Artificial Intelligence Based Projects List_svsembedded_9491535690

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Artificial Intelligence Based Projects List

 

1. AI-Powered Camera Trap:

2. Anomaly Detection:

3. Artificial Intelligence Projects:

4. Augmented Reality Apps:

5. Automated Garbage Collection:

6. Automated Photography Booth:

7. Automated Plant Watering:

8. Autonomous Car Simulation:

9. Autonomous Drone:

10. Autonomous Robot:

11. Autonomous Vehicles:

12. Barcode/QR Code Scanner:

13. Emotion Detection:

14. Emotion Recognition:

15. Energy Management System:

16. Face Detection:

17. Face Recognition Door Lock:

18. Gesture Recognition System:

19. Gesture Recognition:

20. Gesture-Controlled Robot:

21. Handwriting Recognition:

22. Health Monitoring System:

23. Health Monitoring Wearables:

24. Health Monitoring:

25. Healthcare Diagnostics:

26. Home Security Camera:

27. Image Classification:

28. Image Processing Security System:

29. Language Translation:

30. Livestreaming Camera:

31. Music Generation:

32. Natural Language Processing (NLP) Apps:

33. Natural Language Processing:

34. Object Detection

35. Object Recognition:

36. Object Tracking:

37. Pan-and-Tilt Camera System:

38. Plant Health Monitoring:

39. Predictive Analytics:

40. Predictive Maintenance System:

41. Predictive Maintenance:

42. Remote Monitoring

43. Robot Arm Control:

44. Robot-Assisted Elderly Care:

45. Sentiment Analysis:

46. Smart Agriculture:

47. Smart Health Monitoring:

48. Smart Home Automation:

49. Smart Home Controller:

50. Smart Irrigation System:

51. Smart Mirror:

52. Smart Plant Monitoring:

53. Time-Lapse Photography:

54. Traffic Management System:

55. Virtual Pet:

56. Voice Assistant:

57. Voice-Controlled Appliances:

58. Weather Prediction System:

59. Weather Station with Imagery:

60. Wildlife Monitoring Camera: 

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CONTACT: 9491535690, 7842358459

Wireless Multiple Colour ( R-G-B-Y-O ) Detection Robot🤖 Using Raspberry ...

