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In an age where home and workplace security is paramount, the need for simple yet effective alert systems cannot be overstated. This project guides you through the creation of an LED and Buzzer-Based Alert System using a Door Sensor and an Arduino Nano. This system will notify you audibly and visually whenever the door opens or closes, providing an extra layer of security to your environment. Applications and Future Scope This innovative alert system can be applied in various scenarios, such as: Home Security: Detect unauthorized access when the door is opened unexpectedly. Office Monitoring: Alert personnel when secure areas are accessed. Warehouse Management: Track entry and exit in inventory areas. The future scope of this project is vast. By integrating it with IoT platforms, you can enable remote notifications via smartphone, allowing for real-time monitoring of your property. Imagine receiving alerts directly to your device, keeping you informed no matter where you are! Components Required To build this project, gather the following components: - Arduino Nano: The microcontroller that will serve as the brain of the project. - Door Sensor (Magnetic Reed Switch): This sensor will detect when the door is opened or closed. - LEDs (Red and Green): For visual indicators of door status. - Buzzer: Provides an audible alert when the door is opened. - 220Ω Resistor: Used to limit current to the LEDs. - Breadboard and Jumper Wires: For easy and organized connections. - USB Cable for Programming: To connect your Arduino to the computer. - Power Supply (optional): For standalone operation of the system. Steps to Follow 1. Getting Started with Hardware Connections Setting Up the Circuit: 1. Connect the Door Sensor: - Identify the two terminals of the magnetic reed switch. - Connect one terminal to a digital pin on the Arduino (e.g., D2) and the other terminal to the ground (GND). - When the door is closed, the reed switch will be in contact; when opened, it will break the circuit. 2. Wiring the LEDs: - Connect the longer leg (anode) of the red LED to another digital pin (e.g., D3) through a 220Ω resistor. This LED will indicate when the door is open. - Connect the shorter leg (cathode) of the LED to the ground (GND). - If you choose to add a green LED for indicating the door is closed, follow the same connection method but use a different digital pin (e.g., D5). 3. Connecting the Buzzer: - Connect the positive terminal of the buzzer to another digital pin (e.g., D4) and the negative terminal to the ground (GND). Circuit Diagram: 2. Coding the Arduino Nano The heart of your project lies in the code you upload to the Arduino Nano. Here’s a simple code snippet to make your alert system functional: Download the Code: You can download the complete code from [here] 3. Libraries Required This project utilizes basic Arduino functions, so no additional libraries are necessary. However, please always make sure your Arduino IDE is updated to the latest version for the best compatibility. 4. Setting Up in Arduino IDE Follow these steps to upload your code to the Arduino Nano: 1. Install the Arduino IDE: If you haven't already, download and install the Arduino IDE from [the official website](https://www.arduino.cc/en/software). 2. Upload the Code: - Open the Arduino IDE on your computer. - Copy and paste the provided code into a new sketch. - Select the appropriate board (Arduino Nano) and port from the Tools menu. - Click on the upload button (right arrow icon) to program your Arduino. 5. Testing Your System After uploading the code: 1. Connect the Arduino to your power source. 2. Open and close the door connected to the sensor. 3. Observe the LED and listen for the buzzer’s alert. 6. Results Upon completing the project, you will see: - When the door is opened: The red LED lights up, and the buzzer sounds, indicating the door is open. - When the door is closed: The green LED lights up, providing visual confirmation that the door is secured. Suggestions for Improvement - Adding More Sensors: You can integrate additional door sensors for a more comprehensive security system. - Wi-Fi Module Integration: Consider using an ESP8266 or similar module to send notifications to your smartphone for remote monitoring. - Mobile App Development: Develop a simple app to control and monitor the system from your mobile device. Conclusion Congratulations! You've successfully built an LED and Buzzer-Based Alert System using an Arduino Nano. This project not only enhances your understanding of basic electronics and programming but also provides a practical solution for home security. For more innovative projects and skill development programs, be sure to visit Skill-Hub by EmbeddedBrew, where we provide resources to elevate your technical skills!

