To acquire practical proficiency in the design of digital logic circuits, we were tasked to build a 4-digit password-resettable digital locking system, without using any microcontrollers. Using flip-flops as our storage component, we meticulously compared bits utilizing comparator ICs. Supplementary elements including resistors, capacitors, and timer ICs were integrated to facilitate functions such as pull-down and pull-up, debouncing, and clock generation.

Our goal was to construct a UPS to gain practical insights into power electronics. We built a 12V mini UPS, using various power electronic components including power diodes, power MOSFETs, and power resistors. To achieve voltage step-down and step-up functionality, we integrated two transformers into our design. The UPS consisted of two primary sections: a rectifier that converted stepped-down AC voltage to DC voltage, storing it in a battery, and an inverter that converted DC voltage back to AC voltage before stepping it up again for application.

Our objective was to construct a reliable model for detecting plant leaf diseases in order to gain insight into the area of digital signal processing and machine learning. We initially employed k-means clustering on the L*a*b* color space for segmentation, followed by using the HSV color space to identify the infected cluster. Then, we extracted various mathematical properties from both clusters, this time we used the RGB color space and grayscale image. Finally, we applied the KNN algorithm, achieving an overall accuracy of 89%.

We were tasked with building a fully automated system to measure and analyze the I-V characteristics of solar panels, aiming to explore automation in optoelectronics testing. We used an array of power resistors to use as variable load. Resistors were carefully selected so that our setup was able to measure the characteristics of solar panels of various sizes. In our setup, Arduino Uno controlled MOSFETs acted as switches for the resistor array, while a decoder reduced the number of output pins required. To measure solar irradiance, we used an LDR calibrated against an actual pyranometer. Data processing was done in Python to keep costs down compared to MATLAB's expensive licensing requirements.

We developed a smart metering system to explore microprocessor and embedded systems, coupled with IoT applications. Utilizing ESP-32, we established Wi-Fi-based communication. For user interaction, we designed a website interface. Our system not only offers real-time consumption data but also enables users to deactivate low-priority loads during power shortages.

We designed and implemented the I2C protocol in cadence EDA tools. The purpose was to get a grasp on VLSI design. It was aimed specially on understanding basics of RTL design, verification, and physical design. We first designed two state diagrams, one for master and another one for slave. Later, we developed RTL code for them using SystemVerilog. Two slaves were utilized to help ensure proper communication. We used linear testbench to verify our design. Physical design was subsequently completed using Cadence Innovus.  

This project were designed to provide a fundamental understanding of electrical service design for a multi-storied building. We started by carefully placing various fittings and lights, then determining bulb lumen ratings depending on room size and use. We then linked them to switchboards, SDB, and MDB. Certain fittings were also linked to ESDB and EMDB for emergency power, with the EMDB connected to a generator. Due of the significant power usage, we included a transformer. We first determined all necessary breaker and wire ratings. Finally, we estimated the generator and transformer ratings using previous calculations.

The objective of this project was to provide a fundamental understanding of analog integrated circuit design. The main challenge of this project was calibrating various elements and deciding on a trade-off between different performance characteristics. Instead of relying on trial and error, we developed and used a MATLAB algorithm to identify an acceptable match.

Load forecasting is a technique used by power companies to predict the power or energy needed to balance the supply and load demand at all times. It is mandatory for the proper functioning of the electrical industry. It can be classified in terms of time like short-term (a few hours), medium-term (a few weeks up to a year), or long-term (over a year). In this paper, for medium- and long-term forecasting end use and econometric approach is used. Whereas for short-term forecasting various approaches are used like regression models, time series, neural networks, statistical learning algorithms, and fuzzy logic.

This project aimed to provide a foundational understanding of automation, enabling communication between users and various household elements—such as fans, light bulbs, and CCTV camera rotation—through voice control. We utilized the Arduino UNO microprocessor to process Bluetooth signals incoming from the phone and transmit commands to the mentioned elements. In addition, we created a smartphone app for user interaction with MIT App Inventor, utilizing Google Assistant as the speech recognition platform.

The goal of this project was to provide understanding of basic signal processing and the use of numerical techniques. We used audio signals for analyzing FDM at various steps. Our graphical user interface (GUI) offered options for use of both, real-time recording and pre-recorded audio. Additionally, users could adjust the number of signals and audio duration within the GUI. Furthermore, the GUI allowed monitoring of signals in both the time and frequency domains.