Hackaday Links: July 10, 2022

July 10, 2022 by Dan Maloney 9 Comments

We always like to call out a commercial success stemming from projects that got their start on Hackaday.io, and so we’re proud to announce the release of MAKE: Calculus by Joan Horvath and Rich Cameron, a book that takes a decidedly different approach to teaching calculus than traditional courses. Geared to makers and hackers, who generally tend to have a visual style of learning, the book makes heavy use of 3D-printed models to illustrate the relationships between functions. The project started five years ago as a 2017 Hackaday Prize entry, and resulted in a talk at the 2019 Supercon. Their book is now available for preorder, and might be a great way to reacquaint themselves with calc, or perhaps even to learn it for the first time. Continue reading “Hackaday Links: July 10, 2022” →

Talking Head Teaches Laplace Transform

August 9, 2020 by Al Williams 25 Comments

Most people who deal with electronics have heard of the Fourier transform. That mathematical process makes it possible for computers to analyze sound, video, and it also offers critical math insights for tasks ranging from pattern matching to frequency synthesis. The Laplace transform is less familiar, even though it is a generalization of the Fourier transform. [Steve Bruntun] has a good explanation of the math behind the Laplace transform in a recent video that you can see below.

There are many applications for the Laplace transform, including transforming types of differential equations. This comes up often in electronics where you have time-varying components like inductors and capacitors. Instead of having to solve a differential equation, you can perform a Laplace, solve using common algebra, and then do a reverse transform to get the right answer. This is similar to how logarithms can take a harder problem — multiplication — and change it into a simpler addition problem, but on a much larger scale.

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Understanding Math Rather Than Merely Learning It

February 15, 2019 by Al Williams 46 Comments

There’s a line from the original Star Trek where Khan says, “Improve a mechanical device and you may double productivity, but improve man and you gain a thousandfold.” Joan Horvath and Rich Cameron have the same idea about improving education, particularly autodidacticism or self-learning. They share what they’ve learned about acquiring an intuitive understanding of difficult math at the Hackaday Superconference and you can watch the newly published video below.

The start of this was the pair’s collaboration on a book about 3D printing science projects. Joan has a traditional education from MIT and Rich is a self-taught guy. This gave them a unique perspective from both sides of the street. They started looking at calculus — a subject that scares a lot of people but is really integral (no pun intended) to a lot of serious science and engineering.

You probably know that Newton and Leibniz struck on the fundamentals of calculus about the same time. The original papers, however, were decidedly different. Newton’s approach was more physical and less mathematical. Leibniz used formal logic and algebra. Although both share credit, the Leibniz notation won out and is what we use today.

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Calculus In 20 Minutes

September 28, 2017 by Al Williams 56 Comments

If you went to engineering school, you probably remember going to a lot of calculus classes. You may or may not remember a lot of calculus. If you didn’t go to engineering school, you will find that there’s an upper limit to how much electronics theory you can learn before you have to learn calculus. Now imagine Khan Academy, run by an auctioneer and done without computers. Well, you don’t have to imagine it. Thinkwell has two videos that purport to teach you calculus in twenty minutes (YouTube, embedded below).

We are going to be honest. If you need a refresher, these videos might be useful. If you have no idea how to do calculus, maybe these are going to whiz by a little fast. However, either way, the videos have some humor value both from the FedEx commercial-style delivery to the non-computerized graphics (not to mention the glass-breaking sound effects). Of course, the video is about ten years old, but that’s part of its charm.

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Hackaday Prize Entry: Hacker Calculus

May 1, 2017 by Jenny List 42 Comments

Mathematics, as it is taught in schools, sometimes falls short in its mission to educate the pupils. This is the view of [Joan Horvath] and [Rich Cameron], particularly with respect to the teaching of calculus, which they feel has become a purely algebraic discipline that leaves many students in the cold when it comes to understanding the concepts behind it.

Their Hacker Calculus project aims to address this, by returning to [Isaac Newton]’s 1687 seminal work on the matter, Philosophiae Naturalis Principia Mathematica. They were struck by how much the Principia was a work of geometry rather than algebra, and they are seeking to return to [Newton]’s principles in a bid to make the subject more accessible to students left behind when it comes to derivatives and integrals. They intend to refine the geometric approach to create a series of practical items to explain the concepts, both through 3D printed items and through electronics.

We can see that this is an approach that has considerable merit, given that most Hackaday readers will have at some time or other sat through a maths lesson and come away wondering what on earth the teacher was talking about and having been baffled by further attempts to explain it through impenetrable maths-speak. If you were the kid who “got” calculus when the relationship between speed and acceleration – another thing we have [Newton] to thank for describing – was explained in your physics lessons, then you will probably understand.

The pair have some Hackaday Prize history, you may remember them from such previous entries as their 3D prints for the visually impaired project from last year.

Root Mean Square

August 4, 2016 by Al Williams 66 Comments

The first time I was in school for electrical engineering (long story), I had a professor who had never worked in the industry. I was in her class and the topic of the day was measuring AC waveforms. We got to see some sine waves centered on zero volts and were taught that the peak voltage was the magnitude of the voltage above zero. The peak to peak was the voltage from–surprise–the top peak to the bottom peak, which was double the peak voltage. Then there was root-mean-square (RMS) voltage. For those nice sine waves, you took the peak voltage and divided by the square root of two, 1.414 or so.

You know that kid in the front of the class? They were in your class, too. Always raising their hand with some question. That kid raised his hand and asked the simple question: why do we care about RMS voltage? I was stunned when I heard the professor answer, “I think it is because it is so easy to divide by the square root of two.”

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Running Calculus On An Arduino

January 18, 2016 by Will Sweatman 39 Comments

It was Stardate 2267. A mysterious life form known as Redjac possessed the computer system of the USS Enterprise. Being well versed in both computer operations and mathematics, [Spock] instructed the computer to compute pi to the last digit. “…the value of pi is a transcendental figure without resolution” he would say. The task of computing pi presents to the computer an infinite process. The computer would have to work on the task forever, eventually forcing the Redjac out.

Calculus relies on infinite processes. And the Arduino is a (single thread) computer. So the idea of running a calculus function on an Arduino presents a seemingly impossible scenario. In this article, we’re going to explore the idea of using derivative like techniques with a microcontroller. Let us be reminded that the derivative provides an instantaneous rate of change. Getting an instantaneous rate of change when the function is known is easy. However, when you’re working with a microcontroller and varying analog data without a known function, it’s not so easy. Our goal will be to get an average rate of change of the data. And since a microcontroller is many orders of magnitude faster than the rate of change of the incoming data, we can calculate the average rate of change over very small time intervals. Our work will be based on the fact that the average rate of change and instantaneous rate of change are the same over short time intervals.