Face detection has become a fundamental aspect of various AI applications, from security systems to personal devices. With the ESP32-CAM, a low-cost microcontroller with camera capabilities, you can create your own face detection system. This guide will show you how to perform face detection using ESP32-CAM and Python on the Thony Python IDE. Whether you're a hobbyist or a tech enthusiast, this tutorial will help you create a functional project that detects faces in real-time. Prerequisites: ESP32-Cam module FTDI programmer Arduino IDE (installed) Thony Python IDE (installed) Micro-USB cable Jumper wires A local Wi-Fi network Step 1: Setup ESP32-CAM with Thony IDE 1.1 Install Thony Python IDE - Download Thony: Visit [Thony.org](https://thonny.org) and download the IDE for your operating system. - Install Python (If not already installed): Thony IDE will install Python automatically, but if you want a separate installation, go to [Python.org](https://python.org). 1.2 Connect ESP32-CAM to Your System Connect the ESP32-Cam to the FTDI programmer Connect the U0T and U0R pins of the ESP32-Cam to the RX and TX pins of the FTDI programmer. Connect the GND and 5V pins of the ESP32-Cam to the respective FTDI pins. Make sure the IO0 pin is connected to GND for flashing the ESP32-Cam. Install the ESP32 board package in Arduino IDE Open Arduino IDE and go to File > Preferences. In the "Additional Board Manager URLs" field, paste the following link: https://dl.espressif.com/dl/package_esp32_index.json Go to Tools > Board > Board Manager and search for ESP32. Install the ESP32 board package. Select the ESP32-Cam board in Arduino IDE Go to Tools > Board and choose AI-Thinker ESP32-Cam. Set the upload speed to 115200 and the correct port for your FTDI programmer. Upload the Webserver Example Code for Face Detection Open File > Examples > ESP32 > Camera > CameraWebServer. In the code, ensure you add your Wi-Fi SSID and password to connect the ESP32-Cam to your network. Upload the code to the ESP32-Cam by pressing Upload in the Arduino IDE. Once uploaded, remove the GND connection from IO0 and reset the module. Step 2: Get the ESP32-Cam’s IP Address Open Serial Monitor Go to Tools > Serial Monitor in Arduino IDE. Set the baud rate to 115200. Once the ESP32-Cam boots, you should see an IP address displayed in the Serial Monitor. Copy this IP address, as it will be used in the next step. Step 3: Integrate Python for Face Detection Install the OpenCV, Numpy library in Thony Open Thony Python IDE and go to Tools > Manage Packages . Search for opencv-python, Numpy  and install it. This library will handle face detection. Install requests library In the same way, search for and install the requests  library. This is required to interact with the ESP32-Cam’s webserver. 3. Write Python Script for Face Detection Create a New Python Script In Thony, create a new file and name it something like face_detection.py. Write the Code Use the following code to capture the video stream from the ESP32-Cam and detect faces. Run the Python Script - Make sure the ESP32-CAM webserver is running. Replace `'http://your-esp32-cam-ip-address/stream'` with the actual IP address of your ESP32-CAM. - Run the Python script in Thony IDE. A window will pop up displaying the video stream from the ESP32-CAM with detected faces highlighted. Step 4: Code Explanation Let's break down this code for face and eye detection using the ESP32-CAM stream into simple sections for a beginner: 1. Importing Required Libraries import cv2 import urllib.request import numpy as np - cv2: This is the OpenCV library, used for image and video processing. - urllib.request: This is used to fetch data from URLs (in this case, we’ll fetch images from the ESP32-CAM). - numpy (`np`): This is used for handling arrays and matrices. We need it to convert the images we get from the URL into a format OpenCV can process. 2. Loading Pre-Trained Models (Haar Cascades) for Face and Eye Detection f_cas = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') - Cascade Classifier: OpenCV uses pre-trained models (Haar cascades) to detect objects like faces and eyes. - `haarcascade_frontalface_default.xml` is used for detecting faces. - `haarcascade_eye.xml` is used for detecting eyes. The `CascadeClassifier` function loads these XML files, which contain the trained models. 