The system also provides an important motor protection feature. If the temperature of the water pump motor rises above a predefined limit due to overload, continuous operation, or poor cooling, the temperature sensor immediately detects the overheating condition and switches OFF the motor automatically. This prevents motor damage, improves safety, and increases the lifespan of the irrigation pump. LED indicators are used to display the pump status and motor over-temperature condition.

This project is suitable for agricultural fields, gardens, and farms where automatic irrigation and motor safety are essential. The system reduces manual intervention, conserves water, protects the irrigation motor from overheating, and ensures reliable and efficient irrigation without unnecessary motor failures.

The major features of this project are:

  Automatic Irrigation Control – Automatically switches the water pump ON when the soil moisture level is low and turns it OFF when sufficient moisture is reached.

  Motor Temperature Protection – Continuously monitors the water pump motor temperature and automatically turns OFF the motor if it overheats, preventing damage.

  Water Conservation – Supplies water only when required, reducing water wastage and improving irrigation efficiency.

  Motor Safety and Reliability – Protects the irrigation pump from overheating, increasing motor life and reducing maintenance costs.

The major building blocks of this project are:

1. Regulated Power Supply.
2. PIC Microcontroller.
3. Soil moisture sensor.
4. Relay with driver.
5. Crystal oscillator
6. LED Indicators.
7. Temperature sensor
8. Reset Button.

Software’s used:

• PIC-C compiler for Embedded C programming.
• PIC kit 2 programmer for dumping code into Micro controller.
• Express SCH for Circuit design.

Regulated Power Supply:

Block Diagram:

video:

In manual mode, the user can control devices such as bulbs, fans, and sockets using a mobile application (Blynk) and Bluetooth-based voice commands. This allows easy and flexible operation without physical interaction. The ESP32 processes commands received from the user and activates corresponding relays to control the connected appliances.

In automatic mode, the system operates based on real-time sensor inputs. A PIR (Passive Infrared) sensor detects human presence, while an LDR (Light Dependent Resistor) monitors ambient light intensity. Based on these inputs, the system automatically controls lighting and socket operation. Additionally, the system uses temperature and motion sensing to automatically adjust the fan and socket, improving comfort and reducing power consumption.

For enhanced security, when the PIR sensor detects human presence in manual mode, the system sends an alert email to the house owner, enabling remote monitoring and quick response.

Overall, this system integrates IoT, wireless communication, and sensor-based automation to create a smart home solution that improves safety, reduces power consumption, and provides user-friendly control.

Objectives:

 To control home appliances using ESP32.

 To operate devices using Bluetooth voice commands and the Blynk app in manual mode.

 To develop automatic control using PIR sensor, LDR, and temperature sensor.

 To switch bulb, fan, and socket automatically based on conditions.

 To send email alerts when motion is detected.

 To reduce power consumption and improve convenience.

 To provide a simple and user-friendly smart home system.

The major building blocks of this project are:

• Regulated Power supply.
• ESP32 Module.
• Temperature sensor.
• PIR sensor.
• LDR sensor.
• Relays.
• Bulb
• DC fan.
• Three pin sockets.
• Auto/Manual mode.
• Bluetooth.

Software’s used:

• ARDUINO IDE.
• Embedded C language.
• WEB and Bluetooth Technology.

Block Diagram:

video:

The system uses an Arduino Uno as the main controller to measure battery parameters through voltage and temperature sensors. Based on the SOC and SOH values, the controller automatically controls battery charging using a relay. An ESP8266 Wi-Fi module uploads real-time battery data, including voltage, temperature, SOC, and SOH, to the ThingSpeak cloud for remote monitoring, while an LCD displays the system status locally. This IoT-enabled BMS improves battery safety, extends battery life, and supports efficient energy management in electric vehicles.

Objectives:

• To design and develop an IoT-based Battery Management System (BMS) for electric vehicles.
• To monitor battery voltage, temperature, State of Charge (SOC), and State of Health (SOH).
• To control battery charging automatically using a relay based on battery condition.
• To upload real-time battery data to the ThingSpeak cloud using the ESP8266 Wi-Fi module.
• To display battery parameters and charging status on an LCD for easy monitoring.

The major building blocks of this project are:

• Regulated power supply
• Arduino UNO.
• Temperature sensor.
• Voltage sensor.
• Buzzer.
• Three battery packs
• Three Relays.
• Charging Circuit.
• LCD display.
• LED Indicators
• Crystal oscillator
• Reset button.
• ESP8266 Wi-Fi module.
• Load(LEDs).

