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Hackaday Prize Entry : Cosmic Particle Detector Is Citizen Science Disguised As Art

เสาร์, 06/10/2017 - 01:30

Thanks to CERN and their work in detecting the Higgs Boson using the Large Hadron Collider (LHC), there has been a surge of interest among many to learn more about the basic building blocks of the Universe. CERN could do it due to the immense power of the LHC — capable of reaching a beam energy of almost 14TeV. Compared to this, some cosmic rays have energies as high as 3 × 1020 eV. And these cosmic rays keep raining down on Earth continuously, creating a chain reaction of particles when they interact with atmospheric molecules. By the time many of these particles reach the surface of the earth, they have mutated into “muons”, which can be detected using Geiger–Müller Tubes (GMT).

[Robert Hart] is building an array of individual cosmic ray detectors that can be distributed across a landscape to display how these cosmic rays (particles, technically) arrive as showers of muons. It’s a citizen science project disguised as an art installation.

The heart of each individual device will be a set of three Russian Geiger–Müller Tubes to detect the particles, and an RGB LED that lights up depending on the type of particle detected. There will also be an audio amplifier driving a small 1W speaker to provide some sound effects. A solar panel is used to charge the battery, which will feed the converters that generate the logic and high voltages required for the GMT array. The GMT signals pass through a pulse shaper and then through the logic gates, finally being amplified to drive the LEDs and the audio amplifier. Depending on the direction and order in which the particles pass through the GMT’s, the device will produce a bright flash of one of 4 colors — red, green, blue or white. It also triggers generation one of three musical notes — C, F, G or a combination of all three. The logic section uses coincidence detection, which has worked well for his earlier iterations. A coincidence detector is an AND logic which produces an output when two input events occur sufficiently close to each other in time. He’s experimented with several design versions, before settling on a trio of 555 monostable multivibrators to provide the initial pulse shaping, followed by some AND gates. A neat PCB design brings it all together.

While the prototypes are housed in wooden cases, he’s going to experiment with various enclosure and mounting options to see which works best — bollard lamp posts, spheres, something that hangs on a tree or tripod or is put in the ground like a paving block. Future prototypes and installations may include a software, pulse summing and solid-state detectors. Embedded below is a video of his current version of the detector, but there are several other interesting videos on his project page that are worth looking at. And if this has gotten you interested, check out this CERN brochure — LHC, The guide for a simple explanation of particle physics and information on the LHC.

The HackadayPrize2017 is Sponsored by:


Filed under: The Hackaday Prize

NEETS: Electronics Education Courtesy of the US Navy

เสาร์, 06/10/2017 - 00:01

Just about everything the US Government publishes is available to the public. Granted, browsing the GPO bookstore yields a lot of highly specialized documents like a book on how to perform pediatric surgery in hostile environments. However, there are some gems if you know where to look. If you ever wanted to have a comprehensive electronics course, the US Navy’s NEETS (Navy Electricity and Electronics Training Series) is freely available and has 24 modules that cover everything from electron flow through conductors, to tubes, to transistors and integrated circuits.

There are many places you can download these in one form or another. Some of them are in HTML format. Others are in PDF, which might be easier to put on a mobile device. The Internet Archive has them, although sorting by title isn’t quite in numerical order.

Some of the content is a bit dated — the computer section talks about magnetic core and bubble memory, for example, even though the latest revision we know of was in 1998. Of course, there are also references to bits of Navy gear that probably doesn’t mean much to most of us. However, things like the shift register (from module 13) you can see above haven’t changed in a few decades, so you can still learn a lot. The phase splitter in the top banner is even more timeless (you can find it in module 8).

Technician-Level Understanding

However, the core information is very relevant and well-presented. While it is true that you can find lots of college-level electrical engineering material online now for free, those courses are often stuffed full of theory and math. NEETS is aimed at a technician, so it is quite practical and includes sections on things as basic as reading schematics and as sophisticated as RF filters, microwave circuits, and fiber optics.

Not that the course pulls too many punches. The section on oscillators, for example, covers in great detail how different oscillator types work (like the Pierce oscillator from module 9, below). It also covers frequency multipliers. The emphasis isn’t on their design, but understanding the principles of operation is a big step towards being able to design these circuits. There’s also a lot of background information on how a lot of components like ICs are built.

Some of the chapters are better than others. The test equipment module (module 16) is good but covers a lot about calibration stickers and other Navy administrative trivial.  You might not be that interested in RADAR systems, synchros, and gyros, either (then again, maybe you are, but you can always skip the modules you don’t want). Here’s the list (with abbreviated titles):

  • 1 – DC
  • 2 – AC
  • 3 – Circuit Protection and Measurement
  • 4 – Wiring and Schematics
  • 5 – Generators and Motors
  • 6 – Tubes
  • 7 – Solid State Devices/Transistors
  • 8 – Amplifiers
  • 9 – Oscillators, Filters, and Frequency Multipliers
  • 10 – Antennas
  • 11 – Microwaves
  • 12 – Modulation
  • 13 – Digital Logic
  • 14 – Microelectronics/ICs
  • 15 – Synchros, Servos, Gyros
  • 16 – Test Equipment
  • 17 – RF
  • 18 – RADAR
  • 19 – Technician’s Handbook
  • 20 – Glossary
  • 21 – Test Methods
  • 22 – Digital Computers
  • 23 – Magnetic Recording
  • 24 – Fiber Optics

 

Most of the modules have assignments and you can find the answers, too. You may need a little algebra, but not much and certainly nothing beyond that.

