One Man, A Raspberry Pi, and a Formerly Hand Powered Loom

[Fred Hoefler] was challenged to finally do something with that Raspberry Pi he wouldn’t keep quiet about. So he built a machine assist loom for the hand weaver. Many older weavers simply can’t enjoy their art anymore due to the physical strain caused by the repetitive task. Since he had a Pi looking for a purpose, he also had his project.

His biggest requirement was cost. There are lots of assistive looms on the market, but the starting price for those is around ten thousand dollars. So he set the rule that nothing on the device would cost more than the mentioned single board computer. This resulted in a BOM cost for the conversion that came in well under two hundred dollars. Not bad!

The motive parts are simple cheap 12V geared motors off Amazon. He powered them using his own motor driver circuits. They get their commands from the Pi, running Python. To control the loom one can either type in commands into the shell or use the keyboard. There are also some manual switches on the loom itself.

In the end [Fred] met his design goal, and has further convinced his friends that the words Raspberry Pi are somehow involved with trouble.

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Pokemon Go – Bot Edition

A wild Python appeared, and it wants to play Pokemon Go. Python bots are taking over the game when you can’t, and they are good. Since you’re likely to bump into one sooner or later, here’s an overview:

One of the first workable bots and the origin of a lot of (dirty) code, as well as the (not dirty at all) Pokemon Trainer Club client secret, is [Mila432’s] Pokemon Go Bot. One of his initial goals was to make better sense of the API, which worked out better than he hoped.

Not wanting to impetuously destroy the numerous helpful applications that had been built upon the already partially known API, he decided to keep the project off Niantic’s radar. The most recent (and most powerful) version of his bot has not been released. The current version works fine within its limited functionality: Wandering around and looting Pokestops.

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BitCluster Brings a New Way to Snoop Through BitCoin Transactions

Mining the wealth of information in the BitCoin blockchain is nothing new, but BitCluster goes a long way to make sense of the information you’ll find there. The tool was released by Mathieu Lavoie and David Decary-Hetu, PH.D. on Friday following their talk at HOPE XI.

I greatly enjoyed sitting in on the talk which began with some BitCoin basics. The cryptocurrency uses user generated “wallets” which are essentially addresses that identify transactions. Each is established using key pairs and there are roughly 146 million of these wallets in existence now

If you’re a thrifty person you might think you can get one wallet and use it for years. That might be true of the sweaty alligator-skin nightmare you’ve had in your back pocket for a decade now. It’s not true when it comes to digital bits —  they’re cheap (some would say free). People who don’t generate a new wallet for every transaction weaken their BitCoin anonymity and this weakness is the core of BitCluster’s approach.

Every time you transfer BitCoin (BTC) you send the network the address of the transaction when you acquired the BTCs and sign it with your key to validate the data. If you reuse the same wallet address on subsequent transactions — maybe because you didn’t spend all of the wallet’s coins in one transaction or you overpaid and have the change routed back to your wallet. The uniqueness of that signed address can be tracked across those multiple transactions. This alone won’t dox you, but does allow a clever piece of software to build a database of nodes by associating transactions together.

Mathieu’s description of first attempts at mapping the blockchain were amusing. The demonstration showed a Python script called from the command line which started off analyzing a little more than a block a second but by the fourth or fifth blocks hit the process had slowed to a standstill that would never progress. This reminds me of some of the puzzles from Project Euler.

bitcluster-how-it-worksAfter a rabbit hole of optimizations the problem has been solved. All you need to recreate the work is a pair of machines (one for Python one for mondoDB) with the fastest processors you can afford, a 500 GB SSD, 32 GB of RAM (but would be 64 better), Python 64-bit, and at least a week of time. The good news is that you don’t have to recreate this. The 200GB database is available for download through a torrent and the code to navigate it is up on GitHub. Like I said, this type of blockchain sleuthing isn’t new but a powerful open source tool like this is.

Both Ransomware and illicit markets can be observed using this technique. Successful, yet not-so-cautious ransomers sometimes use the same BitCoin address for all payments. For example, research into a 2014 data sample turned up a ransomware instance that pulled in $611k (averaging $10k per day but actually pulling in most of the money during one three-week period). If you’re paying attention you know using the same wallet address is a bad move and this ransomware was eventually shut down.

