REV Journal on Electronics and Communications
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Enhancing Average Secrecy Capacity and Secure Energy Efficiency of SISO System Using RIS with Artificial Jamming over Nakagami-m Fading Channels
No full textThis paper investigates the average secrecy capacity (ASC), secure energy efficiency (SEE) of a single-input single-output (SISO) system assisted by a reconfigurable intelligent surfaces (RIS) and with/without artificial jamming (AJ). We conduct a detailed ASC and SEE of the proposed system under Nakagami-m fading channels. First, we derive the cumulative distribution function (CDF) of the SNR for jamming and non-jamming schemes. The derived CDFs are then used to obtain closed-form theoretical expressions for ASC and SEE. Moreover, the effect of imperfect successive interference cancellation (SIC) at legitimate receivers has been taken into account to better reflect realistic system implementations. The numerical evaluations confirm that the proposed RIS-Jammer-SISO scheme consistently achieves superior secrecy performance when compared to the RIS-Only configuration. A comprehensive analysis has also been conducted to examine the impact of key system parameters, including the number of reflecting elements, jammer positioning, carrier frequency, and SIC accuracy. Finally, the validity of the analytical results has been corroborated through extensive Monte Carlo simulations
Heart Rate Variability Monitoring under Stimulation Input Using Non-Contact CW Radar
Get PDFRecent studies have mainly relied on radar technology for extracting crucial vital signs because of its ability to measure without physical contact. The focus on deriving cardiac interbeat interval and heart rate variability has gained significance due to its complexity and relevance in healthcare. Our investigation involved a detailed analysis of continuous wave radar signals to enhance the extraction of chest wall movement data. Using a convolution algorithm, we eliminated the respiratory component from the signal, while a locally projective noise reduction algorithm helped isolate the heartbeat component. Subsequently, a derivation filter was applied to pinpoint the R peak of the heartbeat, facilitating the collection of IBI and HRV metrics. This methodology proved effective for individuals in a relaxed, motionless state. However, its efficacy in cases of elevated heart rates caused by factors such as exercise or caffeine consumption remained uncertain. For subjects with large changes in heart rate followed by large changes in cardiac IBI, we made a small improvement in the algorithm. By performing a window shift of 10 seconds with an overlap of 1 second. Each 10-second data segment is fed into the algorithm. At each data segment, we perform multiple iterations with decreasing number of neighbors until no further change is made. With this adjustment, the results achieved in the group of subjects using input stimulation to increase heart rate such as exercise, or drinking coffee were indicated a strong correlation of 96.38% between radar-based measurements and reference measurements for this group of subjects, affirming the effectiveness of the proposed method in such scenarios
A Reliable and Secure Crowdfunding Platform Using Decentralized Blockchain
Get PDFCrowdfunding is a significant avenue for raising funds over the Internet to bring ideas into reality without relying on traditional funding sources. However, conventional centralized crowdfunding systems suffer from issues such as trustworthiness and transparency. In other words, it is necessary to ensure the reliability of information regarding project details, progress, and money exchanges, and to store this information in a form that cannot be altered. To ensure that the quality of projects is not degraded due to these causes and resolve the existing limitations, we propose a decentralized crowdfunding system using blockchain technology with two major contributions: "Decentralized Voting", and the "Decentralized Evaluating" methods. The Decentralized Voting Method aims to solve a particular platform's biased review by voting on the project's prospects and credibility. The Decentralized Evaluating Method aims to ensure project quality by exploiting the transparency of the invested projects. In this proposed blockchain-based solution, we used React and TypeScript for the front end and Rust-empowered Substrate at the backend. By using these methods, we verify through simulation that the implemented system works as proposed. This study identifies problems in crowdfunding and their causes, and then proposes a system that uses the two methods described above. The proposed system is expected to be a reliable distributed crowdfunding system
Design of a Configurable 4-Channel Analog Front-End for EEG Signal Acquisition on 180nm CMOS Process
Get PDFIn this work, a 4-channel Analog Front-End (AFE) circuit has been proposed for EEG signal recording. For EEG recording systems, the AFE may handle a wide range of sensor inputs with high input impedance, adjustable gain, low noise, and wide bandwidth. The buffer or current-to-voltage converter block (BCV), which can be set to operate as a buffer or a current-to-voltage converter circuit, is positioned between the electrode and the main amplifier stages of the AFE to achieve high input impedance and work with sensor signal types. A chopper capacitively-coupled instrumentation amplifier (CCIA) is positioned after the BCV as the main amplifier stage of the AFE to reduce input-referred noise and balance the impedance of the overall AFE system. A programmable gain amplifier (PGA) is the third stage of the AFE that allows the overall gain of the AFE to be adjusted. The suggested AFE operates in a wide frequency range of 0.5 Hz to 2 kHz with a high input impedance bigger than 2TΩ, and it is constructed and simulated using a 180nm CMOS process. With the lowest 100-dB CMRR and low input-referred noise of 1.8 µVrms, the AFE can achieve low noise efficiency. EEG signals can be acquired with this AFE system, which is very useful for detecting epilepsy and seizures