Software’s used:

• Arduino IDE Studio compiler for Embedded C programming.
• Express SCH for Circuit design.

Technology Used:

• Thingspeak technology.

video:

The growing demand for green energy has increased the importance of Electric Vehicles (EVs), which mainly use Lithium-Ion batteries due to their high energy density. Since these batteries must operate within safe limits, a Battery Management System (BMS) is essential.

This system uses a NodeMCU ESP8266 microcontroller to monitor battery voltage and temperature through connected sensors. When the battery charge level drops, the controller activates a relay to start the charging process automatically. It also calculates important battery parameters such as State of Charge (SOC) and State of Health (SOH).

The measured voltage, SOC, SOH, and temperature values are displayed on an LCD for real-time monitoring. If the temperature exceeds the safety limit, a buzzer is activated to alert the user.

For remote monitoring, the NodeMCU sends battery data to the ThingSpeak IoT cloud platform using its built-in Wi-Fi. This enables users to track battery performance and system status from anywhere through a mobile phone or computer.

The entire system is programmed using Embedded C in the Arduino IDE, which manages sensor readings, charging control, display updates, safety alerts, and IoT communication.

Objectives:

  To design a battery monitoring system using the NodeMCU ESP8266 microcontroller.

  To measure battery voltage continuously for monitoring battery condition.

  To monitor battery temperature using a temperature sensor to ensure safe operation.

  To calculate battery parameters such as State of Charge (SOC) and State of Health (SOH).

  To automatically control battery charging using a relay module when the battery voltage becomes low.

  To display battery voltage, temperature, SOC, and SOH values on the LCD display for real-time monitoring.

  To provide a safety alert using a buzzer when the temperature exceeds the safe limit.

  To upload battery monitoring data such as voltage, temperature, SOC, and SOH to the cloud using the ThingSpeak IoT platform for remote monitoring.

  To improve battery safety, efficiency, and monitoring using IoT technology.

The major building blocks of this project are:

• Regulated power supply
• NodeMCU Microcontroller.
• Temperature sensor.
• Voltage sensor.
• Buzzer.
• 12V Battery pack
• Relay.
• Charging Circuit.
• LCD display.

Software’s used:

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

video:

Multiple sensors are integrated into the system, including an SR04 ultrasonic sensor for real-time water level measurement, a DHT11 sensor for temperature and humidity monitoring, a temperature sensor for surrounding temperature analysis, a BMP180 sensor for atmospheric pressure and altitude measurement, and a soil moisture sensor to detect ground saturation levels. Sensor data is processed using Python scripts running on the Raspberry Pi.

If any sensor value shows a sudden increase beyond predefined threshold limits, indicating a potential flood risk, an audible buzzer alert is immediately activated to warn nearby inhabitants. Simultaneously, real-time sensor data is displayed on an LCD module and uploaded to the Thingspeak cloud platform for remote monitoring and data visualization. Historical data logging is also supported using an SD card.

Additionally, the system supports basic forecasting by analyzing trends in water level, moisture content, humidity, and atmospheric pressure, enabling early prediction of flood-like conditions. The proposed system is cost-effective, reliable, and suitable for deployment in flood-prone rural and urban areas, contributing to disaster preparedness and risk reduction through real-time monitoring and early warning alerts.

Objectives:

  To design a Python-based IoT flood surveillance system using Raspberry Pi Zero 2W.

  To monitor water level in real time using an ultrasonic (SR04) sensor.

  To measure environmental parameters such as temperature, humidity, pressure, altitude, and soil moisture.

  To detect sudden variations in sensor values indicating potential flood conditions.

  To provide instant local alerts using a buzzer during critical situations.

  To display live sensor data on an LCD and store it for future analysis.

  To upload sensor data to ThingSpeak for remote monitoring and visualization.

  To enable basic flood forecasting through trend analysis of environmental data.

The major building blocks of the project are:

• Power supply.
• DHT11 (temperature and humidity) sensor.
• BMP180 (pressure and altitude) sensor
• Ultrasonic sensor.(water level).
• Moisture sensor.
• Temperature Sensor (SR04).
• LCD display.
• Buzzer.
• SD card.

Software’s used:

• Rasppbian OS.
• IOT Technology.
• Express SCH for Circuit design.

