International Journal of High Speed Electronics and Systems

It is believed that the substrate integrated waveguide (SIW) technology represents the emergent and the most favorable contender for the advancement of the antennas, filters and the other circuit elements operational in the region of microwave as well as the millimeter wave frequencies. SIW layouts offer the advantages of the cost-effective integration of traditional transmission lines and retain the benefits of the classical metallic waveguides, in particular high-quality factors, low losses and the large power handling capability with the self-sufficient electrical shielding. In this paper, a review of the fundamental and operational theory of the SIWs has been discussed and presented. Moreover, the challenges and design considerations in terms of microwave and millimeter filters, the different miniaturization techniques and the most important; its application and implementation for the next-generation 5G communications and beyond has been presented.

Self-sustenance of India in oil and coal has consistently turned down, so electric vehicles are the future need of the society and environment. The heart of an electric vehicle is a battery; its degradation and safety are prime important issues to avoid accidents. Therefore, setting such a mechanism that improves the performance of a battery is a requirement of the society. The main objective of this article was to compare various aging parameters of a battery used in the electric vehicle, in terms of their effects, monitoring methods, and benefits for battery aging. It also aimed to illustrate the information out of touch that can be used for future research. This helps to maintain the clean environment by reducing pollution and economical benefit to the society.

The lifecycle of the battery is mostly exaggerated by the overall energy throughput speed, accumulated heat, and rapid utilization. The adequate utilization and operation of the battery are improved in the flexibility range by the permutation of the battery and the ultracapacitor in the electric vehicle. The overall system performance is determined by the energy management system which plays a significant part in dual-energy storage systems. The major intent of this research is to enhance the performance of electric vehicles which is achieved by maintaining the charge of depleting and charge of sustaining level in the battery and the state of charge in the ultracapacitor. The proposed method controls the state of charge of the battery and the ultracapacitor to make sure the availability of charge throughout the complete settling rate of the battery in the electric vehicle. To attain this condition, the dual converter-based two-stage Artificial Neural Network is initialized. In the first stage of the Artificial Neural Network, the charge sustained in the ultracapacitor is controlled during acceleration which completely depends on the velocity of the vehicles. In contrast to that, in the second stage of the Artificial Neural Network, charge depleting in the UC is trained by connectionless with varying vehicle velocities at deceleration rates. The production and investigation of parameters are not effectively optimized using conventional methods hence the herding and howling characters are combined together and proposed energetic and problem-solving optimization-based metaheuristic algorithm that efficiently tunes the parameters. The SOC rate of the battery for three driving cycles using the proposed method follows FTP75 71.309% at 2474th s and J1015 attained 90.840% at 660th s and the UDDS attained 81.647%. The SOC rate of the Ultracapacitor for three driving cycles using the proposed method follows FTP75 63.518% at 2474 s, J1015 attained 69.332% at 660 s and UDDs attained 67.049%.

The experimental arrangement is executed in MATLAB-Simulink. The state of charge of the battery and the ultracapacitors for the varying drive cycles as FTP75, J1015, and UDDS are experimentally validated and verified with the prevailing methods. The developed method reveals better performance for enhancing the lifespan of the power storeroom system in electric vehicles.

Achieving high-gain and low-noise operation of bulk-driven (BD) operational transconductance amplifier (OTA) with low-power consumption is a challenging task owing to inherent low transconductance and high noise in BD MOSFET. In this paper, we report a self-cascode partial positive feedback (SCPPF) approach-based BD OTA and have fairly compared the results with regular single MOS partial positive feedback (PPF) approach-based BD OTA. In comparison to regular BD PPF OTA, the proposed BD SCPPF OTA yields an improved gain of 68.71 dB, gain-bandwidth of 39.4 kHz, power consumption of 630 nW, output impedance of 0.4 MΩ and common mode rejection ratio of 134.4 dB for same input referred noise of 1.11 μV/√ Hz at frequency of 10 Hz and supply voltage of ±0.5 V. The area consumed by BD SCPPF OTA is 0.0523 mm². The proposed BD SCPPF OTA can be possibly used to improve the performance of biomedical analog front-end sensing and signal processing systems.

Visualization is primarily utilized as a training method to enhance athletic movement quality, increase concentration power, and minimize competition stress on the player while building firm confidence. Physical literacy (PL) provides a valuable lens for analyzing physical activity (PA) movement in more significant social and affective learning processes. This paper presents an Interactive Visualization positioning in physical education (IVPPE) to deal with the signal fluctuations and positioning techniques in visualizing Deep Neural Network (DNN). To ensure the success of their game, athletes are always looking for new ways to improve their health and performance. Using sensors to keep tabs on training and recovery has become more popular among athletes. Currently, sports teams are using sensors to track both the players’ internal and external workloads. It illustrates the multilayer localizer (MLL) based on transfer learning to improve the positioning accuracy and physical literacy positioning model (PLPM) as a health determinant. A variety of data augmentation techniques are used to combat signal fluctuations. As a result, the combined effects of motivation-promoting physical activity-based visualization improve the accuracy ratio to 96.7%, prediction ratio to 96.2%, efficiency ratio to 96.8%, and reduce the error rate to 18.7%, stress level (52.8%) compared to other conventional models and have a positive impact on the localizer and positioning, making a difference in physical activity (PA) levels.