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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.

Creating custom animations on a 0.96" OLED screen using an Arduino Nano can add a dynamic flair to your projects. In this guide, we'll walk you through the detailed steps of converting a GIF into frames, then into bitmaps, and finally into the code required to display them on your OLED. 1. Choosing and Preparing Your GIF - Select Your GIF: Choose a GIF that you want to display on your OLED. Keep in mind the resolution of your OLED screen, which is typically 128x64 pixels. Ensure the GIF is monochrome (black and white) to match the display’s capabilities. - Resize the GIF: Use image editing software like Photoshop, GIMP, or an online tool to resize your GIF to 128x64 pixels (or smaller if needed). 2. Converting GIF to Frames - Extract Frames: Use software like GIMP or an online tool like https://gifgifs.com/split/ to extract the individual frames from your GIF. This will give you a series of images representing each frame of the animation. - Save the Frames: Save each frame as a monochrome BMP or PNG file. Make sure to name them sequentially (e.g., `frame_01.bmp`, `frame_02.bmp`, etc.). 3. Convert Frames to Bitmap (Monochrome) - Convert to Monochrome Bitmap: If your frames are not already monochrome, you’ll need to convert them. Tools like GIMP allow you to convert images to monochrome bitmaps. - Verify the Size and Color Depth: Ensure that each frame is correctly sized (128x64 pixels) and is in 1-bit color depth (monochrome). 4. Convert Bitmaps to C/C++ Code - Use an Image to Code Converter: Utilize tools like the Adafruit GFX Library's ` image2cpp ` tool to convert each monochrome bitmap into an array of bytes. This tool generates the corresponding C++ code that you can directly embed into your Arduino sketch. - Configure the Converter: When using `image2cpp`, ensure you select the correct settings: - Output format: Choose 'Arduino Code' or 'C array'. - Monochrome settings: Ensure the threshold is set for monochrome conversion. - Display settings: Match the settings to your OLED's resolution (128x64). - Generate the Code: Convert each frame and save the generated code. You'll end up with an array for each frame, which looks something like this: ```cpp const unsigned char frame_01 [] PROGMEM = { 0x00, 0x00, 0x3C, 0x42, 0xA5, 0x81, 0xA5, 0x42, 0x3C, 0x00, 0x00 }; ``` 5. Program the Arduino Nano Download the below code to get started with the Animation. 6. Upload and Test - Upload the Sketch: Connect your Arduino Nano to your computer, upload the sketch, and watch your custom animation play on the OLED. - Debug as Needed: If the animation doesn’t display correctly, double-check the frame dimensions, the generated code, and the wiring connections. 7. Optimize and Enhance - Add Transitions: You can add smooth transitions between frames by adjusting delays or implementing more complex animation logic. - Optimize Memory Usage: Since the Arduino Nano has limited memory, consider optimizing the code by reducing the number of frames or compressing the data if needed. Conclusion Displaying custom animations on a 0.96" OLED using an Arduino Nano opens up a world of creative possibilities for your projects. By following the steps outlined above, you can transform any GIF into a series of frames that bring life to your display. For more detailed tutorials and advanced Arduino projects, don't forget to visit our Skill-Hub by EmbeddedBrew. Our Arduino Master Class will take your skills to the next level, empowering you to create even more impressive and sophisticated projects.

