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Working IoT, AI/ML and embedded builds with abstracts, component lists, documentation, and developer support — ready for your final-year submission.
Wireless Charging System for Electric Vehicles with Enhanced Efficiency
The Wireless Charging System for Electric Vehicles (EVs) with Enhanced Efficiency is an innovative solution that aims to simplify and modernize EV charging by eliminating the need for physical connectors. This system leverages electromagnetic induction through a copper coil-based transmitter and receiver, providing a safe, efficient, and contactless power transfer method. The system consists of a primary coil connected to a 12V transformer and a buck-boost converter, which regulates and boosts voltage levels for optimal inductive power transmission. On the receiving end, a secondary coil is used along with a buck converter to stabilize the voltage and safely charge the EV battery. A NodeMCU (ESP8266) microcontroller is integrated to monitor voltage, current, and temperature parameters, and to transmit real-time charging data to a web-based dashboard or mobile app. By removing mechanical plug-in systems, this project reduces wear and tear, increases safety in wet or hazardous environments, and offers automated charging capabilities. The use of efficient power electronics ensures minimal energy loss, making it a cost-effective and scalable solution for future smart charging stations and residential EV setups.
Intelligent Home Automation System for Energy Efficiency and Safety
As the demand for smarter, safer, and more energy-efficient living spaces increases, integrating intelligent automation systems into homes has become a necessity. This project presents an Intelligent Home Automation System for Energy Efficiency and Safety, designed to control and monitor household appliances through a centralized embedded platform. The system is built using the NodeMCU (ESP8266) microcontroller, which offers Wi-Fi connectivity for real-time control and monitoring via a web or mobile interface. The automation setup includes relay-controlled outputs connected to devices such as bulbs and DC motors, enabling smart switching of lights, fans, or curtains. Touch-sensitive buttons are used to manually control appliances, enhancing user convenience while maintaining modern aesthetics. An IR sensor is deployed to detect human presence in specific rooms or areas, allowing the system to automatically turn off lights or fans in unoccupied spaces, thus optimizing energy usage. The hardware components are mounted on a custom-designed PCB board, ensuring a clean and organized layout for safe and reliable operation. The system not only reduces electricity consumption through smart scheduling and presence-based control but also improves household safety by avoiding manual intervention with high-voltage switches. All sensor data and appliance states are transmitted to the cloud or local server using the NodeMCU, allowing users to monitor and control their home remotely. This home automation framework demonstrates a practical, scalable, and cost-effective approach to making modern homes more intelligent, efficient, and secure, particularly suited for urban households, elderly care, and smart city implementations.
RFID-Based Attendance Management System
The RFID-Based Attendance Management System is a smart and efficient solution designed to automate the process of attendance tracking using Radio Frequency Identification (RFID) technology. This system utilizes a NodeMCU (ESP8266) microcontroller integrated with an RFID card reader to scan individual RFID cards assigned to users such as students or employees. When an RFID card is tapped near the reader, the unique ID of the card is read and sent via Wi-Fi to a remote database through the NodeMCU. The system records the date, time, and cardholder identity in real-time, ensuring quick and accurate attendance logging. This data can be accessed through a secure web-based frontend for monitoring, reporting, and analytics. Compared to traditional manual systems, this method is faster, more secure, and tamper-resistant, preventing proxy attendance. The system is ideal for educational institutions, offices, and restricted facilities aiming for a scalable and contactless attendance solution.
Comprehensive Biomedical Monitoring System with Triple Parameter Surveillance
The advancement of IoT-based biomedical systems has paved the way for real-time remote patient monitoring, offering crucial support in medical emergencies and rural healthcare. This project proposes a Comprehensive Biomedical Monitoring System with Triple Parameter Surveillance, which continuously monitors the patient's blood oxygen level (SpO₂), body temperature, and real-time location, and integrates an automated alert mechanism through GSM communication. The system is built using an ESP32 microcontroller, which interfaces with a MAX30102 SpO₂ sensor, a temperature sensor, and a GPS module to collect vital physiological and location data. These parameters are wirelessly transmitted to a Flask-based web server, where the data is displayed on a live dashboard and analyzed using a machine learning model to detect abnormal health conditions such as hypoxia, fever, or sudden changes in location. If any health parameter crosses a predefined threshold, the system immediately activates the GSM module (SIM800L/SIM900) to send an emergency SMS alert containing the patient’s vital data and GPS location to medical responders or family members. This ensures quick response even in areas with poor internet connectivity. By integrating biomedical sensors, intelligent decision-making via machine learning, and dual communication through Wi-Fi and GSM, the system offers a reliable, portable, and scalable healthcare monitoring solution. It is particularly suited for remote patient care, mobile health units, elderly monitoring, and post-operative recovery tracking, where timely alerts and accurate health data are essential for saving lives.
