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Efficient Power Control for UAV Based on Trajectory and Game Theory

Computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea majed alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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An Efficient Text-Independent Speaker Identification Using Feature Fusion and Transformer Model

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Computers, Materials & Continua 2023年第5期75卷 4085-4100页

作者： Arfat Ahmad Khan Rashid Jahangir Roobaea Alroobaea Saleh Yahya Alyahyan Ahmed H.Almulhi majed alsafyani Chitapong Wechtaisong College of Computing Khon Kaen UniversityKhon Kaen40000Thailand Department of Computer Science COMSATS University IslamabadVehari CampusVehari61100Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Community College in DwadmiSharqa UniversityDawadmi17472Saudi Arabia School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand

Automatic Speaker Identification(ASI)involves the process of distinguishing an audio stream associated with numerous speakers’*** common aspects,such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording,make the ASI task much more complicated and *** research proposes a deep learning model to improve the accuracy of the ASI system and reduce the model training time under limited computation *** this research,the performance of the transformer model is *** audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted from the ELSDSR,CSTRVCTK,and Ar-DAD,*** evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on all *** ELSDSR,CSTRVCTK,and Ar-DAD,the highest attained accuracies are 0.99,0.97,and 0.99,*** experimental results reveal that the proposed technique can achieve the best performance for ASI problems.

关键词： Speaker identification signal processing Arabic deep learning transformer

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