3. Defining the ESP32-CAM URL url = 'http://192.168.1.104/capture' - This defines the URL from where the ESP32-CAM streams its video or captures frames. You should replace `'http://192.168.1.104/capture'` with the actual IP address of your ESP32-CAM. Make sure the ESP32-CAM is connected to the same network as your computer. 4. Creating a Display Window cv2.namedWindow("Live Transmission", cv2.WINDOW_AUTOSIZE) - This creates a window named "Live Transmission" to display the camera feed. `cv2.WINDOW_AUTOSIZE` means the window will automatically adjust its size based on the image size. 5. Main Loop to Continuously Capture and Process Frames while True: img_resp = urllib.request.urlopen(url) imgnp = np.array(bytearray(img_resp.read()), dtype=np.uint8) img = cv2.imdecode(imgnp, -1) - `while True:`: This loop continuously fetches frames from the ESP32-CAM. - `urllib.request.urlopen(url)`: This retrieves the image from the ESP32-CAM via the URL. - `np.array(bytearray(img_resp.read()), dtype=np.uint8)`: Converts the image from bytes into a NumPy array so it can be handled by OpenCV. - `cv2.imdecode(imgnp, -1)`: Decodes the NumPy array into an image that OpenCV can work with. 6. Converting the Image to Grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - `cv2.cvtColor` converts the color image (BGR format) into grayscale, which is easier and faster for the detection algorithms (face and eye detection) to process. 7. Detecting Faces in the Image face = f_cas.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5) - `f_cas.detectMultiScale`: This function detects faces in the grayscale image. - `gray`: The grayscale image where faces are to be detected. - `scaleFactor=1.1`: This parameter specifies how much the image size is reduced at each image scale (controls accuracy). - `minNeighbors=5`: Defines the minimum number of neighboring rectangles that need to be detected for an object (face) to be considered valid. 8. Drawing Rectangles Around Detected Faces for x, y, w, h in face: cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3) - `for x, y, w, h in face:`: This loop runs through all the detected faces, where: - `x` and `y` are the coordinates of the upper-left corner of the face. - `w` is the width and `h` is the height of the face. - `cv2.rectangle`: Draws a red rectangle (BGR color `(0, 0, 255)`) around the detected face in the original image (`img`). 9. Detecting and Highlighting Eyes Within the Detected Face roi_gray = gray[y:y+h, x:x+w] roi_color = img[y:y+h, x:x+w] eyes = eye_cascade.detectMultiScale(roi_gray) for (ex, ey, ew, eh) in eyes: cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2) - `roi_gray` and `roi_color`: These define the "Region of Interest" (ROI) where eyes are expected to be found, which is the region inside the detected face. - `eye_cascade.detectMultiScale(roi_gray)`: Detects eyes within the face region in the grayscale image. - `cv2.rectangle`: Draws a green rectangle (BGR color `(0, 255, 0)`) around each detected eye. 10. Displaying the Result cv2.imshow("live transmission", img) - `cv2.imshow`: This function displays the current frame with rectangles around detected faces and eyes in the "Live Transmission" window. 11. Exiting the Program key = cv2.waitKey(5) if key == ord('q'): break - `cv2.waitKey(5)`: Waits for 5 milliseconds for a key press. - `if key == ord('q'):`: If the 'q' key is pressed, the program breaks out of the loop and stops the live video feed. 12. Cleanup cv2.destroyAllWindows() - `cv2.destroyAllWindows`: Closes the window displaying the video when the loop ends (after pressing 'q'). Summary: - Import libraries: OpenCV for image processing, `urllib` for getting images from the ESP32-CAM, and NumPy for array handling. - Haar Cascades: Pre-trained models to detect faces and eyes. - ESP32-CAM URL: Defines the web address from which the camera feed is fetched. - Face & Eye Detection: OpenCV processes each frame, converting it to grayscale for more efficient detection, and uses `CascadeClassifier` to draw rectangles around faces and eyes. - Live Video Stream: Displays the video feed in real time, with face and eye detection applied, until the user presses 'q' to quit. Conclusion: Congratulations! You’ve successfully set up face detection using the ESP32-CAM and Python on the Thony IDE. This project can be extended for various applications such as smart home security, automated attendance systems, or even facial recognition.If you enjoyed this tutorial, be sure to visit our Skill-Hub for the Arduino Master Class, where you can take your tech skills to the next level!