Imagine a world where firefighters no longer have to put their lives at risk to battle towering infernos. Picture a futuristic drone swooping in, equipped with cutting-edge technology, to extinguish flames from a safe distance. This vision is becoming a reality with the advent of the Dragon Firefighter Flying Robot. In this blog, we delve into the groundbreaking advancements in firefighting technology that promise to revolutionize the industry and save countless lives. Current Challenges in Firefighting Firefighting is a perilous profession. Despite rigorous training and protective gear, firefighters face numerous hazards, from structural collapses to toxic smoke inhalation. Traditional firefighting methods often involve direct exposure to extreme heat and dangerous conditions, putting lives on the line with every mission. The necessity for safer, more efficient firefighting solutions is evident. Technological Advances in Firefighting The integration of technology in firefighting has led to significant improvements over the years. Thermal imaging cameras, advanced protective equipment, and automated systems have enhanced the effectiveness and safety of firefighting operations. However, the emergence of robotic technology represents the next quantum leap in firefighting capabilities. What is the Dragon Firefighter Flying Robot? The Dragon Firefighter Flying Robot, developed by a team of innovative engineers and researchers, is a state-of-the-art drone designed specifically for firefighting. Unlike conventional drones, this robotic marvel is equipped with specialized features that enable it to combat fires with unprecedented efficiency and precision. Key Features and Capabilities 1. High-Temperature Resistance: The Dragon Firefighter Flying Robot can withstand extreme temperatures, allowing it to operate in environments where human firefighters cannot. 2. Advanced Sensors: Equipped with thermal imaging and high-definition cameras, the robot can identify hot spots and assess the situation in real-time. 3. Water and Foam Dispersal Systems: The drone is fitted with advanced dispersal systems that can release water or fire-suppressant foam accurately onto the flames. 4. Autonomous Navigation: Utilizing AI and machine learning, the robot can navigate complex environments autonomously, avoiding obstacles and identifying the best paths to the fire source. 5. Remote Operation: Firefighters can control the robot from a safe distance, minimizing risk while maintaining operational control. Deployment and Operation The Dragon Firefighter Flying Robot is deployed from a safe location near the fire site. Once airborne, it uses its advanced sensors to locate the fire's core. The robot then approaches the fire, maintaining a safe distance, and utilizes its dispersal systems to extinguish the flames. Its autonomous navigation capabilities allow it to move efficiently, even in chaotic and unpredictable environments. Case Studies and Real-World Applications The Dragon Firefighter Flying Robot has already been tested in various scenarios, demonstrating its effectiveness in both urban and rural settings. In one notable case, the robot was deployed to a warehouse fire, where it successfully identified and extinguished several hot spots, preventing the fire from spreading and causing further damage. The Future of Firefighting Potential Impact The introduction of the Dragon Firefighter Flying Robot heralds a new era in firefighting. By reducing the risks to human firefighters and increasing the efficiency of fire suppression efforts, this technology has the potential to save lives, protect property, and transform how firefighting operations are conducted worldwide. Ongoing Research and Development The development of the Dragon Firefighter Flying Robot is just the beginning. Researchers are continually working to enhance the robot's capabilities, integrating new technologies such as advanced AI algorithms and improved dispersal systems. Future iterations may include swarm technology, allowing multiple robots to work together seamlessly in large-scale fire incidents. Conclusion The Dragon Firefighter Flying Robot represents a significant leap forward in firefighting technology. By combining advanced robotics with state-of-the-art fire suppression systems, it offers a safer, more efficient way to combat fires. As this technology continues to evolve, it promises to redefine the future of firefighting, ensuring that firefighters can perform their duties with greater safety and effectiveness. For those interested in staying ahead in the ever-evolving field of technology, be sure to visit Skill-Hub by EmbeddedBrew. Enhance your tech skills and stay informed about the latest advancements in robotics, AI, and more. The future is here, and it's time to be a part of it. Reference Drone Life : https://dronelife.com/2023/12/27/dragon-firefighter-robot-fights-fires-from-a-distance/