The Navy has a lot of other training that might be interesting. There’s machine shop training, photography courses, and hydraulics. If you are looking for something more academic, the MITx Circuits and Electronics class is excellent and a great example of what can be done with Internet delivery of training. It’s truly a great time to be teaching yourself more about electronics!


Filed under: Engineering

Portable Jacob’s Ladder for When…You Know… You Need a Portable Jacob’s Ladder

ศุกร์, 06/09/2017 - 22:30

When do you need a portable Jacob’s Ladder? We don’t know, but apparently [mitxela] doesn’t want to leave home (or the laboratory) without one. So he built a portable unit that works for a few minutes on a battery. In the video (see below), he says he wouldn’t presume to claim it was the smallest Jacob’s Ladder ever, but he thought it might be a contender.

The battery is a LiPo cell and although it might last up to four minutes, [mitxela] points out that the transistors probably wouldn’t survive that much on time, despite the heat sinks he put in place. The whole device is 45mm square and 17mm thick. Of course, the wires add some height (about 150mm total).

We were hoping to see more of the insides, but we presume this uses one of the cheap high voltage modules you can procure from the usual Far East sources–or, at least–it could. The rest is just laser cutting and workmanship.

If you haven’t encountered them before (outside of old monster movies), a Jacob’s Ladder lets high voltage ionize the air down at the bottom of the narrow gap. The ionized air is hot and rises, and the current flows through it, despite the electrodes getting further apart. Of course, that means you shouldn’t put on in your zero-gee space station.

You might think a portable Jacob’s ladders is unique. Turns out, it isn’t. If you want something easy (and perhaps not as portable), you can’t get much easier than this one.


Filed under: classic hacks

Retrotechtacular: Reading and Sorting Mail Automatically

ศุกร์, 06/09/2017 - 21:01

We often read about the minicomputers of the 1960s, and see examples of their use in university research laboratories or medium-sized companies where they might have managed the accounts. It’s tempting though to believe that much of the world in those last decades of the analogue era remained untouched by computing, only succumbing in the decade of the microcomputer, or of the widespread use of the Internet.

What could be more synonymous with the pre-computing age than the mail system? Hundreds of years of processing hand-written letters, sorted by hand, transported by horses, boats, railroads and then motor transport, then delivered to your mailbox by your friendly local postman. How did minicomputer technology find its way into that environment?

Thus we come to today’s film, a 1970 US Postal Service short entitled “Reading And Sorting Mail Automatically”. In it we see the latest high-speed OCR systems processing thousands of letters an hour and sorting them by destination, and are treated to a description of the scanning technology.

If a Hackaday reader in 2017 was tasked with scanning and OCR-ing addresses, they would have high-resolution cameras and formidable computing power at their disposal. It wouldn’t be a trivial task to get it right, but it would be one that given suitable open-source OCR software could be achieved by most of us. By contrast the Philco engineers who manufactured the Postal Service’s  scanners would have had to create them from scratch.

This they performed in a curiously analogue manner, with a raster scan generated by a CRT. First a coarse scan to identify the address and its individual lines, then a fine scan to pick out the line they needed. An optical sensor could then pick up the reflected light and feed the information back to the computer for processing.

The description of the OCR process is a seemingly straightforward one of recognizing the individual components of letters which probably required some impressive coding to achieve in the limited resources of a 1960s minicomputer. The system couldn’t process handwriting, instead it was reserved for OCR-compatible business mail.

Finally, the address lines are compared with a database of known US cities and states, and each letter is routed to the appropriate hopper. We are shown a magnetic drum data store, the precursor of our modern hard drives, and told that it holds an impressive 10 megabytes of data. For 1970, that was evidently a lot.

It’s quaint to see what seems to be such basic computing technology presented as the last word in sophistication, but the truth is that to achieve this level of functionality and performance with the technology of that era was an extremely impressive achievement. Sit back and enjoy the film, we’ve placed it below the break.


Filed under: Retrotechtacular

Giving a Camera Mount a Little (Magnetic) Attractiveness

ศุกร์, 06/09/2017 - 18:00

It’s probably safe to say that most hackers and makers don’t really want to fuss around with the details of making video documentation of their work. They would rather spend their time and energy on the actual project at hand…you know — the fun stuff.

[Robert Baruch] has been wanting more mounting options for his camera mount to make it easier and quicker to set up.  One end of his existing camera mount is a clamp. This has been working for [Robert] so far, but he wanted more options. Realizing that he has plenty of ferrous metal surfaces around his shop, he had an idea — make a magnetic base add-on for his camera mount.

In the video, [Robert] walks us through the process of creating this magnetic camera mount add-on, starting with the actual base. It is called a switchable magnetic base (or mag-base as he calls it) and looks like a handy little device. This was surely the most expensive part of the build, but looks like it should last a very long time. Basically it’s a metal box with magnets on the inside, and a rotating switch on the outside. When the switch is in one position, the box’s bottom is magnetic. Rotate the switch to the other position, and the bottom is no longer magnetic. These switchable magnetic bases come with a stud on top for attaching other things to it, which it looks like [Robert] has already done. From there on out though, he explains and shows the rest of the build.

