A 6502 Retrocomputer In A Very Tidy Package

One of the designers whose work we see constantly in the world of retrocomputing is [Grant Searle], whose work on minimal chip count microcomputers has spawned a host of implementations across several processor families.

Often a retrocomputer is by necessity quite large, as an inevitable consequence of having integrated circuits in the period-correct dual-in-line packages with 0.1″ spaced pins. Back in the day there were few micros whose PCBs were smaller than a Eurocard (100 mm x 160 mm, 4″ x 6.3″), and many boasted PCBs much larger.

[Mark Feldman] though has taken a [Grant Searle] 6502 design and fitted it into a much smaller footprint through ingenious use of two stacked Perf+ prototyping boards. This is a stripboard product that features horizontal traces on one side and vertical on the other, which lends itself to compactness. Continue reading “A 6502 Retrocomputer In A Very Tidy Package”

Shape Programmable Matter is More Magnetic Magic

How could you build an artificial tadpole? Or simulate the motion of a cilium? Those would be hard to do with mechanical means — even micromechanical because of their fluid motion. Researchers have been studying shape-programmable matter: materials that can change shape based on something like heat or magnetic field. However, most research in this area has relied on human intuition and trial and error to get the programmed shape correct. They also are frequently not very fast to change shape.

[Metin Sitti] and researchers at several institutions have found a way to make rapidly changing silicone rubber parts (PDF link) that can change shape due to a magnetic field. The method is reproducible and doesn’t seem out of reach for a hackerspace or well-equipped garage lab.

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The Elements Converge for ±.002 in Tolerance

What can be accomplished with just a torch and compressed air? We can think of many things, but bringing a 17-foot-long marine shaft into ±.002 in tolerance was not on our list.

Heat straightening (PDF) utilizes an oxy-acetylene flame that is used to quickly heat a small section of a workpiece. As the metal cools, it contracts more than it expanded when heated, resulting in a changed volume. With skill, any distortions on a shaft can theoretically be straightened out with enough time (and oxy-acetylene). Heat straightening is commonly applied to steel but works on nickel, copper, brass and aluminum additionally.

[Keith Fenner’s] standard process for trueing stock is sensitive enough that even sunlight can introduce irregularities, but at the same time is robust enough to carry out in your driveway. However, even though the only specialty tools you need are a torch, compressed air and work supports, watching [Keith] work makes it clear that heat straightening is as much an art as it is a science. Check out his artistry in the video below the break. Continue reading “The Elements Converge for ±.002 in Tolerance”

Neural Nets And Game Boy Cameras

Released in 1998, the Game Boy camera was perhaps the first digital camera many young hackers got their hands on. Around the time Sony Mavica cameras were shoving VGA resolution pictures onto floppy drives, the Game Boy camera was snapping 256×224 resolution pictures and displaying them on a 190×144 resolution display. The picture quality was terrible, but [Roland Meertens] recently had an idea. Why not use neural networks to turn these Game Boy Camera pictures into photorealistic images?

Neural networks, deep learning, machine learning, or whatever other buzzwords we’re using require training data. In this case, the training data would be a picture from a Game Boy Camera and a full-color, high-resolution image of the same scene. This dataset obviously does not exist so [Roland] took a few close up head shots of celebrities and reduced the color to four shades of gray.

[Roland]'s face captured with the Game Boy Camera (left), and turned into a photorealistic image (right)
[Roland]’s face captured with the Game Boy Camera (left), and turned into a photorealistic image (right)
For the deep machine artificial neural learning part of this experiment, [Roland] turned to a few papers on converting photographs to sketches and back again, real-time style transfer. After some work, this neural network turned the test data back into images reasonably similar to the original images. This is what you would expect from a trained neural network, but [Roland] also sent a few pics from the Game Boy Camera through this deep machine artificial learning minsky. These images turned out surprisingly well – a bit washed out, but nearly lomographic in character.

We’ve seen a lot of hacks with the Game Boy Camera over the years. Everything from dumping the raw images with a microcontroller to turning the sensor into a camcorder has been done. Although [Roland]’s technique will only work on faces, it is an excellent example of what neural networks can do.

Homemade Subaru Head Unit is Hidden Masterpiece

The Subaru BRZ (also produced for Toyota as the GT86) is a snappy sportster but [megahercas6]’s old US version had many navigation and entertainment system features which weren’t useful or wouldn’t work in his native Lithuania. He could have swapped out the built in screen for a large 4G Android tablet/phone, but there’s limited adventure in that. Instead, he went ahead and built his own homemade Navigation system by designing and integrating a whole bunch of hardware modules resulting in one “hack” of an upgrade.