Outage probability of C-NOMA in relaying network with D2D communication pair
Get PDFThis paper considers a cooperative Non-Orthogonal Multiple Access (C-NOMA) network with multiple relays and destination user underlay interference constraints from device-to-device (D2D) users. The selected relay improves the decoding capacity from the base station to multi-users over block Rayleigh fading channels. Three relay selection schemes are proposed, such as the partial relay selection scheme for the first hop (RSFH) and second hop (RSSH) and the maximized decoding capacity by the two-stage relay selection (TSRS). To consider the outage performance of the proposed system, the derivations of the analytical expressions for both relay selection schemes for all destination users are provided, and Monte Carlo Simulations are used to confirm the accuracy of these mathematical analyses. Finally, the effects of system essential parameters such as the number of DF-relay nodes, the fixed-power allocation, perfect/imperfect successive interference cancellation (SIC), and the strength of interference from a D2D pair in various scenarios are investigated
Developing Uplink Power Optimization and ARS Selection Algorithm for Multi-ARS Small Cell Communication System
Get PDFSmall Cell (SC) models and unmanned aerial vehicles (UAVs) acting as aerial relay stations (ARSs) are both promising advancements in the development of upcoming wireless networks that contribute significantly to improving the overall service quality. In this work, we rely on the Multi-ARS Cell-Free (CF) model, where a large number of ARS coordinated by the ground base station (GBS) and cooperate to serve a large number of users within the same frequency and time resources, to develop the uplink of a multi-ARS SC system, in which each user is served by only one ARS. The time division duplex (TDD) mechanism is used for communication protocol, and the Minimum Mean Square Error (MMSE) method is implemented to estimate the uplink channel. We derive an closed-form expression for uplink user throughput. In addition, we introduce the ARS selection method based on channel conditions and propose the Bisection algorithm to optimize uplink power. The system performance is evaluated by the cumulative distribution function (CDF) of user throughput according to different parameters, such as changing the number of ARS, the number of users, the number of antennas, and the length of pilot sequences with/without power optimization. The results reveal that the ARS selection method is effectively resolved to reduce complexity and improve the practicality of the proposed system, and the power optimization problem for better throughput is non-optimization
Performance Evaluation of OpenStack Cloud Integrated with SDN Controller
Get PDFThe Internet is undergoing a revolution thanks to the development of a very popular recent technology trend - “Cloud Computing”. Large, small, and medium-sized businesses organizations, as well as individuals increasingly prefer to use cloud computing services due to their many advantages. Furthermore, the widespread implementation of cloud computing in large enterprises is an indicator of the technological advancement of information technology companies. It helps in the efficient allocation and utilization of infrastructure, power, and resources. In the meantime, centralized control provided by software-defined networking (SDN) allows for flexibility in operations and management. For that reason, there has been an adequate amount of research and application of combining these two objects over the years. In this study, we will evaluate network performance between virtual machines in terms of bandwidth, latency, and system performance interms of CPU, RAM, etc. in OpenStack with and without integration of SDN Controller - Tungsten Fabric (TF)
Underwater Acoustic Target Classification Using Convolutional Neural Network Combined with Continuous Wavelet Transform
Get PDFUnderwater acoustic target (UAT) classification is a critical task in submarine warfare because acoustic waves are the reliable information source that allows sonar operators and commanders to understand the surrounding situation in the operational area. To solve the problem of UAT classification, this paper proposes a convolutional neural network combined with continuous wavelet transform for improving the accuracy of UAT classification. The classification focuses on types of noise emitted from the propellers of different ships. Signal processing methods such as Short Time Fourier Transform, Continuous Wavelets Transform, and CNN are executed to extract signal features that provide information for classifier. Simulation results are achieved with an accuracy of 99.64\% when using the Continuous Wavelets Transform method combined with the convolutional neural network, which is higher than other traditional methods