Some mild steel rod was cut and modified to slip into the pipe. The rod is held in place by a set screw which allows for easy adjustment of the mount’s height. Then he welds the rod to a washer which is, in turn, welded to a tube. After the welding, he takes the whole thing to a deburring wheel to clean it up. After that, the final touches are made with some spray paint and a custom 3D printed cap.

Sprinkled throughout the video are some useful tips, one of them being how he strips the zinc off of the washer with acid prior to welding. The reason for this is that you don’t want to weld over zinc because it produces neurotoxins.

Now [Robert] can attach his camera mount quickly just about anywhere in his shop with the help of his new magnetic base.

There’s no shortage of camera mount hacks that we’ve covered. Here’s another one involving a magnet, but also has an automatic panning feature. Do you need a sliding camera mount? How about a motorized sliding camera mount — enjoy.


Filed under: digital cameras hacks, news

Being a Friend to Man’s Best Friend

ศุกร์, 06/09/2017 - 15:00

When [Jason Dorie] tipped us off on this, he said, “This barely qualifies as a hack.” We disagree, as would any other dog lover who sees how it improved the life of his dog with a simple mood-altering doggie-bed carousel.

[Jason]’s hack lies not so much in the rotating dog bed – it’s just a plywood platform on a bearing powered by a couple of Arlo robot wheels. The hack is more in figuring out what the dog needs. You see, [Thurber] is an old dog, and like many best friends who live a long life, he started showing behavioral changes, including endlessly pacing out the same circular path to the point of exhaustion. Circling in old dogs is often a symptom of canine cognitive dysfunction, which is basically the dog version of Alzheimer’s. Reasoning that the spinning itself was soothing, [Jason] manually turned [Thurber]’s dog bed on the floor. [Thurber] calmed down immediately, so the bittersweetly named “Dementia-Go-Round” was built.

Sadly, [Thurber] was actually suffering from a brain tumor, but he still really enjoyed the spinning and it gave him some peace during his last few days. Looking for hacks to help with human dementia? We’ve had plenty of those before too.


Filed under: Medical hacks, misc hacks

Controlling a Moog Werkstatt with a Capacitive Touch Jankó Keyboard

ศุกร์, 06/09/2017 - 12:00

[Ben Bradley], a member of Freeside Atlanta, built a capacitive touch Jankó keyboard for the Georgia Tech Moog Hackathon. Jankó Keyboards are a 19th-Century attempt to add a more compact piano keyboard. There are three times as many keys as a traditional piano but arranged vertically for (supposedly) greater convenience while playing–an entire octave can be covered with one hand. But yeah, it never caught on.

[Ben]’s project consists of a series of brass plates wired to capacitive touch breakout boards from Adafruit, one for each of the Arduino Mega clone’s four I2C addresses. When a key is touched, the Arduino sends a key down signal to the Werkstatt while using a R-2R ladder to generate voltage for the VCO exponential input.

The most recent Moog Hackathon was the third.  Twenty-five teams competed from Georgia Tech alone, plus more from other schools, working for 48 hours to build interfaces with Moog Werkstatt-Ø1 analog synths, competing for $5,000 in cash prizes as well as Werkstatts for the top three teams.

We’re synth-fiends here on Hackaday: we cover everything from analog synths to voltage controlled filters.

Via Freeside Atlanta, photo by [Nathan Burnham].

 


Filed under: musical hacks

Hackaday Prize Entry: Printing Bones

ศุกร์, 06/09/2017 - 09:00

You would be forgiven to think that 3D printing is only about rolls of filament and tubs of resin. The fact is, there are many more 3D printing technologies out there. Everything from powders to paper can be used to manufacture a 3D model. [Jure]’s Hackaday Prize entry is meant to explore those weirder 3D manufacturing techniques. This is a printer that lays down binder over a reservoir of powder, slowly building up objects made out of minerals.

The key question with a powder printer is exactly what material this printer will use. For this project, [Jure] is planning on printing with hydroxyapatite, a mineral that makes up about 70% of bones by weight. Printing bones — yes, they do that — is quite expensive and has diverse applications.

The design of this printer is about what you would expect. It’s a Cartesian design with a roller to distribute powder, a piston to drop the part down into the frame, and an industrial inkjet printhead designed for wide format printers. It’s a fantastic piece of work and one of the better powder printers we’ve seen, and we can’t wait to see what [Jure] is able to produce with this.

The HackadayPrize2017 is Sponsored by:
Filed under: 3d Printer hacks, The Hackaday Prize

Mini Delta 3D Printer in Action at the Monoprice Booth

ศุกร์, 06/09/2017 - 06:01

When I was at Bay Area Maker Faire a few weekends ago I stopped by the Monoprice booth to chat with [Chris Apland], their head of 3D Printing. Earlier in the week, the company had just announced preorders for their new $169 delta-style 3D printer called the MP Mini Delta.

[Brian Benchoff] covered that launch and I don’t have a lot of details about the machine itself to add. I saw it in action, printing tiny waving cat models. The stock printer can use ABS or PLA and has a build volume of 110mm in diameter and 120mm tall and these preorder units (being sold through Indegogo) will begin shipping in August.