A New Hope in the Fight Against Obesity In a groundbreaking advancement that could revolutionize obesity treatment, engineers have developed a vibrating, ingestible capsule designed to help patients lose weight. As obesity rates continue to climb globally, innovative solutions like this one are critical in providing new, effective treatments. Imagine a tiny device that, once swallowed, can aid in weight loss through simple, mechanical means. This is not a science fiction scenario, but a tangible reality born from cutting-edge research and engineering. Understanding the Vibrating Capsule The ingestible capsule, developed by a team of engineers at MIT, is a small, pill-sized device that can be swallowed. Once inside the stomach, it begins to vibrate, stimulating the mechanoreceptors in the stomach lining. These receptors signal the brain that the stomach is full, helping to reduce appetite and caloric intake. Key Features and Mechanism - Size and Composition: The capsule is approximately the size of a standard dietary supplement pill, making it easy to swallow. It is made from biocompatible materials that are safe for ingestion and eventual excretion. - Vibration Technology: The core innovation lies in the capsule's ability to vibrate at a specific frequency. This vibration targets the stomach's mechanoreceptors, which play a crucial role in regulating feelings of fullness. - Power Source: The capsule contains a miniature battery and a vibrating component, both of which are designed to withstand the acidic environment of the stomach for a predetermined period. Research and Development Process The development of this vibrating capsule involved extensive research and testing. The engineering team conducted numerous experiments to determine the optimal vibration frequency and duration. Animal studies were conducted to assess the capsule's safety and efficacy, followed by initial human trials. Challenges and Solutions - Durability: Ensuring the capsule could withstand the harsh conditions of the stomach without degrading prematurely was a significant challenge. The team utilized advanced materials and coatings to enhance durability. - Safety: The biocompatibility of the capsule materials was rigorously tested to prevent any adverse reactions in patients. The capsule's components were carefully selected to ensure they would pass through the digestive system without causing harm. - Efficacy: Determining the precise vibration frequency that would effectively stimulate the mechanoreceptors without causing discomfort was crucial. The engineers fine-tuned the device through iterative testing. Potential Impact on Obesity Treatment This innovative capsule has the potential to make a significant impact on obesity treatment by offering a non-invasive, drug-free option. Traditional weight loss methods, such as diet and exercise, often require substantial lifestyle changes and can be difficult to maintain. Pharmacological treatments can have side effects and are not suitable for everyone. In contrast, this vibrating capsule offers a simpler, more accessible solution. Advantages Over Traditional Methods - Non-Invasive: Unlike surgical options such as gastric bypass or sleeve gastrectomy, this capsule does not require any invasive procedures. - Ease of Use: Patients can take the capsule as part of their daily routine without the need for special equipment or settings. - Minimal Side Effects: The mechanical action of the capsule minimizes the risk of side effects commonly associated with weight loss medications. Future Prospects and Research Directions The successful development of the vibrating capsule opens the door for further research and enhancements. Future studies could explore: - Long-Term Efficacy: While initial trials are promising, long-term studies are needed to evaluate the sustained effectiveness of the capsule. - Combination Therapies: Researchers could investigate the potential of combining the capsule with other treatments, such as dietary adjustments or pharmacotherapy, to enhance weight loss outcomes. - Customization: Personalizing the capsule's vibration frequency and duration based on individual patient needs could improve its effectiveness. Conclusion: A Step Forward in Weight Management The development of the vibrating, ingestible capsule represents a significant step forward in the quest for effective obesity treatments. This innovative approach offers a promising alternative to traditional methods, potentially transforming the way we address weight management. For those intrigued by this technological breakthrough and interested in enhancing their tech skills, consider exploring Skill-Hub by EmbeddedBrew. Skill-Hub offers a wealth of resources and courses designed to help you stay ahead in the rapidly evolving tech landscape. Visit Skill-Hub today and take the next step in your tech-skill enhancement journey. Reference : MIT News - https://news.mit.edu/2023/engineers-develop-vibrating-ingestible-capsule-1222

