The project contains an overview of the complete system, MEMS sensor is placed at the rods of the handle of the walking device and just by moving the rods we can control the walking device. MEMS sensor is one of the simplest but also most applicable micro-electromechanical systems. They became indispensable in automobile industry, computer and audio-video technology.

In this project MEMS sensor is used as the input to the Arduino nano microcontroller in order to control the walking device. The output of the MEMS sensor is given to the PID microcontroller. MEMS sensor data is fed to the PID microcontroller and the analog data will be serially sent to the Arduino nano MICROCONTROLLER. Depending on the output from the MEMS sensor, microcontroller decides the direction of movement of wheels (either forward, backward, left or right and also stop). With this the person can move in the desired direction he needs.

The major building blocks of this project are:

1. Battery power supply.
2. Arduino Nano Micro controller
3. L298 driver.
4. MEMS sensors.
5. DC motors.
6. LM2596.

Software’s used in the project:

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

Block Diagram:

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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.

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The system was designed such that the energy management of multiple engine motors for Hybrid Electric vehicle using wireless communication modules. This project demonstrates a dual-MOSFET control system using a potentiometer input received via serial communication. The system is built on the Arduino Nano platform and features a 16×2 LCD display for real-time feedback. A single potentiometer value, transmitted from another microcontroller over serial (via SoftwareSerial), is used to proportionally control two MOSFETs representing an engine (EM) and a motor (MO) based on predefined percentage ranges:

• 0–30%: EM is controlled from 0–255 PWM, MO remains off.
• 30–60%: EM decreases from 255–0, MO increases from 0–180 PWM.
• 60–100%: EM turns off, MO increases from 180–255 PWM.

The LCD displays the mapped velocity and power percentage on the first row, and the current status of EM and MO on the second row (e.g., “ENG CONNECTED”, “MOT CONNECTED”). The project showcases smooth transition control using a single input source and efficient serial communication between microcontrollers, useful in electric vehicle hybrid systems or automation applications. The controller unit forms the intellectual part of the device. The future works include giving intelligence to the whole system like if any of the motor senses any fault, the whole system will shutdown.

The major building blocks of this project are:

1. Adapter Power Supply.
2. Arduino Nano.
3. Potentiometer.
4. LCD with driver.
5. Wireless communication modules
6. MOSFET with drivers
7. DC motors with driver.
8. LED indicators.

Software’s used:

1. Embedded C programming.
2. Arduino ide programmer for dumping code into Micro controller.
3. Express SCH for Circuit design.

Block Diagram:

video:

The generated outputs from wind, solar, and hydro sources are processed through rectifier and capacitor circuits to provide stable DC power. Voltage sensors continuously monitor the output voltage from each energy source, and the collected data is processed by the Arduino Nano microcontroller. The measured parameters are displayed on an LCD screen for local monitoring.

To enable smart and remote monitoring, the system integrates an ESP8266 Wi-Fi module that uploads real-time data to the ThingSpeak cloud platform using IoT technology. This allows users to monitor renewable energy generation from anywhere through the internet. The proposed system provides efficient data acquisition, real-time monitoring, improved renewable energy utilization, and low-cost implementation. It is highly suitable for smart grid applications, remote energy management systems, and sustainable power generation projects.

The main objectives of the project are:

• To design a hybrid renewable energy system using solar, wind, and hydro sources.
• To monitor voltage and current from renewable sources and battery in real-time.
• To optimize solar energy harvesting using LDR sensors.
• To display system parameters on a LCD for local monitoring.
• To transmit data to the ThingSpeak IoT platform via a Wi-Fi module.

The major building blocks of this project are:

• Arduino Nano.
• DUAL AXIS Solar cell/plate.
• Wind.
• Hydel.
• LDR sensors.
• ESP8266 WIFI module.
• LCD display.
• Voltage Sensors.
• Servo Motors.

Software’s used:

1. Arduino ide compiler for Embedded C programming.
2. Express SCH for Circuit design.

Regulated power supply:

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The EEG sensor captures real-time brainwave activity, which is processed by the Raspberry Pi to identify specific mental states such as focus or relaxation. These signals are then translated into commands that trigger the relay module, turning the bulb ON or OFF. The system employs machine learning techniques or predefined thresholds to differentiate between intentional commands and background noise.

This project demonstrates the potential of BCI technology in smart home automation, assistive devices, and neurotechnology applications, offering hands-free control solutions for users with motor impairments. Future enhancements may include integration with other appliances, improved signal classification, and cloud-based analytics for real-time monitoring.

The major features of this project are:

• Brain-Computer Interface (BCI) Technology: Enables direct communication between the brain and external devices.
• EEG Sensor Integration: Captures real-time brainwave activity (e.g., focus or relaxation states).
• Raspberry Pi 3 B+ Based Processing: Processes EEG signals and identifies mental states.
• Relay Module Control: Uses brainwave commands to turn a bulb ON or OFF.
• Machine Learning / Threshold-Based Signal Classification: Differentiates intentional brain commands from background noise.
• Assistive Application Focus: Offers hands-free control for individuals with motor impairments.
• Potential for Smart Home Automation: Can be extended to control various home appliances.
• Scalable for Future Enhancements: Supports integration with cloud-based analytics, improved classification algorithms, and more devices.