What was of interest is to hear the shipping estimates the Monoprice team is throwing around. Chris told me that their conservative estimate is that 20,000 of these printers will ship through this preorder, but he is optimistic that by the end of the fourth quarter they’ll be closer to 100,000 units. That is incredible.

Part of the promise here is the out of the box functionality; [Chris] mentioned having a printed cat in your hands within 5 minutes. If it can actually do that without the need for setup and calibration that’s impressive. But I know that even seasoned printing veterans are interested in seeing how fast they can run this tiny delta and still turn out quality prints.

You’ll find the video interview after the break.


Filed under: 3d Printer hacks, Interviews

Sony Unveils Swarm Robots for Kids

ศุกร์, 06/09/2017 - 03:01

Sony recently unveiled Toio, an educational robotics toy for young programmers. We all know Sony as an electronics giant, but they do dabble in robotics from time to time. The AIBO dog family is probably their most famous creation, though there is also QRIO, a bipedal humanoid, and on the stranger side, the Rolly.

Toio consists of two small cube robots which roll around the desktop. You can control them with handheld rings, or run programs on them. The robots are charged by a base station, which also has a cartridge slot. Sony is marketing this as an ecosystem that can be expanded by buying packs which consist of accessories and a software cartridge. It looks like the cartridge is yet another proprietary memory card format. Is Sony ever going to learn?

There isn’t much hard information on Toio yet. We know it will be released in Japan on December 1st and will cost around ¥ 20,000, or about 200 USD. No word yet on a worldwide release.

The striking thing about this kit is how well the two robots know each other’s position. Tape a paper pair of pants, and they “walk” like two feet. Attach a paper linkage between them, and they turn in perfect sync, like two gears. Add some paper strips, and the two robots work together to form a gripper.  We can only guess that Sony is using cameras on the bottom of each robot to determine position — possibly with the aid of an encoded work surface — similar to Anoto paper. Whatever technology it is, here’s to hoping Sony puts out an SDK for researchers and hackers to get in on the fun with these little robots.


Filed under: robots hacks

TORLO is a Beautiful 3D Printed Clock

ศุกร์, 06/09/2017 - 01:31

What if you could build a clock that displays time in the usual analog format, but with the hands moving around the outside of the dial instead of rotating from a central point? This is the idea behind TORLO, a beautiful clock built from 3D printed parts.

The clock is the work of [ekaggrat singh kalsi], who wanted to build a clock using a self-oscillating motor. Initial experiments had some success, however [ekaggrat] encountered problems with the motors holding consistent time, and contacts wearing out. This is common in many electromechanical systems — mechanics who had to work with points ignition will not remember them fondly. After pushing on through several revisions, it was decided instead to switch to an ATtiny-controlled motor which was pulsed once every two seconds. This had the benefit of keeping accurate time as well as making it much easier to set the clock.

The stunning part of the clock, however, is the mechanical design. The smooth, sweeping form is very pleasing to the eye, and it’s combined with a beautiful two-tone colour scheme that makes the exposed gears and indicators pop against the white frame. The minute and hour hands form the most striking part of the design — the indicators are attached to a large ring gear that is turned by the gear train built into the frame. The video below the break shows the development process, but we’d love to see a close-up of how the gear train meshes with the large ring gears which are such an elegant part of the clock.

A great benefit of 3D printing is that it makes designing custom gear trains very accessible. We’ve seen other unconventional 3D printed clock builds before. 


Filed under: 3d Printer hacks, clock hacks

From 50s Perceptrons To The Freaky Stuff We’re Doing Today

ศุกร์, 06/09/2017 - 00:01

Things have gotten freaky. A few years ago, Google showed us that neural networks’ dreams are the stuff of nightmares, but more recently we’ve seen them used for giving game character movements that are indistinguishable from that of humans, for creating photorealistic images given only textual descriptions, for providing vision for self-driving cars, and for much more.

Being able to do all this well, and in some cases better than humans, is a recent development. Creating photorealistic images is only a few months old. So how did all this come about?

Perceptrons: The 40s, 50s And 60s The perceptron

We begin in the middle of the 20th century. One popular type of early neural network at the time attempted to mimic the neurons in biological brains using an artificial neuron called a perceptron. We’ve already covered perceptrons here in detail in a series of articles by Al Williams, but briefly, a simple one looks as shown in the diagram.

Given input values, weights, and a bias, it produces an output that’s either 0 or 1. Suitable values can be found for the weights and bias that make a NAND gate work. But for reasons detailed in Al’s article, for an XOR gate you need more layers of perceptrons.

In a famous 1969 paper called “Perceptrons”, Minsky and Papert pointed out the various conditions under which perceptrons couldn’t provide the desired solutions for certain problems. However, the conditions they pointed out applied only to the use of a single layer of perceptrons. It was known at the time, and even mentioned in the paper, that by adding more layers of perceptrons between the inputs and the output, called hidden layers, many of those problems, including XOR, could be solved.

Despite this way around the problem, their paper discouraged many researchers, and neural network research faded into the background for a decade.

