Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence...
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Spectrum resources are the precious and limited natural *** order to improve the utilization of spectrum resources and maximize the network throughput,this paper studies the resource allocation of the downlink cogniti...
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Spectrum resources are the precious and limited natural *** order to improve the utilization of spectrum resources and maximize the network throughput,this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonalmultiple access(CRN-NOMA).NOMA,as the key technology of the fifth-generation communication(5G),can effectively increase the capacity of 5G *** optimization problem proposed in this paper aims to maximize the number of secondary users(SUs)accessing the system and the total throughput in the *** the constraints of total power,minimum rate,interference and SINR,CRN-NOMA throughput is maximized by allocating optimal transmission ***,for the situation of multiple sub-users,an adaptive optimization method is proposed to reduce the complexity of the optimization ***,for the optimization problem of nonlinear programming,a maximization throughput optimization algorithm based on Chebyshev and convex(MTCC)for CRN-NOMA is proposed,which converts multi-objective optimization problem into single-objective optimization problem to *** the same time,the convergence and time complexity of the algorithm are *** analysis and simulation results show that the algorithm can effectively improve the system *** terms of interference and throughput,the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access(OFDMA).This paper provides important insights for the research and application of NOMA in future communications.
Parkinson’s disease is a disabling condition that affects the quality of life of individuals with both motor and non-motor symptoms. Auditory disorders are one of the non-motor symptoms that individuals will face in ...
Parkinson’s disease is a disabling condition that affects the quality of life of individuals with both motor and non-motor symptoms. Auditory disorders are one of the non-motor symptoms that individuals will face in the early stages of the disease. Therefore, the use of vocal features for early detection of Parkinson’s disease is a crucial aspect of diagnosis. This paper presents a novel approach to Parkinsonts disease diagnosis using vocal features and advanced neural network architectures. In this study, a structure based on dual cross-attention is introduced that combines features extracted from time-frequency representations, specifically the Wavelet Transform (WT) and Tunable Q factor Wavelet Transforms (TQWT), describing both temporal and frequency features of the audio signal simultaneously. Ultimately, a self-attention block is responsible for determining the class assignment. The results demonstrate that the proposed network performs well compared to existing networks in the context of Parkinson’s disease diagnosis.
作者：
Krishna, Kotte. Venkata RamaParthipan, V.Saveetha University
Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University
Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Big Data and Network Security Tamil Nadu Chennai India
An examination of the degree to which GoogleNet and Alexnet are accurate in predicting the academic success of students is the primary objective of this study. Each of the two groups that make up the program contains ...
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This paper introduces an approach with the Transformer Neural networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring...
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A vast number of individuals globally experience Parkinson’s disease (PD), which is classified as a neurological disorder. Identifying biomarkers that can aid in the early detection and monitoring of PD is crucial fo...
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ISBN:
(数字)9798331529710
ISBN:
(纸本)9798331529727
A vast number of individuals globally experience Parkinson’s disease (PD), which is classified as a neurological disorder. Identifying biomarkers that can aid in the early detection and monitoring of PD is crucial for improving patient’s treatment. In this study, we investigated the use of resting-state electroencephalogram(EEG) data for PD classification. We used the resting state EEG(rEEG) data from 15 PD patients on/off medication states. We applied a topological data analysis(TDA) approach to extract different and important features from the rEEG data, specifically entropy. We found that the entropy values was the most decisive feature among all features we examined and result for classification between patients on medication and off medication (with $\mathbf{8 5 \%}$ accuracy). Our innovation centers around the utilization of TDA for examining Parkinson’s disease data with exploring features and structures in higher dimensions and also with extraction of topological features through Persistent Homology.
Highly reliable and flexible control is required for distributed generation(DG) to efficiently connect to the *** inverters play a key role in the control and integration of DG into the power grid and provide advanced...
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Highly reliable and flexible control is required for distributed generation(DG) to efficiently connect to the *** inverters play a key role in the control and integration of DG into the power grid and provide advanced functionalities. In this paper, an energy-based single-phase voltage-source smart inverter(SPV-SSI) of 5 k VA is designed and analyzed in detail. SPV-SSI is capable of supplying the power to local load and the utility load up to the rated capacity of the inverter, injecting the power into the grid, storing the energy in lead-acid battery bank, controlling the voltage at the point of common coupling(PCC) during voltage sags or faults, and making decisions on real-time pricing information obtained from the utility grid through advanced metering. The complete design of smart inverter in dq frame, bi-directional DC-DC buck-boost converter, IEEE standard 1547 based islanding and recloser, and static synchronous compensator(STATCOM) functionalities is presented in this paper. Moreover, adaptive controllers, i. e., fuzzy proportional-integral(F-PI) controller and fuzzy-sliding mode controller(F-SMC) are designed. The performances of F-PI controller and F-SMC are superior, stable, and robust compared with those of conventionally tuned PI controllers for voltage control loop(islanded mode) and current control loop(grid-connected mode).
In order to overcome the challenges caused by flash memories and also to protect against errors related to reading information stored in DNA molecules in the shotgun sequencing method, the rank modulation method has b...
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The dynamics of water within a nanopool of a reverse micelle is heavily affected by the amphiphilic *** this work,the terahertz(THz)spectra of cyclohexane/Igepal/water nonionic reverse micelle mixture are measured by ...
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The dynamics of water within a nanopool of a reverse micelle is heavily affected by the amphiphilic *** this work,the terahertz(THz)spectra of cyclohexane/Igepal/water nonionic reverse micelle mixture are measured by THz timedomain spectroscopy and analyzed with two Debye models and complex permittivity of background with volume *** on the fitted parameters of bulk and fast water,the molar concentration of all kinds of water molecules and hydration water molecule number per Igepal molecule are *** find that slow hydration water has the highest proportion in water when the radius parameterω_(0)<10,while bulk water becomes the main component whenω_(0)≥*** feature radius ratio of nonhydrated and hydrated water to total water nanopool is roughly obtained from 0.39 to 0.85 with increasingω_(0).
The investigation examines the application of Support Vector Machines (SVMs) for blame location and classification in electrical frameworks, pointing to an upgrade of the reliability of critical foundations. Four SVM-...
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