Block Diagram:

video:

An Arduino Nano controls the LED sequencing and acquires optical signals, which are transmitted to a Raspberry Pi 3 A+ for processing. The system uses the Non-Negative Least Squares (NNLS) algorithm and machine learning techniques to estimate Haemoglobin levels accurately without gender-based calibration.

To enhance reliability, an accelerometer detects motion artifacts, while a DS18B20 temperature sensor monitors temperature variations. The system continuously evaluates signal quality using the Signal-to-Noise Ratio (SNR) and displays Haemoglobin concentration, SNR, temperature, and motion status on a 16×2 LCD. The proposed system provides a safe, reusable, and real-time solution for non-invasive Haemoglobin monitoring and health assessment.

Objectives:

1. To develop a non-invasive system for measuring Haemoglobin concentration.
2. To use seven optical wavelengths for accurate Haemoglobin estimation.
3. To acquire and process optical signals using Arduino Nano and Raspberry Pi.
4. To implement the NNLS algorithm for Haemoglobin calculation.
5. To improve measurement accuracy using machine learning techniques.
6. To monitor signal quality using Signal-to-Noise Ratio (SNR) analysis.
7. To detect motion artifacts using an accelerometer sensor.
8. To measure temperature variations using a DS18B20 temperature sensor.
9. To display Haemoglobin level, SNR, temperature, and motion status on an LCD.
10. To provide a safe, reusable, and real-time Haemoglobin monitoring solution.

The major building blocks of this project are:

• Power supply.
• RASPBERRY pi3 A+.
• Arduino NANO.
• Noninvasive Haemoglobin Sensor.
• LCD display.
• Gyroscope.
• Temperature sensor.
• SD card.

Software’s used:

• Arduino IDE.
• Embedded C language.
• Raspbian OS.
• Python Language.
• Machine Learning.

video:

The device features GPS-based geofencing, allowing parents to define a virtual boundary. If the child crosses this predefined safe zone, the system immediately sends an alert via SMS and displays a boundary breach warning on the LCD screen, while also activating the buzzer for local alerting. The wearable is powered by a compact rechargeable battery, making it suitable for continuous use.

An AI algorithm analyzes sensor inputs and user interactions to identify abnormal patterns, enhancing the system’s intelligence and responsiveness. This innovative, compact solution offers a comprehensive approach to child safety, combining proactive monitoring with immediate emergency communication.

Features:

1. AI-driven safety monitoring
2. Real-time GPS tracking
3. GSM module for SMS alerts
4. Virtual boundary (geofencing) alerts
5. Abnormal temperature detection
6. Panic switch for emergency alerts
7. Buzzer for local warnings
8. LCD display for real-time status
9. Smart alert logic using AI patterns

The Major Building blocks of this project are:

• Battery.
• LM2596.
• Arduino NANO.
• LCD display.
• Temperature sensor.
• GSM Modem.
• GPS.
• Buzzer.
• Panic switch.

Software’s used:

• Arduino IDE compiler for Embedded C programming.
• Express SCH for Circuit design.

video:

In this system, solar energy is captured from the solar panel and processed through a MOSFET-based MPPT (Maximum Power Point Tracking) circuit to extract maximum available power. The charging circuit regulates and stores this energy in a rechargeable battery pack. The inverter converts the stored DC power into AC power to drive AC loads. A relay controlled by the Raspberry Pi manages battery charging and load switching operations.

The Raspberry Pi acts as the main controller and performs intelligent energy management by analyzing real-time data from voltage and current sensors. It calculates key battery parameters such as State of Charge (SOC) and State of Health (SOH). Based on these parameters, the system automatically controls charging/discharging cycles to improve battery performance and lifespan.

Multiple sensors including solar voltage and current sensors, battery voltage and current sensors, DHT11 (temperature and humidity sensor), and light intensity sensors continuously monitor environmental and electrical parameters. The system also measures PWM signals, efficiency, and overall system performance.

All real-time data such as solar voltage, solar current, battery voltage, battery current, SOC, SOH, temperature, humidity, light intensity, PWM duty cycle, and efficiency are uploaded to the ThingSpeak cloud platform for remote monitoring and data visualization. The LCD display shows live system status locally, while a buzzer provides alerts during abnormal conditions such as overvoltage, overheating, or low battery levels.

By integrating AI-based predictive analytics with IoT cloud monitoring, the proposed system enhances energy utilization efficiency, ensures optimal battery health, enables smart load management, and provides real-time remote monitoring. This makes the AI-SEMS highly suitable for smart homes, renewable energy systems, and sustainable power management applications.