Backpropagation And Sigmoid Neurons: The 80s

In 1986 neural networks were brought back to popularity by another famous paper called “Learning internal representations by error propagation” by David Rummelhart, Geoffrey Hinton and R.J. Williams. In that paper they published the results of many experiments that addressed the problems Minsky talked about regarding single layer perceptron networks, spurring many researchers back into action.

Also, according to Hinton, still a key figure in the area of neural networks today, Rummelhart had reinvented an efficient algorithm for training neural networks. It involved propagating back from the outputs to the inputs, setting the values for all those weights using something called a delta rule.

Fully connected neural network and sigmoid

The set of calculations for setting the output to either 0 or 1 shown in the perceptron diagram above is called the neuron’s activation function. However, for Rummelhart’s algorithm, the activation function had to be one for which a derivative exists, and for that they chose to use the sigmoid function (see diagram).

And so, gone was the perceptron type of neuron whose output was linear, to be replaced by the non-linear sigmoid neuron, still used in many networks today. However, the term Multilayer Perceptron (MLP) is often used today to refer not to the network containing perceptrons discussed above but to the multilayer network which we’re talking about in this section with it’s non-linear neurons, like the sigmoid. Groan, we know.

Also, to make programming easier, the bias was made a neuron of its own, typically with a value of one, and with its own weights. That way its weights, and hence indirectly its value, could be trained along with all the other weights.

And so by the late 80s, neural networks had taken on their now familiar shape and an efficient algorithm existed for training them.

Convoluting And Pooling

In 1979 a neural network called Neocognitron introduced the concept of convolutional layers, and in 1989, the backpropagation algorithm was adapted to train those convolutional layers.

Convolutional neural networks and pooling

What does a convolutional layer look like? In the networks we talked about above, each input neuron has a connection to every hidden neuron. Layers like that are called fully connected layers. But with a convolutional layer, each neuron in the convolutional layer connects to only a subset of the input neurons. And those subsets usually overlap both horizontally and vertically. In the diagram, each neuron in the convolutional layer is connected to a 3×3 matrix of input neurons, color-coded for clarity, and those matrices overlap by one.

This 2D arrangement helps a lot when trying to learn features in images, though their use isn’t limited to images. Features in images occupy pixels in a 2D space, like the various parts of the letter ‘A’ in the diagram. You can see that one of the convolutional neurons is connected to a 3×3 subset of input neurons that contain a white vertical feature down the middle, one leg of the ‘A’, as well as a shorter horizontal feature across the top on the right. When training on numerous images, that neuron may become trained to fire strongest when shown features like that.

But that feature may be an outlier case, not fitting well with most of the images the neural network would encounter. Having a neuron dedicated to an outlier case like this is called overfitting. One solution is to add a pooling layer (see the diagram). The pooling layer pools together multiple neurons into one neuron. In our diagram, each 2×2 matrix in the convolutional layer is represented by one element in the pooling layer. But what value goes in the pooling element?

In our example, of the 4 neurons in the convolutional layer that correspond to that pooling element, two of them have learned features of white vertical segments with some white across the top. But one of them encounters this feature more often. When that one encounters a vertical segment and fires, it will have a greater value than the other. So we put that greater value in the corresponding pooling element. This is called max pooling, since we take the maximum value of the 4 possible values.

Notice that the pooling layer also reduces the size of the data flowing through the network without losing information, and so it speeds up computation. Max pooling was introduced in 1992 and has been a big part of the success of many neural networks.

Going Deep Deep neural networks and ReLU

A deep neural network is one that has many layers. As our own Will Sweatman pointed out in his recent neural networking article, going deep allows for layers nearer to the inputs to learn simple features, as with our white vertical segment, but layers deeper in will combine these features into more and more complex shapes, until we arrive at neurons that represent entire objects. In our example when we show it an image of a car, neurons that match the features in the car fire strongly, until finally the “car” output neuron spits out a 99.2% confidence that we showed it a car.

Many developments have contributed to the current success of deep neural networks. Some of those are:

  • the introduction starting in 2010 of the ReLU (Rectified Linear Unit) as an alternative activation function to the sigmoid. See the diagram for ReLU details. The use of ReLUs significantly sped up training. Barring other issues, the more training you do, the better the results you get. Speeding up training allows you to do more.
  • the use of GPUs (Graphics Processing Units). Starting in 2004 and being applied to convolutional neural networks in 2006, GPUs were put to use doing the matrix multiplication involved when multiplying neuron firing values by weight values. This too speeds up training.
  • the use of convolutional neural networks and other technique to minimize the number of connections as you go deeper. Again, this too speeds up training.
  • the availability of large training datasets with tens and hundreds of thousands of data items. Among other things, this helps with overfitting (discussed above).
Inception v3 architecture Deep dream hexacopter

To give you some idea of just how complex these deep neural networks can get, shown here is Google’s Inception v3 neural network written in their TensorFlow framework. The first version of this was the one responsible for Google’s psychedelic deep dreaming. If you look at the legend in the diagram you’ll see some things we’ve discussed, as well as a few new ones that have made a significant contribution to the success of neural networks.

The example shown here started out as a photo of a hexacopter in flight with trees in the background. It was then submitted to the deep dream generator website, which produced the image shown here. Interestingly, it replaced the propellers with birds.