Wireless Zigbee Multiple Colour ( R-G-B-Y-O ) Detection Robot🤖 Using Raspberry Pi Pico with OpenCV & Python | Colour Recognition Based Wireless Object Tracking🤖Robot Using Raspberry Pi Pico with OpenCV & Python | Color detection using python and OpenCV | Color Detection with Python | Multiple Color Detection in Real-Time using Python-OpenCV | opencv color detection | Color Detection Using OpenCV | Augmented Reality using OpenCV | Simple Color recognition with Opencv and Python. *********************************************************** If You Want To Purchase the Full Working Project KIT Mail Us: svsembedded@gmail.com Title Name Along With You-Tube Video Link We are Located at Telangana, Hyderabad, Boduppal. Project Changes also Made according to Student Requirements http://svsembedded.com/  https://www.svskits.in/ http://svsembedded.in/  http://www.svskit.com/ M1: +91 9491535690  M2: +91 7842358459 We Will Send Working Model Project KIT through DTDC / DHL / Blue Dart / First Flight Courier Service We Will Provide Project Soft Data through Google Drive 1. Project Abstract / Synopsis 2. Project Related Datasheets of Each Component 3. Project Sample Report / Documentation 4. Project Kit Circuit / Schematic Diagram 5. Project Kit Working Software Code 6. Project Related Software Compilers 7. Project Related Sample PPT’s 8. Project Kit Photos 9. Project Kit Working Video links Latest Projects with Year Wise YouTube video Links 157 Projects  https://svsembedded.com/ieee_2022.php 135 Projects  https://svsembedded.com/ieee_2021.php 151 Projects  https://svsembedded.com/ieee_2020.php 103 Projects  https://svsembedded.com/ieee_2019.php 61 Projects  https://svsembedded.com/ieee_2018.php 171 Projects  https://svsembedded.com/ieee_2017.php 170 Projects  https://svsembedded.com/ieee_2016.php 67 Projects  https://svsembedded.com/ieee_2015.php 55 Projects  https://svsembedded.com/ieee_2014.php 43 Projects  https://svsembedded.com/ieee_2013.php 1100+ Projects https://www.svskit.com/2022/02/900-pr... *********************************************************** Creating a wireless Zigbee-controlled robot that uses a Raspberry Pi Pico for color detection using OpenCV and Python is an exciting project! This project involves both hardware and software components. Here's a high-level overview of the steps you'd need to take: Hardware Components: 1. Raspberry Pi Pico: This microcontroller board will serve as the brain of the robot. 2. Zigbee Module: You'll need a Zigbee module (like XBee) for wireless communication. 3. Motors and Wheels: These components will enable the robot to move. 4. Color Sensor: A color sensor or camera will be used to detect colors. 5. Power Supply: Provide power to both the Raspberry Pi Pico and the motors. Software Steps: 1. Setting up Raspberry Pi Pico: • Install the required MicroPython firmware on the Raspberry Pi Pico. • Set up the Pico to communicate with the Zigbee module using UART. 2. Zigbee Communication: • Implement a communication protocol using UART to send and receive commands between the remote control (Zigbee-connected device) and the robot. 3. Motor Control: • Interface with the motor driver to control the robot's movement (forward, backward, left, right) based on commands received through Zigbee. 4. Color Detection using OpenCV: • Connect a color sensor or camera module to the Raspberry Pi Pico. • Write a Python script that captures images using OpenCV. • Process the images to detect colors using OpenCV's color detection methods. 5. Sending Color Information: • Once a color is detected, send this information back to the remote control using Zigbee communication. 6. Remote Control Interface: • Set up the remote control interface, which can be a separate Zigbee module connected to a computer or smartphone. • Develop a simple user interface to send movement commands and receive color information. 7. Integration: • Combine the motor control, color detection, and Zigbee communication code into a single program running on the Raspberry Pi Pico. 8. Testing and Refinement: • Test your robot's color detection and movement in different environments. • Refine your code to ensure accurate color detection and smooth movement. Remember that this is a complex project that involves both hardware and software aspects. It's important to break down each step into smaller tasks and tackle them one by one. You'll also need to refer to the datasheets of the components you're using, the MicroPython documentation for the Raspberry Pi Pico, and the OpenCV documentation for image processing. Additionally, be prepared to troubleshoot issues that may arise during the development process. Good luck, and have fun creating your Zigbee-controlled color-detecting robot!

AI Wireless Hand Gesture Recognition & Home Automation Using Raspberry P...

