The Car Parking System using 8051 is an automated gate control system designed using Assembly Language Programming (ALP). The system uses IR sensors to detect vehicles at both the entry and exit points. When a vehicle arrives at the entry gate, the IR sensor detects its presence, and the 8051 microcontroller checks for available parking space using a counter logic. If a slot is available, the microcontroller sends a signal to the motor driver to open the gate; otherwise, access is denied. At the exit gate, the IR sensor triggers the gate to open and the counter is updated accordingly. The project was developed using ALP on the Keil IDE and simulated using Proteus. Through this project, I gained hands-on experience in low-level hardware control, sensor integration, and motor interfacing—key skills in embedded system design.

The Smart Flood-Level Monitoring System uses an ESP32 microcontroller and an ultrasonic sensor to measure water levels by calculating the distance to the road surface. It classifies flood severity into Safe, Caution, or Not Safe categories and sends data via Wi-Fi to a Firebase cloud database for real-time monitoring. An Android app displays color-coded alerts to help users and authorities make quick decisions during floods. The system’s modular design supports multiple sensor nodes, enabling easy expansion for city-wide coverage. It also filters out false readings caused by environmental factors to improve accuracy. This project provided hands-on experience in IoT programming, sensor integration, cloud communication, and mobile app development, creating a cost-effective flood monitoring solution.

The Emergency Chat Box project utilizes ESP8266 Wi-Fi modules to enable offline communication between a sender and receiver through a captive portal and WebSockets, without requiring an internet connection. The sender ESP8266 acts as a Wi-Fi Access Point hosting a chat interface, while the receiver connects to this network and displays incoming messages on an OLED screen. Messages are transmitted in real-time with low latency using WebSockets, and the receiver acknowledges messages via a push button, ensuring reliable communication during emergencies. The system’s queueing mechanism manages multiple messages in order, preventing message loss. Developed using Embedded C, this project provided practical experience in embedded system programming, wireless communication, sensor interfacing, and real-time data handling, making it a robust solution for critical offline messaging.

This project automates foot arch classification using Harris mat footprint images transformed into pressure heatmaps. Machine learning models—Support Vector Machines (SVM) and Convolutional Neural Networks (CNN)—classify Pes Cavus, Pes Planus, and Normal Arch with high accuracy. The CNN model achieved 92% accuracy with strong precision, recall, and F1 scores. Using Gaussian-based pressure modeling, the system analyzes plantar pressure without sensors, providing biomechanically valid results. Built with OpenCV and advanced image processing, it includes a React-based web platform for real-time visualization and easy access to pressure data. This sensorless, cost-effective method enables early detection and monitoring of foot abnormalities for clinical and remote healthcare use.