Search results for "arduino OR pro OR portenta OR vision OR shield OR lora"

High-Performance LoRa Communication Device The Elecrow ThinkNode M5 is a compact, portable LoRa communication device designed for off-grid messaging, location sharing, and outdoor use. Powered by an ESP32-S3 processor and an SX1262 LoRa chip, it comes pre-installed with Meshtastic firmware and can be configured conveniently via Bluetooth using the official Meshtastic app. The integrated multi-GNSS module supports GPS, GLONASS, BeiDou, and QZSS for reliable positioning and location sharing. Messages and device information are displayed on the energy-efficient 1.54-inch e-paper screen, while the built-in 1,200 mAh rechargeable battery enables extended mobile operation. With its integrated LoRa antenna, compact housing, RTC, and support for both Meshtastic and MeshCore firmware, the ThinkNode M5 is a practical communication solution for hiking, camping, remote locations, and emergency situations where conventional mobile networks are unavailable. Features LoRa communication Multi-GNSS positioning 1,200 mAh rechargeable battery 1.54-inch e-paper display Built-in RTC Compatible with MeshCore Compatible with Meshtastic Specifications Main Processor (ESP32-S3) CPU/SoC Xtensa 32-bit LX7 dual-core processor, clocked at up to 240 MHz System Memory 512 KB SRAM, 8 MB PSRAM Storage 4 MB Flash, 384 KB ROM Firmware Meshtastic firmware is fully compatible, and the signal is transmitted in LoRa mode EPD (Electronic Paper Display) Display Size 1.54 inch EPD (monochrome Ink screen) Display Materials E-lnk (Electronic Ink） Resolution 200 x 200 Driver Chip SSD1681 (via SPI or I²C interface) Global Fresh Time 2s Wireless Communication Bluetooth Bluetooth Low Energy and Bluetooth 5.0 (phone configuration) LoRa SX1262 LoRa Module, EU 868 MHz（external antenna） Hardware Interfaces USB-C, RP-SMA Function GPS Location (GPS, GLONASS, BeiDou, QZSS), EPD Display, RTC, USB 2.0, PMU power management (built-in 1200 mAh lithium battery), buzzer, etc. Button Knob Switch, Function Button, Page Turn Button, GPS Switch, Reset Button LED Indicator Power supply, GPS/LoRa indication Others Power Input 5 V/1 A, supports USB or lithium battery power supply Power consumption The maximum working current is about 340 mA (CPU + LoRa module), and the low power consumption is about 34 uA Operating Temperature -10~50°C Storage Temperature -20~60°C Relative humidity 10-95%, @ 40°C (non-condensing) Enclosure ABS Plastic Dimensions 82 x 51.6 x 26.3 mm Weight 81 g Included 1x Elecrow ThinkNode M5 1x LoRa antenna 1x USB-C cable 1x Manual Downloads Manual

Read (+) (–)