Introduction In the world of IoT, creating smart devices that can be controlled remotely is both exciting and rewarding. One such project is the Webserver Controlled Spy Car using the ESP32-Cam module. This camera-equipped module allows you to stream live video and control your car's movements through a simple web interface. If you're interested in exploring the world of remote surveillance, this project is for you! Follow the detailed steps below to create your own spy car and gain hands-on experience in embedded systems. Step 1: Components You’ll Need - ESP32-Cam Module - FTDI Programmer (for uploading code to ESP32) - L298N Motor Driver - DC Motors with Wheels (4 for car movement) - Chassis for the Car (any basic car chassis works) - LiPo Battery or Adapter (for powering the car) - Jumper Wires (for connections) - Breadboard (optional for easy connections) Step 2: Setting Up ESP32-Cam for Web Server The ESP32-Cam module can stream video and control the car via a webserver. First, we’ll set up the ESP32-Cam to stream live video over Wi-Fi. 1. Install ESP32 board on Arduino IDE: - Open Arduino IDE. - Go to File > Preferences and in the Additional Board Manager URLs field, paste the following link: ``` https://dl.espressif.com/dl/package_esp32_index.json ``` - Next, go to Tools > Board > Boards Manager and search for ESP32. Install it. 2. Connect the FTDI programmer: - Connect the GND of the FTDI programmer to GND of the ESP32-Cam. - Connect VCC to 5V, RX to U0T, and TX to U0R. - Set the IO0 pin to GND (this puts the ESP32 into programming mode). 3. Upload the code for video streaming: - Use the following sample code to set up the camera streaming: 4. Check video stream: - Once the code is uploaded, open the Serial Monitor and note down the IP address of the ESP32-Cam. - Enter this IP address into a web browser to view the video stream. Step 3: Assembling the Spy Car 1. Motor Driver Connections: - Connect the L298N Motor Driver to the DC motors for car movement. - The IN1, IN2, IN3, and IN4 pins of the L298N should be connected to any 4 GPIO pins on the ESP32-Cam module (for controlling the direction). - Connect the 12V input of the L298N to the LiPo Battery for power. 2. ESP32-Cam Pin Configuration: - Assign GPIO pins to control the motors: - `GPIO12` for IN1, - `GPIO13` for IN2, - `GPIO14` for IN3, - `GPIO15` for IN4. Step 4: Setting Up the Web Server for Car Control In addition to streaming video, we want to control the movement of the car using buttons on a web interface. Here’s how to modify the code: Test the car movements: - Once the code is uploaded, open the web interface by visiting the ESP32-Cam’s IP address. - Click the buttons to move the car in different directions while viewing the live video feed Step 5: Powering and Testing Once all the connections are made: - Power the car with a LiPo battery. - Test the car’s movement and camera stream by accessing the web server from your smartphone or laptop. Conclusion You’ve now built your own webserver-controlled spy car using the ESP32-Cam! This project combines the power of IoT and real-time control, providing a great way to explore remote monitoring. Make sure to visit Skill-Hub by EmbeddedBrew for the Arduino Master Class, where you’ll dive even deeper into microcontroller programming and automation.

