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  • Open Access

    ARTICLE

    Multidomain Correlation-Based Multidimensional CSI Tensor Generation for Device-Free Wi-Fi Sensing

    Liufeng Du1,*, Shaoru Shang1, Linghua Zhang2, Chong Li1, Jianing Yang3, Xiyan Tian1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1749-1767, 2024, DOI:10.32604/cmes.2023.030144 - 17 November 2023

    Abstract Due to the fine-grained communication scenarios characterization and stability, Wi-Fi channel state information (CSI) has been increasingly applied to indoor sensing tasks recently. Although spatial variations are explicitly reflected in CSI measurements, the representation differences caused by small contextual changes are easily submerged in the fluctuations of multipath effects, especially in device-free Wi-Fi sensing. Most existing data solutions cannot fully exploit the temporal, spatial, and frequency information carried by CSI, which results in insufficient sensing resolution for indoor scenario changes. As a result, the well-liked machine learning (ML)-based CSI sensing models still struggling with stable More >

  • Open Access

    ARTICLE

    Nonprecsion (Standard) Psychosocial Interventions for the Treatment of Mental Disorders

    Alan E. Kazdin*

    International Journal of Mental Health Promotion, Vol.24, No.4, pp. 457-473, 2022, DOI:10.32604/ijmhp.2022.022522 - 27 May 2022

    Abstract Advances in precision treatment promise to greatly improve the extent to which therapies for mental disorders are better matched to patient characteristics. At the same, we need to ensure that more readily disseminable and available nonprecison treatments are further developed as well. These treatments refer to standardized interventions that do not have to be individualized and are more readily available. Impetus for this call stems from the treatment gap, namely, the huge difference in the proportion of individuals who are in need of mental health services and who actually receive any form of treatment. The More >

  • Open Access

    ARTICLE

    Optimal Beamforming for Secure Transmit in Practical Wireless Networks

    Qiuqin Yang1, Linfang Li1, Ming-Xing Luo1,*, Xiaojun Wang2

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1863-1877, 2022, DOI:10.32604/cmc.2022.027120 - 18 May 2022

    Abstract In real communication systems, secure and low-energy transmit scheme is very important. So far, most of schemes focus on secure transmit in special scenarios. In this paper, our goal is to propose a secure protocol in wireless networks involved various factors including artificial noise (AN), the imperfect receiver and imperfect channel state information (CSI) of eavesdropper, weight of beamforming (BF) vector, cooperative jammers (CJ), multiple receivers, and multiple eavesdroppers, and the analysis shows that the protocol can reduce the transmission power, and at the same time the safe reachability rate is greater than our pre-defined… More >

  • Open Access

    ARTICLE

    LCF: A Deep Learning-Based Lightweight CSI Feedback Scheme for MIMO Networks

    Kyu-haeng Lee*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5561-5580, 2022, DOI:10.32604/cmc.2022.024562 - 14 January 2022

    Abstract Recently, as deep learning technologies have received much attention for their great potential in extracting the principal components of data, there have been many efforts to apply them to the Channel State Information (CSI) feedback overhead problem, which can significantly limit Multi-Input Multi-Output (MIMO) beamforming gains. Unfortunately, since most compression models can quickly become outdated due to channel variation, timely model updates are essential for reflecting the current channel conditions, resulting in frequent additional transmissions for model sharing between transceivers. In particular, the heavy network models employed by most previous studies to achieve high compression… More >

  • Open Access

    ARTICLE

    Action Recognition Based on CSI Signal Using Improved Deep Residual Network Model

    Jian Zhao1, Shangwu Chong1, Liang Huang1, Xin Li1, Chen He1, Jian Jia2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1827-1851, 2022, DOI:10.32604/cmes.2022.017654 - 30 December 2021

    Abstract In this paper, we propose an improved deep residual network model to recognize human actions. Action data is composed of channel state information signals, which are continuous fine-grained signals. We replaced the traditional identity connection with the shrinking threshold module. The module automatically adjusts the threshold of the action data signal, and filters out signals that are not related to the principal components. We use the attention mechanism to improve the memory of the network model to the action signal, so as to better recognize the action. To verify the validity of the experiment more More >

  • Open Access

    ARTICLE

    Adaptive Scheme for Crowd Counting Using off-the-Shelf Wireless Routers

    Wei Zhuang1,2, Yixian Shen1, Chunming Gao3, Lu Li1, Haoran Sang4, Fei Qian5,*

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 255-269, 2022, DOI:10.32604/csse.2022.020590 - 08 October 2021

    Abstract Since the outbreak of the world-wide novel coronavirus pandemic, crowd counting in public areas, such as in shopping centers and in commercial streets, has gained popularity among public health administrations for preventing the crowds from gathering. In this paper, we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information (CSI) by using common commercial wireless routers. Compared with previous researches on device-free crowd counting, our proposed method is more adaptive to the change of environment and can achieve high accuracy of crowd count estimation. Because the distance between access point More >

  • Open Access

    ARTICLE

    The Arcsine-X Family of Distributions with Applications to Financial Sciences

    Yen Liang Tung1, Zubair Ahmad2, Eisa Mahmoudi2,*

    Computer Systems Science and Engineering, Vol.39, No.3, pp. 351-363, 2021, DOI:10.32604/csse.2021.014270 - 12 August 2021

    Abstract The heavy-tailed distributions are very useful and play a major role in actuary and financial management problems. Actuaries are often searching for such distributions to provide the best fit to financial and economic data sets. In the current study, a prominent method to generate new distributions useful for modeling heavy-tailed data is considered. The proposed family is introduced using trigonometric function and can be named as the Arcsine-X family of distributions. For the purposes of the demonstration, a specific sub-model of the proposed family, called the Arcsine-Weibull distribution is considered. The maximum likelihood estimation method is… More >

  • Open Access

    ARTICLE

    Modeling Liver Cancer and Leukemia Data Using Arcsine-Gaussian Distribution

    Farouq Mohammad A. Alam1, Sharifah Alrajhi1, Mazen Nassar1,2, Ahmed Z. Afify3,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2185-2202, 2021, DOI:10.32604/cmc.2021.015089 - 05 February 2021

    Abstract The main objective of this paper is to discuss a general family of distributions generated from the symmetrical arcsine distribution. The considered family includes various asymmetrical and symmetrical probability distributions as special cases. A particular case of a symmetrical probability distribution from this family is the Arcsine–Gaussian distribution. Key statistical properties of this distribution including quantile, mean residual life, order statistics and moments are derived. The Arcsine–Gaussian parameters are estimated using two classical estimation methods called moments and maximum likelihood methods. A simulation study which provides asymptotic distribution of all considered point estimators, 90% and… More >

  • Open Access

    ARTICLE

    Application of Multi Agent Systems in Automation of Distributed Energy Management in Micro-grid using MACSimJX

    Leo Rajua, R. S. Miltonb, Senthilkumaran Mahadevana

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 483-491, 2018, DOI:10.1080/10798587.2017.1305647

    Abstract The objective of this paper is to monitor and control a micro-grid model developed in MATLABSimulink through Multi Agent System (MAS) for autonomous and distributed energy management. Since MATLAB/Simulink is not compatible with parallel operations of MAS, MAS operating in Java Agent Development Environment (JADE) is linked with MATLAB/Simulink through Multi Agent Control using Simulink with Jade extension (MACSimJX). This allows the micro-grid system designed with Simulink to be controlled by MAS for realizing the advantages of MAS in distributed and decentralized microgrid systems. JADE agents receive environmental information through Simulink and they coordinate to More >

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