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design of Clustering Techniques in Cognitive Radio Sensor Networks

Computer Systems Science & Engineering 2023年第1期44卷 441-456页

作者： R.Ganesh Babu d.hemanand V.Amudha S.Sugumaran department of Electronics and Communication Engineering SRM TRP Engineering College621105Tamil NaduIndia department of Computer Science and Engineering Sriram Engineering College602024Tamil NaduIndia department of Electronics and Communication Engineering Saveetha School of Engineering602105Tamil NaduIndia department of Electronics and Communication Engineering Vishnu Institute of TechnologyBhimavaram534202Andhra PradeshIndia

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)*** Cognitive Radio(CR)system is based on the Adaptive Swarm distributed Intelligent based Clustering algorithm(ASdIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence *** this research paper,the proposed ASdIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their *** this research,multiple random Sec-ondary Users(SUs),and PUs are considered for ***,the pro-posed ASdIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization *** results showed that the proposed ASdIC algorithm reduced the node power of 9.646%compared to the existing ***,ASdIC algorithm reduced 24.23%of SUs average node power compared to the existing *** of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB *** proposed ASdIC delivers low false alarm rate compared to other existing optimization algorithms in the primary *** results showed that the proposed ASdIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

关键词： Adaptive swarm distributed clustering cognitive radio clustering algorithm distributed swarm intelligent energy efficient distributed cluster-based sensing multi modal optimization

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Human Action Recognition Using difference of Gaussian and difference of Wavelet

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Big data Mining and Analytics 2023年第3期6卷 336-346页

作者： Gopampallikar Vinoda Reddy Kongara deepika Lakshmanan Malliga duraivelu hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui department of Computer Science and Engineering(AI&ML) CMR Technical CampusHyderabad 501401India. department of Information Technology Kakatiya Institute of Technology and ScienceWarangal 506015India. department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Hyderabad 500100India. department of Computer Science and Engineering S.A.Engineering College(Autonomous)Thiruverkadu 600077India. department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India. department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. IdMS Team STI LaboratoryFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 25003Morocco.

Human Action Recognition(HAR)attempts to recognize the human action from images and *** major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral *** proposed descriptor uses a difference of Gaussian(doG)filter to extract scale-invariant features and a difference of Wavelet(doW)filter to extract spectral *** create a composite feature vector for a particular test action picture,the discriminant of Guassian(doG)and difference of Wavelet(doW)features are *** discriminant Analysis(LdA),a widely used dimensionality reduction technique,is also used to eliminate duplicate ***,a closest neighbor method is used to classify the *** and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform *** average accuracy of doG+doW is observed as 83.6635%while the average accuracy of discrinanat of Guassian(doG)and difference of Wavelet(doW)is observed as 80.2312%and 77.4215%,*** average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation doG+doW is observed as 62.5231%while the average accuracy of difference of Guassian(doG)and difference of Wavelet(doW)is observed as 60.3214%and 58.1247%,*** the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

关键词： human action recognition difference of Gaussian difference of wavelet linear discriminant analysis Weizmann UCF 11 accuracy

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