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.

IoT Home Automation - DHT11(T/H) - Air Quality Monitoring ESP32 & Blynk ...

IoT Home Automation | Temperature, Humidity & Air Quality Monitoring using ESP32 & Blynk 2.0 | IoT Home Automation - DHT11(T/H) - Air Quality Monitoring ESP32 & Blynk App Notification📱Email Alert | How to Monitor Air Quality | Air Quality Monitoring System | ESP8266 | Blynk IOT Projects | Control System using ESP32 & Blynk 2.0 | Home Automation using ESP32 & Blynk 2.0 - IoT Projects Ideas | IoT based Air quality Monitoring system | Air pollution monitoring using MQ135 & ESP32 | IoT | esp32 | Home Automation using ESP32 & Blynk 2.0 - IoT Projects Ideas | Temperature, Humidity & Air Quality Monitoring. *********************************************************** 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 looks like you're interested in setting up an IoT home automation system using the DHT11 sensor to monitor temperature and humidity, an ESP32 microcontroller, and the Blynk app for notifications and email alerts. This is a great project idea! Here's a step-by-step guide to help you get started: Components Needed: 1. ESP32 development board. 2. DHT11 sensor (for temperature and humidity measurement). 3. Breadboard and jumper wires. 4. USB cable for power and programming. 5. Blynk app (downloadable from app stores). Steps: 1. Set Up the Hardware: • Connect the DHT11 sensor to the ESP32 using jumper wires. • Connect the VCC pin of the DHT11 to the 3.3V pin on the ESP32. • Connect the GND pin of the DHT11 to the GND pin on the ESP32. • Connect the DATA pin of the DHT11 to a GPIO pin on the ESP32 (e.g., GPIO 2). 2. Install Arduino IDE and ESP32 Board Support: • Download and install the Arduino IDE from the official website. • Open the Arduino IDE and go to "File" "Preferences". • Add the following URL to the "Additional Boards Manager URLs": https://dl.espressif.com/dl/package_e... • Go to "Tools" "Board" "Boards Manager", search for "esp32", and install the ESP32 board support. 3. Install Required Libraries: • In the Arduino IDE, go to "Sketch" "Include Library" "Manage Libraries". • Search and install the following libraries: • "DHT sensor library" by Adafruit. • "Blynk" by Blynk Inc. 4. Create a Blynk Account and Get Auth Token: • Download the Blynk app on your smartphone and create an account. • Create a new Blynk project and obtain the authentication token from the email sent to you. 6. Air Quality Monitoring: • You can add an additional sensor (e.g., MQ-135) to measure air quality. Connect it to the ESP32 and update the code accordingly. 7. Email Alert Configuration: • Blynk provides an email notification widget that you can add to your project. • Configure the widget to send an email when a specific condition (e.g., high temperature or humidity) is met. 8. Upload the Code: • Connect your ESP32 to your computer via USB. • Select the correct board and port in the Arduino IDE. • Click the "Upload" button to upload the code to your ESP32. 9. Monitor the Data: • Open the Serial Monitor in the Arduino IDE to view the data being read from the DHT11 sensor. 10. Monitor Data on Blynk App: • Open your Blynk app and check if the temperature and humidity values are being updated on the Value Display widgets. • Set up email alerts based on your chosen conditions.

GPS Clock Using Raspberry Pi Pico (RP2040) Arm Cortex-M0+ & LCD Display

GPS Clock Using Raspberry Pi Pico (RP2040) Arm Cortex-M0+ & LCD Display | Make a GPS Clock With Arduino - Projects | Arduino GPS Clock | raspberry pi | raspberry pi pico | raspberry pi real time clock | raspberry pi pico w | raspberry | raspberry pi pico gps | raspberry pi pico oled | raspberry pi pico gps tracker | raspberry pi 4 | raspberry pico | real time clock raspberry pi pico | raspberry pi pico real time clock | real time clock | raspberry pi pico nmea | real time clock raspberry pi | raspberry pi pico thonny | thonny raspberry pi pico | raspberry pi microcontroller | raspberry pi pico tutorial | raspberry pi zero. *********************************************************** 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 GPS clock using a Raspberry Pi Pico (RP2040) with an Arm Cortex-M0+ processor and an LCD display is a fun and educational project. In this project, we will use the Pico's GPIO pins to communicate with a GPS module and an LCD display. The GPS module will provide the time and date information, and the LCD will display the clock. Here's a step-by-step guide to building the GPS clock: Components Required: 1. Raspberry Pi Pico (RP2040) 2. GPS module (e.g., NEO-6M or similar) 3. LCD display (e.g., 16x2 or 20x4) 4. Breadboard and jumper wires 5. Power supply for the Raspberry Pi Pico Step 1: Setting up the Hardware 1. Connect the GPS module to the Raspberry Pi Pico using jumper wires. Typically, the GPS module communicates over UART, so connect the TX pin of the GPS module to the RX pin (GPIO1) of the Pico and vice versa. Also, connect the GND and VCC (usually 3.3V) pins between the devices. 2. Connect the LCD display to the Raspberry Pi Pico using jumper wires. You will need at least six GPIO pins to control the LCD: RS, E, D4, D5, D6, and D7. Step 2: Setting up the Software 1. Install the latest version of Circuit Python on the Raspberry Pi Pico. You can find instructions on how to do this on the official Raspberry Pi Pico website. 2. Once Circuit Python is installed, you will see the Pico as a USB drive when connected to your computer. Create a new Python file (e.g., gps_clock.py) on the Pico and edit it using a text editor. 3. Install the necessary libraries for the GPS module and the LCD display. You can use the Adafruit Circuit Python GPS library for the GPS module and the Adafruit Circuit Python Char LCD library for the LCD display. Download the libraries and copy them to the lib folder on your Pico. Step 3: Writing the Code Now, let's write the Python code for the GPS clock. The code will read the time information from the GPS module and display it on the LCD. Step 4: Running the GPS Clock 1. Save the Python code on the Raspberry Pi Pico as gps_clock.py. 2. Safely eject the Pico from your computer and connect it to a power supply. 3. The GPS clock should now be up and running. The LCD will display the current time and date as received from the GPS module. Please note that the GPS module requires a clear view of the sky to get a fix on the GPS satellites. It may take a few minutes for the module to acquire its first fix. Once it has a fix, it should update the time and date regularly. Always ensure that you have a stable power supply for the Raspberry Pi Pico and any connected components to avoid unexpected behaviour or damage to the devices.