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39 Experiments with Raspberry Pi and Arduino This book is about Raspberry Pi 3 and Arduino camera projects. The book explains in simple terms and with tested and working example projects, how to configure and use a Raspberry Pi camera and USB based webcam in camera-based projects using a Raspberry Pi. Example projects are given to capture images, create timelapse photography, record video, use the camera and Raspberry Pi in security and surveillance applications, post images to Twitter, record wildlife, stream live video to YouTube, use a night camera, send pictures to smartphones, face and eye detection, colour and shape recognition, number plate recognition, barcode recognition and many more. Installation and use of popular image processing libraries and software including OpenCV, SimpleCV, and OpenALPR are explained in detail using a Raspberry Pi. The book also explains in detail how to use a camera on an Arduino development board to capture images and then save them on a microSD card. All projects given in this book have been fully tested and are working. Program listings for all Raspberry Pi and Arduino projects used in this book are available for download on the Elektor website.

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Raspberry Pi 5 provides two four-lane MIPI connectors, each of which can support either a camera or a display. These connectors use the same 22-way, 0.5 mm-pitch “mini” FPC format as the Compute Module Development Kit, and require adapter cables to connect to the 15-way, 1 mm-pitch “standard” format connectors on current Raspbery Pi camera and display products.These mini-to-standard adapter cables for cameras and displays (note that a camera cable should not be used with a display, and vice versa) are available in 200 mm, 300 mm and 500 mm lengths.

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Raspberry Pi 5 provides two four-lane MIPI connectors, each of which can support either a camera or a display. These connectors use the same 22-way, 0.5 mm-pitch “mini” FPC format as the Compute Module Development Kit, and require adapter cables to connect to the 15-way, 1 mm-pitch “standard” format connectors on current Raspbery Pi camera and display products.These mini-to-standard adapter cables for cameras and displays (note that a camera cable should not be used with a display, and vice versa) are available in 200 mm, 300 mm and 500 mm lengths.

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The Arduino Pro Portenta Vision Shield brings industry-rated features to your Portenta. This hardware add-on will let you run embedded computer vision applications, connect wirelessly or via Ethernet to the Arduino Cloud or your own infrastructure, and activate your system upon the detection of sound events. Features 324x324 pixels camera sensor: use one of the cores in Portenta to run image recognition algorithms using the OpenMV for Arduino editor 100 Mbps Ethernet connector: get your Portenta H7 connected to the wired Internet 2 onboard microphones for directional sound detection: capture and analyse sound in real-time JTAG connector: perform low-level debugging of your Portenta board or special firmware updates using an external programmer SD-Card connector: store your captured data in the card, or read configuration files The Vision Shield has been designed to fit on top of the Arduino Portenta family. The Portenta boards feature multicore 32-bit ARM Cortex processors running at hundreds of megahertz, with megabytes of program memory and RAM. Portenta boards come with WiFi and Bluetooth. Embedded Computer Vision Made Easy Arduino has teamed up with OpenMV to offer you a free license to the OpenMV IDE, an easy way into computer vision using MicroPython as a programming paradigm. Download the OpenMV for Arduino Editor from our professional tutorials site and browse through the examples we have prepared for you inside the OpenMV IDE. Companies across the whole world are already building their commercial products based on this simple-yet-powerful approach to detect, filter, and classify images, QR codes, and others. Debugging With Professional Tools Connect your Portenta H7 to a professional debugger through the JTAG connector. Use professional software tools like the ones from Lauterbach or Segger on top of your board to debug your code step by step. The Vision Shield exposes the required pins for you to plug in your external JTAG. Camera Himax HM-01B0 camera module Resolution 320 x 320 active pixel resolution with support for QVGA Image sensor High sensitivity 3.6μ BrightSense pixel technology Microphone 2 x MP34DT05 Length 66 mm Width 25 mm Weight 11 gr For more information, check out the tutorials provided by Arduino here.

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The uArm Swift Pro is a high quality robotic arm that can be used in a wide range of applications. The uArm Swift Pro was developed and optimized for use in education, which means that many packages are already available for open source platforms such as ROS. The uArm Swift Pro has a position repeatability of 0.2 mm and is also equipped with a stepper motor and a 12-bit encoder. These are just a few reasons that make the uArm Swift Pro an excellent choice for educational use. Another great feature is the 3D printing kit that converts the uArm Swift Pro into a 3D printer in less than 1 minute. The uArm supports the following development platforms/systems: UFACTORY SDK Arduino Python ROS GRABCAD OpenMV Smartphone App The smartphone app for iOS is already available in the App Store and enables easy control and monitoring of the robotic arm. The app for Android is in development and will be available soon. An example of the Machine Vision The following GIF shows the uArm in combination with the OpenMV Machine Vision Cam M7 and the facial recognition applications that can be implemented in MicroPython. Specifications Degrees of Freedom: 4 Repeatability: Up to 0.2 mm Payload: 500 g Working Range: 50-320 mm Positioning Speed: 100 m/s Position Feedback: 12-bit Encoder Dimensions: 150 x 140 x 281mm Weight: 2.2 kg Included UFactory uArm Swift Pro Body Bluetooth & Vacuum Gripper Downloads Datasheet

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Elektor GREEN and GOLD members can download their digital edition here. Not a member yet? Click here. The AlertAlfred AI Security SystemPowered by a Raspberry Pi 5 and the Hailo 8L Module AI in Electronics DevelopmentAn Update After Only One Year Intro to AI AlgorithmsPrompt: Which Algorithms Implement Each AI Tool? Single-Board Computers for Artificial Intelligence ProjectsBackground and Overview From Sensor Data to Machine Learning ModelsGesture Detection with an Accelerometer and Edge Impulse Build a Leaky Integrate-and-Fire Spiking NeuronArtificial Intelligence Without Software ChatGPT for Electronic DesignDoes GPT-4o Do It Any Better? Bringing AI to the Edge with ESP32-P4 Exploring Speech Functions on Raspberry Pi ZeroWhen Overclocking Gives Freedom of Speech The Growing Role of Edge AIA Trend Shaping the Future Unlocking the Power of Edge AIA Conversation with François de Rochebouët of STMicroelectronics A VHDL Clock Made with ChatGPT AI’s Real ImpactSayash Kapoor on “AI Snake Oil” and More The Latest Stuff From BeagleBoardBeagleY-AI, BeagleV-Fire, BeagleMod, BeaglePlay and BeagleConnect Freedom Mosquito Detection Using Open Datasets and Arduino Nicla Vision AI Today and Tomorrow: Insights from Espressif, Arduino, and SparkFun Artificial Intelligence Timeline BeagleY-AIThe Latest SBC for AI Applications AI in FocusPerspectives from the Elektor Community Machine Vision with OpenMVCreate a Soda Can Detector A Conversation with the Digital MindChatGPT vs Gemini Skilling Me Softly with This Bot?Is the AI Revolution in the Electronic Field Failing Due to a Lack of Social Precision?

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Elektor GREEN and GOLD members can download their digital edition here. Not a member yet? Click here. Arduino Portenta Machine Control and Arduino Portenta H7A CAN-to-MQTT Gateway Demo Project Unboxing the Elektor LCR Meter with David Cuartielles MicroPython Enters the World of Arduino Connected Projects, SimplifiedDive Into the Arduino Cloud Introduction to TinyMLBig Is Not Always Better Arduino K-Way Writing Arduino Sketches Just Got Better Get to Know Arduino Getting Started with the Portenta X8Manage Software Securely with Containers Build, Deploy, and Maintain Scalable, Secure ApplicationsWith Arduino Portenta X8 Featuring NXP’s i.MX 8M Mini Applications Processor and EdgeLock SE050 Secure Element How I Automated My HomeArduino CEO Fabio Violante Shares Solutions Altair 8800 SimulatorHardware Simulation of a Vintage Computer MS-DOS on the Portenta H7Run Old-School Software on Contemporary Hardware Grow It YourselfA Digitally Controlled, Single-Box Solution for Indoor Farming Save the Planet With Home Automation?MQTT on the Arduino Nano RP2040 Connect Go Professional with Arduino Pro Smart Ovens Take a Leap Into the Future Tagvance Builds Safer Construction Sites with Arduino Santagostino Breathes Easywith Remote Monitoring that Leverages AI for Predictive Maintenance Security Flies High with RIoT Secure’s MKR-Based Solution Open-Source Brings a New Generation of Water Management to the World SensoDetect Deforestation with Sound Analysis The Mozzi Arduino Library for Sound SynthesisInsights from Tim Barrass The New Portenta X8 (with Linux!) and Max Carrier Redefine What’s Possible How Using Arduino Helps Students Build Future Skills Must-Haves for Your Electronics Workspace The Importance of Robotics in Education Dependable IoT Based Upon LoRa Unboxing the Portenta Machine Control 8-Bit Gaming with Arduboy Reducing Water Usage at Horseback Riding TracksAn IoT to Constantly Monitor Soil Humidity and Temperature Levels The Panettone ProjectA sourdough starter management and maintenance system Supporting Arduino Resellers Space Invaders with Arduino Art with ArduinoInspiring Insights from Artists and Designers Arduino Product Catalogue The Future of Arduino

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Raspberry Pi 5 provides two four-lane MIPI connectors, each of which can support either a camera or a display. These connectors use the same 22-way, 0.5 mm-pitch “mini” FPC format as the Compute Module Development Kit, and require adapter cables to connect to the 15-way, 1 mm-pitch “standard” format connectors on current Raspbery Pi camera and display products.These mini-to-standard adapter cables for cameras and displays (note that a camera cable should not be used with a display, and vice versa) are available in 200 mm, 300 mm and 500 mm lengths.

Read (+) (–)

Raspberry Pi 5 provides two four-lane MIPI connectors, each of which can support either a camera or a display. These connectors use the same 22-way, 0.5 mm-pitch “mini” FPC format as the Compute Module Development Kit, and require adapter cables to connect to the 15-way, 1 mm-pitch “standard” format connectors on current Raspbery Pi camera and display products.These mini-to-standard adapter cables for cameras and displays (note that a camera cable should not be used with a display, and vice versa) are available in 200 mm, 300 mm and 500 mm lengths.

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This hands-on bundle lets you build real Edge AI applications with the Raspberry Pi Raspberry Pi AI HAT+ (13 TOPS) The Raspberry Pi AI HAT+ (13 TOPS) is an expansion board designed for the Raspberry Pi 5, featuring an integrated Hailo AI accelerator. This add-on offers a cost-effective, efficient, and accessible approach to incorporating high-performance AI capabilities, with applications spanning process control, security, home automation, and robotics. The AI HAT+ connects via the Raspberry Pi 5’s PCIe Gen3 interface. When the Raspberry Pi 5 is running a current version of the Raspberry Pi OS, it automatically detects the onboard Hailo accelerator, making the neural processing unit (NPU) available for AI tasks. Additionally, the rpicam-apps camera applications included in Raspberry Pi OS seamlessly support the AI module, automatically using the NPU for compatible post-processing functions. Raspberry Pi Camera Module 3 Raspberry Pi Camera Module 3 is a compact camera from Raspberry Pi. It offers an IMX708 12-megapixel sensor with HDR, and features phase detection autofocus. Camera Module 3 is available in standard and wide-angle variants, both of which are available with or without an infrared cut filter. Camera Module 3 can be used to take full HD video as well as stills photographs, and features an HDR mode up to 3 megapixels. Its operation is fully supported by the libcamera library, including Camera Module 3’s rapid autofocus feature: this makes it easy for beginners to use, while offering plenty for advanced users. Camera Module 3 is compatible with all Raspberry Pi computers. Book: Edge AI Made Practical – AI Projects for the Raspberry Pi with the AI HAT+ Edge AI is transforming everyday devices by putting intelligence where it matters most: directly inside the hardware. With on-device inference, a camera can recognize a visitor instantly, a phone can translate speech without streaming audio to the cloud, and a wearable can detect anomalies in real time—fast, private, and reliable even when the network disappears. This book is your practical guide to building exactly those kinds of systems with the Raspberry Pi AI HAT+ and the Hailo-8L accelerator. You’ll start with clear foundations: core AI and machine-learning concepts, how neural networks work, and what truly distinguishes Edge AI from cloud AI—plus an honest look at ethical considerations and future impacts. This bundle contains: Book: Edge AI Made Practical Raspberry Pi AI HAT+ (13 TOPS) Raspberry Pi Camera Module 3 Active Cooler for Raspberry Pi 5 FPC Display Cable for Raspberry Pi 5 (200 mm) Elektor Component Kit 40-pin GPIO Header Traffic Light Module LED red 5 V with built-in resistor LED yellow 5 V with built-in resistor LED green 5 V with built-in resistor LED blue 5 V with built-in resistor Breadboard (400 Tie-points) 10 Dupont wires male-female DHT22 sensor module Servo motor Required Raspberry Pi 5

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AI Projects for the Raspberry Pi with the AI HAT+ Edge AI is transforming everyday devices by putting intelligence where it matters most: directly inside the hardware. With on-device inference, a camera can recognize a visitor instantly, a phone can translate speech without streaming audio to the cloud, and a wearable can detect anomalies in real time—fast, private, and reliable even when the network disappears. This book is your practical guide to building exactly those kinds of systems with the Raspberry Pi AI HAT+ and the Hailo-8L accelerator. You’ll start with clear foundations: core AI and machine-learning concepts, how neural networks work, and what truly distinguishes Edge AI from cloud AI—plus an honest look at ethical considerations and future impacts. Then it’s straight to hands-on physical computing. Step by step, you’ll set up Raspberry Pi OS, power and cooling, and develop in Python using the Thonny IDE. You’ll learn GPIO basics with lights and servos, mount the AI HAT+ hardware, install and verify the Hailo software stack, and connect the right camera—official modules or USB webcams, even multiple cameras. From your first pipeline to real projects, you’ll run person detection, pose estimation, segmentation, and depth estimation, then level up with YOLO object detection: smart alerts, guest counters, and custom extensions. You’ll even connect vision to motion by combining gesture recognition with servo-driven mechanisms, including a robotic arm. With troubleshooting tips, hardware essentials, and a practical Python refresher, this book turns Edge AI from buzzword into buildable reality.

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