Researching about the utilization of Quantum Dots in Photovoltaic cells to optimize the performance of Solar cells using machine learning models.

• Designed and implemented an IoT-based mobile application that would stream real-time data of several parameters of the smelting lines and suggest ideal readings for the machinery's efficient functioning.• The data was collected from the voltmeters through an Arduino system and was transmitted to the mobile using a Bluetooth module. The information was streamed through Firebase (a cloud-based database service).• Several visual data interpretation methods were implemented (Radial Gauges, Bar Graph, Pie Charts).• Optimized the power consumption of the industrial smelting lines by monitoring overvoltage supply, such that the machine runs more efficiently.

• Designed and implemented a predictive algorithm for processing data received by an antenna system with significant noise to find the direction of the signal approach with better accuracy.• Implemented and simulated the neural network algorithm in MATLAB and Python.• Improved the accuracy of the direction-finding system drastically from 3 degrees RMS error to 1 degree.