MeatBagPnP Makes You The Automatic Pick And Place

November 29, 2017 by Anool Mahidharia 36 Comments

It’s amazing how hackers are nowadays building increasingly complex hardware with SMD parts as small as grains of sand. Getting multilayer PCB’s and soldering stencils in small quantities for prototyping is easier than ever before. But Pick-and-Place — the process of taking parts and stuffing them on the PCB in preparation for soldering — is elusive, for several reasons. For one, it makes sense only if you plan to do volume production as the cost and time for just setting up the PnP machine for a small run is prohibitive. And a desktop PnP machine isn’t yet as ubiquitous as a 3D printer. Placing parts on the board is one process that still needs to be done manually. Just make sure you don’t sneeze when you’re doing it.

Of course the human is the slow part of this process. [Colin O’Flynn] wrote a python script that he calls MeatBagPnP to ease this bottleneck. It’s designed to look at a row in a parts position file generated from your EDA program and highlight on a render of the board where that part needs to be placed. The human then does what a robotic PnP would have done.

A bar code scanner is not necessary, but using one does make the process a bit quicker. When you scan a code on the part bag, the script highlights the row on the spreadsheet and puts a marker on the first instance of it on the board. After you’ve placed the part, pressing the space bar puts a marker on the next instance of the same value. The script shows it’s done after all parts of the same value are populated and you can then move on to the next part. If you don’t have a bar code scanner handy, you can highlight a row manually and it’ll tell you where to put that part. Check it out in the video below.

Of course, before you use this tool you need some prior preparation. You need a good PNG image of the board (both sides if it is double-sided) scaled so that it is the same dimensions as the target board. The parts position file generated from your EDA tool must use the lower left corner of the board as the origin. You then tell the tool the board dimensions and it scales up everything so that it can put the red markers at the designated XY positions. The script works for single and double-sided boards. For a board with just a few parts, it may not be worth the trouble of doing this, but if you are trying to manually populate a complex board with a lot of parts, using a script like this could make the process a lot less painful.

The project is still fresh and rough around the edges, so if you have comments or feedback to offer, [Colin] is listening.

[Colin]’s name ought to ring a bell — he’s the hacker who built ChipWhisperer which took 2nd Prize at The Hackaday Prize in 2014. The MeatBagPnP project is a result of having worked at building increasingly complex boards manually and trying to make the process easier. In addition to the walk-through of how the script works after the break we’ve embedded his other video from three years back when he was stuffing parts — including BGA’s — the hard way and then reflowing them in a Chinese oven with hacked firmware.

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Tensorflow Tutorial Uses Python

November 25, 2017 by Al Williams 1 Comment

Around the Hackaday secret bunker, we’ve been talking quite a bit about machine learning and neural networks. There’s been a lot of renewed interest in the topic recently because of the success of TensorFlow. If you are adept at Python and remember your high school algebra, you might enjoy [Oliver Holloway’s] tutorial on getting started with Tensorflow in Python.

[Oliver] gives links on how to do the setup with notes on Python versions. Then he shows some basic setup operations. From there, he has the software “learn” how to classify random points that either fall into a circle or don’t. Granted, this is easy enough to do with traditional programming, so it isn’t a great practical example, but it is illustrative for learning purposes.

Given that it is easy to algorithmically decide which points are in the circle and which are not, it is simple to develop training data. It is also easy to look at the result and see how close it is to the actual circle. You’ll see that it takes a lot of slow learning before the result space looks like a circle and not a triangle or some other odd shape.

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Smarter Phones In Your Hacks With TensorFlow Lite

November 23, 2017 by Steven Dufresne 11 Comments

One way to run a compute-intensive neural network on a hack has been to put a decent laptop onboard. But wouldn’t it be great if you could go smaller and cheaper by using a phone instead? If your neural network was written using Google’s TensorFlow framework then you’ve had the option of using TensorFlow Mobile, but it doesn’t use any of the phone’s accelerated hardware, and so it might not have been fast enough.

Google has just released a new solution, the developer preview of TensofFlow Lite for iOS and Android and announced plans to support Raspberry Pi 3. On Android, the bottom layer is the Android Neural Networks API which makes use of the phone’s DSP, GPU and/or any other specialized hardware to speed up computations. Failing that, it falls back on the CPU.

Currently, fewer operators are supported than with TensforFlor Mobile, but more will be added. (Most of what you do in TensorFlow is done through operators, or ops. See our introduction to TensorFlow article if you need a refresher on how TensorFlow works.) The Lite version is intended to be the successor to Mobile. As with Mobile, you’d only do inference on the device. That means you’d train the neural network elsewhere, perhaps on a GPU-rich desktop or on a GPU farm over the network, and then make use of the trained network on your device.

What are we envisioning here? How about replacing the MacBook Pro on the self-driving RC cars we’ve talked about with a much smaller, lighter and less power-hungry Android phone? The phone even has a camera and an IMU built-in, though you’d need a way to talk to the rest of the hardware in lieu of GPIO.

