Top 10 Embedded Systems Articles of 2018 – Readers’ Favorites

At year‑end we revisit the most‑read pieces that shaped 2018 in the embedded world. Security concerns and AI excitement were front‑and‑center, yet classic topics like RTOS internals and embedded design still dominated the conversation. Below are the ten articles that captured the most readership. If your favorite is missing, drop a comment and let us know.

Event Flag Groups: Essential Utilities and Data Structures – Colin extends the RTOS Revealed series, delving into event flag groups and their practical use in real‑time operating systems.

Flash 101: NAND vs NOR – Embedded designers must weigh numerous factors when selecting flash memory. This first article in the series explains the key differences between NOR and NAND flash.

Applying Machine Learning in Embedded Systems – Machine learning has become a viable engineering tool when approached with an understanding of its requirements and limits. It’s more accessible than many think.

A Peek Inside Amazon FreeRTOS – Amazon FreeRTOS simplifies cloud integration, but the documentation can be opaque. This post peeks under the hood to reveal what developers can expect from AWS’s RTOS offering.

You Think Your Software Works? Prove It! – Formal methods are moving from research labs to mainstream development, especially in high‑assurance systems. They can be adopted incrementally and combined with C or C++ code.

A Fresh Look at Embedding a Web Server – Embedded engineers frequently tackle web servers and HTTP protocols. Traditional data‑transfer methods may no longer be optimal; this article explores modern alternatives.

How Embedded Projects Run Into Trouble: Jack’s Top Ten – Number Ten – This series outlines common pitfalls in embedded projects, released weekly for a deeper dive into each issue.

Achieving Memory Safety Without Compromise – The newest generation of safe programming languages eschews garbage collectors and runtimes, delivering both memory safety and fine‑grained control.

Machines Can See, Hear, and Analyze – Specialized processors and software are enabling embedded systems to perform AI tasks locally, moving the power of machine learning from the cloud to the edge.

We hope you enjoyed revisiting these influential pieces. If we missed your favorite, let us know in the comments below.