GPS is the acronym for Global Positioning System. It is employed to find the position where the user is located on the earth. This information is provided by the GPS with the help of the data it receives from the satellites.

The main controlling device of the whole system is a microcontroller. GPS receiver and LCD display are interfaced to it. The GPS receiver continuously feds the vehicle location coordinates and speed of the vehicle data it receives from the satellites to the Microcontroller. The Microcontroller processes the data and displays the data on the LCD screen. To perform the task, Microcontroller is loaded with an intelligent program written in embedded ‘C’ language.

The major features of this project are:

1. Displaying the location coordinates of the vehicle.
2. Displaying speed of the vehicle.

The major building blocks of this project are:

1. Microcontroller.
2. Regulated Power Supply
3. GPS receiver.
4. LED Indicators.
5. LCD Display with driver.

Software’s used:

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

Regulated Power Supply:

Block Diagram:

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The main controlling device of the system is a microcontroller. The IR sensors and LCD are interfaced with the microcontroller. When a vehicle passes through the IR sensor arrangement, the sensors detect its movement and send signals to the microcontroller. The microcontroller processes these signals to calculate the total number of vehicles and determine their speed based on the time taken to travel between two IR sensor pairs. The calculated vehicle count and speed are then displayed on the LCD. To perform these tasks, the microcontroller is programmed using Embedded C language.

The main features of this project are:

1. Automatic Vehicle Counting: Accurately counts the number of vehicles passing through the IR sensor arrangement in real time.
2. Vehicle Speed Measurement: Calculates the speed of each vehicle using IR sensors and displays the result along with the vehicle count on a 16×2 LCD.

The main building blocks of the project are:

1. Regulated Power Supply.
2. PIC Microcontroller.
3. IR sensors.
4. LCD with driver.
5. Reset.
6. Crystal oscillator.
7. LED indicators.

Software’s used:

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

Regulated power supply:

Block Diagram:

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The system includes a pH sensor to detect acidity or alkalinity of water, and a TDS sensor to determine the total dissolved solids present in the water. All sensor data are read and processed by the ESP32 and displayed locally on an LCD display through an I2C communication interface.

Using the built-in Wi-Fi capability of ESP32, the measured parameters are uploaded to the cloud platform ThingSpeak for real-time monitoring and data storage. This IoT-based monitoring system helps in early detection of water contamination and enables remote monitoring through the cloud. The proposed system is low-cost, efficient, and suitable for applications such as drinking water monitoring, industrial water quality checking, and environmental water analysis.

The main objectives of the project are:

  To design and develop an IoT-based water quality monitoring system using the ESP32 for real-time data processing and communication.

  To measure important water quality parameters such as temperature, pH level and total dissolved solids (TDS) using sensors like pH sensor and TDS sensor.

  To display the real-time water parameter values on an LCD display through an I2C communication interface for local monitoring.

  To upload and store water quality data in the cloud using the IoT platform ThingSpeak for remote monitoring and analysis.

  To develop a low-cost, efficient, and continuous monitoring system that can be used in drinking water systems, industries, and environmental water monitoring applications.

The major building blocks of this project are:

• Adapter Power supply.
• ESP32 Microcontroller.
• PH sensor.
• TDS sensor.
• LCD display.

Software’s used:

• Arduino IDE for compiling and dumping code into Microcontroller.
• Thingspeak Cloud.
• Express SCH for Circuit design.

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A piezoelectric sensor is a device that uses the piezoelectric effect, to measure changes in pressure, acceleration, temperature, strain, or force by converting them to an electrical charge. The prefix piezo- is Greek for ‘press’ or ‘squeeze’.

The main controlling part of the whole system is Micro controller. In this project Piezo plate along with voltage sensor, LCD are interfaced to micro controller section. Micro controller reads the voltage values from the piezo plate and will be display on the LCD

The energy from the piezo plate is stored into the rechargeable battery through charging circuit. Battery power is fed as input to the three USB ports through lm2596 voltage regulator which distribute the different powers to the three USBs for fast and slow charging of electric devices.

Major features of this project:

1. Electricity generation from piezo plates.
2. Monitoring generated voltage on LCD module.

Major blocks present in this system:

1. ARDUINO UNO Micro controller
2. Piezo sensors.
3. Rechargeable Battery.
4. Voltage sensor.
5. Charging circuit
6. LCD display.
7. LM2596 Voltage regulator.
8. TWO USB ports.

Software’s used:

1. Arduino IDE.
2. Express SCH for Circuit Design.

Regulated power supply:

Block Diagram:

video:

Components Used:

• Power supply
• PIC Microcontroller
• Bidirectional DC/DC converter.
• Non-inverting Buck-Boost converter.
• Mode switching.
• Operational dead zone.

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.

Block Diagram:

video:

The sensor outputs are primarily analog signals, which are converted into digital data using the Analog-to-Serial conversion capability of the Arduino Nano’s built-in Analog-to-Digital Converter (ADC). The Arduino Nano collects and processes the sensor readings and communicates the data serially to a Raspberry Pi Zero 2W for advanced processing and cloud connectivity.

The Raspberry Pi implements Artificial Intelligence (AI) and Machine Learning (ML) algorithms to analyze the collected sensor data and predict the potability status of the water based on trained models. The predicted results and real-time sensor readings are displayed locally on an LCD display for user monitoring. In cases where the measured parameters exceed safe thresholds, an abnormal condition alert is generated using a buzzer, providing an immediate warning to users.

For remote monitoring and data storage, the system uploads the collected sensor data and prediction results to the ThingSpeak cloud platform, enabling real-time visualization, data logging, and further analysis through IoT connectivity. The integration of sensor-based monitoring, AI-driven prediction, and cloud-based data management ensures an efficient and intelligent solution for drinking water quality assessment.

This system provides a cost-effective, real-time, and automated water monitoring solution, which can help improve public health by ensuring safe drinking water through continuous monitoring and predictive analytics.

The main objectives of the project are:

  To monitor drinking water quality in real time using multiple sensors such as temperature, pH, turbidity, TDS, Water level and Light intensity.

  To collect and process sensor data using Arduino Nano, where analog signals from sensors are converted into digital values using the built-in Analog-to-Digital Converter (ADC).

  To transmit sensor data to Raspberry Pi Zero 2W through serial communication for advanced processing and system control.

  To implement Artificial Intelligence (AI) and Machine Learning (ML) algorithms on the Raspberry Pi to analyze sensor data and predict whether the water is potable or not.

  To provide real-time status monitoring by displaying sensor readings, prediction results, and water quality status on an LCD display.

  To generate an alert during abnormal conditions by activating a buzzer when water parameters exceed safe limits.

  To upload sensor data and prediction results to the ThingSpeak cloud platform for remote monitoring, data visualization, and storage.

  To develop a low-cost and efficient IoT-based smart water monitoring system that helps ensure safe drinking water and improves public health.

The major building blocks of this project are:

• Power supply.
• Arduino Nano.
• Raspberry pi zero 2w.
• SD card.
• PH sensor.
• Temperature sensor.
• Turbidity
• TDS sensor.
• Water level and Light intensity.
• LCD display.
• Buzzer.

Software’s used:

• Arduino IDE for compiling and dumping code into Microcontroller.
• Raspbian OS.
• Thingspeak Cloud.
• Express SCH for Circuit design.
• AI and Machine learning algorithm.

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A user-friendly web dashboard allows the user to start or stop the motor, change its direction (clockwise or anticlockwise), adjust its speed using a slider, and perform an emergency stop whenever required. The NodeMCU receives the commands from the web interface and controls the DC motor driver, which regulates the motor’s speed using Pulse Width Modulation (PWM) and changes its rotation direction based on user input.

An LCD display is used to show the current operating status, including motor speed and direction, providing real-time feedback to the user. Separate power supplies are used for the control circuit and the motor to ensure reliable and safe operation. The system offers accurate motor control, remote accessibility, and ease of operation, making it suitable for industrial automation, robotics, conveyor systems, laboratory equipment, and educational applications.