AI Wireless Hand Gesture Recognition & Home Automation Using Raspberry Pi Pico with OpenCV & Python | Gesture Recognition System Using OpenCV Python | Home Automation Using Raspberry Pi Pico | Hand gesture recognition using Deep Learning OpenCV Python Tutorial |Full source code | Arduino Gesture Control Robot | opencv hand gesture recognition | hc-05 bluetooth with arduino | Colour Recognition Based Wireless Object Tracking🤖Robot Using Raspberry Pi Pico with OpenCV & Python. *********************************************************** If You Want To Purchase the Full Working Project KIT Mail Us: svsembedded@gmail.com Title Name Along With You-Tube Video Link We are Located at Telangana, Hyderabad, Boduppal. Project Changes also Made according to Student Requirements http://svsembedded.com/  https://www.svskits.in/ http://svsembedded.in/  http://www.svskit.com/ M1: +91 9491535690  M2: +91 7842358459 We Will Send Working Model Project KIT through DTDC / DHL / Blue Dart / First Flight Courier Service We Will Provide Project Soft Data through Google Drive 1. Project Abstract / Synopsis 2. Project Related Datasheets of Each Component 3. Project Sample Report / Documentation 4. Project Kit Circuit / Schematic Diagram 5. Project Kit Working Software Code 6. Project Related Software Compilers 7. Project Related Sample PPT’s 8. Project Kit Photos 9. Project Kit Working Video links Latest Projects with Year Wise YouTube video Links 157 Projects  https://svsembedded.com/ieee_2022.php 135 Projects  https://svsembedded.com/ieee_2021.php 151 Projects  https://svsembedded.com/ieee_2020.php 103 Projects  https://svsembedded.com/ieee_2019.php 61 Projects  https://svsembedded.com/ieee_2018.php 171 Projects  https://svsembedded.com/ieee_2017.php 170 Projects  https://svsembedded.com/ieee_2016.php 67 Projects  https://svsembedded.com/ieee_2015.php 55 Projects  https://svsembedded.com/ieee_2014.php 43 Projects  https://svsembedded.com/ieee_2013.php 1100+ Projects https://www.svskit.com/2022/02/900-pr... *********************************************************** It sounds like you're interested in creating a project involving AI-based wireless hand gesture recognition and home automation using a Raspberry Pi Pico, OpenCV, and Python. This can be a fascinating project that combines computer vision, machine learning, and hardware control. I'll provide you with a general outline of how you can approach this project: Project Overview: The goal of this project is to create a system that recognizes hand gestures using computer vision techniques, translates those gestures into commands, and controls home automation devices wirelessly using a Raspberry Pi Pico. Components Needed: 1. Raspberry Pi Pico 2. Camera module (compatible with Raspberry Pi) 3. Home automation devices (smart bulbs, smart plugs, etc.) 4. Breadboard and jumper wires 5. Optional: Power supply for Raspberry Pi Pico and any connected devices Project Steps: 1. Setting Up Raspberry Pi Pico: • Install the Thonny IDE on your computer for programming the Raspberry Pi Pico. • Set up the Raspberry Pi Pico for programming using MicroPython. 2. Installing OpenCV: • Install OpenCV library for Python on the Raspberry Pi Pico. • This will allow you to capture video frames from the camera module and perform image processing. 3. Collecting Hand Gesture Data: • Prepare a dataset of different hand gestures for training your gesture recognition model. • Record video samples of yourself performing various gestures (e.g., open hand, closed fist, thumbs up, etc.). 4. Training the Gesture Recognition Model: • Preprocess the video frames to extract relevant features (e.g., hand position, contours, etc.). • Train a machine learning model (e.g., SVM, Random Forest, etc.) using the extracted features and labeled gestures. 5. Wireless Communication Setup: • Configure the Raspberry Pi Pico to communicate wirelessly, for example, using Bluetooth or Wi-Fi. • Implement a mechanism for sending gesture recognition results to your home automation devices. 6. Home Automation Integration: • Set up the necessary libraries or APIs to control your smart home devices. This might involve using libraries like gpiozero for controlling GPIO pins on the Raspberry Pi Pico. 7. Gesture Recognition and Home Automation Logic: • Capture video frames from the camera module using OpenCV. • Process the frames using the trained gesture recognition model to identify the gesture. • Depending on the recognized gesture, trigger appropriate commands to control the home automation devices. 8. Testing and Refinement: • Test the system with different hand gestures and monitor the response of your home automation devices. • Refine the gesture recognition model and system logic as needed for better accuracy and performance.