The major building blocks of this project are:

1. Power Supply.
2. Raspberry pi3 B+.
3. EEG amplifier bio amp EXG pill.
4. Relay along with Bulb.
5. Arduino nano.
6. LCD Display.

Software’s used:

• Python programming.
• Raspbian OS.
• Express SCH for Circuit design.
• Machine learning.

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Vehicle-to-Vehicle Communication System project is to avoid the roadway collisions and to drivers prior to the collision. The ultrasonic sensors are used as a distance measurement device for this system, a low power, high-performance microcontroller is used to control the overall operations systematically. Accident sensor is used to detect the accidents, gas sensor is used to detects the gas, fire sensor is used to detect the fire and emergency button is used to send the voice alert in panic situations. Li-Fi technology is used to transmits the data using light waves and receiving the data using solar wirelessly. It helps better communication system between two vehicles by calculating distance and vehicle status like accident, gas, fire, emergency of other vehicles will be display on LCD and also voice alert will be coming from speaker. The main controlling device of the project is Arduino Nano, to achieve this task microcontroller loaded program written in embedded C language.

Objectives:

• Li-Fi Technology Implementation: Utilizing Li-Fi technology for high-speed data transmission between vehicles, ensuring quick and reliable communication.
• Accident Prevention: Implementing a system that can detect potential accidents and take preventive measures to avoid collisions.
• Data Transmission: In the event of an emergency such as an accident, gas leak, or fire, the affected vehicle would transmit a distress signal encoded in light waves.
• Reception and Display: Nearby vehicles equipped with Li-Fi receivers would capture these signals. The received data could be displayed on an LCD screen within the vehicle, alerting the driver to the situation ahead.
• Voice Alerts: Simultaneously, the system could generate voice alerts through the vehicle’s speaker system to ensure the driver is immediately made aware of the emergency.

The main building blocks of the project are:

• TWO Battery power supply.
• Two Arduino Nano Boards.
• LM2596 regulators.
• Ultrasonic Sensor
• LDR.
• SOLAR.
• 12V LEDs.
• Two LCD displays.
• Two vehicles.
• Accident sensor.
• Fire sensor.
• GAS sensor.
• Apr33a3 voice module.

Technologies used:

• Li-Fi Technology.

Software’s used:

• Arduino IDE studio compiler to write the program.
•  Express SCH for Circuit design.

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An Arduino Nano serves as the central control unit, monitoring system battery voltage using voltage sensor. The transmitted wireless power is received by a secondary coil in the receiving vehicle, converted from AC to DC, and regulated using a TP4056 charging module to safely charge a 3.7V Li-ion battery. Continuously monitor the battery voltage on LCD display, while a buzzer provides audible alerts for fault or threshold conditions.

This system eliminates the need for conventional charging cables, reduces wear and tear of connectors, and enhances safety and convenience. The proposed V2V wireless charging model demonstrates a practical solution for emergency charging, roadside assistance, and smart EV ecosystems, contributing toward sustainable and intelligent transportation systems.

The main objectives of the project are:

 To design a wireless charging system for electric vehicles.

  To enable vehicle-to-vehicle power transfer without using physical cables.

  To use inductive coupling for safe and efficient wireless energy transmission.

  To monitor battery voltage using sensor.

  To control and manage the system using an Arduino Nano.

  To display charging status and voltage on an LCD display.

  To provide alerts using a buzzer for abnormal conditions.

  To demonstrate a safe and portable charging solution for emergency situations.

The major building blocks of the project are:

• Regulated power supply.
• 12V Rechargeable Battery.
• Arduino NANO Microcontroller.
• Wireless Power Transmitting and Receiving Coil
• Voltage sensor.
• LCD display.
• Buzzer.
• Pulse generator.
• TP4056IC.
• 7V LI –ION Battery.

Software’s used:

1. Arduino IDE for compiling and dumping code into controller
2. Express SCH for Circuit design.

video:

The robot section is controlled by a microcontroller interfaced with DC motors driven by an L298N motor driver for movement. An ESP32-CAM module provides real-time live video streaming, enabling remote visual monitoring through a mobile device. LoRa technology is used for long-range, low-power wireless communication between the robot unit and the remote-control section, where delivery status and alerts are displayed on an LCD.

To enhance security, a vibration sensor detects unauthorized handling or theft attempts or battery voltage goes low immediately triggering a buzzer alert and sending warning messages to the remote unit. An ultrasonic sensor detects obstacles, and upon detection, the microcontroller activates blue alert lights to ensure safe navigation. Battery voltage and current are continuously monitored to provide low-battery alerts.

