The major features of this project are:

• pi camera and image processing based automatic WELDING DEFECT detection.
• Automatic obstacle detection using Ultrasonic sensor.
• Automatic obstacle detection and vehicle control system.
• Automatic line following using IR sensors.
• Automatic object detection using IR sensor.
• IOT based alert intimation to the user.
• Automatic welding defect detection and monitoring and also alerting system.
• To achieve this task using Raspberry pi3processor.

The major building blocks of this project are:

1. Li-ion battery Power Supply.
2. Raspberry pi3.
3. Pi camera.
4. IR sensors.
5. Ultrasonic sensor.
6. SD card.
7. LCD display.
8. DC motor with L293d motor driver

Software’s used:

• Raspbian OS.
• Python Language.
• Image Processing.

Block diagram:

video:

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In this project we are monitoring two types (food, non food) items using RFID technology in warehouse using IOT. The proposed system uses machine learning algorithm to effectively predict and categorize items in inventory. An RFID system consists of an antenna or coil, a transceiver (with decoder) and a transponder (RF tag) electronically programmed with unique information.

Each item consists of RFID TAG. We place the two RFID reader modules at entrance and exit which are interfaced to the raspberry pi3 processor. The raspberry pi module continuously tracks the items and updates them into the web page (like no of items delivered and stocked and received). The status of the project will display on LCD module.

The major building blocks of this project are:

• Power Supply.
• Raspberry pi3
• TWO RFID readers.
• RFID tags.
• LCD display with driver.
• SD card.

Software’s used:

1. Linux OS.
2. Python Language.
3. Express SCH for Circuit design.
4. Machine learning algorithm.

Block diagram:

video:

In order to further maintain peace and provide security to people now a day, Closed-circuit television (CCTV) surveillance system is being utilized. This study focused on the design and implementation of a low-cost smart security camera using Raspberry Pi (RPI) and OpenCV. The system was designed to be used inside a warehouse facility. It has human detection, fire detection and gas detection capability that can provide precaution to potential crimes and potential fire. The credit card size Raspberry Pi (RPI) with Open-Source Computer Vision (OpenCV) software handles the image processing for the alarms and sends captured pictures to user’s email via Wi-Fi.

The device which is able to perform the task is a Raspberry Pi processor. Whenever pi camera detects any motion then camera capture that image and sending to the respective mail via Wi-Fi. When the GAS sensor detects the gas, then the system gives buzzer alerts. When the flame sensor detects the flame, then raspberry pi switch ON the water sprinkler through DC water motor. To perform this task, Raspberry Pi processor is programmed using embedded ‘Linux’.

The objectives of the project are:

1. Capturing of Person images using pi camera.
2. Captured image is sent through email.
3. It sprinkler the water when fire is detected.
4. It activates the buzzer when the gas is detected.
5. Motion detection using image processing.

The project provides learning’s on the following advancements:

1. Learn about Image processing and IOT Technology.
2. Raspberry pi working and programming.
3. Learn about Relay.
4. Learn about Gas and fire sensor.

The major building blocks of this project are:

• Power supply.
• Raspberry pi3.
• SD card.
• Pi camera.
• GAS Sensor.
• Fire sensor.
• Buzzer.
• Relay with DC water motor.

Software’s used:

1. Python language.
2. Linux OS.
3. Express SCH for Circuit design.

Block Diagram:

Video:

This robot consists of pi camera to recognize the face using image processing. This robot takes the persons to their selected destination by following the lines. The robot greets a known person. If the robot recognizes unknown person it will asks to select a destination if it is an unknown person. This robot consists of push buttons to select the destination like class room1, class room 2 and lab1, lab2. This robot consists of voice module to gives the voice output. This robot consists of ultrasonic sensor to detect the obstacles in its path. If the robot detects the obstacle, it will stop automatically.

The device which is able to perform the task is a Raspberry Pi processor. To perform this task, Raspberry Pi processor is programmed using embedded ‘Linux’.

The objectives of the project are:

1. Capturing of Person images using pi camera.
2. Voice based assisting system.
3. Obstacle detection using ultrasonic sensor.
4. Design a Line following robot to greet and guiding the persons at colleges.
5. IR sensor-based line following system.
6. Raspberry pi and Pi camera-based face recognition.

The project provides learning’s on the following advancements:

1. Learn about Image processing Technology.
2. Raspberry pi working and programming.
3. Interfacing pi camera to the raspberry pi.

