The ESP32 utilizes its built-in Wi-Fi capability to upload attendance records to a web server or cloud database in real time. This enables administrators to monitor attendance remotely, maintain digital records, and generate reports without manual intervention. Unauthorized or unregistered RFID cards are rejected, ensuring secure and reliable attendance management.

The proposed system eliminates the drawbacks of traditional manual attendance methods, such as proxy attendance, time consumption, and record maintenance errors. It provides a fast, contactless, and cost-effective attendance solution suitable for schools, colleges, universities, offices, industries, laboratories, libraries, and other organizations. By combining RFID technology with IoT-based web monitoring, the system enhances efficiency, accuracy, and data accessibility for modern attendance management.

The main objectives of the project are:

• To develop an automated attendance system using RFID technology and the ESP32 microcontroller.
• To identify users accurately using EM-18 RFID reader and unique RFID tags.
• To record attendance automatically without manual intervention.
• To display attendance status instantly on an I2C LCD display.
• To upload attendance records to a web server/cloud database using the ESP32’s Wi-Fi.
• To prevent proxy attendance by allowing only registered RFID tags.
• To maintain a secure and centralized digital attendance database.
• To reduce the time and errors associated with manual attendance systems.

The major building blocks of this project are:

1. Power Supply.
2. ESP32 Microcontroller.
3. EM-18 RFID reader and tags.
4. LCD display.

Software’s used:

1. Arduino IDE for compiling and dumping code into Microcontroller.
2. Express SCH for Circuit design.
3. WEB technology.

video:

An Arduino Nano collects sensor data and transmits it to a Raspberry Pi 4, which serves as the central processing unit. The Raspberry Pi performs image processing and machine learning–based classification to determine the freshness status of fruits (good or spoiled). The camera enables live video streaming, allowing users to visually inspect the fruits remotely.

All collected data, including environmental parameters, gas concentration, and fruit condition, are displayed on a web-based dashboard in real time. The system also includes an LCD for local display and a buzzer alert to notify users when spoilage is detected. By combining sensor fusion and AI-based image analysis, the proposed system enhances food safety, reduces waste, and enables smart refrigerator management through remote monitoring and decision-making.

The main objectives of this project are:

• To design and develop an intelligent refrigerator monitoring system for detecting spoilage in fruits like apples and bananas.
• To integrate multiple sensors (gas, temperature, humidity, IR, and NIR) for real-time environmental and freshness monitoring.
• To implement AI/ML-based image processing techniques for classifying fruits as fresh (good) or spoiled (bad).
• To enable live video streaming of fruits using a camera for remote visual inspection.
• To develop a web-based dashboard for displaying sensor data, fruit selection, and spoilage status in real time.
• To provide early warning alerts (buzzer/notification) when spoilage or abnormal conditions are detected.
• To ensure seamless communication between Arduino Nano and Raspberry Pi for efficient data processing.
• To reduce food wastage by enabling timely detection and monitoring of fruit quality.
• To create a low-cost, user-friendly smart refrigeration system for domestic and commercial use.

The major building blocks of this project are:

1. Regulated power supply.
2. AC – DC converter.
3. Raspberry pi4.
4. SD card.
5. Pi camera.
6. Buzzer.
7. Arduino Nano.
8. MQ-135 sensor.
9. Methane sensor.
10. DHT11 sensor.
11. IR sensor.
12. NIR sensor.
13. Arduino Nano.
14. LCD display.
15. Buzzer.

Software’s used:

• Raspbian OS.

• Machine learning & Artificial Intelligence (AI) algorithm.
• Express SCH for Circuit design.
• WEB application.

Block Diagram:

video:

To monitor motor performance parameters, the INA219 sensor is used for measuring voltage, current, and power consumption of the DC motor in real time. These parameters, along with motor status are continuously displayed on an LCD module as well as on the IoT web dashboard. The web interface allows users to remotely control the motor speed levels and direction from any smart device connected to the network.

The proposed system provides an efficient, low-cost, and user-friendly solution for industrial automation and remote motor management applications. It improves monitoring accuracy, enhances operational flexibility, and enables real-time control through IoT technology.

Objectives:

• To design and develop an IoT-based DC motor speed and direction control system using the ESP32 microcontroller.
• To control the DC motor operation such as clockwise, counterclockwise, speed variation, and stop function using the L298 Motor Driver.
• To monitor motor parameters like voltage, current, and power consumption using the INA219 sensor.
• To display motor status and electrical parameters on an LCD display in real time.
• To develop a web dashboard for remote monitoring and control of the DC motor through Wi-Fi connectivity.
• To improve automation, efficiency, and user convenience using IoT technology.

The major building blocks of this project are:

• Adapter Power supply.
• ESP32.
• L298 Motor Driver.
• DC motor.
• INA219.
• LCD display with I2C.

Software’s used:

• ARDUINO IDE.
• Embedded C language.
• IOT Technology.

Block Diagram:

video:

An Arduino Nano collects sensor data and transmits it to a Raspberry Pi 4, which serves as the central processing unit. The Raspberry Pi performs image processing and machine learning–based classification to determine the freshness status of fruits (good or spoiled). The camera enables live video streaming, allowing users to visually inspect the fruits remotely.