This project demonstrates a low-cost, efficient, and IoT-enabled solution for real-time DC motor speed and direction control, improving automation, flexibility, and user convenience through wireless technology.

The main objectives of the project are:

1. IOT based controlling of DC motor speed and direction.
2. Usage WEB technology.
3. Monitoring DC motor speed and direction on LCD display.

The major building blocks of this project are:

1. Adapter Power supply.
2. NodeMCU.
3. DC Motor with driver.
4. LCD Module.

Software’s used:

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

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Now a day’s every system is automated in order to face new challenges. In the present days Automated systems have less manual operations, flexibility, reliability and accurate. Due to this demand every field prefers automated control systems. Especially in the field of electronics automated systems are giving good performance.

The main controlling device of the whole system is a Microcontroller. IR sensor and LCD are interfaced to microcontroller. Number of objects passing through conveyer belt will be calculated by microcontroller from the input it get from IR sensors and displays the count on the LCD screen. To perform this task, microcontroller is loaded with an intelligent program written using embedded ‘C’ language.

The main objectives of the project are:

1. Counting number of objects passing through conveyer belt.
2. Simpler design.

The major building blocks of the project are:

1. Regulated power supply
2. Microcontroller
3. crystal oscillator
4. Reset
5. LED Indicators
6. IR sensor
7. LCD with driver.
8. DC motor for conveyer belt.

Regulated Power Supply:

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The Digital image processing plays important role in the sign capturing and detection system. The image processing algorithms to takes the necessary action for resizing the captured signs. The objective of the proposed work is to recognize the speed limits on speed-limit signs automatically using pi camera and raspberry pi.

The proposed system will get the image of the real world from the Pi camera which is interfaced to the raspberry pi and deep learning are used to detect the speed limit of road signs. We can create a data set into the raspberry pi. This data set contains a greater number of speed limit signs. The raspberry pi processor takes the input from pi camera and it will compare with the data set and based on that the system identifies the speed limit sign and shows on LCD module and also it will announce the voice through speaker. The coding for this whole system is in python and for image processing we are using deep learning.

The major features of this project are:

• Camera based automatic speed limit detection using deep learning.
• The main objective of proposed system is to detect the speed limit road sign automatically and display on LCD display.
• Voice based alerting system.
• This system avoids the road accidents.

The major building blocks of this project are:

1. Power Supply.
2. Raspberry pi zero 2 W.
3. Pi camera.
4. LCD display.
5. Speaker.

Software’s used:

• Linux OS.
• PYTHON LANGUAGE.
• Deep learning for image processing.

Block Diagram:

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UWB Microwave Sensor is a Doppler transceiver module. It has a built-in Dielectric Resonator Oscillator (DRO) and a pair of Microstrip patch antenna array, making it ideal for usage in motion detection equipment.

This project makes a use of microwave sensor to detect the human behind the wall. Raspberry pi processor which is interfaced to the UWB signal transmitter module. Arduino UNO microcontroller which is connected to the UWB receive module and which receives the reflected signal from object.

The main controlling device of the project is raspberry pi processor. Microwave transmitter is interfaced to the raspberry pi processor. Microwave receiver module, LCD display and Buzzer is interfaced to the Arduino UNO microcontroller. When the user switch ON the system, UWB TX module will transmits the microwave signals through the wall and Tx signal reflect off objects and return to the receiver. If the sensor detects the human behind the wall, message will display on LCD along with buzzer. To achieve this task microcontroller loaded program written in embedded C language and raspberry pi loaded program written in python language.

The major building blocks of the project are:

1. Regulated Power supply.
2. Raspberry Pi processor.
3. Arduino UNO.
4. UWB Microwave transceiver module.
5. LCD display with driver.
6. Buzzer.

Software’s used:

1. Raspbian OS, Python language for raspberry pi.
2. Arduino IDE studio for dumping the code into the Arduino microcontroller.
3. Express SCH for Circuit design.

Regulated Power Supply:

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