You can try out TensorFlow Lite fairly easily by going to their GitHub and downloading a pre-built binary. We suspect that’s what was done to produce the first of the demonstration videos below.

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Learn About Blockchains By Building One

November 23, 2017 by Jenny List 27 Comments

What do we curious Hackaday scribes do when we want to learn about something? First port of call: search the web.

When that something is blockchain technology and we’re looking for an explanation that expands our cursory overview into a more fundamental understanding of the basic principles, there is a problem. It seems that to most people blockchains equate to one thing: cryptocurrencies, and since cryptocurrencies mean MONEY, they then descend into a cultish frenzy surrounded by a little cloud of flying dollar signs. Finding [Daniel van Flymen]’s explanation of the fundamentals of a blockchain in terms of the creation of a simple example chain using Python was thus a breath of fresh air, and provided the required education. Even if he does start the piece by assuming that the reader is yet another cryptocurrency wonk.

We start by creating a simple class to hold all the Python functions, then we are shown a single block. In his example it’s a JSON object, and it contains the payload in the form of a transaction record along with the required proof-of-work and hash. We’re then taken through a very simple proof-of-work algorithm, before being shown how the whole can be implemented as very simple endpoints.

You are not going to launch a cryptocurrency using this code, and indeed that wasn’t our purpose in seeking it out. But if you are curious about the mechanics of a blockchain and are equally tired of evangelists of The Blockchain who claim it will cure all ills but can’t explain it in layman’s terms, then this relatively simple example is for you.

The wrong way to build a blockchain image: Jenny List. #FarmLife.

Tiny Tensor Brings Machine Deep Learning To Micros

November 13, 2017 by Al Williams 25 Comments

We’ve talked about TensorFlow before — Google’s deep learning library. Crunching all that data is the province of big computers, not embedded systems, right? Not so fast. [Neil-Tan] and others have been working on uTensor, an implementation that runs on boards that support Mbed-OS 5.6 or higher.

Mbed of course is the embedded framework for ARM, and uTensor requires at least 256K of RAM on the chip and an SD card less than (that’s right; less than) 32 GB. If your board of choice doesn’t already have an SD card slot, you’ll need to add one.

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(Nearly) All Your Computers Run MINIX

November 11, 2017 by Jenny List 68 Comments

Are you reading this on a machine running a GNU/Linux distribution? A Windows machine? Or perhaps an Apple OS? It doesn’t really matter, because your computer is probably running MINIX anyway.

There once was a time when microprocessors were relatively straightforward devices, capable of being understood more or less in their entirety by a single engineer without especially God-like skills. They had buses upon which hung peripherals, and for code to run on them, one of those peripherals had better supply it.

A modern high-end processor is a complex multicore marvel of technological achievement, so labyrinthine in fact that unlike those simple devices of old it may need to contain a dedicated extra core whose only job is to manage the rest of the onboard functions. Intel processors have had one for years, it’s called the Management Engine, or ME, and it has its own firmware baked into the chip. It is this firmware, that according to a discovery by [Ronald Minnich], contains a copy of the MINIX operating system.

If you are not the oldest of readers, it’s possible that you may not have heard of MINIX. Or if you have, it might be in connection with the gestation of [Linus Torvalds]’ first Linux kernel. It’s a UNIX-like operating system created in the 1980s as a teaching aid, and for a time it held a significant attraction as the closest you could get to real UNIX on some of the affordable 16-bit desktop and home computers. Amiga owners paid for copies of it on floppy disks, it was even something of an object of desire. It’s still in active development, but it’s fair to say its attraction lies in its simplicity rather than its sophistication.

It’s thus a worry to find it on the Intel ME, because in that position it lies at the most privileged level of access to your computer’s hardware. Your desktop operating system, by contrast, sees the hardware through several layers of abstraction in the name of security, so a simple OS with full networking and full hardware access represents a significant opportunity to anyone with an eye to compromising it. Placing tinfoil hats firmly on your heads as the unmistakable thwop of black helicopters eases into the soundscape you might claim that this is exactly what they want anyway. We would hope that if they wanted to compromise our PCs with a backdoor they’d do it in such a way as to make it a little less easy for The Other Lot. We suspect it’s far more likely that this is a case of the firmware being considered to be an out-of-sight piece of the hardware that nobody would concern themselves with, rather than a potential attack vector that everyone should. It would be nice to think that we’ll see some abrupt updates, but we suspect that won’t happen.

Intel I7 processor underside: smial [FAL].

Understanding Floating Point Numbers

October 2, 2017 by Al Williams 67 Comments

People learn in different ways, but sometimes the establishment fixates on explaining a concept in one way. If that’s not your way you might be out of luck. If you have trouble internalizing floating point number representations, the Internet is your friend. [Fabian Sanglard] (author of Game Engine Black Book: Wolfenstein 3D) didn’t like the traditional presentation of floating point numbers, so he decided to explain them a different way.

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