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In recent years, there have been plenty of demands and growth in the autonomous vehicle industry, and thus, challenges of designing highly efficient photonic radars that can detect and range any target with the resolution of a few centimeters have been encountered. The existing radar technology is unable to meet such requirements due to limitations on available bandwidth. Another issue is to consider strong attenuation while working under diverse atmospheric conditions at higher frequencies. The proposed model of photonic radar is developed considering these requirements and challenges using the frequency-modulated direct detection technique and considering a free-space range of 750 m. The result depicts improved range detection in terms of received power and an acceptable signal-to-noise ratio and range under adverse climatic situations.

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

This paper presents a novel, scalable, and modular multiport power electronic topology for the integration of multiple resources. This converter is not only scalable in terms of the integration of multiple renewable energy resources (RES) and storage devices (SDs) but is also scalable in terms of output ports. Multiple dc outputs of a converter are designed to serve as input to the stack-ing modules (SMs) of the modular multilevel converter (MMC). The proposed multiport converter is bidirectional in nature and superior in terms of functionality in a way that a modular universal converter is responsible for the integration of multiple RES/SDs and regulates multiple dc output ports for SMs of MMC. All input ports can be easily integrated (and controlled), and output ports also can be controlled independently in response to any load variations. An isolated active half-bridge converter with multiple secondaries acts as a central hub for power processing with multiple renewable energy resources that are integrated at the primary side. To verify the proposed con-verter, a detailed design of the converter-based system is presented along with the proposed control algorithm for managing power on the individual component level. Additionally, different modes of power management (emulating the availability/variability of renewable energy sources (RES)) are exhibited and analyzed here. Finally, detailed simulation results are presented in detail for the validation of the proposed concepts and design process. Funding: This research was funded by Taif University Researchers Supporting Project, number (TURSP-2020/228). Scopus

In this paper, the closed loop speed controller parameters are optimized for the permanent magnet synchronous motor (PMSM) drive on the basis of the indirect field-oriented control (IFOC) technique. In this derive system under study, the speed and current controllers are implemented using the fractional order proportional, integral, and derivative (FOPID) controlling technique. FOPID is considered as efficient techniques for ripple minimization. The hybrid grey wolf optimizer (HGWO) is applied to obtain the optimal controllers in case of implementing conventional PID as well as FOPID controllers in the derive system. The optimal controller parameters tend to enhance the drive response as ripple content in speed and current, either during steady state time or transient time. The drive system is modeled and tested under various operating condition of load torque and speed. Finally, the performance for PID and FOPID are evaluated and compared within MATLAB/Simulink environment. The results attain the efficacy of the operating performance with the FOPID controller. The result shows a fast response and reduction of ripples in the torque and the current.