An integrated GPS-GSM system is proposed to track vehicles using Google

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Social distancing is a necessary precaution measure taken in order to have more control over the outbreak of infectious diseases such as COVID-19. Most of Social distancing monitoring approaches are based on Bluetooth and mobile phones that require an app to be downloaded on all phones. This paper proposes a different approach to monitor social distancing, using cameras, and combining different computer vision algorithms. The approach utilizes the concept of inverse perspective mapping (IPM) together with the camera's intrinsic information to produce a bird's eye view with real-world coordinates of the frame being processed from a video source. The process starts with image enhancement, foreground detection using Gaussian Mixture Model (GMM) background subtraction, tracking using Kalman filter, computing real-world distance measurements between individuals, and detecting those who have been in less than 2 meters apart as they are considered to be in contact. This tool could assist the efforts of the governments to contain the virus. It can be implemented in closed areas or institutions, monitor the extent of people's commitment, and provide analysis and a faster approach to detect possibly corona suspicion cases. The approach is tested on the task decomposition data set, which included frames of closed areas and the camera's intrinsic parameters. Another data set was created with different scenarios to increase the confidence level of our algorithm. The results showed the success of our approach in detecting the violation in social distancing with accurate measures of the realworld coordinates.

This study proposes a fuzzy system for tracking the maximum power point of a PV system for solar panel. The solar panel and maximum power point tracker have been modeled using MATLAB/Simulink. A simulation model consists of PV panel, boost converter, and maximum power point tack MPPT algorithm is developed. Three different conditions are simulated: 1) Uniform irradiation; 2) Sudden changing; 3) Partial shading. Results showed that fuzzy controller successfully find MPP for all different weather conditions studied. FLC has excellent ability to track MPP in less than 0.01 second when PV is subjected to sudden changes and partial shading in irradiation.

One of the major obstacles for successful mass production of carbon nanotubes (CNTs) is performing quick and precise characterization of the properties of a given batch of nanotubes. In this paper, we have identified a set of intermediate steps that will lead to a comprehensive, scalable set of procedures for analyzing nanotubes. The proposed methodology was originated with data processing of Raman spectra of multi-wall carbon nanotubes (MWCNT) turfs and image enhancement of SEM micrographs. Image analysis techniques of SEM images were employed and stereological relations were determined for SEM images of CNT structures; these results were utilized to estimate the morphology of the turf (i.e. CNTs alignment and curvature) using an artificial neural networks (ANN) classifier. This model was also used to investigate the link between Raman spectra of CNTs and the quality of the turf morphology. This novel methodology will improve our capability to control the quality of the grown nanotubes through the use of this system in a supervised growth environment.