By 2011, convolutional neural networks with max pooling, and running on GPUs had achieved better-than-human visual pattern recognition on traffic signs with a recognition rate of 98.98%.

Processing And Producing Sequences – LSTMs

The Long Short Term Memory (LSTM) neural network is a very effective form of Recurrent Neural Networks (RNN). It’s been around since 1995 but has undergone many improvements over the years. These are the networks responsible for the amazing advancements in speech recognition, producing captions for images, producing speech and music, and more. While the networks we talked about above were good for seeing a pattern in a fixed size piece of data such as an image, LSTMs are for pattern recognition in a sequence of data or for producing sequences of data. Hence, they do speech recognition, or produce sentences.

LSTM neural network and example

They’re typically depicted as a cell containing different types of layers and mathematical operations. Notice that in the diagram, the cell points back to itself, hence the name Recurrent neural network. That’s because when an input arrives, the cell produces an output, but also information that’s passed back in for the next time input arrives. Another way of depicting it is by showing the same cell but at different points in time — the multiple cells with arrows showing data flow between them are really the same cell with data flowing back into it. In the diagram, the example is one where we give an encoder cell a sequence of words, one at a time, the result eventually going to a “thought vector”. That vector then feeds the decoder cell which outputs a suitable response, one word at a time. The example is of Google’s Smart Reply feature.

LSTMs can be used for analysing static images though, and with an advantage over the other types of networks we’ve see so far. If you’re looking at a static image containing a beach ball, you’re more likely to decide it’s a beach ball rather than a basket ball if you’re viewing the image as just one frame of a video about a beach party. An LSTM will have seen all the frames of the beach party leading up to the current frame of the beach ball and will use what it’s previously seen to make its evaluation about the type of ball.

Generating Images With GANs Generative adversarial network

Perhaps the most recent neural network architecture that’s giving freaky results are really two networks competing with each other, the Generative Adversarial Networks (GANs), invented in 2014. The term, generative, means that one network generates data (images, music, speech) that’s similar to the data it’s trained on. This generator network is a convolutional neural network. The other network is called the discriminator and is trained to tell whether an image is real or generated. The generator gets better at fooling the discriminator, while the discriminator gets better at not being fooled. This adversarial competition produces better results than having just a generator.

StackGAN’s bird with text

In late 2016, one group improved on this further by using two stacked GANs. Given a textual description of the desired image, the Stage-I GAN produces a low resolution image missing some details (e.g. the beak and eyes on birds). This image and the textual description are then passed to the Stage-II GAN which improves the image further, including adding the missing details, and resulting in a higher resolution, photo-realistic image.

Conclusion

And there are many more freaky results announced every week. Neural network research is at the point where, like scientific research, so much is being done that it’s getting hard to keep up. If you’re aware of any other interesting advancements that I didn’t cover, please let us know in the comments below.


Filed under: Featured, Interest, software hacks

Raspberry Pi Malware Mines BitCoin

พฤ, 06/08/2017 - 22:31

According to Russian security site [Dr.Web], there’s a new malware called Linux.MulDrop.14 striking Raspberry Pi computers. In a separate posting, the site examines two different Pi-based trojans including Linux.MulDrop.14. That trojan uses your Pi to mine BitCoins some form of cryptocurrency. The other trojan sets up a proxy server.

According to the site:

Linux Trojan that is a bash script containing a mining program, which is compressed with gzip and encrypted with base64. Once launched, the script shuts down several processes and installs libraries required for its operation. It also installs zmap and sshpass.

It changes the password of the user “pi” to “\$6\$U1Nu9qCp\$FhPuo8s5PsQlH6lwUdTwFcAUPNzmr0pWCdNJj.p6l4Mzi8S867YLmc7BspmEH95POvxPQ3PzP029yT1L3yi6K1”.

In addition, the malware searches for network machines with open port 22 and tries to log in using the default Raspberry Pi credentials to spread itself.

Embedded systems are a particularly inviting target for hackers. Sometimes it is for the value of the physical system they monitor or control. In others, it is just the compute power which can be used for denial of service attacks on others, spam, or — in the case — BitCoin mining. We wonder how large does your Raspberry Pi botnet needs to be to compete in the mining realm?

We hope you haven’t kept the default passwords on your Pi. In fact, we hope you’ve taken our previous advice and set up two factor authentication. You can do other things too, like change the ssh port, run fail2ban, or implement port knocking. Of course, if you use Samba to share Windows files and printers, you ought to read about that vulnerability, as well.


Filed under: news, Raspberry Pi, security hacks

Nuts and Bolts: Keeping it Tight

พฤ, 06/08/2017 - 21:00

It’s not much of a stretch to say that without nuts and bolts, the world would fall apart. Bolted connections are everywhere, from the frame of your DIY 3D printer to the lug nuts holding the wheels on your car. Though the penalty for failure is certainly higher in the latter than in the former, self-loosening of nuts and bolts is rarely a good thing. Engineers have come up with dozens of ways to make sure the world doesn’t fall apart, and some work better than others. Let’s explore a few of these methods and find out what works, what doesn’t work, and in the process maybe we’ll learn a little about how these fascinating fasteners work.

