Though the node voltage method and loop current method are the most widely taught, another powerful method is modified nodal analysis (MNA). MNA often results in larger systems of equations than the other methods but is easier to implement algorithmically on a computer. To use modified nodal analysis we have to write one equation for each node not attached to a voltage source (as in standard nodal analysis), and augment these equations with an equation for each voltage source. 

Our project simulated a power system in three parts. Part 1 focused on power generation using hydroelectric and parallel generators, demonstrating control of power generation based on demand. Part 2 involved designing the transmission network and implementing a load-shedding scheme using a three-phase source. Part 3 simulated real-world factory power consumption using induction motors as loads and synchronous motors for power factor correction. The goal was to simulate overall system power generation, transmission losses, consumed power, and relevant information.

This project focuses on non-intrusive load monitoring (NILM) using deep neural networks (DNNs) such as LSTM, DAE, and Sequence-to-Point models. The aim is to disaggregate energy consumption for different appliances using the REDD dataset. A sliding window approach was adopted to enable real-time disaggregation, with the goal of improving performance through tuning and testing. The evaluation of disaggregated energy consumption results is based on metrics such as MSE, MAE, Relative Error, and F1 Score. The project aims to compare the effectiveness of different DNNs in accurately identifying and monitoring individual appliances' energy usage. 

Our Project was to develop an automatic, low-cost, handy watering device for indoor and outdoor plants. Besides keeping the owner updated about his Plants and the condition of the weather, we created a model that can send the data to our database. In this project, we intend to develop a system that will water plants when it's needed. Also, it will be able to control the moisture of the plant at any time. Also, the temperature, humidity, and rain update can be monitored from an online cloud server. Our model can take Images of the plant every 1 Hour and Send them to Google Drive. It is an Arduino-based project. 

The program in MATLAB compares various digital modulation techniques for OFDM simulations. It converts input data into binary form, applies modulation schemes like BPSK, QPSK, QAM, 16-QAM, and 32-QAM, and adds pilot values. The modulated data is transformed into time-domain signals using IFFT and passed through a simulated channel with fading and noise. At the receiver end, demodulation is performed, and the output is visualized for performance analysis. The project demonstrates OFDM data transmission and facilitates comparison of modulation techniques.