The system is built around a Lenovo 4G phone-tablet running android and supporting GPS, GLONASS as well as the Chinese BeiDou satellite navigation systems. He removed the original daughter board handling the USB OTG connection on the tablet, and replaced it with his version so he could connect it to his external USB board via a flat ribbon cable. The USB board contains a Cypress 4-port USB hub. One port is used as the USB HID device to allow external buttons for system control — Power, Volume Up/Down, Fwd/Rev, Play/Pause, and Phone Answer/Hangup. The second port is used as a regular USB input to allow connecting external devices such as flash drives. The third one goes to a reversing camera while the fourth port goes to a USB DAC.

The USB DAC is another hardware board by itself and also includes a Bluetooth module which integrates his phone’s audio and control functions with the on-board system. There’s also an audio mixer which allows him to use the phone audio without having to miss out on the navigation prompts from the tablet. Both boards also contain several peripheral circuits such as amplifiers and DC power supplies. Audio to the speakers is routed through six LM3886 based power amplifier boards. And the GPS module receives its own special low-noise amplifier board to ensure extremely strong reception at all times. That’s a total of ten boards custom built for this project. He’s also managed to source all the original harness connectors so his system is literally a snap in replacement. The final assembly looks pretty dashing.

For some strange reason, the Lenovo tablet uses 4.35V as the ‘fully charged” value for its LiPo instead of the more common 4.20V, so even with the whole system connected to a hefty 12V lead acid battery from which he’s deriving the 4.20V charging voltage for the tablet, it still complains about “low battery” — and he’s looking for advice on how he can resolve that issue short of blowing up the LiPo by using the higher charge voltage. Besides that, he’s (obviously a kickass) hardware designer and a little bit rusty on the software and programming side of things, for which he’s looking for inputs from the community. His introductory video is almost 30 minutes long, but the shorter demo video after the break shows the system after installation in his car. He’s posted all of his Altium hardware source files on the project page, but until he shares PDF versions, it would be difficult for most of us to look at his work.

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An Overview Of The Dreaded EMC Tests

There is one man whose hour-long sessions in my company give me days of stress and worry. He can be found in a soundless and windowless room deep in the bowels of an anonymous building in a town on the outskirts of London. You’ve probably driven past it or others like it worldwide, without being aware of the sinister instruments  that lie within.

The man in question is sometimes there to please the demands of the State, but there’s nothing too scary about him. Instead he’s an engineer and expert in electromagnetic compatibility, and the windowless room is a metal-walled and RF-proof EMC lab lined with ferrite tiles and conductive foam spikes. I’m there with the friend on whose work I lend a hand from time to time, and we’re about to discover whether all our efforts have been in vain as the piece of equipment over which we’ve toiled faces a battery of RF-related tests. As before when I’ve described working on products of this nature the specifics are subject to NDAs and in this case there is a strict no-cameras policy at the EMC lab, so yet again my apologies as any pictures and specifics will be generic.

There are two broadly different sets of tests which our equipment will face: RF radiation, and RF injection. In simple terms: what RF does it emit, and what happens when you push RF into it through its connectors and cables? We’ll look at each in turn as a broad overview pitched at those who’ve never seen inside an EMC lab, sadly there simply isn’t enough space in a Hackaday article to cover every nuance.

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Project Zero Finds A Graphic Zero Day

After finding the infamous Heartbleed vulnerability along with a variety of other zero days, Google decided to form a full-time team dedicated to finding similar vulnerabilities. That team, dubbed Project Zero, just released a new vulnerability, and this one’s particularly graphic, consisting of a group of flaws in the Windows Nvidia Driver.

Most of the vulnerabilities found were due to poor programming techniques. From writing to user provided pointers blindly, to incorrect bounds checking, most vulnerabilities were due to simple mistakes that were quickly fixed by Nvidia. As the author put it, Nvidia’s “drivers contained a lot of code which probably shouldn’t be in the kernel, and most of the bugs discovered were very basic mistakes.”

When even our mice aren’t safe it may seem that a secure system is unattainable. However, there is light at the end of the tunnel. While the bugs found showed that Nvidia has a lot of work to do, their response to Google was “quick and positive.” Most bugs were fixed well under the deadline, and google reports that Nvidia has been finding some bugs on their own. It also appears that Nvidia is working on re-architecturing their kernel drivers for security. This isn’t the first time we’ve heard from Google’s Project Zero, and in all honesty, it probably won’t be last.