This book is about the Arduino microcontroller and the Arduino concept. The visionary Arduino represented a new innovation in microcontroller hardware in 2005, the concept of open source hardware, making a broad range of computing accessible for all.
This book, “Arduino V: AI and Machine Learning,” is an accessible primer on Artificial Intelligence and Machine Learning for those without a deep AI and ML background. The author concentrates on Artificial Intelligence (AI) and Machine Learning (ML) applications for microcontroller–based systems. The intent is to introduce the concepts and allow readers to practice on low cost, accessible Arduino hardware and software. Readers should find this book a starting point, an introduction, to this fascinating field. A number of references are provided for further exploration.
Author(s): Steven F. Barrett
Series: Synthesis Lectures on Digital Circuits & Systems
Publisher: Springer
Year: 2023
Language: English
Pages: 212
City: Cham
Preface
Approach of the Book
References
Acknowledgments
Contents
About the Author
1 Getting Started
1.1 Overview
1.2 The Big Picture
1.3 Arduino Quickstart
1.3.1 Quick Start Guide
1.3.2 Arduino Development Environment Overview
1.3.3 Sketchbook Concept
1.3.4 Arduino Software, Libraries, and Language References
1.3.5 Writing an Arduino Sketch
1.4 Application: LED Strip
1.5 Summary
1.6 Problems
2 Arduino Nano 33 BLE Sense
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2.1 Overview
2.2 Arduino Nano 33 BLE Sense SoC Board
2.3 Arduino Nano 33 BLE Sense Features
2.4 NINA B306 Module Subsystems
2.4.1 B306 Module Memory
2.5 NINA B306 Module Peripherals
2.5.1 Pulse Width Modulation (PWM) Channels
2.5.2 Serial Communications
2.5.3 Bluetooth Low Energy (BLE)
2.6 Nano 33 BLE Sense Peripherals
2.6.1 Nine Axis IMU (LSM9DS1)
2.6.2 Barometer and Temperature Sensor (LPS22HB)
2.6.3 Relative Humidity and Temperature Sensor (HTS221)
2.6.4 Digital Proximity, Ambient Light, RGB, and Gesture Sensor (APDS–9960)
2.6.5 Digital Microphone (MP34DT05)
2.7 Application: Bluetooth BLE Greenhouse Monitor
2.8 Summary
2.9 Problems
3 Arduino Nano 33 BLE Sense Power and Interfacing
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3.1 Overview
3.2 Arduino Power Requirements
3.3 Voltage Regulators
3.3.1 Powering the Nano 33 From Batteries
3.4 Interfacing Concepts
3.5 Input Devices
3.5.1 Switches
3.6 Output Devices
3.6.1 Light Emitting Diodes (LEDs)
3.6.2 Serial Liquid Crystal Display (LCD)
3.7 Motor Control Concepts
3.7.1 DC Motor
3.8 Application: Dagu Magician Robot
3.8.1 Requirements
3.8.2 Circuit Diagram
3.8.3 Dagu Magician Robot Control Algorithm
3.8.4 Testing the Control Algorithm
3.9 Summary
3.10 Problems
4 Artificial Intelligence and Machine Learning
4.1 Overview
4.2 A Brief History of AI and ML Developments
4.3 K Nearest Neighbors
4.4 Decision Trees
4.5 Application: KNN Classifier
4.6 Application: Decision Trees
4.7 Summary
4.8 Problems
5 Fuzzy Logic
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5.1 Overview Concepts
5.2 Theory
5.2.1 Establish Fuzzy Control System Goal, Inputs, and Outputs
5.2.2 Fuzzify Crisp Sensor Values
5.2.3 Apply Rules
5.2.4 Aggregate Active Rules and Defuzzify Output
5.3 Arduino eFLL
5.3.1 Example: Simple
5.3.2 Example: Advanced
5.4 Application
5.5 Summary
5.6 Problems
6 Neural Networks
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6.1 Overview
6.2 Biological Neuron
6.3 Perceptron
6.3.1 Training the Perceptron Model
6.3.2 Single Perceptron Run Mode
6.3.3 Sorting Tomatoes
6.4 Multiple Perceptron Model
6.4.1 Three Perceptron Run Mode
6.5 Perceptron Challenges
6.6 Artificial Neural Network (ANN)
6.6.1 Single Neuron Model
6.6.2 Single Neuron Run Mode
6.6.3 Artificial Neural Networks
6.6.4 ANN Convergence
6.7 Deep Neural Networks–Introduction to Software Tools
6.8 Application: ANN Robot Control
6.9 Summary
6.10 Problems
Index
Index