Imagine a world where artificial skin can provide robots and prosthetic limbs with a sense of touch almost indistinguishable from human skin. A world where technology mimics nature so closely that it revolutionizes the way we interact with machines. This isn't a scene from a sci-fi movie; it's the cutting-edge innovation by researchers from KAIST, South Korea, bringing us one step closer to this remarkable future. Researchers at KAIST's College of Engineering have achieved a significant breakthrough by developing a skin-like tactile sensor that mimics the human sense of touch. This pioneering technology promises to transform various fields, from robotics to healthcare, by providing machines with the ability to sense and respond to their environment with unprecedented accuracy. Key Features and Innovations 1. High Sensitivity and Precision The tactile sensor developed by the KAIST team boasts high sensitivity, allowing it to detect minute pressure changes, vibrations, and even temperature variations. This level of precision is crucial for applications requiring delicate touch and responsiveness, such as in robotic surgery or advanced prosthetics. 2. Flexibility and Durability One of the standout features of this sensor is its flexibility. Designed to closely mimic human skin, it can bend, stretch, and conform to various shapes without compromising its functionality. Additionally, it exhibits remarkable durability, withstanding repeated use and harsh environmental conditions. 3. Bio-Compatibility The sensor's materials are biocompatible, making it safe for use in medical applications. This is particularly important for prosthetics, where the sensor can directly interact with human skin without causing adverse reactions. Applications and Future Prospects The potential applications of this skin-like tactile sensor are vast and varied. In the field of robotics, it can enhance the dexterity and sensitivity of robotic hands, enabling them to perform tasks that require a delicate touch. In healthcare, it can be integrated into prosthetic limbs, providing amputees with a sense of touch and improving their quality of life. Furthermore, this technology holds promise for developing advanced human-machine interfaces, paving the way for more intuitive and responsive interaction with electronic devices. Conclusion The development of this skin-like tactile sensor by Korean researchers marks a significant milestone in the realm of tactile technology. By closely mimicking the human sense of touch, this innovation opens up new possibilities in robotics, healthcare, and beyond, promising a future where technology and human senses are seamlessly integrated. For those passionate about staying ahead in the tech world and enhancing their tech skills, visit Skill-Hub by EmbeddedBrew. Dive into a wealth of resources and courses designed to keep you at the forefront of technological advancements. Reference: [ KAIST News ]

Sure, here’s a step-by-step guide to create a home automation system that controls two devices and displays temperature data from a DHT22 sensor on an LCD using NodeMCU and Blynk: Step 1: Gather Materials - NodeMCU (ESP8266) - DHT22 temperature and humidity sensor - 16x2 LCD display with I2C module - Two relays (for controlling devices) - Breadboard and jumper wires - Power supply (5V for relays, typically USB for NodeMCU) - Blynk app installed on your smartphone Step 2: Set Up Blynk 1. Create a Blynk Account: Download the Blynk app from the App Store or Google Play and create an account. 2. Create a New Project: In the Blynk app, create a new project. Select "NodeMCU" as your device and note down the authentication token sent to your email. 3. Add Widgets:    - Add a button widget for each device you want to control.    - Add a labeled value widget to display temperature data.    - Optionally, add a gauge or graph widget to visualize temperature data. Step 3: Set Up Hardware 1. Connect the DHT22 Sensor:    - VCC to 3.3V on NodeMCU    - GND to GND on NodeMCU    - Data to digital pin D4 on NodeMCU 2. Connect the LCD Display:    - Connect the I2C module to the LCD.    - SDA to D2 on NodeMCU    - SCL to D1 on NodeMCU    - VCC to 5V on NodeMCU    - GND to GND on NodeMCU 3. Connect the Relays:    - Relay 1 IN pin to D5 on NodeMCU    - Relay 2 IN pin to D6 on NodeMCU    - VCC to 5V    - GND to GND Step 4: Install Libraries In your Arduino IDE, install the following libraries: - Blynk Library: Go to Sketch > Include Library > Manage Libraries, search for "Blynk", and install. - DHT Sensor Library: Search for "DHT sensor library" and install. - LiquidCrystal I2C Library: Search for "LiquidCrystal I2C" and install. Step 5: Write the Code Step 6: Upload Code to NodeMCU 1. Connect your NodeMCU to your computer via USB. 2. Open the Arduino IDE and select the correct board and port. 3. Upload the code to your NodeMCU. Step 7: Configure Blynk App 1. Button Widgets: Set one button to V1 and the other to V2 for controlling the relays. 2. Labeled Value Widget: Set to V5 to display the temperature data. Step 8: Power Up and Test 1. Ensure all connections are secure. 2. Power up your NodeMCU and relays. 3. Open the Blynk app and test the buttons to control your devices. 4. Check the LCD display and Blynk app to see the temperature readings from the DHT22 sensor. Conclusion You’ve now built a basic home automation system using NodeMCU and Blynk! This setup allows you to control two devices remotely and monitor temperature data in real-time. Explore additional projects and skills on our website and continue enhancing your IoT expertise with Skill-Hub by EmbeddedBrew. Happy building!