The major building blocks of this project are:

• Battey Power supply.
• Raspberry pi3.
• SD card.
• Pi camera.
• Push buttons.
• IR sensors.
• Ultrasonic sensor.
• DC motor with l293d motor driver.
• Speaker.

Software’s used:

1. Python language.
2. Linux OS.
3. Express SCH for Circuit design.

Block Diagram:

video:

The aim of this project is to design a braille system read the book for visually impaired person without any difficulty.It takes braille input and gives text and voice output.It takes input of voice and image and gives output as Braille. This system uses image processing OpenCV algorithm to extract the letters from scanned image. By using pi camera this system can scan the image. This system uses both Arduino UNO and raspberry pi processor. We are connecting solenoid valves long with relay to gives the braille output. Here relay works as switch to ON/OFF the solenoid valves. This system will display the text on LCD module. To achieve this task raspberry pi loaded program written in python language.

The major features of this project are:

• Design a braille system using raspberry pi.
• Pi camera, OpenCV based image processing.
• It takes braille input and gives text and voice output.
• It takes input of voice and image and gives output as Braille.

Components used:

• Rechargeable battery.
• Raspberry pi3.
• Arduino uno.
• Pi camera.
• Two Switches for enable mic/camera.
• Six Relays with six solenoid valves.
• Six Buttons as six raised dots.
• MIC.
• Vibration Motor.
• Re-Read button.
• SD card.
• Speaker.
• LCD display.

Software’s used:

• OpenCV image processing.
• Linux OS.
• Python language.
• Arduino ide studio.
• Embedded C language.

Block Diagram:

video:

The main aim of this project is to monitoring of traffic signal violation and its penalty system using Raspberry Pi. In this work, Radio Frequency Identification (RFID) along with Image Processing techniques is used.

Using radio frequency identification (RFID) technology, this project aims to create an intelligent traffic system. It is developed and operated in such a way that fines are tracked independently. RFID technology, passive tags, a processing unit (computer), and a communication system were all utilized with low-cost equipment. The first level is represented by data collection via RFID, which scans any identifiable tag connected to cars.

In this pi camera is mounted on traffic signals which is interfaced to the raspberry pi for continuously monitoring of vehicles. When any vehicle break traffic rules, particularly signal jumps then pi camera capture the vehicle image and sending mail to the vehicle owner along with vehicle image and penality. And also display the vehicle number on LCD display. The concept provided in this title will help to reduce road accidents and avoidance.  The SD card is a key part of the Raspberry Pi; it provides the initial storage for the Operating System and files. Storage can be extended through many types of USB connected peripherals.

The major features of this project are:

1. Automatic traffic lights control.
2. Automated Penalty Collection for Traffic Signal Violation.
3. Using RFID technology for vehicle details.
4. Using Raspberrypi, PIC Microcontroller to achieve this task.

The major building blocks of this project are:

• Regulated Power Supply.
• PIC Microcontroller.
• Traffic signals.
• Raspberry pi.
• RFID Reader.
• Pi camera.
• SD Card.
• LCD display.

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.
4. Linux OS, Python language.
5. Image processing technology.

Regulated Power Supply:

Block Diagram:

video:

The Fruit Harvesting Robot is an automated system designed for harvesting fruits in orchards. It features a 4-degree-of-freedom (4DOF) robotic arm controlled by a Raspberry Pi. The robot navigates through the orchard using a combination of line following with IR sensors and a Raspberry Pi Camera for precise movement and fruit detection. Machine learning algorithms are used to identify ripe fruits and plan the arm’s harvesting actions. This system aims to increase harvesting efficiency and reduce manual labor in fruit orchards.

The major features of this project are:

• pi camera and Machine Learning based automatic fruit detection.
• Automatic line following using IR sensors.
• Automatic tree detection using IR sensor.
• To achieve this task using Raspberry pi3processor.

The major building blocks of this project are:

1. Li-ion battery Power Supply.
2. Raspberry pi3.
3. Pi camera.
4. IR sensors.
5. SD card.
6. DC motor with L293d motor driver.
7. Robot body along with ARM.

Software’s used:

• Raspbian OS.
• Python Language.
• Machine learning algorithm.

video:

When smoke or unauthorized movement is detected, the robot activates a buzzer to alert individuals in the vicinity. Concurrently, the system sends instant SMS via GSM to predefined contacts, providing immediate notification of potential emergencies. The integration of Wi-Fi connectivity allows for remote control of the robot, enhancing user interaction and situational awareness.

