Editor’s note
Welcome to the DigiKey eMagazine Volume 18 – Edge AI. As technology continues to evolve at a rapid pace, we’re constantly exploring innovative solutions that shape the future of industries from IoT to AI and machine vision. In this issue, we’ve curated a selection of articles that offer valuable insights into these game-changing technologies and how they’re transforming the way we approach engineering challenges. In our first feature, we delve into predictive maintenance through AI-powered data acquisition, highlighting how current sensors can play a pivotal role in optimizing efficiency and minimizing downtime. Staying on the topic of AI, we also explore tinyML at the Edge – examining three unique use cases that demonstrate how machine learning can be deployed directly within resource-constrained devices for smarter, more efficient systems. For those venturing into the world of multicore microcontrollers, we break down why they’re essential for IoT devices at the Edge and provide practical advice on getting started with these powerful, parallel-processing units. We also take a deep dive into the crucial, yet often overlooked, aspect of data preparation in machine learning – offering clarity on why clean, structured data is the foundation of successful ML projects. On the hardware front, we explore how to design and deploy smart machine vision systems rapidly, empowering you with the tools needed to integrate visual intelligence into applications across industries. And lastly, we turn our focus to GMSL cameras, which have been road-tested and are driving innovation into new markets, presenting opportunities that are redefining how we capture and process visual data. This issue is packed with cutting-edge information and practical tips to keep you ahead of the curve in the world of technology. We hope these articles inspire fresh ideas and new possibilities as you navigate the exciting developments in your field.
4 Use a current sensor to efficiently acquire data for predictive maintenance with AI 8 3 uses for tinyML at the Edge 12 Why and how to get started with multicore microcontrollers for IoT devices at the Edge 18 How to build an AI-powered toaster 22 Special feature: retroelectro Programming a calculator to form concepts: the organizers of the Dartmouth Summer Research Project 28 What is data-preparation in ML, and why is it crucial for success? 30 How to rapidly design and deploy smart machine vision systems 36 Road-tested GMSL cameras drive into new markets 40 Understanding computer and machine vision 50 How machine vision is advancing automation now
we get technical
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