Welcome to EmbeddedBrew Innovations Crafting Tomorrow's Technology Today Get to Know Us At EmbeddedBrew Innovations, we are passionate about turning ideas into reality by designing and developing cutting-edge embedded systems. As a pioneering company in the embedded technology space, we specialize in innovative projects, practical workshops, and comprehensive online courses that empower individuals and businesses to stay ahead in the fast-evolving world of technology. Our mission is to fuse creativity with engineering to create solutions that simplify complex problems and improve everyday life. Whether you're a tech enthusiast or a professional, EmbeddedBrew is where innovation meets expertise. Learn More Services We offer a range of services tailored to meet the needs of individuals and businesses in the embedded systems space Custom Embedded Projects From idea conceptualization to complete product development, we design custom embedded solutions for industries including IoT, automation, and more. Our projects are driven by a commitment to excellence, ensuring that your solutions are not only innovative but reliable and scalable. Workshops & Training Hands-on training programs and workshops designed for all levels, from beginners to advanced professionals. Learn the essentials of embedded systems, Arduino, IoT, robotics, and more from our team of experts. Online Courses Enroll in our self-paced or instructor-led courses to deepen your knowledge in key areas of embedded technology. Our courses cover a variety of topics, including microcontrollers, sensor integration, and real-time systems, providing you with the skills needed to excel in today's tech-driven world. Join Us at EmbeddedBrew! We are always on the lookout for passionate individuals who share our vision of technological innovation. If you are interested in joining our team, please fill out the Crew Information Form. Join Us

Getting started with Keypad module and Arduino Nano Here are the steps to get started with a Keypad module and an Arduino Nano to display values on the Serial Monitor Description: In this tutorial, we will learn how to interface a keypad module with an Arduino Nano and display the pressed keys on the Serial Monitor. Keypads are a simple and effective way to add input functionality to your Arduino projects. Components Needed - Arduino Nano - Keypad module (4x4 or 4x3) - Jumper wires - Breadboard (optional) - USB cable to connect Arduino Nano to the computer Step 1: Connect the Keypad to the Arduino Nano 1. Identify the Keypad Pins: Most keypads have a set of row and column pins. A 4x4 keypad typically has 8 pins (4 for rows and 4 for columns), while a 4x3 keypad has 7 pins (4 for rows and 3 for columns). 2. Connect the Keypad Pins to Arduino: Use jumper wires to connect the keypad pins to the Arduino Nano. Here's an example connection for a 4x4 keypad: - Keypad Pin 1 (Row 1) to Arduino Digital Pin 2 - Keypad Pin 2 (Row 2) to Arduino Digital Pin 3 - Keypad Pin 3 (Row 3) to Arduino Digital Pin 4 - Keypad Pin 4 (Row 4) to Arduino Digital Pin 5 - Keypad Pin 5 (Column 1) to Arduino Digital Pin 6 - Keypad Pin 6 (Column 2) to Arduino Digital Pin 7 - Keypad Pin 7 (Column 3) to Arduino Digital Pin 8 - Keypad Pin 8 (Column 4) to Arduino Digital Pin 9 Step 2: Install the Keypad Library 1. Open the Arduino IDE: 2. Go to Library Manager: Navigate to `Sketch` -> `Include Library` -> `Manage Libraries`. 3. Search for Keypad Library: In the Library Manager, type "Keypad" in the search bar. 4. Install the Library: Find the "Keypad by Mark Stanley, Alexander Brevig" and click "Install". Step 3: Write the Arduino Code 1. Open a New Sketch in Arduino IDE: 2. Include the Keypad Library: #include Step 4: Upload and Test the Code 1. Connect the Arduino Nano to your Computer: Use the USB cable. 2. Select the Correct Board and Port: Go to `Tools` -> `Board` -> `Arduino Nano`, and then `Tools` -> `Port` to select the appropriate COM port. 3. Upload the Sketch: Click the upload button in the Arduino IDE. 4. Open the Serial Monitor: Navigate to `Tools` -> `Serial Monitor` and set the baud rate to 9600. Step 5: Press Keys on the Keypad With everything set up, press the keys on the keypad. You should see the corresponding key values displayed on the Serial Monitor. Project Gallery All Documents : Download the below code to get started with Keypad module. Click Here to Download Download Video Tutorial : Conclusion : You have successfully interfaced a keypad with an Arduino Nano and displayed the key presses on the Serial Monitor. This basic setup can be expanded to create more complex projects, such as password input systems, menu navigation, and more. Also check our website for more projects and explore the Skill-Hub by EmbeddedBrew. comments debug Comments Write a comment Write a comment Share Your Thoughts Be the first to write a comment.

