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As the number of electric vehicles (EVs) within society rapidly increase, the concept of maximizing its efficiency within the electric smart grid becomes crucial. This research presents the impacts of integrating EV charging infrastructures within a smart grid through a vehicle to grid (V2G) program. It also observes the circulation of electric charge within the system so that the electric grid does not become exhausted during peak hours. This paper will cover several different case studies and will analyze the best and worst scenarios for the power losses and voltage profiles in the power distribution system. Specifically, we seek to find the optimal location as well as the ideal number of EVs in the distribution system while minimizing its power losses and optimizing its voltage profile. Verification of the results are primarily conducted using GUIs created on MATLAB. These simulations aim to develop a better understanding of the potential impacts of electric vehicles in smart grids, such as power quality and monetary benefits for utility companies and electric vehicle users 

Image processing is an important technique that is used in many fields, such as self-driving vehicles or facial recognition. One method is called image convolution, which involves many calculations that manipulate the pixels of an image to produce a new image with a desired effect. This is computation intensive and requires a significant amount of time when run on a traditional computer processing unit (CPU). Since image processing is used for real-time applications, such as those mentioned above, it is essential that convolution algorithms run as quickly as possible. A common way to speed up image convolution algorithms is to take advantage of the highly parallel structure of graphical processing units (GPU) to perform concurrent calculations. One problem with GPU applications is that they are often limited by the latency delays associated with transferring data between the CPU and the GPU. Previous works have looked into different ways to address this issue and optimize GPU programs. This research aims to explore different memory implementations and compare them to see which is best at optimizing data transfers.