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Elektor GREEN en GOLD leden kunnen deze uitgave hier downloaden. Nog geen lid? Klik hier om een lidmaatschap af te sluiten. Kleine warmtebeeldcameraeen op Arduino UNO gebaseerde DHZ-oplossing Project-update #3: ESP32-gebaseerde energiemeterintegratie en test met Home Assistant 2024: een AI-odysseeobjectdetectie verbeteren met verfijnde technieken De Raspberry Pi gaat AInieuwe kit bevat M.2 HAT+ met AI-versneller Sensoren voor weerstationswat is de beste keuze? Lees uw analoge watermeter uit met AI (1)koppel uw oude watermeter met het IoT! GSM-alarmdraadloze garagebeveiliging Low-power Thread-devices geoptimaliseerd en onder de loep genomenweinig vermogen... weinig inspanning? Uit het leven gegrepende genderkloof Zelfbouw-nevelkamermaak onzichtbare straling zichtbaar SparkFun Thing Plus Mattereen veelzijdig Matter-gebaseerd IoT development board IoT-retrofittenRS232-apparaten klaarstomen voor Industry 4.0 Stimuleer het IoT met 8-bit MCU’s Technologie stimuleert duurzaamheidvooruitgang leidt in veel toepassingen tot efficiënter energiegebruik AWS voor Arduino en co. (1)AWS IoT ExpressLink in de praktijk Luchtstroomdetector met alleen een Arduinozonder externe sensoren! Lekdetectorverbonden met de Arduino-cloud Kristallenvreemde onderdelen Universele logger voor de tuinAI-tuinieren komt een stapje dichterbij Analoge 1kHz-generatorsinussen met geringe vervorming Miletus: webapps offline gebruikeninclusief toegang tot het systeem en het apparaat! Van 4G naar 5Gis het echt zo eenvoudig? Alle begin......gaat symmetrisch

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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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This complete STM32 microcontroller programming course features a textbook, a component kit, hands-on projects, and a comprehensive online course. It is ideal for step-by-step learning of embedded systems programming in MicroPython using a practical, hands-on approach. A Practical Introduction to Embedded Systems with the STM32 This course is designed for people who are new to embedded systems and looking for a structured, example-driven way to get started. A kit of parts comprising LEDs and resistors, switches, sensors and actuators, displays, a breadboard and wires, and more is included. These are used in the course to illustrate example applications. No prior experience with MicroPython or embedded development is required. Each section features hands-on examples and mini projects designed to reinforce key concepts and inspire deeper exploration. By the end of the course, you’ll be able not only to reproduce the examples but also to build on them with your own ideas and applications. What Will You Learn? Microcontroller programming in MicroPython on the STM32 using the Thonny IDE Working with Digital I/O, read buttons and encoders, control LEDs and relays Read analog inputs, voltages, and analog sensors Generating analog output signals and PWM Use serial communication like UART, I²C and SPI to control displays and read digital sensors and SD cards Managing time Working with interrupts Real-time sensor input and control via buttons, LEDs, and displays Control actuators like relays and servo motors Who Is It For? Students and self-learners exploring embedded systems Makers and IoT enthusiasts looking to improve their hardware skills Educators and trainers seeking ready-to-teach material What's Inside the Box? Access to the full course on the Elektor Academy Pro Learning Platform STM32 NUCLEO-F411RE microcontroller board Book: MicroPython for STM32 Downloadable project files for every module Component Box: 2× LED, red, 5 mm LED, green, 5 mm 3× Resistor, 470 Ω, 0.25 W LDR Potentiometer, 10 kΩ, linear Pushbutton Rotary encoder module Relay module DHT22 Humidity & Temperature Sensor TM1637-compatible 4-digit 7-segment display MPU-6050 IMU with headers SSD1306-compatible I²C OLED display Micro SD card adapter with header Buzzer SG90 Micro Servo ILI9341-compatible SPI 240×320 TFT display 20× Jumper wires Breadboard

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Program, build, and master over 50 projects with MicroPython and the RP2040 microprocessor The Raspberry Pi Pico is a high-performance microcontroller module designed especially for physical computing. Microcontrollers differ from single-board computers, like the Raspberry Pi 4, in not having an operating system. The Raspberry Pi Pico can be programmed to run a single task very efficiently within real-time control and monitoring applications requiring speed. The ‘Pico’ as we call it, is based on the fast, efficient, and low-cost dual-core ARM Cortex-M0+ RP2040 microcontroller chip running at up to 133 MHz and sporting 264 KB of SRAM, and 2 MB of Flash memory. Besides its large memory, the Pico has even more attractive features including a vast number of GPIO pins, and popular interface modules like ADC, SPI, I²C, UART, and PWM. To cap it all, the chip offers fast and accurate timing modules, a hardware debug interface, and an internal temperature sensor. The Raspberry Pi Pico is easily programmed using popular high-level languages such as MicroPython and or C/C++. This book is an introduction to using the Raspberry Pi Pico microcontroller in conjunction with the MicroPython programming language. The Thonny development environment (IDE) is used in all the projects described. There are over 50 working and tested projects in the book, covering the following topics: Installing the MicroPython on Raspberry Pi Pico using a Raspberry Pi or a PC Timer interrupts and external interrupts Analogue-to-digital converter (ADC) projects Using the internal temperature sensor and external temperature sensor chips Datalogging projects PWM, UART, I²C, and SPI projects Using Wi-Fi and apps to communicate with smartphones Using Bluetooth and apps to communicate with smartphones Digital-to-analogue converter (DAC) projects All projects given in the book have been fully tested and are working. Only basic programming and electronics experience is required to follow the projects. Brief descriptions, block diagrams, detailed circuit diagrams, and full MicroPython program listings are given for all projects described. Readers can find the program listings on the Elektor web page created to support the book.

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VOYAGE DANS LES RÉSEAUX NEURONAUX (1ÈRE PARTIE)Les neurones artificiels TEST DE PRÉCONFORMITÉ CEM POUR UN PROJET ALIMENTÉ EN COURANT CONTINUPartie 1 : RSIL double continu CHARGE ÉLECTRONIQUE EN CC ET CAJusqu?à 400 V et 10 A (crête) DÉMARRER EN ÉLECTRONIQUE (9)N'ayez pas peur du choc... un peu de « self control » suffit ! TRAITEMENT D'IMAGES AVEC LE MODULE JETSON NANO DE NVIDIA (1ÈRE PARTIE)Matériel et logiciel ZOOM SUR LES TRANSFORMATEURS SECTEURComportement transitoire à l?allumage et l?extinction « YES WE CAN » AVEC PICAN 3Module de bus CAN pour le Raspberry Pi 4 CENTRALE SOLAIRE SUR BALCONUn investissement vite amorti PRISE DE VUE ET DIFFUSION VIDÉO EN CONTINU AVEC UN RASPBERRY PI 4La caméra Raspberry Pi HQ en action UTILISATION D?ÉCRANS DANS LES PROJETS RASPBERRY PIExtrait : écrans à diodes électroluminescentes organiques (OLED) PROPELLER 2 DE PARALLAX (4)Envoi de chaînes de caractères 60 ANS D?ELEKTORRegard sur septembre DANS L?ANTRE DE?Menno van der Veen et de ses tubes amplificateurs CIRCUITS HYBRIDES ROSE DES VENTS AVEC LE CAPTEUR GY-271Ou pourquoi il faut faire des huit pour calibrer un capteur MESUREZ VOTRE EMPREINTEÉlectronique et émissions de gaz à effet de serre THERMOSTAT CONNECTÉ À ESP32Conservez votre vin à la bonne température ! LÉVITATION MAGNÉTIQUE, VERSION NUMÉRIQUEComparateur analogique remplacé par un ESP32 Pico ULTIMATE ARDUINO UNO HARDWARE MANUAL - FR TRANSLATIONExtrait : chargeur d?amorçage du microcontrôleur principal MICROPYTHON POUR L'ESP32 ET SES COPAINSPartie 2 : piloter facilement les écrans matriciels CARTE MADMACHINE SWIFTIOUn langage récent pour du matériel tout nouveau SUR LE VIFPassions incertaines HEXADOKUCasse-tête pour elektorniciens

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The Elektor Arduino Nano MCCAB Training Board contains all the components (incl. Arduino Nano) required for the exercises in the "Microcontrollers Hands-on Course for Arduino Starters", such as light-emitting diodes, switches, pushbuttons, acoustic signal transmitters, etc. External sensors, motors or assemblies can also be queried or controlled with this microcontroller training system. Specifications (Arduino Nano MCCAB Training Board) Power Supply Via the USB connection of the connected PC or an external power supply unit (not included) Operating Voltage +5 Vcc Input Voltage All inputs 0 V to +5 V VX1 and VX2 +8 V to +12 V (only when using an external power supply) Hardware periphery LCD 2x16 characters Potentiometer P1 & P2 JP3: selection of operating voltage of P1 & P2 Distributor SV4: Distributor for the operating voltagesSV5, SV6: Distributor for the inputs/outputs of the microcontroller Switches and buttons RESET button on the Arduino Nano module 6x pushbutton switches K1 ... K6 6x slide switches S1 ... S6 JP2: Connection of the switches with the inputs of the microcontroller Buzzer Piezo buzzer Buzzer1 with jumper on JP6 Indicator lights 11 x LED: Status indicator for the inputs/outputs LED L on the Arduino Nano module, connected to GPIO D13 JP6: Connection of LEDs LD10 ... LD20 with GPIOs D2 ... D12 Serial interfacesSPI & I²C JP4: Selection of the signal at pin X of the SPI connector SV12 SV9 to SV12: SPI interface (3.3 V/5 V) or I²C interface Switching output for external devices SV1, SV7: Switching output (maximum +24 V/160 mA, externally supplied) SV2: 2x13 pins for connection of external modules 3x3 LED matrix(9 red LEDs) SV3: Columns of the 3x3 LED matrix (outputs D6 ... D8) JP1: Connection of the rows with the GPIOs D3 ... D5 Software Library MCCABLib Control of hardware components (switches, buttons, LEDs, 3x3 LED matrix, buzzer) on the MCCAB Training Board Operating Temperature Up to +40 °C Dimensions 100 x 100 x 20 mm Specifications (Arduino Nano) Microcontroller ATmega328P Architecture AVR Operating Voltage 5 V Flash Memory 32 KB, of which 2 KB used by bootloader SRAM 2 KB Clock Speed 16 MHz Analog IN Pins 8 EEPROM 1 KB DC Current per I/O Pins 40 mA on one I/O pin, total maximum 200 mA on all pins together Input Voltage 7-12 V Digital I/O Pins 22 (6 of which are PWM) PWM Output 6 Power Consumption 19 mA Dimensions 18 x 45 mm Weight 7 g Included 1x Elektor Arduino Nano Training Board MCCAB 1x Arduino Nano

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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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Le téléchargement intégral de ce numéro est disponible pour nos membres GOLD et GREEN sur le site Elektor Magazine ! Pas encore membre ? Cliquez ici. l'ESP-BLASTUn mini drone-fusée ESP32 qui vole à plus de 100 km/h atelier sans fil (1)Modules radio déverrouillés : émetteur-récepteur sub-GHz CC1101 433 MHz émetteur AMAvec le générateur d'ondes carrées/sinusoïdales sur Raspberry Pi Pico STM32Cube Innovation ChallengeParticipez avec votre meilleur projet STM32C5 CoffeeCallerComment une pause café a inspiré un PCB sur mesure audiotroniquesDistorsion harmonique totale Elektor à 65 ansProjets de septembre et octobre au fil des ans Bluetooth Channel Sounding pour le suivi des équipementsMesure de distance avec une précision centimétrique pour l’IoT de nouvelle génération 2026 : une Odyssée de l'IAL'informatique quantique n'est pas le prochain GPU protéger le cloud exige de sécuriser la périphérie premières expériences avec le HackRF ProTests pratiques avec le nouveau matériel SDR mise à niveau du modem de GPRS/2G vers LTE/4GExemple d'un panneau de contrôle d'alarme sans fil antenne en bouclePartie 1 : Une antenne ondes courtes à directivité réglable, à réaliser soi-même convertisseur de radio Internet en AMUn ESP32-S3 comme émetteur projet 2.0 mioty : aperçu généralL'approche de Fraunhofer pour les LPWAN sagesse d'atelierConseils pratiques d'experts Walter, le module IoT compactPartie 2 : Accéder aux services météorologiques et aux services de données

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IMAGE PROCESSING WITH THE NVIDIA JETSON NANO (PART 2)Image Recognition Using Edge Impulse ELEKTOR JUMPSTARTER NEWSUpcoming Campaigns AN OPEN-SOURCE GPS TRACKING PLATFORMTraccar Maps Vehicle Tracking Without the Need for a Third-Party Cloud Server JOY-IT LCR-T7 MULTI-FUNCTION TESTERTesting Passives, Discrete Semiconductors and IR Remote Controls NOISE SYNTHESIZERFrom Noise to Music with the PRBSynth1 STARTING OUT IN ELECTRONICSEasier than Imagined! ... Continuing with the Coil UNDERSTANDING THE NEURONS IN NEURAL NETWORKS (PART 2)Logical Neurons ISSUES WITH SECURITY? FIGHT FIRE WITH FIRE!Flashbulb-Protected Analogue Memory Add-on For the Tamper-Evident Box LCR METER POSTER BLUETOOTH BEACONS IN PRACTICEBeacons Light the Way Ahead C PROGRAMMING ON RASPBERRY PICommunicating over Wi-Fi (Sample Chapter) EMC PRE-COMPLIANCE TEST FOR YOUR DC-POWERED PROJECT (PART 2)The Hardware and How to Use It HANDS ON THE PARALLAX PROPELLER 2 (PART 5)Inside the Smart Pin MODBUS OVER WLAN (PART 1)Hardware and Programming HOMELAB TOURSWhere the Junior Computer Is Brought to Life Again BUILD YOUR OWN HIGH-PRECISION CALIBRATOR-10 V to +10 V, 0 to 40 mA, 0.001% ARDUINO NANO RP2040 CONNECTRaspberry Pi RP2040 + Wi-Fi + Bluetooth THE PHYSICAL BODY OF ARTIFICIAL INTELLIGENCE ERR-LECTRONICSCorrections, Updates and Readers’ Letters CREATE GUIS WITH PYTHONIntroducing guizero CO2 METER KIT FOR THE CLASSROOMAn ESP8266-Based Device from the University of Applied Sciences Aachen NOSTALGIC MK484 MW/LW RADIO...Always Fun to Build! ELEKTOR @ 60Let There Be Light! HEXADOKUThe Original Elektorized Sudoku

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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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Le téléchargement intégral de ce numéro est disponible pour nos membres GOLD et GREEN sur le site Elektor Magazine ! Pas encore membre ? Cliquez ici. L'architecture de processeur open-source RISC-V16 cartes et MCU à connaître un lecteur audio avec égaliseur basé sur un FPGAMixage audio numérique avec un Arduino MKR Vidor 4000 tête laser pour l’horloge de sable basé sur Raspberry Pi PicoDessiner avec la lumière participez au concours STM32 Edge AI système de contrôle environnemental multi-capteurs pour les plantesMesure sans fil de l'approvisionnement en eau et de la luminosité porte automatique contrôlée par l'IA et MaixduinoReconnaissance faciale avec une caméra l’électronique embarquée en 2024L’IA va redéfinir l’industrie Calcul en mémoire basé sur la charge chez EnCharge AI des opérations d'IA avec 10 fois moins d'énergie et des coûts divisés par 20 Une carte pour le développement et l’entraînement des modèles ML d’analyse des vibrations Elektor Mini-WheelieKit robot gyropode (robot autostabilisé) MCUViewerMCUViewer outil de débogage open source multiplateforme isolateur USB 2.0Isolation éléctrique pour les périphériques USB anticipation et actionApplication pratique de la maintenance prédictive SPoE – compatibilité électromagnétiquePaire unique avec Power-over-Ethernet à travers les yeux d'EMC rétro-techCréer un monde nouveau avec la télévision couleur surveillance ECGavec des modules Hexabitz et STM32CubeMonitor la bataille pour l’IA en périphérie HaLow atteint une distance Wifi record de 16 km à 900 MHz première puce embarquée CHERI RISC-V et programme d'accès anticipé la détection des incendies de forêt de troisième génération utilise des liaisons satellites sur le vifDélices et supplices du choix démarrer en électronique......Filtrage et contrôle de la tonalité Kit d'horloge quasi-analogiqueNouvelle version d'un classique d'Elektor une approche modulaire de test des capteursCarte de test de capteurs basée sur l'ESP32-S3 2025 : une odyssée de l'IAL'essor des modèles de fondation et leur impact sur l'accessibilité de l'IA Synthétiseur MIDI autonome Raspberry Pi (1)Préparation d'une plateforme pour des expériences d’IA en périphérie projet 2.0Corrections, mises à jour et courrier des lecteurs Le RISC-V AI, un processeur à tout faire : CPU, GPU, DSP, FPGA paroles de PDG : fraîcheur, silence et finesse programmation Dual-Core avec le Raspberry Pi PicoLe monde de la programmation parallèle

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World’s Most Popular ROS Platform TurtleBot is the most popular open source robot for education and research. The new generation TurtleBot3 is a small, low cost, fully programmable, ROS based mobile robot. It is intended to be used for education, research, hobby and product prototyping. Affordable Cost TurtleBot was developed to meet the cost-conscious needs of schools, laboratories and companies. TurtleBot3 is the most affordable robot among the SLAM-able mobile robots equipped with a 360° Laser Distance Sensor LDS-01. ROS Standard The TurtleBot brand is managed by Open Robotics, which develops and maintains ROS. Nowadays, ROS has become the go-to platform for all the roboticists around the world. TurtleBot can be integrated with existing ROS-based robot components, but TurtleBot3 can be an affordable platform for whom want to get started learning ROS. Extensibility TurtleBot3 encourages users to customize its mechanical structure with some alternative options: open source embedded board (as a control board), computer and sensors. TurtleBot3 Waffle Pi is a two-wheeled differential drive type platform but it is able to be structurally and mechanically customized in many ways: Cars, Bikes, Trailers and so on. Extend your ideas beyond imagination with various SBC, sensors and motors on a scalable structure. Modular Actuator for Mobile Robot TurtleBot3 is able to get a precise spatial data by using 2 DYNAMIXEL’s in the wheel joints. DYNAMIXEL XM series can be operated by one of 6 operating modes (XL series: 4 operating modes): Velocity control mode for wheels, Torque control mode or Position control mode for joint, etc. DYNAMIXEL can be used even to make a mobile manipulator which is light but can be precisely controlled with velocity, torque and position control. DYNAMIXEL is a core component that makes TurtleBot3 perfect. It is easy to assemble, maintain, replace and reconfigure. Open Control Board for ROS The control board is open-sourced in hardware wise and in software wise for ROS communication. The open source control board OpenCR1.0 is powerful enough to control not only DYNAMIXEL’s but also ROBOTIS sensors that are frequently being used for basic recognition tasks in cost effective way. Various sensors such as Touch sensor, Infrared sensor, Color sensor and a handful more are available. The OpenCR1.0 has an IMU sensor inside the board so that it can enhance precise control for countless applications. The board has 3.3 V, 5 V, 12 V power supplies to reinforce the available computer device lineups. Open Source The hardware, firmware and software of TurtleBot3 are open source which means that users are welcomed to download, modify and share source codes. All components of TurtleBot3 are manufactured with injection molded plastic to achieve low cost, however, the 3D CAD data is also available for 3D printing. Specifications Maximum translational velocity 0.26 m/s Maximum rotational velocity 1.82 rad/s (104.27 deg/s) Maximum payload 30 kg Size (L x W x H) 281 x 306 x 141 mm Weight (+ SBC + Battery + Sensors) 1.8 kg Threshold of climbing 10 mm or lower Expected operating time 2h Expected charging time 2h 30m SBC (Single Board Computers) Raspberry Pi 4 (2 GB RAM) MCU 32-bit ARM Cortex-M7 with FPU (216 MHz, 462 DMIPS) Remote Controller RC-100B + BT-410 Set (Bluetooth 4, BLE) Actuator XL430-W210 LDS (Laser Distance Sensor) 360 Laser Distance Sensor LDS-01 or LDS-02 Camera Raspberry Pi Camera Module v2.1 IMU Gyroscope 3 AxisAccelerometer 3 Axis Power connectors 3.3 V/800 mA5 V/4 A12 V/1 A Expansion pins GPIO 18 pinsArduino 32 pin Peripheral 3x UART, 1x CAN, 1x SPI, 1x I²C, 5x ADC, 4x 5-pin OLLO DYNAMIXEL ports 3x RS485, 3x TTL Audio Several programmable beep sequences Programmable LEDs 4x User LED Status LEDs 1x Board status LED1x Arduino LED1x Power LED Buttons and Switches 2x Push buttons, 1x Reset button, 2x Dip switch Battery Lithium polymer 11.1 V 1800 mAh / 19.98 Wh 5C PC connection USB Firmware upgrade via USB / via JTAG Power adapter (SMPS) Input: 100-240 VAC 50/60 Hz, 1.5 A @maxOutput: 12 VDC, 5 A Downloads ROS Robot Programming GitHub E-Manual Community

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