Creating an online clock using NodeMCU and an LCD display is an exciting project that combines the power of Wi-Fi connectivity with the simplicity of microcontrollers. Follow these steps to build your own online clock. Materials Needed: - NodeMCU (ESP8266) - LCD Display (16x2 or 20x4) with I2C module - Breadboard and jumper wires - USB cable for programming NodeMCU - Internet connection Step 1: Set Up the Hardware 1. Connect the LCD Display to NodeMCU:    - Connect the VCC pin of the LCD to the 3.3V pin on the NodeMCU.    - Connect the GND pin of the LCD to a GND pin on the NodeMCU.    - Connect the SDA pin of the LCD to the D2 pin on the NodeMCU.    - Connect the SCL pin of the LCD to the D1 pin on the NodeMCU. 2. Power the NodeMCU:    - Connect the NodeMCU to your computer using the USB cable to power it up and upload the code. Step 2: Install Required Libraries 1. Install the Arduino IDE:    - Download and install the Arduino IDE from the [Arduino website]( https://www.arduino.cc/en/software ). 2. Add ESP8266 Board to Arduino IDE:    - Open Arduino IDE, go to `File > Preferences`.    - In the "Additional Board Manager URLs" field, add the following URL: ` http://arduino.esp8266.com/stable/package_esp8266com_index.json`    - Go to `Tools > Board > Boards Manager`, search for `ESP8266` and install the `esp8266` platform. 3. Install Libraries:      - Go to `Sketch > Include Library > Manage Libraries`.      - Search for and install the following libraries:      - `LiquidCrystal_I2C` (for controlling the LCD via I2C)      - `NTPClient` (for getting time from an NTP server)      - `ESP8266WiFi` (for connecting NodeMCU to Wi-Fi) Step 3: Write the Code 1. Include Libraries and Define Variables: 2. Set Up Wi-Fi and Time Client: 3. Display Time and Date: Step 4: Upload the Code 1. Upload Code to NodeMCU:    - Select the correct board and port in the Arduino IDE (`Tools > Board > NodeMCU 1.0 (ESP-12E Module)` and `Tools > Port`).    - Click the upload button to upload the code to the NodeMCU. 2. Monitor the Serial Output:    - Open the Serial Monitor (`Tools > Serial Monitor`) to see the connection status and debug messages. Step 5: Test and Debug 1. Check LCD Display:    - Ensure the LCD displays the current time and date.    - If the display is not working, check the connections and ensure the I2C address of the LCD (0x27 in this case) matches your hardware. 2. Verify Time Accuracy:    - The time displayed should update every second.    - If the time is incorrect, check your internet connection and the NTP server configuration. Conclusion Congratulations! You have successfully created an online clock using NodeMCU and an LCD display. For more exciting projects, visit our website and explore Skill-Hub by EmbeddedBrew to learn new skills in embedded systems.

