An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node's nature...
A novel method for evaluating compensation networks in IPT systems is proposed for a fair comparison. An optimal control strategy is adopted in the comparison to ensure operation under optimal conditions. Results indi...
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Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficie...
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Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RSP is an efficient solution for big data processing and ***,a challenge for implementing RSP is determining an appropriate sample size for RSP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.
The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the p...
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The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.
作者：
Gok, GorkemBoyaci, AytugUlas, MustafaFirat University
Faculty of Technology Department of Software Engineering Elaziǧ Turkey Air Force Academy
National Defence University Department of Computer Engineering İstanbul Turkey Firat University
Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey
This study is meant to research the evolution of intrusion detection and network monitoring within computer, cloud-based systems, IIoT, and mobile environments. The source has outlined the novel technologies in IDS, f...
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Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims t...
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Mobile edge computing (MEC) extracts the resource closer to end users;however, resource orchestration and management are still mentioned as the important issues. We propose the software-defined networking (SDN) techni...
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Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of ...
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Nowadays Wearable sensor-based Human Activity Recognition (HAR) is gaining popularity for its affordability and low computational demands. These sensors are widely used in healthcare and surveillance. However, using s...
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Dynamic inductive power transfer (IPT) can be used to charge moving electric vehicles (EVs) through the magnetic coupling between primary coils in a road with secondary coils onboard an EV. However, consistency in cha...
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