All collected data, including environmental parameters, gas concentration, and fruit condition, are displayed on a web-based dashboard in real time. The system also includes an LCD for local display and a buzzer alert to notify users when spoilage is detected. By combining sensor fusion and AI-based image analysis, the proposed system enhances food safety, reduces waste, and enables smart refrigerator management through remote monitoring and decision-making.

The main objectives of this project are:

• To design and develop an intelligent refrigerator monitoring system for detecting spoilage in fruits like apples and bananas.
• To integrate multiple sensors (gas, temperature, humidity and  IR) for real-time environmental and freshness monitoring.
• To implement AI/ML-based image processing techniques for classifying fruits as fresh (good) or spoiled (bad).
• To enable live video streaming of fruits using a camera for remote visual inspection.
• To develop a web-based dashboard for displaying sensor data, fruit selection, and spoilage status in real time.
• To provide early warning alerts (buzzer/notification) when spoilage or abnormal conditions are detected.
• To ensure seamless communication between Arduino Nano and Raspberry Pi for efficient data processing.
• To reduce food wastage by enabling timely detection and monitoring of fruit quality.
• To create a low-cost, user-friendly smart refrigeration system for domestic and commercial use.

The major building blocks of this project are:

1. Regulated power supply.
2. AC – DC converter.
3. Raspberry pi4.
4. SD card.
5. Pi camera.
6. Arduino Nano.
7. MQ-135 sensor.
8. Methane sensor.
9. DHT11 sensor.
10. IR sensor.
11. Arduino Nano.
12. LCD display.
13. Buzzer.

Software’s used:

• Raspbian OS.
• Machine learning & Artificial Intelligence (AI) algorithm.
• Express SCH for Circuit design.
• WEB application

video:

The Raspberry Pi analyzes physiological data, mouse behavior, and facial expressions to determine the user’s stress level in real time. The stress status is displayed on an LCD and also monitored through a web interface with live video streaming. The system provides an intelligent and non-intrusive solution for stress monitoring in workplaces, healthcare, and research applications.

The main objectives of this project are:

1. To monitor the user’s stress level in real time.
2. To measure heart rate and SpO₂ using the MAX30102 sensor.
3. To detect mouse clicking force using a strain gauge sensor.
4. To capture and analyze facial expressions using a Pi Camera.
5. To perform facial recognition with bounding box and emotion detection.
6. To display stress status and physiological parameters on an LCD.
7. To provide live video streaming and stress monitoring through a web interface.
8. To store monitoring data for future analysis and reference.

The major building blocks of this project are:

1. Adapter power supply.
2. Raspberry pi4.
3. Arduino Nano.
4. SD card.
5. LCD display.
6. MAX30102 Sensor.
7. Strain gage sensor.
8. Pi camera.

Software’s used:

• Raspbian OS.
• Express SCH for Circuit design.
• Web technology.

video:

This system detects such types of faults line-line fault, open circuit fault and line-ground fault. This proposed model uses the concept of Ohms law to detect the faults which is quick, reliable and cost effective. This system consists of ESP8266 WI-FI module to send the data into the WEB.

Here the current sensing circuits made a set of resistors are interfaced to Arduino micro controller at ADC port for providing digital data to the microcontroller. Hence the fault is detecting in transmission lines, this data upload into the WEB Dashboard also microcontroller will display the fault name on LCD module and activate Buzzer for alerts. This system will give the indication of particular phase fault through RED, GREEN and YELLOW LEDs. This project consists of fire and smoke sensors to detect the fire and smoke accidents and alerts the person through Buzzer. To achieve this task microcontroller loaded program written in embedded C language.

Objectives:

• Design a three-phase fault analysis system.
• It will detect line-line fault, open circuit fault, line-ground fault.
• R, Y, B fault indication using LEDs.
• Automatic fire and smoke detection and alerting system.
• Alert in abnormal condition using Buzzer.
• Visible alerts using LCD display.
• WEB based alerting system.

The main blocks of this project are:

• Adapter power supply.
• Arduino NANO Microcontroller.
• Three phase transmission lines.
• Resistors.
• LCD with driver.
• ESP8266 Wi-Fi module.
• Buzzer.
• RED, GREEN, YELLOW LEDs.
• Fire sensor.
• Smoke sensor.

Software’s used:

• Embedded C programming.
• Arduino IDE studio for dumping code into Micro controller.
• Express SCH for Circuit design.

video:

In this application, the PZEM module interfaces with an LCD display and a website display to show real-time data, allowing users to monitor grid parameters on both local and remote platforms. The LCD provides immediate feedback on key electrical parameters like voltage, current, active power, and frequency, offering a convenient and compact solution for localized monitoring. Additionally, the website display enables remote monitoring, accessible from any device with internet access, using a web-based interface. This can be achieved through the integration of IoT device, such as ESP32, which send collected data to a web platform.