This bundle includes the Arduino UNO Q (2 GB) and the book "Arduino UNO Q and AI". The Arduino UNO Q is the first UNO board with a hybrid dual-brain architecture, combining a powerful Linux processor with a real-time microcontroller – bringing advanced computing and precise control together on one board. Powered by a Qualcomm Dragonwing QRB2210 MPU running Debian Linux and a STM32U585 MCU for real-time tasks, the UNO Q is built for next-generation applications. From Edge Computing and AI to robotics and automation, it delivers high performance without sacrificing ease of use. Simply connect your peripherals and get started – no extra hardware required. Features Dual-core architecture: Linux MPU + real-time MCU Qualcomm Dragonwing QRB2210 with Debian Linux support STM32U585 microcontroller for deterministic control Runs Arduino sketches via Zephyr OS Ideal for AI, IoT, robotics, and industrial projects Specifications Microprocessor (MPU) Qualcomm Dragonwing QRB2210:Quad-core Arm Cortex-A53 @ 2.0 GHzAdreno GPU 3D graphics accelerator2× ISP (13 MP + 13 MP or 25 MP) @ 30 fps Microcontroller (MCU) STM32U585Arm Cortex-M33 up to 160 MHz2 MB flash memory786 KB SRAM RAM 2 GB LPDDR4 Power Supply From USB-C connector 5 V max at 3 AInput Voltage (VIN): 7-24 V Storage 16 GB eMMC USB 1× USB-C port with host/device role switching, power role switch and video output Connectivity Wi-Fi 5 (2.4/5 GHz) with onboard antennaBluetooth 5.1 with onboard antenna Interfaces I²C/I³CSPIPWMCANUARTPSSIGPIOJTAGADC Video Video output support via USB-CMIPI DSI pins on JMEDIA header Extra 4× RGB user-controllable LEDs8×13 Blue LED Matrix1× Qwiic connector voltage 3V3, I²C1× User push-buttonJCTL: MPU Remote Debug connector Audio Microphone IN / Headphone OUT / Line OUT on JMISC MPU Operating System Linux Debian OS with upstream support Real-time Operating System Arduino Core on Zephyr OS Containerization Docker and Docker Compose support Support Operating Systems for Arduino App Lab Windows: Windows 10 or later (64-bit)macOS: macOS 11 or later (64-bit)Linux: Ubuntu 22.04 or later, and Debian Trixie (64-bit) Dimensions 68.85 × 53.34 mm (UNO form factor) Downloads Datasheet User Manual Pinout Schematics Book: Arduino UNO Q and AI – Learn to Build Intelligent Embedded Systems Build smarter embedded systems with Arduino UNO Q. This book gives you the tools, knowledge, and confidence to turn ideas into intelligent, working solutions using the Arduino UNO Q platform. Discover how to build intelligent embedded systems with the Arduino UNO Q and AI. Unlock the full potential of the Arduino UNO Q, a next-generation platform that combines the real-time power of the STM32U585 microcontroller with the flexibility of a Qualcomm Dragonwing QRB2210 microprocessor. Learn how to rapidly prototype real-world applications using the Arduino IDE for low-level embedded control and Python in Arduino App Lab for high-level development. Build confidence through hands-on projects that guide you step by step from basic board features to complete working systems. Explore ready-to-use, AI based Arduino App Lab examples and see how they can jump-start your development and reduce time to deployment. Step into the world of Edge AI with a clear, practical introduction to Edge Impulse Studio—no prior AI experience required. Follow a complete, real-world workflow to create a Keyword Spotting AI application, covering data collection, model training, optimization, and on-device inference using the Edge Impulse Studio. Bridge the gap between embedded systems and machine learning and learn how to bring intelligence directly onto your hardware. Perfect for embedded engineers, educators, students, and makers looking to stay ahead in AI-enabled product development. This bundle contains: Arduino UNO Q (2 GB) (normal price: €50) Book: Arduino UNO Q and AI (normal price: €35)

Read (+) (–)