At the destination, access to the parcel compartment is protected using a password-based locking system. A 4×4 keypad mounted on the robot allows the user to enter the password. If the correct password is entered, the solenoid lock opens for a predefined duration and closes automatically afterward. Multiple incorrect password attempts trigger an alert to prevent unauthorized access.

Overall, the proposed system provides a secure, intelligent, and remotely monitored delivery solution suitable for food delivery, courier services, and e-commerce applications, combining live streaming, theft detection, obstacle avoidance, and secure access control in a single robotic platform.

The objectives of the project include:

1. To design an RC-based delivery robot for food and e-commerce applications.
2. To enable long-range wireless control and alert communication using LoRa.
3. To provide live video monitoring using the ESP32-CAM.
4. To implement theft detection and security using sensors and alerts.
5. To ensure secure parcel delivery through a password-protected locking system.
6. To detect obstacles and provide alert indications for safe navigation.
7. To monitor battery status and display system information on an LCD.

The major building blocks of this project are:

• Rechargeable battery.
• ESP32 Camera.
• Arduino Nano.
• DC Motors with L293D driver.
• Blue light.
• Head light.
• Ultrasonic sensor.
• DC motor with l298 driver.
• Buzzer.
• Keypad.
• PIC Microcontroller.
• LCD display.
• Voltage &Current sensor.
• Solenoid Lock.
• Vibration sensor.
• Relay.
• LoRa Modules.

Software’s used:

1. Arduino IDE for compiling and dumping code into ESP32 Camera and Arduino NANO.
2. PIC-C compiler for Embedded C programming.
3. PIC kit 2 programmer for dumping code into PIC Micro controller.
4. Express SCH for Circuit design.

video:

The main controlling device of the whole system is an arduino nano Microcontroller. Eye blink sensor and Buzzer and RF transmitter are connected to Microcontroller. This sensor data continuously monitors by the Arduino nano microcontroller. If the sensor detects drowsiness it will process to the microcontroller, then microcontroller active the Buzzer and transmits this to the vehicle section through RF transmitter. This data received by RF receiver and stop the vehicle ignition automatically.  To perform the task, Microcontroller is loaded with an intelligent program written in embedded ‘C’ language.

The main objectives of the project are:

• Automatic drowsiness detection using eye blink sensor.
• Automatic vehicle ignition control system.
• Using wireless RF technology.
• Audible alerts using buzzer.
• To achieve this task using Arduino nano microcontroller.

The main blocks of this project are:

• 9v Battery power.
• Arduino nano microcontroller.
• Eye Blink sensor.
• RF transmitter and RF receiver modules.
• Encoder and Decoder.
• DC Motor

Software’s used:

1. Embedded C programming.
2. Arduino IDE Studio for dumping code into Micro controller.
3. Express SCH for Circuit design.

Block diagram:

video:

Arduino nano microcontroller is employed to generate gating pulses for the converters, monitor system performance, and manage operational modes. Through IoT technology, the user can remotely switch between G2V and V2G modes, ensuring flexible control and monitoring over the internet using ESP8266 WI-FI module. The proposed system ensures efficient energy utilization, enhances grid stability, and enables EVs to act as distributed energy storage units, thereby supporting the development of smart grids and sustainable energy systems.

Modes of Operation There are two basic modes of operation: Grid-to Vehicle(G2V) mode and Vehicle-to-Grid (V2G) mode.

Grid-to Vehicle(G2V) mode:

In this mode of operation, the vehicle battery is charged from grid. Grid’s AC voltage is rectified to DC by an AC to DC converter operating in the rectifier mode. The output of this converter is fed to the DC link. The DC-to-DC converter operates in buck mode by stepping down the DC link voltage to the required battery charging voltage.

Vehicle-to-Grid Mode of Operation:

In this operating mode, energy stored in the battery is fed back to the grid. The DC voltage from the battery is stepped up using the DC-DC converter operating in boost mode and is then converted to AC using the AC-DC converter operating in the inverter mode and is fed to grid. To feed the power back to grid, output of inverter should be synchronized with that of the grid. Here the proposed grid synchronization technique is using a hysteresis current control.

The major features of this project are:

• Achieve flow of power between power grid and the electric vehicle in both the directions
• Use of Bi directional AC-DC Converter that acts as rectifier and inverter alternatively in two modes respectively
• Use of Bi Directional DC-DC Converter that acts as step down converter and step up converter alternatively in two modes respectively.
• Wireless mode selection using IOT technology.

The major building blocks of this project:

• Utility Grid
• AC-DC Bidirectional Converter.
• DC-DC Bidirectional Converter.
• Vehicle Battery.
• NodeMCU Microcontroller.
• Power Supply.
• LED indicator.
• ESP8266 WI-FI Module.

Software’s used:

1. Arduino IDE programmers for dumping code into Microcontroller.
2. Embedded C programming.
3. Express SCH for Circuit design.

Block Diagram:

video: