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  • Open Access

    ARTICLE

    Research on Indoor Passive Positioning Technology Based on WiFi

    Lei Sun1, Ling Tan1,*, Wenjie Ma1, Jingming Xia2

    Journal on Internet of Things, Vol.2, No.1, pp. 23-35, 2020, DOI:10.32604/jiot.2020.09075 - 06 August 2020

    Abstract In recent years, WiFi indoor positioning technology has become a hot research topic at home and abroad. However, at present, indoor positioning technology still has many problems in terms of practicability and stability, which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process. Aiming at the instability of RSS and the more complicated data processing, this paper proposes a low-frequency filtering method based on fast data convergence. Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data; data convergence combines the mean and multi-data parallel analysis process More >

  • Open Access

    ARTICLE

    Green Synthesis of Silver Nanoparticles Using Annona diversifolia Leaf Extract and Their Antimicrobial Application

    Rogelio Solorzano-Toala1, Daniel Gonzalez-Mendoza2,*, Benjamin Valdez-Salas3, Vianey MendezTrujillo4, Federico Gutierrez-Miceli1, Ernesto Beltran-Partida3, Olivia Tzintzun-Camacho2

    Journal of Renewable Materials, Vol.8, No.9, pp. 1129-1137, 2020, DOI:10.32604/jrm.2020.09845 - 03 August 2020

    Abstract The aim of this study was the synthesis of silver nanoparticle using Annona diversifolia Safford. The silver nanoparticles obtained were analyzed by spectroscopic methods and dynamic light scattering methods. The inhibition of AgNPs was evaluated against Bacillus cereus, Klebsiella pneumoniae and Enterobacter aerogenes. The results showed that AgNPs have high values at 3 keV and particle size between 45 to 58 nm with a homogenous morphology. The AgNPs showed growth inhibition against Klebsiella pneumoniae and Enterobacter aerogenes. Therefore studies are needed to confirm the potential antimicrobial of different AgNP from A. diversifolia in Gram negative and Gram positive bacteria. More >

  • Open Access

    ARTICLE

    Research on Clothing Simulation Design Based on Three-Dimensional Image Analysis

    Wenyao Zhu1, 2, Xue Li3, Young-Mi Shon4, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 945-962, 2020, DOI:10.32604/cmc.2020.010089 - 23 July 2020

    Abstract Traditional clothing design models based on adaptive meshes cannot reflect. To solve this problem, a clothing simulation design model based on 3D image analysis technology is established. The model uses feature extraction and description of image evaluation parameters, and establishes the mapping relationship between image features and simulation results by using the optimal parameter values, thereby obtaining a three-dimensional image simulation analysis environment. On the basis of this model, by obtaining the response results of clothing collision detection and the results of local adaptive processing of clothing meshes, the cutting form and actual cutting effect More >

  • Open Access

    ARTICLE

    Median Filtering Detection Based on Quaternion Convolutional Neural Network

    Jinwei Wang1, 2, 3, 4, Qiye Ni3, Yang Zhang3, Xiangyang Luo2, *, Yunqing Shi5, Jiangtao Zhai3, Sunil Kr Jha3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 929-943, 2020, DOI:10.32604/cmc.2020.06569 - 23 July 2020

    Abstract Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics. Therefore, more attention has been paid to the forensics research of median filtering. In this paper, a median filtering forensics method based on quaternion convolutional neural network (QCNN) is proposed. The median filtering residuals (MFR) are used to preprocess the images. Then the output of MFR is expanded to four channels and used as the input of QCNN. In QCNN, quaternion convolution is designed that can better mix the information of different channels than traditional methods. The More >

  • Open Access

    ARTICLE

    The Optimization Study about Fault Self-Healing Restoration of Power Distribution Network Based on Multi-Agent Technology

    Fuquan Huang1, Zijun Liu1, Tinghuang Wang1, Haitai Zhang2, *, Tony Yip3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 865-878, 2020, DOI:10.32604/cmc.2020.010724 - 23 July 2020

    Abstract In order to quickly and accurately locate the fault location of the distribution network and increase the stability of the distribution network, a fault recovery method based on multi-objective optimization algorithm is proposed. The optimization of the power distribution network fault system based on multiagent technology realizes fast recovery of multi-objective fault, solve the problem of network learning and parameter adjustment in the later stage of particle swarm optimization algorithm falling into the local extreme value dilemma, and realize the multi-dimensional nonlinear optimization of the main grid and the auxiliary grid. The system proposed More >

  • Open Access

    ARTICLE

    Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection

    Shi Li1, Xinyan Cao1, *, Yiting Nan2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 777-788, 2020, DOI:10.32604/cmc.2020.010870 - 23 July 2020

    Abstract Stance detection is the task of attitude identification toward a standpoint. Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting. Moreover, because the target is not always mentioned in the text, most methods have ignored target information. In order to solve these problems, we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory (LSTM) and the excellent extracting performance of convolutional neural networks (CNNs). The method can obtain multi-level features that consider both local More >

  • Open Access

    ARTICLE

    A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX

    Jun Li1, 2, Jieren Cheng2, *, Naixue Xiong3, Lougao Zhan4, Yuan Zhang1

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 723-741, 2020, DOI:10.32604/cmc.2020.011272 - 23 July 2020

    Abstract Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health. However, big data faces many ongoing serious challenges in the process of collection, storage, and use. Among them, data security and privacy problems have attracted extensive interest. In an effort to overcome this challenge, this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions (SGX). First of all, we define SGX as a trusted edge computing node, design data access module, data protection module, and data… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

    Fei Li1, *, Jiayan Zhang1, Edward Szczerbicki2, Jiaqi Song1, Ruxiang Li 1, Renhong Diao1

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 653-681, 2020, DOI:10.32604/cmc.2020.011264 - 23 July 2020

    Abstract The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to More >

  • Open Access

    ARTICLE

    Semi-GSGCN: Social Robot Detection Research with Graph Neural Network

    Xiujuan Wang1, Qianqian Zheng1, *, Kangfeng Zheng2, Yi Sui1, Jiayue Zhang1

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 617-638, 2020, DOI:10.32604/cmc.2020.011165 - 23 July 2020

    Abstract Malicious social robots are the disseminators of malicious information on social networks, which seriously affect information security and network environments. Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks. Supervised classification based on manual feature extraction has been widely used in social robot detection. However, these methods not only involve the privacy of users but also ignore hidden feature information, especially the graph feature, and the label utilization rate of semi-supervised algorithms is low. Aiming at the problems of shallow feature extraction and low label utilization rate in… More >

  • Open Access

    ARTICLE

    An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network

    Ye Wang1, Bixin Liu2, Hongjia Wu1, Shan Zhao1, Zhiping Cai1, *, Donghui Li3, *, Cheang Chak Fong4

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 355-367, 2020, DOI:10.32604/cmc.2020.09835 - 23 July 2020

    Abstract With the continuous development of e-commerce, consumers show increasing interest in posting comments on consumption experience and quality of commodities. Meanwhile, people make purchasing decisions relying on other comments much more than ever before. So the reliability of commodity comments has a significant impact on ensuring consumers’ equity and building a fair internet-trade-environment. However, some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits. Those improper ways of self-profiting have severely ruined the entire online shopping industry. Aiming to detect and prevent these deceptive comments More >

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