AIMS Electronics and Electrical Engineering最新文献

Electric vehicles (EVs) are an emerging technology that contribute to reducing air pollution. This paper presents the development of a 200 kW DC charger for the vehicle to grid (V2G) application. The bidirectional dual active bridge (DAB) converter was the preferred fit for a high-power DC-DC conversion due its attractive features. A particle swarm optimization (PSO) algorithm was used to online auto-tune the optimal K_P and K_I value with minimize error voltage. Then, by concerning the controller with fixed gains have limitation in response during dynamic change, the PSO was improved to allow re-tuning and update the new K_P and K_I upon step changes or disturbances through a time-variant approach. The proposed controller, online auto-tuned PI using PSO with re-tuning (OPSO-PI-RT) and one-time (OPSO-PI-OT) execution were compared under desired output voltage step changes and load step changes in terms of steady-state error and dynamic performance. The OPSO-PI-RT method was a superior controller with 98.16% accuracy and faster controller with 85.28 s^-1 average speed compared to OPSO-PI-OT using controller hardware-in-the-loop (CHIL) approach.

Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction, features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check ups.This work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic.

An analytical SS model is presented to observe the subthreshold swing (SS) of a junctionless gate-all-around (GAA) FET with ferroelectric in this paper. For the gate structure, a multilayer structure of metal-ferroelectric-metal-insulator-semiconductor (MFMIS) was used, and the SS was calculated in $15 leqslant {P_r} leqslant 30,mu C/c{m^2}$ and $0.8 leqslant {E_c} leqslant 1.5,MV/cm$, which are the ranges of remanent polarization and coercive field suggested in various experiments in the case of HZO as the ferroelectric material. It was found that the SSs from the presented analytical SS model agree well with those derived from the relationship between drain current and gate voltage using a 2D potential distribution in the range of device parameters used for simulation. As a result of analyzing the SS of the junctionless GAA FET with ferroelectric using the analytical SS model presented in this paper, the SS decreased because the voltage across the inner gate decreased when the ferroelectric thickness increased. It was observed that the condition of SS < 60 mV/dec was sufficiently obtained according to changes in device parameters such as channel length, channel radius and ferroelectric thickness, and that the SS maintained a constant value according to the ratio of remanent polarization and coercive field P_r/E_c. As P_r/E_c increases, the SS increases as the ferroelectric capacitance increases. As the channel length becomes smaller, the change in SS according to P_r/E_c is more severe.

Autonomous vehicles are at the forefront of future transportation solutions, but their success hinges on reliable perception. This review paper surveys image processing and sensor fusion techniques vital for ensuring vehicle safety and efficiency. The paper focuses on object detection, recognition, tracking, and scene comprehension via computer vision and machine learning methodologies. In addition, the paper explores challenges within the field, such as robustness in adverse weather conditions, the demand for real-time processing, and the integration of complex sensor data. Furthermore, we examine localization techniques specific to autonomous vehicles. The results show that while substantial progress has been made in each subfield, there are persistent limitations. These include a shortage of comprehensive large-scale testing, the absence of diverse and robust datasets, and occasional inaccuracies in certain studies. These issues impede the seamless deployment of this technology in real-world scenarios. This comprehensive literature review contributes to a deeper understanding of the current state and future directions of image processing and sensor fusion in autonomous vehicles, aiding researchers and practitioners in advancing the development of reliable autonomous driving systems.

Currently, interdigital capacitive (IDC) sensors are widely used in science, industry and technology. To measure the changes in capacitance in these sensors, many methods such as differentiation, phase delay between two signals, capacitor charging/discharging, oscillators and switching circuits have been proposed. These techniques often use high frequencies and high complexity to measure small capacitance changes of fF or aF with high sensitivity. An analog interface based on a capacitance multiplier for capacitive sensors is presented. This study includes analysis of the interface error factors, such as the error due to the components of the capacitance multiplier, parasitic capacitances, transient effects and non-ideal parameters of OpAmp. A design approach based on an IDC sensor to measure the quality of edible oils is presented and implemented. The quality relates to the total polar compounds (TPC) and consequently to relative electrical permittivity begin{document}$ {varepsilon }_{r} $end{document} of the oils. A measurement system has been implemented to measure the capacitance of the IDC sensor, which depended on begin{document}$ {varepsilon }_{r} $end{document}. The simulation and experimental results showed that, for a capacitance multiplication factor equal to 1000, changes of 3.3 µs/100 fF can be achieved with an acceptable level of noise, which can be easily measured by a microcontroller.

Modulation classification (MC) is a critical task in wireless communication systems, enabling the identification of the modulation class in the received signals. In this paper, we analyzed a novel multi-layer convolutional neural network (CNN) to extract hierarchical features directly from the raw baseband samples. Moreover, we compared the training and testing accuracy of the CNN model for various decimation rates, input sample size and the number of convolutional layers. The results showed that the three-layer CNN model provided better classification accuracy with less computation cost. Furthermore, we observed that the MC performance of the proposed CNN model was better than the other deep learning (DL) and cumulant-based models.