Simple ECG Proves You Aren’t Heartless After All

November 7, 2016 by Al Williams 13 Comments

We don’t think of the human body as a piece of electronics, but a surprising amount of our bodies work on electricity. The heart is certainly one of these. When you think about it, it is pretty amazing. A pump the size of your fist that has an expected service life of nearly 100 years.

All that electrical activity is something you can monitor and–if you know what to look for–irregular patterns can tell you if everything is OK in there. [Ohoilett] is a graduate student in the biomedical field and he shares some simple circuits for reading electrocardiogram (ECG) data. You can see a video fo the results, below.

Continue reading “Simple ECG Proves You Aren’t Heartless After All” →

All About Eve

November 5, 2016 by Al Williams 33 Comments

Most programming languages today look fairly similar. There’s small differences, of course (Python using spaces, Ruby and Perl have some odd-looking constructs). In the 1960s and 1970s, though, a lot of programming languages were pretty cryptic. Algol, APL, and LISP are great examples of unusual looking programming languages. Even FORTRAN and PL/1 were hard to read. RPG and COBOL were attempts to make programming more accessible, although you could argue that neither of them took over the world. Most programming languages today have more similarity to FORTRAN than either of those two languages.

A new programming language, Eve, claims to be based on years of research in programming from a human perspective instead of from the computer’s. The result is a language that works by pattern matching instead of the usual flow of control. It is also made to live inside of Markdown documents that can serve as documentation. You can see a video about Eve, below.

Neither of these are totally new ideas. SNOBOL, AWK, and Prolog all have some pattern-matching involved. [Donald Knuth] was promoting literate programming back in the 1980s. However, Eve understands modern constructs like web browsers.

Continue reading “All About Eve” →

Personal Compass Points To Your Spawn Point

November 5, 2016 by Al Williams 22 Comments

A conventional compass points north (well, to magnetic north, anyway). [Videoschmideo] wanted to make a compass that pointed somewhere specific. In particular, the compass — a wedding gift — was to point to a park where the newlywed couple got engaged. Like waking up in a fresh new Minecraft world, this is their spawn point and now they can always find their way back from the wilderness.

The device uses an Arduino, a GPS module, a compass, and a servo motor. Being a wedding gift, it also needs to meet certain aesthetic sensibilities. The device is in an attractive wooden box and uses stylish brass gears. The gears allow the servo motor to turn more than 360 degrees (and the software limits the rotation to 360 degrees). You can see a video of the device in operation, below.

Continue reading “Personal Compass Points To Your Spawn Point” →

Retrotechtacular: FAX As A Service In 1984

November 4, 2016 by Al Williams 29 Comments

If you tell someone these days to send you something via FAX, you are likely to get a look similar to the one you’d get if you asked them to park your horse. But in 1984, FAX was a mysterious new technology (well, actually, it wasn’t, but it wasn’t yet common to most people).

FedEx–the people who got famous delivering packages overnight–made a bold move to seize a new market: Zapmail (not to be confused with the modern mass mailing service). The idea was simple (you can see a commercial for it in grainy VHS splendor below): Overnight is great, but sometimes you need something sent across the country now. A FedEx driver picks up your documents, carries them to a FedEx office. There the documents FAX to another FedEx office where another driver delivers the printed copy. The process took two hours to get a paper document from one side of the continent to another.

Continue reading “Retrotechtacular: FAX As A Service In 1984” →

Drum On A Chip–Not That Kind Of Chip

November 3, 2016 by Al Williams 5 Comments

Comedian Mitch Hedberg had a theory about Pringles potato chips. His theory is the company formed to make tennis balls. But instead of a truckload of rubber, someone accidentally sent them potatoes, so they made the best of it. Certainly the Pringles can is an iconic brand all by itself. The cans also have a lot of hacker history, since they are commonly used for WiFi cantennas (even though it might not be the best choice of cans). People also use them to build pinhole cameras, macro lenses, and a variety of cannon-like devices.

[Ian H] uses the short Pringles cans to build a drum kit. Clearly, the little cans aren’t going to make very much sound on their own, but with a piezo speaker element used in reverse, the cans become touch sensors that feed an Arduino and drive a MIDI device. You can see a video of the result, below.

Continue reading “Drum On A Chip–Not That Kind Of Chip” →

Minimal Arduino Clock

November 2, 2016 by Al Williams 19 Comments

Making a clock with a common microcontroller like an Arduino isn’t very difficult. However, if you’ve tried it, you probably discovered that keeping track of wall time is difficult without some external hardware. [Barzok] has a very minimal clock build. It takes a handful of LED arrays with an integrated driver, an Arduino Nano, a real-time clock module, and a voltage regulator.

Continue reading “Minimal Arduino Clock” →

Machine Learning: Foundations

November 2, 2016 by Al Williams 33 Comments

When you want a person to do something, you train them. When you want a computer to do something, you program it. However, there are ways to make computers learn, at least in some situations. One technique that makes this possible is the perceptron learning algorithm. A perceptron is a computer simulation of a nerve, and there are various ways to change the perceptron’s behavior based on either example data or a method to determine how good (or bad) some outcome is.

What’s a Perceptron?

I’m no biologist, but apparently a neuron has a bunch of inputs and if the level of those inputs gets to a certain level, the neuron “fires” which means it stimulates the input of another neuron further down the line. Not all inputs are created equally: in the mathematical model of them, they have different weighting. Input A might be on a hair trigger, while it might take inputs B and C on together to wake up the neuron in question.
Continue reading “Machine Learning: Foundations” →