Book: Mastering the Arduino Uno R4 Based on the low-cost 8-bit ATmega328P processor, the Arduino Uno R3 board is likely to score as the most popular Arduino family member, and this workhorse has been with us for many years. Eleven years later, the long-overdue successor, the Arduino Uno R4, was released. It is built around a 48 MHz, 32-bit Arm Cortex-M4 microcontroller and provides significantly expanded SRAM and Flash memory. Additionally, a higher-precision ADC and a new DAC are added to the design. The Uno R4 board also supports the CAN Bus with an interface. Two versions of the board are available: Uno R4 Minima, and Uno R4 WiFi. This book is about using these new boards to develop many different and interesting projects with just a handful of parts and external modules. All projects described in the book have been fully tested on the Uno R4 Minima or the Uno R4 WiFi board, as appropriate. The project topics include the reading, control, and driving of many components and modules in the kit as well as on the relevant Uno R4 board, including LEDs 7-segment displays (using timer interrupts) LCDs Sensors RFID Reader 4x4 Keypad Real-time clock (RTC) Joystick 8×8 LED matrix Motors DAC (Digital-to-analog converter) LED matrix WiFi connectivity Serial UART CAN bus Infrared controller and receiver Simulators … all in creative and educational ways with the project operation and associated software explained in great detail. Arduino Uno R4 WiFi The Arduino Uno R4 is powered by the Renesas RA4M1 32-bit ARM Cortex-M4 processor, providing a significant boost in processing power, memory, and functionality. The WiFi version comes with an ESP32-S3 WiFi module in addition to the RA4M1, expanding creative opportunities for makers and engineers. The Arduino Uno R4 runs at 48 MHz, which provides a 3x increase over the popular Uno R3. Additionally, SRAM has been upgraded from 2 kB to 32 kB, and flash memory from 32 kB to 256 kB to support more complex projects. Responding to community feedback, the USB port is now USB-C, and the maximum power supply voltage has been raised to 24 V with an enhanced thermal design. The board includes a CAN bus and an SPI port, enabling users to reduce wiring and perform parallel tasks by connecting multiple shields. A 12-bit analog DAC is also provided on the board. Specifications Microcontroller Renesas RA4M1 (ARM Cortex-M4) USB USB-C Programming Port Pins Digital I/O Pins 14 Pins Analog input pins 6 DAC 1 RTC 1 PWM pins 6 Communication UART 1x I²C 1x SPI 1x Qwiic I²C connector 1x CAN 1x CAN Bus Power Circuit operating voltage 5 V Input voltage (VIN) 6-24 V DC Current per I/O Pin 8 mA Clock speed Main core 48 MHz Memory RA4M1 256 kB Flash, 32 kB RAM LED Matrix 12 x 8 (96 red LEDs) Dimensions 68.9 x 53.4 mm Downloads Datasheet Schematics This bundle contains: Book: Mastering the Arduino Uno R4 (normal price: €45) Arduino Uno R4 WiFi (normal price: €30)

Read (+) (–)

The Arduino Pro Mini is a microcontroller board based on the ATmega328P. It has 14 digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, an on-board resonator, a reset button, and holes for mounting pin headers. A six pin header can be connected to an FTDI cable or SparkFun breakout board to provide USB power and communication to the board. The Arduino Pro Mini is intended for semi-permanent installation in objects or exhibitions. The board comes without pre-mounted headers, allowing the use of various types of connectors or direct soldering of wires. The pin layout is compatible with the Arduino Mini. The Arduino Pro Mini was designed and is manufactured by SparkFun Electronics. Specifications Microcontroller ATmega328P Board Power Supply 5-12 V Circuit Operating Voltage 5 V Digital I/O Pins 14 PWM Pins 6 UART 1 SPI 1 I²C 1 Analog Input Pins 6 External Interrupts 2 DC Current per I/O Pin 40 mA Flash Memory 32 KB of which 2 KB used by bootloader SRAM 2 KB EEPROM 1 KB Clock Speed 16 MHz Dimensions 18 x 33.3 mm (0.7 x 1.3") Downloads Eagle files Schematics

Read (+) (–)