The grid monitoring system aims to enhance energy management, ensure power quality, and optimize consumption by providing accurate and real-time information. It can be used in various applications, such as solar power systems, industrial facilities, and residential energy monitoring, making it an essential tool for users seeking to improve grid performance, troubleshoot electrical issues, and reduce energy waste. The combination of real-time display and remote monitoring ensures greater control over energy resources, thereby contributing to sustainability and cost savings.

Objectives:

Monitor Electrical Parameters: Track key values like voltage, current, power (kWh), and frequency in real-time.

Local Display: Show real-time data on an LCD screen for quick reference and monitoring.

Remote Access: Provide a web interface for users to monitor grid data from anywhere using an internet-connected device.

Track Energy Consumption: Monitor and display energy usage (kWh) to help users manage energy consumption.

Check Power Quality: Track voltage and frequency to identify any power quality issues, like fluctuations.

Improve Efficiency: Detect issues and optimize the grid system for better performance and energy efficiency.

Easy-to-Use Interface: Make the LCD display and web interface simple and user-friendly for easy access and interpretation of data.

Future Scalability: Allow for easy future expansion, adding more sensors or devices as needed.

Data Logging: Save historical data to help analyze trends and generate reports for decision-making.

Better Energy Management: Help users understand their energy use and make changes to save energy and reduce costs.

Components Used:

• Regulated Power Supply.
• GRID.
• ESP32 Microcontroller.
• PZEM MODULE.
• LCD Display.
• Load.

Software’s Used:

1. Arduino IDE studio compiler for dumping program.
2. Express SCH for Circuit design.

video:

The dashboard provides detailed weather parameters such as temperature, humidity, atmospheric pressure, wind speed, cloud conditions, visibility, overcast status, sunrise time, sunset time, current date, time, and location details. The NodeMCU continuously updates the weather information through Wi-Fi connectivity, ensuring accurate and real-time monitoring.

The system is compact, low-cost, and energy efficient, making it suitable for smart home applications, environmental monitoring, educational projects, and IoT-based weather stations. The project demonstrates the practical implementation of Internet of Things (IoT) technology for live weather analysis and remote monitoring applications.

Objectives

• To design and develop a live weather monitoring dashboard using NodeMCU ESP8266.
• To connect the NodeMCU to the internet through Wi-Fi for real-time data access.
• To display live weather information on a web-based dashboard.
• To monitor weather parameters such as temperature, humidity, atmospheric pressure, and wind speed.
• To display cloud conditions, overcast status, and visibility details.
• To provide location-based weather information including sunrise and sunset timings.
• To display real-time date and time information on the dashboard.
• To create a low-cost and efficient IoT-based weather monitoring system.
• To enable remote weather monitoring using mobile phones or web browsers.
• To demonstrate the application of IoT technology in environmental monitoring systems.

Components Used:

• Power supply.
• NodeMCU.

Software’s used:

1. Embedded C programming.
2. Arduino IDE programmer for dumping code into Micro controller.
3. Express SCH for Circuit design.
4. WEB technology.

video:

The proposed system is an IoT-based Non-Invasive Glucometer and Hemoglobin Monitoring System using the ESP32 microcontroller. The glucose and hemoglobin concentrations are estimated using non-invasive optical measurement techniques. The system operates on the principle that variations in the intensity of near-infrared (NIR) light, after passing through the finger and received by a photodetector, are correlated with changes in blood glucose levels. Similarly, hemoglobin concentration is estimated using optical signal variations processed through dedicated measurement circuits.

The ESP32 processes the acquired signals and displays the measured values on an LCD display. In addition, the system hosts a web-based interface (IoT webpage), allowing users to monitor glucose and hemoglobin levels remotely through any device with a web browser. This enables real-time data access without the need for external communication modules.

Powered through a RPS, the system is compact, economical, and user-friendly, making it suitable for continuous health monitoring, early diabetes detection, and anemia screening in home healthcare and clinical environments.

Objectives:

• To design and develop a non-invasive system for measuring blood glucose and hemoglobin levels without the need for finger-pricking.
• To utilize near-infrared (NIR) optical sensing techniques for estimating glucose and hemoglobin concentrations through the human finger.
• To implement the ESP32 microcontroller for processing sensor data efficiently and accurately.
• To develop an IoT-based web interface for real-time monitoring of health parameters using any web browser.
• To display the measured glucose and hemoglobin values on an LCD screen for local monitoring.

The major building blocks of this project are:

• Regulated Power supply.
• ES32.
• NIR module for glucose measuring.
• NON-INVASIVE Hemoglobin sensing circuit.
• LCD display.

Software’s used:

• ARDUINO IDE.
• Embedded C language.

Block Diagram:

video:

The objectives of the project include:

• To interface R307 fingerprint module with Raspberry Pi.
• To store fingerprint templates with corresponding blood groups.
• To display blood group information on LCD using I2C.
• To generate buzzer alert for wrong fingerprint detection.
• To develop a web page for registration and database management.
• To enable Wi-Fi-based remote access.

The major building blocks of this project are:

• Regulated power supply.
• Raspberry pi zero 2w.
• Fingerprint module.
• LCD display.
• Buzzer.
• SD card.

Software’s used:

• Raspbian OS.
• WEB technology.
• Express SCH for Circuit design.

video: