Aiming at the characteristics of abnormally slow and diffuse electroencephalogram (EEG) signals of Alzheimer's disease (AD), based on the latent variable analysis (LVA) theory, this study proposed an EEG diagnosis method based on principal component analysis (PCA) and energy feature recognition of potential signals. Firstly, the original electroencephalogram (EEG) signal was reduced into potential signal by principal component analysis (PCA), and then the potential signal was divided into delta, theta, alpha and beta frequency bands by fast Fourier transform (FFT), and then the energy features of the potential signal were extracted. By comparing the results of AD and control group, the distribution of EEG potential signals in AD group was scattered, and the AD rhythm was slowed down in low dimensional space (manifested as the main activity shifting from high frequency to low frequency). Secondly, support vector machine (SVM) classification analysis was performed combined with the energy characteristics of the potential signals, and it was found that the potential signals could effectively identify AD patients, and the recognition effect was better than that of the original EEG signals. In addition, increasing the number of latent signals can improve the SVM classification performance, for example, in the θ-band, the joint features can achieve 94.12% classification accuracy. It is shown that the proposed method preserves the valid information of the original EEG while eliminating the redundant information between EEG channels, which ultimately improves the classification accuracy.
Vehicular edge computing (VEC) is a new paradigm that combines edge computing with vehicular networks to facilitate the efficient processing of data generated by vehicles. In this approach, vehicles offload their computing tasks to nearby roadside units (RSUs) for faster processing. However, VEC faces several challenges in handling offloaded tasks due to the overloading problem on edge servers. As a result, VEC’s performance can deteriorate significantly. To mitigate these challenges, this paper proposes a server capacity planning-based task computation offloading (SCPTCO) approach for vehicular networks (VNs). Server capacity planning is the process of estimating the required capacity of edge servers to handle the offloaded computing tasks from vehicles. Our proposed SCPTCO approach ensures the efficient utilization of edge servers by estimating the required server capacity. Furthermore, we designed a mobility model that considers vehicle movements at various speeds to simulate real-life scenarios. The results of the simulation demonstrated that our proposal performed better than the baseline approaches in all scenarios. It can significantly reduce both service time and task failure rate by 21.3% and 72.2%, respectively, compared to the local edge offloading with random capacity (LEORC) scheme. Moreover, when compared to a local edge offloading with equal capacity (LEOEC) scheme, the reduced rates are around 11.6% and 55.8%, respectively.
A novel non-coherent acoustic underwater multiple-input multiple-output (MIMO) communication system is proposed and analyzed in this article. The proposed system uses space modulation techniques and relies on the time difference of arrival (TDOA) to determine active transmitters. In particular, at each symbol time, only one transmitter is active and transmits an unmodulated data towards the receiver. Via TDOA, the receiver localizes active transmitter and retrieve corresponding data bits. It is revealed in this study that leveraging TDOA with acoustic signals, the receiver can discern the index of the active transmitter precisely and attain very low error probability independent of any channel state information. As well, modulating the index of transmitters, as in space modulation, attains multiplexing gain and enhances the overall data rate. Analytical derivation of the error performance of the proposed system is presented and substantiated through comparison with Monte Carlo simulation results. Reported results reveal that the proposed system is a low complexity technique that is independent of the channel fading distribution, and can be designed to attain zero error probability with proper system dimensions.
A fifth-order gap-coupled resonator loaded ultra-wide band (UWB) filtenna is proposed in this research article. The band-stop Butterworth filter is designed at the ISM frequency of 5.8 GHz and an ultra-wideband circular-shaped microstrip patch is integrated with the extended end of the filter. The filter is designed using the concept of the gap-coupled microstrip stub sections with a metallic via connected at the end. It yields all the necessary capacitive and inductive effects to show its band-stop property at the ISM band. It produces a sharp narrow--band notch at 5.8 GHz and helps the antenna to utilize all the available frequency bands by eliminating the possibility for the antenna to interfere with the 5.8 GHz ISM band.
The Cooperative Spectrum Sensing (CSS) based Cognitive Radio-Internet of Things (CR-IoT) is an advantageous solution to address the spectrum scarcity problem by allowing the unlicensed users to share the available frequency bands. However, this system is vulnerable to malicious attacks due to degrading the detection performance. These attacks consist of the presence of Malicious Users (MUs), who aim to mislead the Fusion Center (FC) with fake or falsification sensing information. This work proposes multiple features-based Machine Learning (ML) like Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR) and Decision Tree (DT) algorithms, to identify MUs in Orthogonal Frequency Division Multiplexing (OFDM)-based CR-IoT networks. The performance of the proposed scheme is evaluated through simulations. The numerical results show that the proposed scheme achieves an enhanced security where MUs are correctly identified, a better detection gain and an enhanced system sum rate compared with other schemes.