All-in-One AI Learning and Development Platform for Edge Intelligence This NVIDIA Jetson-based AI Starter Kit is a versatile platform for learning, development, and hands-on experimentation in Edge AI. Designed for use with the NVIDIA Jetson Orin Nano, the kit combines an 11.6-inch IPS display, an 8-megapixel pan-tilt camera, and an AI voice interaction system. It includes 30 functional modules and 38 carefully designed Python courses that progress from beginner to advanced level. The course content covers Python fundamentals, sensor technology, AI vision, and image recognition. With step-by-step practical exercises ranging from basic programming to real-world computer vision applications, this kit provides an ideal platform for students, makers, developers, and anyone looking to explore artificial intelligence and edge computing. Please note: The NVIDIA Jetson Orin Nano is not included. Features Designed for the NVIDIA Jetson Orin Nano AI voice interaction module 8 MP camera with pan-tilt control Supports object and face tracking No soldering or complex wiring required Built-in 11.6-inch IPS display 30 functional modules 38 step-by-step Python lessons I²C, UART, and GPIO expansion interfaces Portable carrying case included Specifications Main Processor Jetson Orin Nano Number of Sensors 30 Sensor Board Design Integrated sensor board, no soldering or complex wiring required Screen 11.6-inch IPS Screen Screen Resolution 1366 x 768 Camera IMX219 8MP Expansion Interfaces 2x I²C, 1x UART, 1x IO D13, 1xIO D12 Programming Environment Based on Python Number of Tutorials 38 creative tutorials Target Audience Students, teachers, hardware enthusiasts Application Scenarios Ideal for learning AI programming, Jetson Orin Nano basics Dimensions 300 x 200 x 100 mm Module List Module Quantity Temperature & Humidity Sensor 1 pcs Button 4 pcs Ultrasonic Ranging Sensor 1 pcs Light Sensor 1 pcs Linear Potentiometer 1 pcs Buzzer 1 pcs LCD 1 pcs Infrared Remote 1 pcs Relay 1 pcs NFC 1 pcs PIR Motion Sensor 1 pcs Joystick 1 pcs 4-Digital Display 1 pcs LED 1 pcs Microphone 1 pcs Speaker 1 pair Vibration Motor 1 pcs Touch Sensor 1 pcs 8x8 RGB Matrix 1 pcs Atmospheric pressure sensor 1 pcs Tilt Switch 1 pcs Stepper Motor 1 pcs Accelerometer & Gyro 1 pcs Gas Sensor (MQ2) 1 pcs Hall Sensor 1 pcs Rotary Encoder 1 pcs U-shaped photoelectric switch 1 pcs NTC 1 pcs Servo 2 pcs Monocular camera 1 pcs Included 1x Elecrow AI Starter Kit for Jetson Orin Nano 1x IR Remote Control 1x NFC Card 1x 128 GB SD Card 1x 40-pin GPIO Cable 2x 22-pin FPC Cable 2x 24-pin FPC Cable 1x Jetson DP & USB Adapter Board Not included NVIDIA Jetson Orin Nano Downloads Manual Wiki OpenClaw Install and Config Guide

Read (+) (–)

Le téléchargement intégral de ce numéro est disponible pour nos membres GOLD et GREEN sur le site Elektor Magazine ! Pas encore membre ? Cliquez ici. Raspberry Pi Pico comme analyseur de spectredes FFT avec du matériel bon marché régulateur de tension linéaire ±40 Vune alimentation de qualité pour l'amplificateur de puissance Fortissimo-100... et d'autres ! la communication sans fil des microcontrôleurs devient flexiblel'EEPROM ouvre des perspectives de mise en réseau pour les microcontrôleurs sans fil 5000 ? à gagner !participez au concours STM32 Wireless Innovation Design Contest 2023 : l'odyssée de l'IAdémarrer avec l'interpréteur de code de ChatGPT LoRa, un couteau suisse (1)le protocole LoRa et ses avantages puit de courant réglable avec générateur d'horloge intégrétestez vos alimentations, convertisseurs de tension et batteries les nouvelles cartes Arduino UNO R4 Minima et WiFi des potentiomètres logarithmiquesqui sont exponentiels ! Carte d'interface pour pilote de moteurUn BoB pour un pilote de moteur à courant continu de 5 A avec une taille de 3×3 mm sur le vifdanger de l'électronique une architecture cellulaire offre-t-elle la plus basse consommation pour l'Internet des objets (IdO) ?caractéristiques énergétiques des technologies LTE-M et NB-IoT pour le déploiement de réseaux étendus à basse consommation (LPWAN) communication sans fil dans les systèmes IoT à l'aide de modules Arduino MKRnormes de communication des modules Arduino pour l'IoT pertes en courant alternatif dans les composants magnétiquesévitez la surchauffe des inductances ! optimiser son déploiement cloud grâce aux appareils de mesure implémentation de Matter : que faut-il pour déployer les appareils Matter ? le YARD Stick Oneun outil de test sans fil pour les fréquences jusqu?à 1 GHz relais à verrouillagedrôle de composant, la série PIC O?Clock ? prendre le pouls du tempsconception d'un récepteur de signaux horaires SDR Due Diligence DirectiveBusiness as Usual Will Not Do démarrer en électronique?amplificateur de tension enregistreur d'infrasons avec l'Arduino Pro Miniun projet extrait du livre 'Arduino & Co.' d'Elektor compteur d'énergie basé sur le cloudavec un module ESP32 et un capteur de tension/courant PZEM-004T guide de programmation Bare-Metal (2)timer précis, UART et débogage Hexadoku

Read (+) (–)

Elektor GREEN en GOLD leden kunnen deze uitgave hier downloaden.Nog geen lid? Klik hier om een lidmaatschap af te sluiten. Kiesschijftelefoon als afstandsbedieningvoor licht kies 1; voor koffie kies 2 GPS-gebaseerde snelheidsmeternooit meer bekeuringen RGB-stroboscoop met Arduinokleurige toepassing van een nuttig instrument Draadloze nooddrukknopmeer veiligheid met LoRa Alle begin......volgt de emitter Vrij te kiezen onafhankelijke hystereseniveaus voor comparatorenmet simulaties, spreadsheets en algebra Impedantie-analyzer op basis van een ESP32simpel, weinig onderdelen en goedkoop! Oost West Lab Bestzelfbouw moet gestimuleerd! Het MCCAB Arduino Nano Training Boardalle hardware voor de “Microcontrollers Hands-On Course” Uit het leven gegrepenmodern luddisme Sensor-ABC: de DS18B20 temperatuursensoraansluiting op de 1-Wire bus Gaan Matter en Thread het Smart Home redden?nieuwe standaarden om het slimme huis te vereenvoudigen Een kwestie van samenwerkingontwikkelen met het Thing Plus Matter Board en Simplicity Studio Elektor IIIIinfographicIoT en sensoren Matter, ExpressLink, Rainmaker – waar hebben we het over?vraaggesprek met Amey Inamdar van Espressif De juiste keuze van microcontroller-ontwikkelkitsvoor IoT- en IIoT-toepassingen Condensatoren gedragen zich niet altijd capacitief! Een NTP-klok met CircuitPythonwaarom zou je deze programmeertaal gebruiken? Bouw een cool IoT-display'met de Phambili Newt De doppler-bewegingssensor HB100theorie en praktijk Een gids voor puristisch programmeren (deel 1)voor STM32 en andere controllers Siglent SDM3045X-multimeter Microprocessoren voor embedded systemenvreemde onderdelen Microcontroller-documentatie verklaarddeel 3: blokschema’s en meer Low-power LoRa-weerstationbouw zelf een weerstation met een groot bereik Transverter voor de 70cm-band Klimaat aan ingenieurssnelheid is geboden! Hexadoku

Read (+) (–)

The Elektor LoRa NodeVersatile, long-range, 868-MHz remote control with state feedback and STM32 Inside InteractiveCorrections & Updates || Questions & Answers Analogue Electronics DesignCase Study #1 — Section 2: Preamplifier optimized for MEMS microphone My IoT Button: A Button for the WebPart 1: IoT architecture BASIC for the ESP32 and ESP8266Programming with Annex WiFi RDS ESP32 Doorbell via Telegram“The UPS Man Always Rings Once” Small Circuits RevivalCapita Selecta from the Elektor Project Suggestions Box My First LoRaWANWith Blue Pill, LoRa Breakout Board and The Things Network Elektor Connexions 2003EL “A Strong Supporter of Open Internet”Interview with Wienke Giezeman, Initiator of The Things Network The Meadow F7 BoardA great board for .NET developers Practical ESP32 Multitasking (2)Task priorities Sigfox and the IoT (3)First steps on the network Raspberry Pi Bash Command Cheat Sheet Europe's Most Successful Start-up Accelerator?HighTechXL, Eindhoven, The Netherlands Review: The Enviro+ RPi-HATMeasure air quality with a Raspberry Pi and the Enviro+ HAT Steeped in Electronicsordering components from Ukraine and Russia Review: Andonstar AD407Better than its predecessor? Lab, Sweet LabA glimpse of The Holy Place — no unauthorized entry. Optical Probe for OscilloscopesMeasure brightness fluctuations of lighting systems The 'TABULA' Project - An UpdateTangibles with user feedback How-To Calculate the Prospective Short-Circuit Current or PSCCand choose the proper circuit breaker TMS1000 Series Microcontrollers Peculiar Parts, the series Starting Out in Electronics (2) Easier than imagined! Developer's ZoneTips & Tricks, Best Practices and Other Useful Information Locating Wayward Wires Track and trace concealed and defective cables with ease and certainty Review: Joy-IT DMSO2D72 Portable 3-in-1 Oscilloscope Homebrew PC for the Electronics LabTips on Component Choice and Construction The Elektor ‘Intelekt’ Chess Computer (1981)Tiny Chess 86 ported to the Intel 8088 Beyond ElectronicsPCB Art - Pushing the Limits of Industrial Manufacturing

Read (+) (–)

This bundle contains the AI Fusion Lab Kit – which includes over 450 components – and the book Physical AI with Raspberry Pi, offering a comprehensive, hands-on introduction to AI, electronics, and Python programming. Build practical projects that combine computer vision, voice interaction, sensors, motors, and large language models. The included book guides you step by step from Raspberry Pi setup and hardware control to advanced applications using OpenCV, MediaPipe, YOLO, OpenAI services, and local Ollama models. SunFounder AI Fusion Lab Kit for Raspberry Pi (450+ parts) The AI Fusion Lab Kit transforms a Raspberry Pi into a complete platform for learning artificial intelligence, electronics, and Python programming. It supports Raspberry Pi 5, 4B, 3B+, 3B, and Zero 2 W, as well as popular AI models and services including ChatGPT, Gemini, Grok, DeepSeek, Qwen, Doubao, and Ollama. With its camera, Pan-Tilt HAT, 10-axis sensor module, microphone, speaker, and electronic components, the kit enables users to build interactive AI projects that combine voice, vision, movement, and sensor data. It is suitable for beginners, students, makers, and anyone who wants to explore practical AI applications. Build Multimodal AI Projects Combine large language models with speech recognition, text-to-speech, camera vision, sensors, and breadboard circuits. Create projects that can listen, speak, see, react, and interact with their environment while learning how modern AI systems work. Explore AI Vision and Object Tracking Use YOLO, OpenCV, and MediaPipe to detect and track objects, colors, faces, and human movement. The motorized Pan-Tilt HAT allows the camera to follow targets automatically, demonstrating how computer vision and motion control work together. Voice AI with the Fusion HAT+ The Fusion HAT+ simplifies hardware control through a unified Python library and supports safe Raspberry Pi shutdown. Its integrated microphone and speaker make it easy to develop voice-controlled assistants and other conversational AI applications. Compatible with Raspberry Pi 5, 4B, 3B+, 3B, and Zero 2 W Supports multiple cloud-based and local AI models Includes camera, Pan-Tilt HAT, 10DOF module, microphone, and speaker Projects covering voice AI, computer vision, sensors, and electronics Video lessons, documentation, and sample code Book: Physical AI with Raspberry Pi Using the Raspberry Pi and the AI Fusion Lab Kit, this book takes you step by step from the fundamentals of hardware interfacing to the development of intelligent systems capable of seeing, listening, speaking, and making decisions. Whether you are a student, educator, maker, or professional engineer, you will gain valuable experience through projects designed to build both knowledge and confidence. Beginning with system setup, GPIO programming, sensors, displays, motors, and automation techniques, the book gradually introduces more advanced topics, including speech recognition, text-to-speech synthesis, conversational AI, object recognition, and machine learning. Along the way, you will learn how to create voice-controlled devices, intelligent assistants, camera tracking systems, environmental monitoring solutions, and AI-powered embedded applications. Explore modern technologies such as OpenAI services, Ollama local large language models, OpenCV, MediaPipe, and YOLO object detection while gaining practical experience with real-world applications used in robotics, industrial automation, smart devices, and autonomous systems. You’ll discover how intelligent software can interact with sensors, cameras, motors, and displays to create systems that respond naturally to the world around them. Rather than simply following code examples, you will be encouraged to experiment, customize, and expand every project. The techniques and skills developed throughout this book provide a solid foundation for future innovation, rapid prototyping, research, and product development. If you are ready to move beyond traditional electronics and explore the future of intelligent embedded systems, this book will guide you every step of the way. This bundle contains: SunFounder AI Fusion Lab Kit for Raspberry Pi (normal price: €100) Book: Physical AI with Raspberry Pi (normal price: €45) Not included Raspberry Pi

Read (+) (–)

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

Read (+) (–)