Main Objectives:

• To maximize solar energy utilization by implementing a MOSFET-based MPPT controller for efficient power extraction from solar panels.
• To monitor and manage battery health by calculating State of Charge (SOC) and State of Health (SOH) using an intelligent Battery Management System (BMS).
• To implement AI-based energy management for optimizing charging, discharging, and load control operations to improve overall system efficiency.
• To provide real-time monitoring and remote access of solar, battery, and environmental parameters through IoT cloud platforms such as ThingSpeak.
• To enhance the reliability and sustainability of renewable energy systems through smart load management, fault detection, and efficient energy storage utilization.

The major building blocks of this project are:

• Power supply.
• Raspberry pi.
• SOLAR Panel.
• Charging circuit.
• Temperature sensor.
• Voltage sensor.
• Current sensor.
• Light Intensity Sensor/
• DHT11(temperature & humidity) sensor.
• Buzzer.
• 12v Battery pack.
• Relay.
• LCD display.
• LED Indicators
• MPPT module.
• ESP8266 wi-fi module.

Software’s used in the project:

1. Raspbian OS.
2. Python language.
3. Express SCH for Circuit design.
4. Machine learning (ML).

video:

The system also incorporates a temperature sensor to continuously monitor ambient temperature and display both the current temperature and selected fan speed on an LCD screen in real time. To enhance safety, a buzzer-based alert mechanism is included to notify the user whenever the temperature exceeds a preset threshold value. The proposed system offers efficient motor control, real-time monitoring, improved user interaction, and temperature-based safety alerts, making it suitable for smart home appliances, industrial cooling systems, and energy-efficient ventilation applications.

The main objectives of the project are:

• To design and implement a speed control system for a BLDC Fan motor using an Arduino Uno and a Keypad based control method.
• To provide user-friendly control through keypad, each corresponding to a predefined speed level.
• To display real-time speed levels and Temperature on an LCD screen for easy monitoring and user interaction.
• High temperature alerts using Buzzer.

The major building blocks of the project are:

1. Regulated Power Supply.
2. Arduino UNO microcontroller.
3. 4X4 keypad
4. BLDC fan motor
5. IR sensor
6. LCD display.
7. Temperature sensor.
8. Buzzer.

Software’s used:

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

Regulated Power Supply:

BLOCK DIAGRAM:

video:

The proposed system consists of a glucose sensor, temperature sensor, and MQ-135 acetone sensor to measure blood glucose level, body temperature, and breath acetone concentration respectively. These sensors are interfaced with an Arduino microcontroller, which collects and transfers the sensor data to a Raspberry Pi processor. The Raspberry Pi acts as the main controlling unit and is programmed using embedded Linux. A 4×4 keypad and selection switches are provided for entering dataset values and selecting automatic or manual operation modes. Using the collected sensor data and user input, the Decision Tree J48 algorithm processes the information and predicts the chances of diabetes in a person. The prediction result is displayed on an LCD screen. By integrating machine learning with embedded systems, the project provides an efficient, low-cost, and user-friendly solution for early diabetes prediction and health monitoring.

Objectives:

1. To develop an intelligent diabetes prediction system using Machine Learning techniques.
2. To implement the Decision Tree J48 algorithm for accurate early prediction of diabetes.
3. To measure important health parameters such as blood glucose level, body temperature, and breath acetone concentration using sensors.
4. To interface sensors with Arduino and Raspberry Pi for real-time data acquisition and processing.
5. To provide both automatic and manual modes for user-friendly operation.
6. To allow users to enter dataset values through a 4×4 keypad for prediction analysis.
7. To display the diabetes prediction results clearly on an LCD screen.
8. To design a low-cost, efficient, and portable healthcare monitoring system.
9. To improve early diagnosis and reduce the risk of severe diabetes-related complications.
10. To integrate embedded systems and machine learning for smart healthcare applications.

The major building blocks of the project are:

1. Power supply.
2. Raspberry Pi processor.
3. Glucosensor.
4. Temperature Sensor
5. Arduino UNO.
6. 4X4 Keypad
7. Selection switch
8. LCD display
9. MQ-135.

Software’s used:

1. Raspbian OPERATING SYSTEM.
2. Python Language.
3. Express SCH for Circuit design.
4. Decision Tree algorithm.

Block diagram:

video: