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35 Touch Develop & MicroPython Projects The BBC micro:bit is a credit sized computer based on a highly popular and high performance ARM processor. The device is designed by a group of 29 partners for use in computer education in the UK and will be given free of charge to every secondary school student in the UK. The device is based on the Cortex-M0 processor and it measures 4 x 5 cm. It includes several important sensors and modules such as an accelerometer, magnetometer, 25 LEDs, 2 programmable push-button switches, Bluetooth connectivity, micro USB socket, 5 ring type connectors, and a 23-pin edge connector. The device can be powered from its micro USB port by connecting it to a PC, or two external AAA type batteries can be used. This book is about the use of the BBC micro:bit computer in practical projects. The BBC micro:bit computer can be programmed using several different programming languages, such as Microsoft Block Editor, Microsoft Touch Develop, MicroPython, and JavaScript. The book makes a brief introduction to the Touch Develop programming language and the MicroPython programming language. It then gives 35 example working and tested projects using these language. Readers who learn to program in Touch Develop and MicroPython should find it very easy to program using the Block Editor or any other languages. The following are given for each project: Title of the project Description of the project Aim of the project Touch Develop and MicroPython program listings Complete program listings are given for each project. In addition, working principles of the projects are described briefly in each section. Readers are encouraged to go through the projects in the order given in the book.

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This book is about teaching the Python programming language using the Raspberry Pi 4 computer. The book makes an introduction to Raspberry Pi 4 and then teaches Python with the topics: variables, strings, arrays, matrices, tuples, lists, dictionaries, user functions, flow of control, printing, keyboard input, graphics, GUI, object oriented programming and many more topics. The book is aimed for beginners, students, practising engineers, hobbyists, and for anyone else who may want to learn to program in Python. The book includes many example programs and case studies. All the example programs and case studies have been tested fully by the author and are all working. The example programs aim to teach the various programming concepts of Python. The case studies cover the use of Python in the analysis and design of electronic circuits. Some of the case study topics are: Resistor colour code identification Resistive potential divider circuits Resistive attenuator design Zener diode voltage regulator design RC and RLC transient circuits Circuit frequency response Saving data on external memory stick Mesh and node circuit analysis using matrices Resonance in RLC circuits Transistor Biasing analysis Transistor amplifier design Design of active filters Interfacing hardware with GPIO, I²C and SPI Using Wi-Fi with Python and TCP/IP and UDP programs Using Bluetooth from Python Full program listings of all the programs used in the book are available at the Elektor website of the book. Readers should be able just to copy and use these programs in their Raspberry Pi projects without any modifications.

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Diese Ausgabe steht allen GOLD- und GREEN-Mitgliedern auf der ElektorMagazine-Website zum Download bereit! Sie sind noch kein Mitglied? Hier klicken! Was genau ist eigentlich Edge-KI?Intelligenz direkt im Gerät Wir stellen vor: Edge Impulse StudioKI-Modelle für Edge-Geräte einfach erstellen und bereitstellen Keyword Spotting mit Edge ImpulseErfassen, Trainieren und Implementieren Intelligente Gerätesteuerung per Sprachbefehl mit Nordic Thingy:53 Edge-KISchlüsselbegriffe zum Verständnis von Edge-KI und Machine Learning Crashkurs: Einstieg in Edge ImpulseWie Sie mit dem Arduino Nano 33 BLE Sense ein ML-Modell erfassen, trainieren und bereitstellen Ein neues Kapitel für ArduinoVom Hobby-Board zur Edge-Computing-Plattform Erste Schritte mit Objekterkennung auf Edge-Geräten Fehlererkennung auf LeiterplattenComputer Vision mit dem Raspberry Pi KI-Skalierung für kleinste Geräte Optimierung der Energieeffizienz bei batteriebetriebenen Edge-KI-Geräten KI-ToasterWenn Edge-KI das Frühstück rettet Thundercomm Rubik Pi 3Raspberry Pi trifft Edge-KI Unternehmensführung, Embedded ML und die Edge-Revolution Vision-Language-Modelle für Edge-GeräteKaskadierte Modelle für höhere Zuverlässigkeit Lernen Sie Edge Impulse kennenFragen aus der Elektor-Community Projekt-Update #5: Energiemessgerät mit ESP32Haushaltsgeräte mit Edge-KI erkennen Bewegungserkennung mit Anomalie-ErkennungEin End-to-End-Tutorial Intelligentes Belüftungssystem: Verbindung von Schall- und UmweltdatenEin Dual-MCU-Ansatz mit Machine Learning für die automatische Steuerung von Fenstern und Jalousien Sprachsteuerung für Ohrhörer und Headsets Edge-KI: Intelligente Geräte auf dem Vormarsch

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