This comprehensive approach not only facilitates rapid response to fire incidents and security breaches but also combines fire safety with robotic mobility. The Smart Fire Sentinel aims to significantly improve safety measures in homes and businesses, offering a proactive and efficient solution for emergency management and surveillance.

The main objective of this project is:

• Realtime fire and intruder detection and alerting system.
• GSM based SMS alerts.
• Wi-Fi based wireless controlling of robot direction and movement.
• PIR based automatic motion detection.
• Smoke sensor based automatic smoke detection.

The major building blocks of this project are:

• Battery power supply.
• Raspberry Pi3.
• Pi camera.
• SD card.
• LCD display.
• Buzzer.
• GSM.
• smoke.
• PIR.
• L293D with DC motors.

Software’s Used:

1. Python language.
2. Linux Operating System.
3. IOT technology.

Block diagram:

video:

The main aim of this project is to design a surveillance system for bike riders using raspberry pi3, pi camera. This system using CNN machine learning and open CV image processing is used to detect the helmet of bike rider and triple ride will be display on LCD module.

The main controlling device of the project is raspberry pi3 processor. Pi camera and LCD module is interfaced to the raspberry pi processor. When the user switch on the surveillance system, it will start live streaming and capture the image, if the biker rider doesn’t wear the helmet and if three people on a bike using machine learning and sending these captured images into the mail.

1. Capturing of Person images using pi camera.
2. Captured image is sent through email.
3. Using machine learning CNN and open CV image processing to detect the triple ride and helmet.
4. To achieve this task using Raspberry Pi3 processor.

The project provides learning’s on the following advancements:

1. Learn about Image processing and IOT Technology.
2. Raspberry pi working and programming.
3. Pi camera interfacing to the raspberry pi.

The major building blocks of this project are:

• Power supply.
• Raspberry pi3.
• SD card.
• Pi camera.
• LCD display.

Software’s used:

• Python language.
• OpenCV image processing.
• Linux OS.
• CNN machine learning.

video:

The main of the project is to design a Wi-Fi controlled Robot along with robotic arm for virtual telepresence using Raspberry pi and pi camera.

In this project the smartphone reads the accelerometer and magnetometer data of the direction in which the user turns his head, say, right or left. This data is sent to the modem over Wi-Fi and to the Raspberry Pi board, which, in turn, provides these values as inputs to the servo motors.

In this user will control the robot like forward, backward, left and right directions and control the ARM also from web browser like up and down, open and close motions with the help of spur gear and warm gear mechanism.

The Raspberry Pi is a low cost, credit-card sized computer that can be used in electronics projects. It is a capable little device that enables people of all ages to explore computing, and to learn how to program in languages like Scratch and Python. It’s capable of doing everything you’d expect a desktop computer to do, from browsing the internet and playing high-definition video, to making spreadsheets, word-processing, and playing games.

The task of the project is achieved by using Raspberry pi. This robot with a Pi camera is placed in a remote location to capture the environment in visual form using Raspberry Pi. The captured visuals are displayed on the user’s virtual reality (VR) headset. The robot can also be moved in any direction (in which the user turns his head, say, right or left) through a web applications installed in the user’s smartphone

The main objectives of the project:

• The robot should be able to move forward, backward, left and right on its four wheels with the components on board by receiving commands from WEB browser.
• ARM also can move open and close, up and down directions for pick and place the objects by receiving the commands from web browser.
• The pi camera is to capture clear, real-time visuals of the area in front. The camera moves in the same direction as the VR headset.
• Video stream data from the camera should be sent to the Raspberry Pi over the Wi-Fi for being displayed to the user’s screen.
• Using Raspberry pi to achieve this task.

The major building blocks of this project are:

• Power bank for raspberrypi3.
• 12v1ahm rechargeable battery for robot
• Raspberry pi3processor.
• SD card.
• Pi camera
• VR head set
• Servo motors
• L293d motor drivers.
• DC motors
• Robot chases.
• Smartphone with internet connection.

Software’s used in the project:

1. Python Programming.
2. Linux OS, Express SCH for Circuit design.

Applications Used:

• Wireless IMU is used to control the servo motors.
• Dual Screen Mod Browser is used for live video.
• Network analyzer to get the IP address of raspberry pi.

Block diagram:

Video