Mechanical Image Acquisition With A Nipkow Disc

June 28, 2017 by Jenny List 36 Comments

If you mis-spent your teenage years fishing broken televisions from dumpsters and either robbing them for parts or fixing them for the ability to watch The A Team upstairs rather than in the living room as I did, then it’s possible that you too will have developed a keen interest in analogue television technology. You’ll know your front porch from your blanking interval and your colour burst, you might say.

An illustration of a simple Nipkow disk. Hzeller (CC BY-SA 3.0).

There was one piece of television technology that evaded a 1980s dumpster-diver, no 625-line PAL set from the 1970s was ever going to come close to the fascination of the earliest TV sets. Because instead of a CRT and its associated electronics, they featured a spinning disk with a spiral pattern of holes. These mechanical TV systems were quickly superseded in the 1930s by all-electronic systems, so of the very few sets manufactured only a fraction have survived the intervening decades.

The spinning disk in a mechanical TV is referred to as a Nipkow disk, after its inventor, [Paul Gottlieb Nipkow]. [Nipkow] conceived and patented the idea of a spinning disk with a spiral of holes to dissect an image sequentially into a series of lines in the 1880s, but without the benefit of the electronic amplification that would come a few decades later was unable to produce a viable system to demonstrate it. It would be in the 1920s before [John Logie Baird] would develop the first working television system using [Nipkow]’s invention.

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A Crash Course In Thingiverse Customizer

June 27, 2017 by Al Williams 19 Comments

OpenSCAD is a great way to create objects for 3D printing (or other purposes), especially if you are already used to programming. For things like front panels, it is great because you can easily make modifications and — if you wrote your code correctly–everything will just adjust itself to new positions.

However, what if you have a general-purpose piece of code, and you want people to have the ability to customize it? For example, consider this code:

$fn=100;
difference()
{
  cube([25,25,5]);
  translate([4,4,-1]) cylinder(h=7,r=2);
  translate([25-4,4,-1]) cylinder(h=7,r=2);
  translate([4,25-4,-1]) cylinder(h=7,r=2);
  translate([25-4,25-4,-1]) cylinder(h=7,r=2);
}

That creates the plate with four drill holes you see on the right.

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The PDP-1: The Machine That Started Hacker Culture

June 27, 2017 by Dan Maloney 32 Comments

One of my bucket list destinations is the Computer History Museum in Mountain View, California — I know, I aim high. I’d be chagrined to realize that my life has spanned a fair fraction of the Information Age, but I think I’d get a kick out of seeing the old machines, some of which I’ve actually laid hands on. But the machines I’d most like to see are the ones that predate me, and the ones that contributed to the birth of the hacker culture in which I and a lot of Hackaday regulars came of age.

If you were to trace hacker culture back to its beginning, chances are pretty good that the machine you’d find at the root of it all is the Digital Equipment Corporation’s PDP-1. That’s a tall claim for a machine that was introduced in 1959 and only sold 53 units, compared to contemporary offerings from IBM that sold tens of thousands of units. And it’s true that the leading edge of the explosion of digital computing in the late 50s and early 60s was mainly occupied by “big iron” machines, and that mainframes did a lot to establish the foundations for all the advances that were to come.

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MEMS: The Biggest Word In Small

June 26, 2017 by Al Williams 20 Comments

What’s tiny and on track to be worth $22 billion dollars by 2018? MEMS (Micro Electrical Mechanical Systems). That’s a catch-all phrase for microscopic devices that have moving parts. Usually, the component sizes range from 0.1 mm to 0.001 mm, which is tiny, indeed. There are some researchers working with even smaller components, sometimes referenced as NEMS (Nano Electrical Mechanical Systems).

Resonant Cantilever by [Pcflet01], CC BY-SA 3.0

MEMS have a wide range of applications including ink jet printers, accelerometers, gyroscopes, microphones, pressure sensors, displays, and more. Many of the sensors in a typical cell phone would not be possible without MEMS. There are many ways that MEMS devices are built, but just to get a flavor, consider the cantilever (see right), one of the most common MEMS constructions.

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Hacking An Inspection Microscope

June 26, 2017 by Elliot Williams 74 Comments

Sometimes I need to be able to take photographs of very small things, and the so-called macro mode on my point-and-shoot camera just won’t cut it. And it never hurts to have an inspection scope on hand for tiny soldering jobs, either, though I prefer a simple jeweler’s loupe in one eye for most tasks. So I sent just over $40 off to my close friend Alibaba, and a few weeks later was the proud owner of a halfway usable inspection scope that records stills or video to an SD card.

Unfortunately, it’s only halfway useable because of chintzy interface design and a wobbly mount. So I spent an afternoon, took the microscope apart, and got it under microcontroller control, complete with WiFi and a scripting language. Much better! Now I can make microscope time-lapses, but much more importantly I can take blur-free photos without touching the wiggly rig. It was a fun hack, so I thought I’d share. Read on!

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Catastrophic Forgetting: Learning’s Effect On Machine Minds

June 23, 2017 by Steven Dufresne 22 Comments

What if every time you learned something new, you forgot a little of what you knew before? That sort of overwriting doesn’t happen in the human brain, but it does in artificial neural networks. It’s appropriately called catastrophic forgetting. So why are neural networks so successful despite this? How does this affect the future of things like self-driving cars? Just what limit does this put on what neural networks will be able to do, and what’s being done about it?

Numerical weights in an artificial neural network

Neurons in the brain

The way a neural network stores knowledge is by setting the values of weights (the lines in between the neurons in the diagram). That’s what those lines literally are, just numbers assigned to pairs of neurons. They’re analogous to the axons in our brain, the long tendrils that reach out from one neuron to the dendrites of another neuron, where they meet at microscopic gaps called synapses. The value of the weight between two artificial neurons is roughly like the number of axons between biological neurons in the brain.

To understand the problem, and the solutions below, you need to know a little more detail.

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Go Small, Get Big: The Hack That Revolutionized Bioscience

June 23, 2017 by Dan Maloney 11 Comments

Few people outside the field know just how big bioscience can get. The public tends to think of fields like physics and astronomy, with their huge particle accelerators and massive telescopes, as the natural expressions of big science. But for decades, biology has been getting bigger, especially in the pharmaceutical industry. Specialized labs built around the automation equipment that enables modern pharmaceutical research would dazzle even the most jaded CERN physicist, with fleets of robot arms moving labware around in an attempt to find the Next Big Drug.

I’ve written before on big biology and how to get more visibility for the field into STEM programs. But how exactly did biology get big? What enabled biology to grow beyond a rack of test tubes to the point where experiments with millions of test occasions are not only possible but practically required? Was it advances in robots, or better detection methodologies? Perhaps it was a breakthrough in genetic engineering?

Nope. Believe it or not, it was a small block of plastic with some holes drilled in it. This is the story of how the microtiter plate allowed bioscience experiments to be miniaturized to the point where hundreds or thousands of tests can be done at a time.

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