Goggles for Blind Person using Ultrasonic Sensor and Buzzer Alert

Goggles for Blind Person using Ultrasonic Sensor and Buzzer Alert | Voice Alert | Goggles for blind person using ultrasonic sensor and buzzer price | ultrasonic glasses for the blind project pdf | ultrasonic glasses for the blind project ppt | Diy goggles for blind person using ultrasonic sensor and buzzer | ultrasonic glasses for the blind project report | smart glasses for blind using Arduino | ultrasonic glasses for the blind research paper | ultrasonic glasses for the blind applications | Virtual Eye for Blind using IOT | Virtual Eye for Blind People Using Deep Learning. *********************************************************** If You Want To Purchase the Full Working Project KIT Mail Us: svsembedded@gmail.com Title Name Along With You-Tube Video Link We are Located at Telangana, Hyderabad, Boduppal. Project Changes also Made according to Student Requirements http://svsembedded.com/  https://www.svskits.in/ http://svsembedded.in/  http://www.svskit.com/ M1: +91 9491535690  M2: +91 7842358459 We Will Send Working Model Project KIT through DTDC / DHL / Blue Dart / First Flight Courier Service We Will Provide Project Soft Data through Google Drive 1. Project Abstract / Synopsis 2. Project Related Datasheets of Each Component 3. Project Sample Report / Documentation 4. Project Kit Circuit / Schematic Diagram 5. Project Kit Working Software Code 6. Project Related Software Compilers 7. Project Related Sample PPT’s 8. Project Kit Photos 9. Project Kit Working Video links Latest Projects with Year Wise YouTube video Links 157 Projects  https://svsembedded.com/ieee_2022.php 135 Projects  https://svsembedded.com/ieee_2021.php 151 Projects  https://svsembedded.com/ieee_2020.php 103 Projects  https://svsembedded.com/ieee_2019.php 61 Projects  https://svsembedded.com/ieee_2018.php 171 Projects  https://svsembedded.com/ieee_2017.php 170 Projects  https://svsembedded.com/ieee_2016.php 67 Projects  https://svsembedded.com/ieee_2015.php 55 Projects  https://svsembedded.com/ieee_2014.php 43 Projects  https://svsembedded.com/ieee_2013.php 1100+ Projects https://www.svskit.com/2022/02/900-pr... *********************************************************** Creating goggles for blind individuals using an ultrasonic sensor and a buzzer is a fascinating and potentially helpful project. The idea behind this concept is to use the ultrasonic sensor to detect obstacles or objects in the wearer's surroundings and then provide feedback through a buzzer to help them navigate safely. Here's a basic outline of how you could approach this project: Materials Needed: 1. Arduino or Raspberry Pi (microcontroller) 2. Ultrasonic sensor (such as HC-SR04) 3. Buzzer or small speaker 4. Goggles or wearable frame 5. Power source (battery or portable power bank) 6. Wires and basic electronic components 7. 3D-printed or custom-designed frame to hold the components Steps: 1. Design and Build: • Begin by designing or selecting a comfortable and wearable frame for the goggles. This frame should have space to house the ultrasonic sensor, microcontroller, and buzzer. You can use 3D printing or other materials to create this frame. 2. Ultrasonic Sensor Integration: • Attach the ultrasonic sensor to the front of the goggles. Make sure it is positioned in a way that it can detect objects in the user's path. Connect the sensor to the microcontroller using appropriate wiring. 3. Microcontroller Programming: • Write the code for the microcontroller (Arduino or Raspberry Pi) to read data from the ultrasonic sensor. The sensor will provide distance measurements, allowing the device to detect obstacles. • Program the microcontroller to interpret the distance data and determine if an obstacle is within a certain range (e.g., too close to the user). 4. Buzzer Feedback: • Integrate the buzzer into the goggles and connect it to the microcontroller. • Write code to control the buzzer based on the sensor data. For example, you could make the buzzer emit different patterns or frequencies of sound based on the distance to the obstacle. Louder and more frequent beeps could indicate closer obstacles. 5. Testing and Calibration: • Test the device in different environments to ensure accurate obstacle detection and appropriate feedback through the buzzer. • Calibrate the system to adjust sensitivity levels and feedback patterns as needed. 6. User Interface (Optional): • Consider adding a simple user interface, such as buttons or switches, to allow the wearer to adjust settings or turn the device on and off. 7. Safety and User Testing: • Prioritize user safety throughout the design and testing process. The device should provide useful information without causing distraction or harm to the user. • Collaborate with blind individuals or experts in the field to gather feedback and make improvements to the device.

Colour Recognition Based Wireless Object Tracking🤖Robot Using Raspberry ...

