Energy Signal-Aided Secure Beamforming and Self-Energy Recycling in Full-Duplex Wireless-Powered Relay Networks

In this paper, the object of study is secure transmission and green energy transfer in full-duplex (FD) wireless-powered relay (WPR) secure systems, where an FD relay collects the power from radio-frequency signs and transmits the information in the face of multiple eavesdroppers. In order to improve the efficiency and safety of the contemporaneous wireless energy and information delivery, we propose a joint energy-signal- (ES-) aided secure beamforming and time-switch scheme under the self-power circulation protocol at the relay. The question formulated in this paper is to maximize the confidentiality rate according to energy restrictions at both the relay and energy receiver. As the question is non-salient and hard to resolve directly, we transform it into two sub-problems. For the first sub-problem, a two-level optimization technique is suggested to separately gain the optimal beamforming as well as the ES covariance. The extrinsic rank is a single-variable majorization question, which can be solved by single-dimensional (1D) examination. We attain an optimal solution to the inner level by a semi-definite relaxation (SDR) technique. For the second sub-problem, we again use 1D search to solve this problem. Moreover, we prove that SDR always exists as a level-1 optimal resolution. Mathematical outcomes show that the suggested plan can achieve a considerable gain of confidentiality rate by comparison with other benchmark plans.

Investigating Wi-Fi, Bluetooth, and Bluetooth Low-Energy Signal Characteristics for Integration in Vehicle–Pedestrian Collision Warning Systems

The purpose of the study is to investigate the comparative field performance of Wi-Fi, Bluetooth Classic (Bluetooth) and Bluetooth Low Energy (BLE) signal modes for integration in vehicle–pedestrian collision warning systems. The study compares these wireless signal modes to find out which one is most appropriate to be utilized in these systems and provides better results in terms of accuracy and functionality. Five factors including received signal strength indicator (RSSI)-distance relationship, rainfall effects on the signals, motion effects, non-line of sight effects and signal transmission rates were selected for evaluation. These factors were selected considering the requirements of vehicle–pedestrian collision warning systems and compared with each other based on experimental outcomes. The results of the experiments indicated the overall superiority of BLE mode over Wi-Fi and Bluetooth modes to be utilized in these systems. Application of this mode may provide the possibility of fast collision warnings thanks to low signal transmission intervals and high probability of simultaneous signal detections by multiple signals scanners. Moreover, the capability of this mode to accurately estimate distance and position is higher than Wi-Fi mode and not significantly different from Bluetooth mode.

Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers

Simultaneous wireless information and power transfer (SWIPT) is a promising technology to achieve wide-area energy transfer by sharing the same radio frequency (RF) signal and infrastructure of legacy wireless communication systems. To enlarge the effective range of energy transfer in practice, it is desirable to have a hybrid signaling SWIPT scheme, which combines a high-power multitone energy signal with a low-power broadband information signal. This paper presents a systematic study on the performance of hybrid signaling SWIPT systems with memoryless nonlinear transmitter power amplifiers (PAs). Using PA efficiency and signal-to-noise-and-distortion ratio (SNDR) as the metrics to measure the efficiency of energy transfer and information transmission, respectively, we derive the tradeoff between these two metrics for two PA classes, two nonlinear PA models, and two SNDR definitions. Our results reveal insights into the fundamental performance tradeoff inherent in SWIPT systems using hybrid signaling schemes.

A Dynamic Data Driven and Data Segregation Approach Image Restoration Using Neural Networks

Image restoration is the method of restoring an image to its original state by removing noise and blur. Image disclarity is crucial to maintain in a variety of cases, including photography, where motion blur is caused by camera shake when taking images, radar imaging, where the impact of image system reaction is removed, and so on. Image noise is an unwanted signal that appears in an image from a sensor, such as a power / energy signal, or from the atmosphere, such as rain or snow. Coding artefacts, resolution limitations, transmission noise, object motion, camera shake, or a confluence of events could cause image degradation. With the intention of separating HF and LF objects, image decomposition is used to decompose the distorted image into a pattern layer (High Frequency Component) and a framework layer (Low Frequency Component).

Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation

Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.

Bivalence Fuzzified Decision Stump Bootstrap Aggregation for Energy and Cost-Efficient 6G Communication

Future Sixth generation (6G) wireless networks are anticipatedto offer entirecoverage, improved spectral, energyandcost-efficient communication.The 6G will enable a network collectivelyand offer seamless wireless connectionsbetween the devices. While the deployment of 5G is ongoing, mobile communication networks are still suffering many basic challenges such as high-energy consumption and operating costs. To address these issues, it is very important to consider and develop new technologies in next-generation mobile communication, namely 6G. Novel machine learning can potentially assist the 6G to obtain better communication. Bivalence Fuzzified Decision Stump Bootstrap Aggregating (BFDSBA) model is introduced for energy and cost efficient communication. The BFDSBA model considers the nodes i.e. devices in the forecasting process before the data communication in the 6G network. The Bootstrap Aggregative technique utilizes set of weak learners as Bivalence Fuzzified Decision Stump. For each device in the network, energy, signal strength, and bandwidth is measured. Based on the estimated resources, efficient devices are selected for the 6G network architectural design. This in turn helps to improvedata communication with lesser cost in6G networks. The result exposesimprovement of BFDSBA model than the conventional methods.

Speed control of an SPMSM using a tracking differentiator-PID controller scheme with a genetic algorithm

In this paper, a tracking differentiator-proportional integral and derivative (TD-PID) control scheme is proposed to control the speed of a surface mount permanent magnet synchronous motor (SPMSM). The TD is used to generate the necessary transient profile for both the reference and the output speed, which are compared with each other to produce the error signals that feed into the PID controller. In addition to the TD unit parameters, the PID controller’s parameters are tuned to achieve the optimum new multi-objective performance index, comprised of the integral of the time absolute error (ITAE), the absolute square of the control energy signal (USQR), and the absolute value of the control energy signal (UABS) and utilizing a genetic algorithm (GA). A nonlinear model of the SPMSM is considered in the design and the performance of the proposed TD-PID scheme was validated by comparing its performance with that of a traditional PI controller in a MATLAB environment. Different case studies were tested to show the effectiveness of the proposed scheme, results including peak overshoot, energy consumption, control signal chatter, and 30% improvement in the OPI, with variable reference speeds, load torque, and parameters uncertainties. Illustrate the proposed scheme's success compared with PI controller.

Testing and Analysis Fault of Induction Motor for Case Study Misalignment Installation Using Current Signal with Energy Coefficient

An induction motor is a key device for an industrial machine. The installation misalignment of the motor will result in derating problems and energy consumption that is generally used to analyze signal faults using the fast Fourier transform (FFT) method. Problems with the rotor affect the non-stationary signal and FFT can be utilized to analyze this problem inefficiently. This paper proposed the testing and analysis of faults in an eccentric rotor at different levels using the stator current detection technique and the calculation of the energy signal coefficient via the wavelet decomposition (WD) method. The experimental results showed that an increase in eccentricity had a linear relation with the energy signal, where R2 was 80.81%. Moreover, the test results illustrated that the proposed method was more efficient than FFT and applicable to motor fault analysis and application in the industrial.