of the most prominent candidate bands for future 5G communication systems. In the design of conventional antenna and metamaterial surfaces (mushroom, slotted), Rogers-5880 substrate having relative permittivity, thickness and loss tangent of 2.2, 0.254 mm, and 0.0009 respectively have been used. The conventional rotated Y-shaped antenna offers a satisfactory wider bandwidth (0.87 GHz) at 38.06 GHz… More >

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