Getting Started with NodeMCU ESP8266 Module Download and Install NodeMCU ESP8266 module for IoT Applications. Description: NodeMCU is a popular open-source IoT platform that uses the ESP8266 Wi-Fi module. Here’s a simple guide to help you get started with NodeMCU using the Arduino IDE. Step 1: Install Arduino IDE 1. Download Arduino IDE: - Go to the [Arduino IDE download page]( https://www.arduino.cc/en/software ). - Download the version compatible with your operating system (Windows, macOS, Linux). 2. Install Arduino IDE: - Follow the installation instructions specific to your operating system. - Once installed, open the Arduino IDE. Step 2: Set Up Arduino IDE for NodeMCU 1. Add ESP8266 Board Manager URL: - Open Arduino IDE. - Go to `File` > `Preferences`. - In the “Additional Board Manager URLs” field, enter the following URL: `http://arduino.esp8266.com/stable/package_esp8266com_index.json`. - Click `OK`. 2. Install ESP8266 Board Package: - Go to `Tools` > `Board` > `Boards Manager`. - Search for “esp8266” in the Boards Manager window. - Click on “Install” for the “esp8266” by ESP8266 Community. 3. Select NodeMCU Board: - Go to `Tools` > `Board`. - Scroll down and select “NodeMCU 1.0 (ESP-12E Module)”. Step 3: Connect NodeMCU to Your Computer 1. Connect NodeMCU: - Use a USB cable to connect your NodeMCU to your computer. 2. Install USB Driver (if necessary): - For Windows, you may need to install the CH340 USB driver. Download it from [here]( https://sparks.gogo.co.nz/ch340.html ). - For macOS, the driver usually installs automatically, but if not, it can be downloaded from the same link. Step 4: Upload Your First Sketch 1. Open Blink Example: - Go to `File` > `Examples` > `ESP8266` > `Blink`. 2. Select the Right Port: - Go to `Tools` > `Port` and select the COM port that your NodeMCU is connected to. 3. Upload the Sketch: - Click on the upload button (right arrow) in the Arduino IDE. - Wait for the code to compile and upload. You should see the onboard LED blinking if the upload is successful. Step 5: Explore More Projects Now that you have successfully uploaded your first sketch, you can explore more advanced projects. Here are some ideas to get you started: - Wi-Fi Controlled LED: Control an LED using a web interface. - Temperature and Humidity Monitoring: Use DHT11/DHT22 sensors to monitor environmental conditions. - Home Automation: Create a simple home automation system using MQTT. Project Gallery All Documents : Use the Below code for Practice #define LED D0 // Led in NodeMCU at pin GPIO16 (D0). void setup() { pinMode(LED, OUTPUT); // LED pin as output. } void loop() { digitalWrite(LED, HIGH);// turn the LED off.(Note that LOW is the voltage level but actually //the LED is on; this is because it is acive low on the ESP8266. delay(1000); // wait for 1 second. digitalWrite(LED, LOW); // turn the LED on. delay(1000); // wait for 1 second. } Click Here to Download Download Video Tutorial : Conclusion : For more projects and tutorials, visit our website. Don’t forget to check out Skill-Hub by EmbeddedBrew for a comprehensive range of courses on embedded systems. comments debug Comments Write a comment Write a comment Share Your Thoughts Be the first to write a comment.