Atm face regonistion transcation
Ensuring secure and contactless ATM transactions has become a major concern in today’s digital banking landscape. This project proposes a multi-factor authentication system titled “ATM Face Recognition Transaction System”, which integrates facial recognition using ESP32-CAM with RFID-based user identification and a Flask-based machine learning backend for real-time verification and secure transaction authorization. The system initiates when a user presents an RFID card to an RFID reader, which reads the unique card ID and verifies it as the first layer of authentication. Upon successful card detection, the ESP32-CAM module captures a live image of the user's face and transmits it to a Python Flask server, where a trained machine learning face recognition model compares it against a pre-registered dataset. If the face matches the card owner, the transaction is authorized, and the user is granted access to the ATM functions. If the face does not match or is unrecognized, the transaction is denied, ensuring that stolen or cloned RFID cards cannot be used fraudulently. The system operates over Wi-Fi, enabling seamless integration between the ESP32-CAM, Flask web server, and database of registered users. This dual-factor authentication approach—RFID card + facial recognition—provides an enhanced layer of security, reduces physical contact, and paves the way for future touchless ATM interfaces. This project demonstrates a cost-effective, scalable, and secure ATM authentication system suitable for smart banking applications, combining IoT, embedded systems, and AI-powered face recognition.
Atm transcation with otp sending with card detection
In the modern era of digital banking, securing ATM transactions against unauthorized access has become increasingly critical. This project presents an innovative solution titled “ATM Transaction with OTP Verification and Card Detection”, which combines RFID-based user authentication with One-Time Password (OTP) verification to enhance ATM security. The system uses an RFID card reader and RFID card for initial user identification. Once the user presents a valid card, the system reads the card data via the NodeMCU (ESP8266) microcontroller, which communicates wirelessly with a Python Flask-based web server. Upon successful card detection, the server generates a secure OTP (One-Time Password) and sends it to the registered mobile number or email ID associated with the RFID card. The user is then prompted to enter the OTP at the interface (physical keypad or web-based form). If the OTP matches the server record and is entered within the valid time window, the transaction is approved and access to the ATM functionality is granted. If the OTP is invalid or expired, access is denied. This dual-layer authentication system ensures that even if the RFID card is lost or stolen, unauthorized users cannot perform transactions without access to the legitimate OTP. The entire communication between the NodeMCU and the server is handled over Wi-Fi, ensuring real-time verification and secure data transfer. This project demonstrates a cost-effective, secure, and scalable solution for ATM transaction authentication, blending IoT and web technologies to enhance safety in financial operations.
smart agri with solenoid and npk sensor
Modern agriculture demands smart, scalable, and autonomous solutions to tackle challenges such as crop disease, wildlife intrusion, and inefficient irrigation. This project presents a Smart Agriculture System integrating IoT and Machine Learning (ML) to monitor environmental conditions, detect leaf diseases, and deter wildlife in real time. At the core, an ESP32 microcontroller collects data from a temperature sensor, soil moisture sensor, water level sensor, and other components. A solenoid valve, controlled through a relay, along with a water pump, automates irrigation based on soil conditions. For plant health, an ESP32-CAM, interfaced via a camera converter, captures leaf images which are processed using ML algorithms through a Flask-based web server to identify signs of disease. Wildlife detection is achieved through real-time video analysis, triggering a buzzer, LED light, or servo motor-based deterrent system to scare animals away. A toy-based setup with a container simulates the real-time farm environment, while an external adaptor powers the system for stability. The dashboard offers real-time monitoring, historical logs, and remote actuation features. This solution promotes precision agriculture, resource optimization, and crop protection through intelligent automation.