What to expect

This Elektor edition guest-edited by Edge Impulse explores the edge AI workflow, from data collection and model training to deployment and optimization:

End-to-End Edge AI
A hands-on object detection series that walks through the complete machine learning pipeline on embedded hardware.
High-Resolution, High-Speed Object Counting (Nvidia Jetson Nano, TensorRT)
Pushing inference speed and precision for visual counting applications.
PCB Defect Detection with Computer Vision (Raspberry Pi)
Smarter manufacturing through embedded vision.
Smart Building Ventilation with Sensor Fusion
AI-powered environmental optimization for energy efficiency.
Analog Meter Reading (Arduino Nicla Vision)
A TinyML approach to automating analog infrastructure.
Smart Appliance Control Using Voice Commands (Nordic Thingy:53)
Speech recognition at the edge.
Liquid Classification with TinyML (Seeed Wio Terminal + TDS Sensor)
Combining IoT sensing and embedded ML for fluid analysis.
Surgery Inventory Object Detection
Applying real-time vision to healthcare logistics.

Plus, the magazine includes exclusive interviews with: Edge Impulse co-founders Zach Shelby and Jan Jongboom; Qualcomm Technologies VP of product management Manny Singh; and EDGE AI FOUNDATION CEO Pete Bernard. Also included are a Tech the Future essay titled "AI at the Edge: Powering the Next Generation of Devices," an Industry Case Study on GlobalSense, and more.

What the Heck Is Edge AI Anyway?
Bringing Intelligence to the Device
Meet Edge Impulse Studio
Easily Build and Deploy Edge AI Models
Keyword Spotting with Edge Impulse
Collect, Train, and Deploy
Smart Appliance Control Using Voice Commands with the Nordic Thingy:53
Key Terms for Understanding Edge AI and Machine Learning
Crash Course: Getting Started with Edge Impulse
Learn to Collect, Train, and Deploy an ML Model with the Arduino Nano 33 BLE Sense
A New Chapter for Arduino
From Hobby Board to Edge Computing Powerhouse
Getting Started with Object Detection on Edge Devices
PCB Defect Detection
Computer Vision with Raspberry Pi
Scaling AI to the Smallest Devices
Optimizing Power Efficiency in Battery-Driven Edge AI Devices
AI Toaster
When Edge AI Meets Breakfast
Thundercomm Rubik Pi 3
Raspberry Pi Familiarity Meets Edge AI
Leadership, Embedded ML, and the Edge Revolution
Vision Language Models for the Edge
Cascading Models for Better Reliability
Get to Know Edge Impulse
Questions from the Elektor Community
Project Update #5: ESP32-Based Energy Meter
Using Edge AI to Recognize Household Loads
Motion Recognition with Anomaly Detection
An End-to-End Tutorial
Smart Ventilation System: Fusing Sound and Environmental Data
A Dual-MCU Machine Learning Approach for Automated Window and Louver Control
Bringing Voice Control to Earbuds and Headsets
AI at the Edge: Powering the Next Generation of Devices

What to expect

Contents

Details

Customer Reviews