Month: August 2026

EE Has Always Been Cool

A few weeks ago, we hosted our Legends of Semiconductor dinner, bringing together some of the people who helped build Canada’s semiconductor industry and the next generation of founders and engineers.

One of the legends was Professor Emeritus Adel Sedra, former Dean of Engineering at the University of Waterloo. For many electrical engineering students, Sedra needs no introduction. Microelectronic Circuits, the textbook he co-authored with the late Kenneth Smith, has shaped generations of electrical engineers and, by some estimates, has been used by 75% of electrical engineering students around the world.

At the dinner, the conversation turned to the textbook and which edition each of us had used. Mine was the second edition.

There was only one problem: I had lost my copy.

I was privileged not only to learn from Sedra’s textbook, but also to be one of the students in his classroom when he was a professor of electrical engineering at the University of Toronto. That second edition was the textbook I used as his student more than 30 years ago.

I suspect I lost it during one of our moves. I looked again after the dinner, but couldn’t find it. So I did the next best thing. I found a used copy of the second edition. Same edition, probably similar age, definitely not my copy. I bought it anyway.

This time, I wanted Professor Sedra to autograph it. So I brought the book to Waterloo, where Sedra was meeting with EE students at Williams Cafe. I crashed the party and hijacked their meeting for a few minutes to get my textbook signed.

While I was there, I started chatting with the students and sharing some of my experience as an electrical engineer. One student asked whether I thought EE was getting the spotlight again, after software had taken centre stage for the past couple of decades.

I told them EE is cool again!

Think about what is happening with AI. Everyone talks about the models. But underneath them are GPUs, memory, networking, semiconductors and an enormous amount of computing hardware. All of that hardware needs electricity. Lots of electricity. The entire AI stack, from the chips doing the computation to the power infrastructure keeping data centres running, is bringing electrical engineering back to centre stage.

Sedra immediately corrected me: “EE has always been cool!”

The whole table had a good laugh. And, of course, he was right.

The technologies may be different, but many of the fundamentals are the same. More importantly, so is the way of thinking.

I may never find my original textbook, but I now have the same second edition on my bookshelf again.

This time, signed by my professor!

The Underwater FishCam Livestream That Brought Our Family Together

Two weeks ago, I shared a short note about a fun side project I could only work on at the cottage.

I can now pull back the curtain: it is my underwater livestreaming FishCam. At first glance, it sounds simple: put an underwater camera in the lake and livestream it to YouTube.

Well, not quite.

I deliberately designed the system with a constraint. Instead of putting a powerful computer beside the camera, I chose a tiny, low-powered computer at the cottage to do the minimum, while my “data centre”—an old iMac at my Toronto home—does the heavy lifting.

Why?

For those of us who spend most of our time building in the cloud, abundant compute, memory, bandwidth, and reliable networking are easy to take for granted. Constraints are a forcing function. Running a live HD video stream on a resource-constrained edge device forced me to rethink almost every design decision.

Building at the edge presents a different set of technical challenges.
Who would have thought that streaming HD video could overwhelm the Raspberry Pi’s Wi-Fi subsystem and crash it? Those crashes then corrupted the microSD card, causing even more crashes.

Or that keeping a Raspberry Pi consuming roughly two watts—less power than many LED light bulbs—streaming smoothly would require tuning memory, processing power, and bandwidth?

Given those constraints, today is the first day the entire system has run for more than 24 hours without crashing. I’ll write a separate post about these technical challenges, all relevant to my professional world. As the world moves toward Physical AI, these constraints are important. We cannot assume abundant compute, high-speed networks, low latency, or unlimited power will always be available.

Imagine the vision system of an untethered robot operating at home, on a farm, underwater, or in space. The edge has very different constraints from the cloud.

Ironically, the biggest surprise was not technical. It was how much fun the project created for our family and friends.

The underwater camera has a built-in LED light that turns on after dark. It attracts mayfly nymphs and other small aquatic organisms, which in turn attract fish. Nighttime quickly became the most exciting time to watch.
One evening, some of us were at the cottage with friends, some were in Toronto, and some were travelling overseas. We all watched the same YouTube livestream together.

My mom, who turns 80 next year, and I held our phones up to the TV, waiting for a big fish to swim by.

When one appeared, we both burst with joy and shouted, “Wow, here is a big one!”

It felt like a modern version of fishing.

This is version 1.0. Fish detection, species identification, and other features are coming.


Sometimes the best way to understand where technology is going is to build something completely unnecessary.

More to come. Here is the live YouTube stream from the cottage.

P.S. Don’t forget to come back after dark. We have a party down there every night!