Ball Tracking with OpenCV | red - green - blue - yellow - Colour Recognition Based Wireless Object Tracking🤖Robot Using Raspberry Pi Pico with OpenCV & Python | Object tracking in Python using openCV | Color recognition with Opencv and Python | Ball Tracking with OpenCV | Object Tracking Based on Color Recognition with OpenCV and Python | Wireless Robot Control Using Raspberry Pi Pico. *********************************************************** If You Want To Purchase the Full Working Project KIT Mail Us: svsembedded@gmail.com Title Name Along With You-Tube Video Link We are Located at Telangana, Hyderabad, Boduppal. Project Changes also Made according to Student Requirements http://svsembedded.com/  https://www.svskits.in/ http://svsembedded.in/  http://www.svskit.com/ M1: +91 9491535690  M2: +91 7842358459 We Will Send Working Model Project KIT through DTDC / DHL / Blue Dart / First Flight Courier Service We Will Provide Project Soft Data through Google Drive 1. Project Abstract / Synopsis 2. Project Related Datasheets of Each Component 3. Project Sample Report / Documentation 4. Project Kit Circuit / Schematic Diagram 5. Project Kit Working Software Code 6. Project Related Software Compilers 7. Project Related Sample PPT’s 8. Project Kit Photos 9. Project Kit Working Video links Latest Projects with Year Wise YouTube video Links 157 Projects  https://svsembedded.com/ieee_2022.php 135 Projects  https://svsembedded.com/ieee_2021.php 151 Projects  https://svsembedded.com/ieee_2020.php 103 Projects  https://svsembedded.com/ieee_2019.php 61 Projects  https://svsembedded.com/ieee_2018.php 171 Projects  https://svsembedded.com/ieee_2017.php 170 Projects  https://svsembedded.com/ieee_2016.php 67 Projects  https://svsembedded.com/ieee_2015.php 55 Projects  https://svsembedded.com/ieee_2014.php 43 Projects  https://svsembedded.com/ieee_2013.php 1100+ Projects https://www.svskit.com/2022/02/900-pr... *********************************************************** Creating a color recognition-based object tracking wireless robot control system using a Raspberry Pi Pico, OpenCV, and Python involves several steps. This project will allow the robot to detect and track objects of a specific color and then control its movement wirelessly. Here's a basic outline of the process: Hardware Required: 1. Raspberry Pi Pico RP2040 ARM CORETEX M0+ 2. Robot chassis with motors and wheels 3. Motor driver board (e.g., L298N or L293D) 4. Webcam or camera module compatible with Raspberry Pi Pico 5. Power source (batteries or power supply) Software Required: 1. Thonny IDE (Python IDE for Raspberry Pi) 2. Python libraries: OpenCV and PiGPIO (for motor control) Steps: 1. Set Up Raspberry Pi Pico: • Connect the motor driver to the Raspberry Pi Pico's GPIO pins. • Connect the motors to the motor driver. 2. Install Required Libraries: • Install OpenCV and PiGPIO libraries on your Raspberry Pi Pico using Thonny IDE. 3. Capture and Process Video: • Use the Raspberry Pi Pico's camera module or a USB webcam to capture video frames. • Process the frames using OpenCV to identify objects of the desired color using color thresholding techniques. 4. Object Tracking: • Implement object tracking algorithms (e.g., color-based tracking) to continuously locate the target object within the video frames. 5. Wireless Control: • Set up a wireless communication protocol between your control device (e.g., smartphone or computer) and the Raspberry Pi Pico. You can use technologies like Bluetooth or Wi-Fi for this purpose. 6. Control Logic: • Create a control logic that receives instructions wirelessly and controls the robot's movement based on the tracked object's position. • Convert the object's position within the frame to motor control signals. For example, if the object is on the left side of the frame, increase the left motor speed, and if it's on the right, increase the right motor speed. 7. Motor Control: • Use the PiGPIO library to control the motors' speed and direction through the motor driver board. • Adjust motor speeds based on the control logic's instructions to navigate the robot toward the tracked object. 8. Execution: • Run the Python script on the Raspberry Pi Pico to start the color recognition-based object tracking and wireless control process. • Use your control device to wirelessly send commands to the robot and observe its movement. 9. Testing and Optimization: • Test the system and make necessary adjustments to the color recognition and tracking algorithms for better accuracy and responsiveness. • Optimize the motor control logic for smoother and more accurate movement. you will need to dive deeper into each step, referring to documentation and tutorials for each specific component and library. Additionally, ensure safety precautions while working with the hardware components, especially the motors and power supply.