What Doesn’t Work Transverse vibration leads to self-loosening. Source: BoltScience.com

There are plenty of ways for a bolted joint to fail, but vibration-induced self-loosening is perhaps the most insidious. Anyone who has ever pounded on a stuck bolt or used an impact wrench to remove a rusty nut knows that vibration really helps. Put that same joint into service and subject it to the right kind of vibration, and there’s a good chance the connection will self-loosen and cause the joint to fail.

In the 1960s, German engineer Gerhard Junker studied self-loosening and came to the conclusion that transverse vibrations were responsible for the failure of bolted connections. He devised a simple test apparatus that provided rapid transverse vibrations while monitoring fastener preload tension with a load cell. Graphing preload as a function the number of vibratory cycles yielded clues as to the effectiveness of various locking methods. The test became known as the Junker test and as standard DIN 65151, it remains the gold standard for testing self-loosening.

There are some fascinating videos out there showing Junker tests in action, and some are downright scary. Typically, we’ll throw a simple helical spring lock washer on a stud or bolt, torque down the nut nice and snug, and call it a day, feeling like we’ve made a secure joint. But nothing could be further from the truth. In fact, the video below shows that not only do lock washers add very little security to bolted connections, none of the other common methods — plain washers, nylon insert nuts, and stacked nuts — provide much help either.

What Does Work Jam nut in action. Source: BoltScience.com

Obviously, the video above is aimed at marketing the company’s fancy wedge-locking washers, and it’s pretty clear that they work well. But why do they work when a simple lock washer fails? To answer that, it pays to look at what else works, something that wasn’t tested in the video — a properly installed jam nut.

A jam nut is a low-profile nut, typically about half the height of a standard nut, that’s installed below the larger nut. When the jam nut is installed, it’s tightened only to about a quarter to a half of the full final torque. The thick nut is installed next and torqued to the final value while the jam nut is held in place with a wrench. This effectively pulls the bolt up through the jam nut. The threads of the bolt are then in contact with the top flanks of the threads within the jam nut, while simultaneously contacting the upper or pressure flanks of the top nut. With the top and bottom nuts providing opposing forces on the bolt, the nuts are far less likely to self-loosen.

A similar mechanism is at work in the wedge-locking washers. The two halves of the washer have interlocking wedges, the angle of which exceeds the pitch of the bolt threads. As the bolt is tightened, the higher pitch of the washers pulls the bolt back upwards, providing an opposing force to jam the threads and prevent the fastener from self-loosening.

If we look at all the locking methods that fail, they all have something in common: they all rely on friction. Jam nuts and wedge-lock washers work by providing tension to oppose the transverse vibrations that lead to self-loosening and are therefore much more effective.

Of course, there are other methods of locking threaded fasteners. Adhesive thread lockers come to mind, as do more complicated methods like wired nuts and tabbed washers, and they can be very effective methods. But for low cost and ease of installation, it’s hard to beat a simple jam nut to keep the world from falling apart.

Featured image source: Nord-LockGroup


Filed under: Engineering, Featured, Hackaday Columns

Adding Character To The C64

พฤ, 06/08/2017 - 18:00

The venerable Commodore 64, is there anything it can’t do? Like many 1980s computer platforms, direct access to memory and peripherals makes hacking easy and fun. In particular, you’ll find serial & parallel ports are ripe for experimentation, but the Commodore has its expansion/cartridge port, too, and [Frank Buss] decided to hook it up to a two-line character LCD.

Using the expansion port for this duty is a little unconventional. Unlike the parallel port, the expansion port doesn’t have a stable output, as such. The port contains the data lines of the 6510 CPU and thus updates whenever RAM is read or written to, rather then updating in a controlled fashion like a parallel port does. However, [Frank] found a way around this – the IO1 and IO2 lines go low when certain areas of memory are written to. By combining these with latch circuitry, it’s possible to gain up to 16 parallel output lines – more than enough to drive a simple HD44780 display! It’s a testament to the flexibility of 74-series logic.
It’s all built on a C64 cartridge proto-board of [Frank]’s own design, and effort was made to ensure the LCD works with BASIC for easy experimentation. It’s a tidy mod that could easily be built into a nice enclosure and perhaps used as the basis for an 8-bit automation project. Someone’s gotta top that Amiga 2000 running the school district HVAC, after all!


Filed under: classic hacks

Suffer No Substitutes — The Hudspith Steam Bicycle Is One-Of-A-Kind

พฤ, 06/08/2017 - 15:00

In a bit of punky, steam-based tinkering, Brittish engineer [Geoff Hudspith]’s obsession for steam and passion for cycles fused into the Hudspith Steam Bicycle.

Built and improved over the past thirty years, the custom steam engine uses a petrol and kerosene mix for fuel, reaching a top speed of 32km/h and has a range of 16km on one tank of water. While in motion, the boiler is counter-balanced by the water tank on the rear as well as the flywheel, water pump, and the other components. However, [Hudspith] says he doesn’t have an easy go of it carrying the bike up the flight of stairs to his flat — as you can imagine. A steam whistle was fitted to the bike after insistence from others — and perhaps for safety’s sake as well, since it does take a bit of distance to stop the bike.

Many people have offered large sums for it — and at least one house in exchange for the bike — but [Hudspith] has held on to this one-of-a-kind steam-machine. A little more about the development of the bicycle can be read here! A video of the bike in action is waiting after the break.