In this tutorial, we will walk you through the steps to monitor DHT (Digital Humidity and Temperature) sensor values on the Blynk app using an Arduino board. This project allows you to remotely monitor the temperature and humidity data from the DHT sensor on your smartphone. Materials Needed: - Arduino board (e.g., Uno, Nano) - DHT11 or DHT22 sensor - Jumper wires - Breadboard - USB cable - Internet connection - Blynk app installed on your smartphone Step 1: Setting Up the Hardware 1. Connect the DHT Sensor to the Arduino:      - DHT11/DHT22 Pin Configuration:      - VCC to 5V or 3.3V pin on Arduino      - GND to GND pin on Arduino      - Data pin to a digital pin on Arduino (e.g., D2) DHT Sensor -> Arduino ------------------------ VCC -> 5V GND -> GND DATA -> D2 2. Wiring Diagram:    Ensure you connect the pins correctly to avoid any damage to the sensor or the Arduino. Step 2: Setting Up the Blynk App 1. Download and Install the Blynk App:    - Available on Google Play Store (Android) and Apple App Store (iOS). 2. Create a New Project:    - Open the Blynk app and create a new project.    - Choose your device (e.g., Arduino Uno).    - Note the Auth Token sent to your email. 3. Add Widgets:    - Add a “Gauge” or “Value Display” widget for temperature.    - Add a “Gauge” or “Value Display” widget for humidity.    - Configure the widgets to display values from virtual pins (e.g., V5 for temperature and V6 for humidity). Step 3: Programming the Arduino 1. Install the Required Libraries:      - Open the Arduino IDE and install the following libraries:      - Blynk library      - DHT sensor library Sketch -> Include Library -> Manage Libraries...    - Search for "Blynk" and install it.    - Search for "DHT sensor library" and install it. 2. Write the Arduino Code:    - Use the following sample code given below.    - Replace `YourWiFiSSID`, `YourWiFiPassword`, and `YourAuthToken` with your actual WiFi credentials and Blynk Auth Token. 3. Upload the Code:    - Connect your Arduino to your computer via USB and upload the code. Step 4: Monitoring the Data 1. Open the Blynk App:    - Start the project by pressing the play button in the Blynk app. 2. View the Sensor Data:    - The temperature and humidity values should now appear on the widgets you configured.    - You can now monitor the DHT sensor values in real-time from your smartphone. Conclusion : By following these steps, you have successfully set up a system to monitor DHT sensor values on the Blynk app using an Arduino. This project is a great way to learn about IoT and how to connect sensors to a mobile app for remote monitoring. Also check our website for more projects and explore Skill-Hub by EmbeddedBrew to enhance your Skills. Happy experimenting!

In this tutorial, we'll walk you through the steps to control an LED using Blynk 2.0 with a NodeMCU, bypassing BlynkEdgent for a straightforward approach. Follow these steps to get your project up and running: Step 1: Gather Your Materials - NodeMCU (ESP8266) - LED - Resistor (220 ohms) - Breadboard - Jumper wires - USB cable for programming the NodeMCU - Computer with Arduino IDE installed Step 2: Set Up Blynk 2.0 Account 1. Create an Account: Sign up for a free account on [Blynk]( https://blynk.io/ ). 2. Create a New Template: Go to the Blynk console, create a new template, and configure the template settings such as name, hardware (ESP8266), and connection type (Wi-Fi). 3. Add Datastream: Define a datastream for the digital pin that will control the LED (e.g., Digital Pin D1). Step 3: Configure the Blynk Mobile App 1. Download the App: Install the Blynk app from the App Store or Google Play. 2. Log In: Open the app and log in with your Blynk account. 3. Create New Project: Create a new project and link it to the template you created in the Blynk console. 4. Add Widget: Add a button widget to control the LED. Link the button to the datastream you set up (e.g., Digital Pin D1). Step 4: Set Up the Hardware 1. Connect the LED:    - Place the LED on the breadboard.    - Connect the positive leg (anode) of the LED to a digital pin on the NodeMCU (e.g., D1).    - Connect the negative leg (cathode) to one end of the resistor.    - Connect the other end of the resistor to the GND pin on the NodeMCU. Step 5: Program the NodeMCU 1. Install Blynk Library: Open the Arduino IDE, go to Sketch > Include Library > Manage Libraries, and search for "Blynk." Install the Blynk library. 2. Install ESP8266 Board: In the Arduino IDE, go to File > Preferences, and add the following URL to the Additional Boards Manager URLs: ` http://arduino.esp8266.com/stable/package_esp8266com_index.json` . Then go to Tools > Board > Boards Manager, search for "ESP8266," and install it. 3. Code: Use the following code to set up the Blynk connection and control the LED. 4. Upload the Code: Connect your NodeMCU to your computer using the USB cable, select the appropriate board and port in the Arduino IDE, and upload the code. Step 6: Test Your Setup 1. Open the Blynk App: Ensure your NodeMCU is powered and connected to your Wi-Fi. 2. Control the LED: Use the button widget in the Blynk app to turn the LED on and off. Conclusion Congratulations! You have successfully set up your NodeMCU to control an LED using Blynk 2.0. Experiment with additional widgets and sensors to expand your project. Also check our website for more projects and explore Skill-Hub by EbeddedBrew to enhance your Skills in Embedded Systems and IoT.