Illicit markets like Silk Road are another application for BitCluster. Prior research methods relied on mining comments left by customers to estimate revenue. Imagine if you had to guess at how well Amazon was doing reading customer reviews and hoping they mentioned the price? The ability to observe BTC payment nodes is a much more powerful method.

A good illicit market won’t use just one wallet address. But to protect customers they use escrow address and these do get reused making cluster analysis possible. Silk Road was doing about $800k per month in revenue at its height. The bulk of purchases were for less than $500 with only a tiny percentage above $1000. But those large purchases were likely to be drug purchases of a kilo or more. That small sliver of total transactions actually added up to about a third of the total revenue.

bitcluster-logoIt’s fascinating to peer into transactions in this manner. And the good news is that there’s plenty of interesting stuff just waiting to be discovered. After all, the blockchain is a historical record so the data isn’t going anywhere. BitCluster is intriguing and worth playing with. Currently you can search for a BTC address and see total BTC in and out, then sift through income and expense sorted by date, amount, etc. But the tool can be truly great with more development. On the top of the wishlist are automated database updates, labeling of nodes (so you can search “Silk Road” instead of a numerical address), visual graphs of flows, and a hosted version of the query tool (but computing power becomes prohibitive.)

MicroPython on the ESP8266: Kicking the Tires

Scripting languages are for large computers, right? “Real” embedded device work is a hellish, never-ending cycle of code, compile, and re-flash. Well, I used to think so too, but with the proliferation of scripting and other interactive languages to microcontrollers over the last few years, the hurdle to interactive development on the small chips has gotten a lot lower.

On the ESP8266 platform, I’ve tried out NodeMCU’s Lua and ESP8266 BASIC. (For the last half-year, I’ve been using the awesome Mecrisp-Stellaris almost exclusively on the STM32F1xx and F4xx chips, but haven’t dipped into ESP8266 Forth yet.)

NodeMCU is great because it’s got everything you could want built in, and through cloud services it’s easy to get a tailored build made that maximizes free flash memory for your projects. I just don’t dig the asynchronous Lua thing (you might, try it!). ESP BASIC has a different set of libraries, and is missing MQTT for my purposes. Still it’s pretty slick, and worth a look.

So when the MicroPython folks announced that they were releasing the binary builds for the ESP, I thought it was time to give it a spin. I’ve used Python for nearly twelve years now, so it’s like a comfortable shoe for me. Would MicroPython be the same on the ESP8266? The short answer is yes and no.

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Hackaday Prize Entry: Open Source FFT Spectrum Analyzer

Every machine has its own way of communicating with its operator. Some send status emails, some illuminate, but most of them vibrate and make noise. If it hums happily, that’s usually a good sign, but if it complains loudly, maintenance is overdue. [Ariel Quezada] wants to make sense of machine vibrations and draw conclusions about their overall mechanical condition from them. With his project, a 3-axis Open Source FFT Spectrum Analyzer he is not only entering the Hackaday Prize 2016 but also the highly contested field of acoustic defect recognition.

open_fft_machineFor the hardware side of the spectrum analyzer, [Ariel] equipped an Arduino Nano with an ADXL335 accelerometer, which is able to pick up vibrations within a frequency range of 0 to 1600 Hz on the X and Y axis. A film container, equipped with a strong magnet for easy installation, serves as an enclosure for the sensor. The firmware [Ariel] wrote is an efficient piece of code that samples the analog signals from the accelerometer in a free running loop at about 5000 Hz. It streams the digitized waveforms to a host computer over the serial port, where they are captured and stored by a Python script for further processing.

From there, another Python script filters the captured waveform, applies a window function, calculates the Fourier transform and plots the spectrum into a graph. With the analyzer up and running, [Ariel] went on testing the device on a large bearing of an arbitrary rotating machine he had access to. A series of tests that involved adding eccentric weights to the rotating shaft shows that the analyzer already makes it possible to discriminate between different grades of imbalance.

