This article deals with fabrication and machinability analysis of squeeze cast Al 7075/h-BN/Graphene hybrid nanocomposite (HNC), which has been fabricated by reinforcing hexagonal boron nitride (0.5 wt% h-BN) and graphene nanoparticles (1 wt% GNPs). In order to utilize the self-lubricating property of h-BN and GNPs, their uniform mixing is essential, hence before squeeze casting of HNC ball milling (BM) technique has been employed which enables uniform mixing and also eliminates the agglomeration effect of nanoparticles. Scanning electron microscopy (SEM) and optical microscopic (OM) investigation confirm the uniform mixing of nanoparticles as well as refinement in the grain size. In order to examine the hardness of the proposed HNC, mechanical properties were investigated and observed improvement of 31.25%, 10.93% and 10.27% in the UTS, microhardness (Vickers) and Rockwell hardness respectively as compared to unreinforced Al 7075 alloy fabricated by stir casting. Based on the obtained results machinability analysis is performed considering numerous machining process parameter during CNC turning to investigate the influence of cutting speed (CS), feed rate (FR) and depth of cut (DOC) on surface roughness (SR), generated forces, tool wear and chip morphology of squeeze cast HNC subjected to dry and minimum quantity lubrication (MQL) machining. Finally, the acquired results are presented with the aid of comparative graphical presentation with squeeze casted conventional aluminium alloy.

With the increase in crime and terror rate globally, automated video surveillance, is the need of the hour. Surveillance along with the detection and tracking has become extremely important. Human detection and tracking is ideal, but the random nature of human movement makes it extremely difficult to track and classify as suspicious activities. The primary objective of this is to detect the suspiciously abandoned object recorded by the closed-circuit television cameras (CCTV). The main aim of this project is to ease the load on the controller at the main CCTV station by generating and alarm, whenever there is a detection of an abandoned object. To solve the problem, we first proceeded by the background subtraction such that we obtain the foreground image. Further, we calculated the inter-pixel distance and used area-based thresholding so as to differentiate between the person and the object. The object will further be tracked for a previously set time, which will help the system to decide whether or not the object is abandoned or not. Such a system that can ease the load on single CCTV controller can be deployed in places which require high discipline and security and are more prone to suspicious activities like Airports, Metro station, Railway Stations, entrances and exits of buildings, ATMs, and similar public places.