Controlling an LED with NodeMCU through Arduino IoT Cloud is a fantastic beginner project that introduces you to the world of IoT (Internet of Things). Follow these simple steps to get started: Step 1: Set Up Arduino IoT Cloud 1. Create an Arduino Account:    - Go to [Arduino Create]( https://create.arduino.cc/ ) and sign up for a free account or log in if you already have one. 2. Access Arduino IoT Cloud:    - Navigate to the [Arduino IoT Cloud]( https://create.arduino.cc/iot ) from the Arduino Create dashboard. 3. Set Up a New Thing:    - Click on "Create Thing" to set up a new IoT device.    - Give your Thing a name, such as "LED_Controller." Step 2: Configure the Device 1. Add a Device:    - Click on "Add Device" and select "Set up a third-party device."    - Choose "ESP8266" and select NodeMCU 1.0 from the list of supported boards. 2. Generate Device ID and Secret Key:    - Follow the instructions to generate a Device ID and Secret Key. Note these down as they are needed later. 3. Install the Arduino IoT Cloud Library:    - Open the Arduino IDE and install the `ArduinoIoTCloud` and `Arduino_ConnectionHandler` libraries via the Library Manager. Step 3: Write the Code 1. Set Up the Sketch:    - Open a new sketch in the Arduino IDE.    - Include the necessary libraries at the beginning of your sketch:   #include  "thingProperties.h" 2. Define the LED Pin:    - Define the pin where the LED is connected (e.g., D2 on NodeMCU): const int ledPin = D2; 3. Setup Function:    - Initialize the LED pin and IoT Cloud connection: void setup() { // Initialize the serial and wait for the port to open: Serial.begin(9600); delay(1500); initProperties(); // Connect to Arduino IoT Cloud ArduinoCloud.begin(ArduinoIoTPreferredConnection); // Initialize LED pin pinMode(ledPin, OUTPUT); // Sync initial state ArduinoCloud.addCallback(ArduinoIoTPreferredCallback); } 4. Loop Function:    - Use the loop function to keep the connection active: void loop() { ArduinoCloud.update(); } 5. Callback Function:    - Create a function to handle the LED control: void onLedChange() { digitalWrite(ledPin, ledState); } Step 4: Connect and Upload 1. Connect NodeMCU:    - Connect your NodeMCU board to your computer via USB. 2. Upload the Sketch:    - Select the correct board and port from the Tools menu in the Arduino IDE.    - Click "Upload" to upload the code to your NodeMCU. 3. Configure Network Credentials:    - In the `thingProperties.h` file, enter your Wi-Fi SSID and password. const char SSID[] = "your_SSID"; const char PASS[] = "your_PASSWORD"; Step 5: Create a Dashboard 1. Add a Dashboard:    - In Arduino IoT Cloud, go to the Dashboards section and create a new dashboard. 2. Add a Widget:    - Add a switch widget to control the LED.    - Link the widget to the `ledState` variable. 3. Control Your LED:    - Use the dashboard switch to turn the LED on and off from anywhere in the world! Conclusion By following these steps, you'll successfully control an LED using NodeMCU and Arduino IoT Cloud. This project provides a solid foundation for more complex IoT applications. Also checkout our website for more projects and explore the Skill-Hub by EmbeddedBrew for Enhancing your Skills in IoT. Happy tinkering!