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Hacklet 114 – Python Powered Projects

Python is one of today’s most popular programming languages.  It quite literally put the “Pi” in Raspberry Pi. Python’s history stretches back to the late 1980’s, when it was first written by  Guido van Rossum. [Rossum] created Python as a hobby project over the 1989 Christmas holiday. He wanted a language that would appeal to Unix/C hackers. I’d say he was pretty successful in that endeavor. Hackers embraced Python, making it a top choice in their projects. This week’s Hacklet focuses on some of the best Python-powered projects on Hackaday.io.

pytoolWe start with [Jithin] and Python Powered Scientific Instrumentation tool, his entry in the 2015 Hackaday Prize. [Jithin] has created an “electronics lab in a box” style tool that can compete with commercial products with price tags in the thousands. Python Powered Scientific Instrumentation tool uses simple microcontroller powered hardware to create programmable gain amplifiers, waveform generators, LCR meters, CC sources and more. The microcontroller handles all the real-time operations. Data processing happens on a connected PC running Python scripts. Popular Python libraries like Scipy make signal processing and waveform displays easy.

 

pymusicNext up is [Bill Peterson] with jamPi. [Bill] loves his music keyboard, but hates having to lug around a laptop, audio interface, and all the associated cables. He needed a device which would be as flexible as a PC-based synthesizer, but as simple and compact as a MIDI sound module. JamPi does all this and more. [Bill] is using fluidsynth to generate sound. The control and interface software is handled in Python with the help of the fluidsynth.py module. All this functionality is wrapped up in a simple box with a 2 line character LCD. Now [Bill] is ready to jam anytime, anywhere.

openmv-featureNext is [i.abdalkader] with OpenMV, his entry in the 2014 Hackaday Prize. [i.abdalkader’s] goal was to create “the Arduino of machine vision”. He’s well on his way to accomplishing that. In 2015, OpenMV had a successful Kickstarter campaign. After a few manufacturing glitches, customers are now receiving their devices. OpenMV is a low-cost Python-powered machine vision device. An ARM microcontroller coupled to a simple image sensor makes up the core of the device. The camera is programmed in MicroPython, with the help of many image processing libraries created by the OpenMV team. [i.abdalkader] even created his own IDE using Glade and PyGTK.

pyfaceFinally we have [osannolik] with Calibration and Measurement Tool. Have you ever want to display a few debug parameters from your embedded project, but didn’t have the display real estate (or any display at all)? What about changing a parameter without pulling out your JTAG setup and firing up your debugger? [Osannolik] has created a simple Python powered PC-based front end which can be used as a Swiss army knife for developing embedded systems. Variables can be displayed in real-time, parameters changed. Even graphs are available thanks to pyqtgraph.

If you want more Python-powered goodness, check out our new Python-powered project list! Did I miss your project? Don’t be shy, just drop me a message on Hackaday.io. That’s it for this week’s Hacklet. As always, see you next week. Same hack time, same hack channel, bringing you the best of Hackaday.io!

FPGA Powers Blazingly Fast LED Matrix Audio Visualizer

[Sam Miller], [Sahil Gupta], and [Mashrur Mohiuddin] worked together on a very fast LED matrix display for their final project in ECE 5760 at Cornell University.

Real time!
Real time!

They started, as any good engineering students, by finding a way to make their lives easier. [Sam] had built a 32×32 LED matrix for another class. So, they made three more and ended up with a larger and more impressive 64×64 LED display.

They claim their motivation was the love of music, but we have a suspicion that the true reason was the love all EEs share for unnaturally bright LEDs; just look at any appliance at night and try not be blinded.

The brains of the display is an Altera DE2-115 FPGA board. The code is all pure Verilog. The FFT and LED control are implemented in hardware on the FPGA; none of that Altera core stuff. To generate images and patterns they wrote a series of python scripts. But for us it’s the particle test shown in the video below that really turns our head. This system is capable of tracking and reacting to a lot of different elements on the fly why scanning the display at about 310 FPS. They have tested display scanning at twice that speed but some screen-wrap artifacts need to be worked out before that’s ready for prime time.

The team has promised to upload all the code to GitHub, but it will likely be a while before the success hangover blows over and they can approach the project again. You can view a video interview and samples of the visualizations in the videos after the break.

Thanks to their Professor, [Bruce Land], for submitting the tip! His students are always doing cool things. You can even watch some of his excellent courses online if you like: Here’s one on the AVR micro-controller.

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