A steam-powered bike is cool and all, but how about a tank? Or — better yet — a Raspberry Pi?

[Thanks for the tip, Jasmine!]


Filed under: classic hacks, Engine Hacks, transportation hacks

Automotive Radar and the Doppler Effect

พฤ, 06/08/2017 - 12:00

With more and more cars driving themselves, there is an increasing demand for precise environment aware sensors. From collision avoidance to smooth driving, environmental awareness is a must have for any self-driving cars. Enter automotive radar: cool, precise and relatively cheap. Thanks to a donated automotive radar module, [Shahriar] gifts us with a “tutorial, experiment and teardown.”

Before digging into the PCB, [Shahriar] explains the theory. With just enough math for the mathmagically inclined and not too much for the math adverse, [Shahriar] goes into the details of how automotive radar is different from normal stationary radar.

Only after a brief overview of the Doppler effect, [Shahriar] digs into the PCB which reveals three die-on-PCB ASICs responsible for generating and receiving 77GHz FMCW signals coupled to a 2D array of antennas. Moreover, [Shahriar] points out the several microwave components such as “rat-race couplers” and “branchline couplers.” Additionally, [Shahriar] shows off his cool PCB rulers from SV1AFN Design Lab that he uses as a reference for these microwave components. Finally, a physical embodiment of the Doppler effect radar is demonstrated with a pair of Vivaldi horn antennas and a copper sheet.

We really like how [Shahriar] structures his video: theory, followed by a teardown and then a physical experiment to drive his lesson home. If he didn’t already have a job, we’d say he might want to consider teaching. If the video after the break isn’t enough radar for the day, we’ve got you covered.


Filed under: radio hacks

Solar Bulldozer Gets Dirty

พฤ, 06/08/2017 - 09:00

As the threat of climate change looms, more and more industries are starting to electrify rather than using traditional fuel sources like gasoline and diesel. It almost all cases, the efficiency gains turn out to be environmentally and economically beneficial. Obviously we have seen more electric cars on the roads, but this trend extends far beyond automobiles to things like lawn equipment, bicycles, boats, and even airplanes. The latest in this trend of electrified machines comes to us from YouTube user [J Mantzel] who has built his own solar-powered bulldozer.

The fact that this bulldozer is completely solar-powered is only the tip of the iceberg, however. The even more impressive part is that this bulldozer was built completely from scratch. The solar panel on the roof charges a set of batteries that drive the motors, and even though the bulldozer is slow it’s incredibly strong for its small size. It’s also possible for it to operate on solar alone if it’s sunny enough, which almost eliminates the need for the batteries entirely. It’s also built out of stainless steel and aluminum, which makes it mostly rust-proof.

This is an impressive build that goes along well with [J Mantzel]’s other projects, most of which center around an off-grid lifestyle. If that’s up your alley, there is a lot of inspiration to be had from his various projects. Be sure to check out the video of his bulldozer below as well. You don’t have to build an off-grid bulldozer to get started in the world of living off-the-grid, though, and it’s easy to start small with just one solar panel and a truck.

Thanks to [Darko] for the tip!


Filed under: green hacks

Helix Display Brings Snake Into Three Dimensions

พฤ, 06/08/2017 - 06:00

Any time anyone finds a cool way to display in 3D — is there an uncool way? — we’re on board. Instructables user [Gelstronic]’s method involves an array of spinning props to play the game Snake in 3D.

The helix display consists of twelve props, precisely spaced and angled using 3D-printed parts, each with twelve individually addressable LEDs. Four control groups of 36 LEDs are controlled by the P8XBlade2 propeller microcontroller, and the resultant 17280 voxels per rotation are plenty to produce an identifiable image.

In order to power the LEDs, [Gelstronic] used wireless charging coils normally used for cell phones, transferring 10 W of power to the helix array.  A brushless motor keeps things spinning, while an Arduino controls speed and position via an encoder. All the links to the code used are found on the project page, but we have the video of the display in action is after the break.

There are some decidedly easier ways to achieve a volumetric display, as well as some that feel ripped straight from a video game.

[Thanks for the tip, Itay!]


Filed under: hardware, led hacks

Robotic Arms Controlled By Your….. Feet?

พฤ, 06/08/2017 - 03:00

The days of the third hand’s dominance of workshops the world over is soon coming to an end. For those moments when only a third hand is not enough, a fourth is there to save the day.

Dubbed MetaLimbs and developed by a team from the [Inami Hiyama Laboratory] at the University of Tokyo and the [Graduate School of Media Design] at Keio University, the device is designed to be worn while sitting — strapped to your back like a knapsack — but use while standing stationary is possible, if perhaps a little un-intuitive. Basic motion is controlled by the position of the leg — specifically, sensors attached to the foot and knee — and flexing one’s toes actuates the robotic hand’s fingers. There’s even some haptic feedback built-in to assist anyone who isn’t used to using their legs as arms.

The team touts the option of customizeable hands, though a soldering iron attachment may not be as precise as needed at this stage. Still, it would be nice to be able to chug your coffee without interrupting your work.

It’s rather enviable that they are taking a productive route with this tech instead of the obvious super villain solution and attaching lasers to MetaLimbs’ arms.

[via /r/Robotics]


Filed under: robots hacks, wearable hacks