Smart cars are becoming the future of vehicle industry, most newly developed vehicles are equipped with communication modules that transmits and receive data for many purposes including insurance policy requirements, vehicle security, and for safety purposes. Cellular V2X requires all vehicles to have this module to transmit vehicle data and to receive feedback, so that V2X services can be implemented successfully. However, not all vehicles are equipped with such communication modules; therefore, the purpose of this paper is to design a communication module that enables all vehicles that has OBDII (On-Board Diagnostic module) standard to reliably transmit and receive related V2X data using Cellular networks, such as 3G, 4G, etc. through using affordable embedded system.

V2X (Vehicle-to-Everything) evolution over cellular networks has been an excelling topic with the advances of high throughput and low latency LTE networks, and the introduction of 5G networks. According to recent researches, obtaining acceptable End-to-End (E2E) delay has been a challenging design process since data travels through several steps from the originating source to the data center and vice versa. V2X latency comprises mainly of three levels: source processing, cellular network, and data center processing. Delay reduction can be achieved on the three levels. However, many conventional solutions have not reached the required and acceptable range of latency to enable V2X communication over cellular networks. In this paper, a general review of challenges to make V2X feasible on cellular network has been discussed, and the proposed solutions in the literature has been introduced. As a conclusion, a various types of aiding tools to design and test V2X tools are given, so that a right path should be taken to consider challenges and improving design metrics.

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Queueing forms a daily routine of our lives wherever there are limitations in resources with competition. The ongoing Coronavirus Disease of 2019 (COVID-19) pandemic undeniably has its impacts on people's daily life. That in turn affected the number of customers who used to go outside for different outdoor activities. In this paper, a shopping mall is considered as a case study to propose an appropriate queueing model within these current pandemic circumstances. The proposed model consists of an open network with 10 nodes represent different real-existing serving stations. The queueing model is first analysed to check its stability that is evident analytically on the long run. The performance of the proposed network model is investigated using different measures. Based on recorded arrival and service rates, the mathematical derivations show that the traffic intensity of the system is still below 1, hence ensures stability. Also, the average number of customers in the network is 16.76 customers with only 2.66 customers awaiting in queues.Furthermore, the mean time a customer spends in the queue is only 0.16 min from a total spend on the network about 1.006 min. This indicates that the majority of time customers spend is in service with a very short waiting time due to current COVID-19 pandemic consequences, and the waiting time and queue lengths will undoubtedly increase once normal social life resumes.

High Dynamic Range (HDR) images capturing, and reproduction have been an essential area to investigate by researchers, especially after the escalating acceleration in computers processing capabilities and the widespread of digital cameras. In this paper, parallel components of HDR algorithm are employed using field programmable gate array (FPGA) to produce HDR image. The implementation involves different pre-processing stages to separate images, color matrices, and then group the similar colors to perform a series of arithmetic operations. In consequence, the algorithm has inherited parallelism to exploit so that the FPGA provides parallel computational speed. The algorithm is implemented using Simulink and ModelSim-Altera MATLAB software to produce the response curve and HDR color components signals. After preforming the tone mapping, the results show that the HDR image provides much greater details and a truer color.