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Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 496-509页

作者： Ruhul Amin Khalil Nasir Saeed Mohammad Inayatullah Babar tariqullah jan Sadia Din IEEE Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar 25120Pakistan the Department of Electrical Engineering Northern Border UniversityArar 73222Saudi Arabia College of Science and Engineering Hamad Bin Khalifa UniversityDoha 34110Qatar

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater ***,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic *** communication technologies are already used for communication in the underwater environment;however,lacking localization *** and magnetic induction communication achieves higher data rates for short *** the contrary,acoustic waves provide a low data rate for long-range underwater *** proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final ***,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.

关键词： Bayesian multidimensional scaling(BMDS) hybrid Cramer-Rao lower bound(H-CRLB) internet of underwater things(IoUT) signals of opportunity(SOA)approach

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Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network

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Computers, Materials & Continua 2023年第7期76卷 1213-1247页

作者： Muhammad Rizwan Sana Ul Haq Noor Gul Muhammad Asif Syed Muslim Shah tariqullah jan Naveed Ahmad Department of Electronics University of PeshawarPeshawar25120Pakistan Department of Electronics Engineering Korea Polytechnic UniversitySiheungKorea Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Computer Science Prince Sultan UniversityRiyadh11586Saudi Arabia

Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time *** addition,the performance of a model decreases as the subject’s distance from the camera *** study proposes a 3D separable Convolutional Neural Network(CNN),considering the model’s computa-tional complexity and recognition *** 20BN-Jester dataset was used to train the model for six gesture *** achieving the best offline recognition accuracy of 94.39%,the model was deployed in real-time while considering the subject’s attention,the instant of performing a gesture,and the subject’s distance from the *** being discussed in numerous research articles,the distance factor remains unresolved in real-time deployment,which leads to degraded recognition *** the proposed approach,the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the ***,the capability of feature extraction,degree of relevance,and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding(t-SNE),Mathew’s Correlation Coefficient(MCC),and the McNemar test,*** observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level.

关键词： 3D separable CNN computational complexity hand gesture recognition human-computer interaction

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Fractional Processing Based Adaptive Beamforming Algorithm

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Computers, Materials & Continua 2023年第7期76卷 1065-1084页

作者： Syed Asghar Ali Shah tariqullah jan Syed Muslim Shah Ruhul Amin Khalil Ahmad Sawalmeh Muhammad Anan Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science Northern Border UniversityArar91431Saudi Arabia Department of Software Engineering Alfaisal UniversityRiyadh11533Saudi Arabia

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity *** effectiveness of the algorithm will be studied and evaluated in this *** this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of *** algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational *** FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight *** derivation of the algorithm is provided and supported by mathematical convergence *** is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various *** results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter *** outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

关键词： Adaptive beamforming adaptive array fractional processing least mean square fractional least mean square

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