Sometimes the end of a product’s production run is surrounded by publicity, a mix of a party atmosphere celebrating its impact either good or bad, and perhaps a tinge of regret at its passing. Think of the last rear-engined Volkswagens rolling off their South American production lines for an example.
Then again, there are the products that die with a whimper, their passing marked only by a barely visible press release in an obscure corner of the Internet. Such as this week’s discontinuances from Intel, in a series of PDFs lodged on a document management server announcing the end of their Galileo (PDF), Joule (PDF), and Edison (PDF) lines. The documents in turn set out a timetable for each of the boards, for now they are still available but the last will have shipped by the end of 2017.
It’s important to remember that this does not mark the end of the semiconductor giant’s forray into the world of IoT development boards, there is no announcement of the demise of their Curie chip, as found in the Arduino 101. But it does mark an ignominious end to their efforts over the past few years in bringing the full power of their x86 platforms to this particular market, the Curie is an extremely limited device in comparison to those being discontinued.
Will the departure of these products affect our community, other than those who have already invested in them? It’s true to say that they haven’t made the impression Intel might have hoped, over the years only a sprinkling of projects featuring them have come our way compared to the flood featuring an Arduino or a Raspberry Pi. They do seem to have found a niche though where there is a necessity for raw computing power rather than a simple microcontroller, so perhaps some of the legion of similarly powerful ARM boards will plug that gap.
So where did Intel get it wrong, how did what were on the face of it such promising products fizzle out in such a disappointing manner? Was the software support not up to scratch, were they too difficult to code for, or were they simply not competitively priced in a world of dirt-cheap boards from China? As always, the comments are open.
Header image: Mwilde2 [CC BY-SA 4.0].
One of the biggest challenges of traveling to Mars is that it’s far away. That might seem obvious, but that comes with its own set of problems when compared to traveling to something relatively close like the Moon. The core issue is weight, and this becomes a big deal when you have to feed several astronauts for months or years. If food could be grown on Mars, however, this would make the trip easier to make. This is exactly the problem that [Clinton] is working on with his Martian terrarium, or “marsarium”.
The first task was to obtain some soil that would be a good analog of Martian soil. Obtaining the real thing was out of the question, as was getting similar dirt from Hawaii. [Clinton] decided to make his own by mixing various compounds from the hardware store in the appropriate amounts. From there he turned to creating the enclosure and filling it with the appropriate atmosphere. Various gas canisters controlled by gas solenoid valves mixed up the analog to Martian atmosphere: 96% dioxide, 2% argon, and 2% nitrogen. The entire experiment was controlled by an Intel Edison with custom circuits for all of the sensors and regulating equipment. Check out the appropriately dramatic video of the process after the break.
While the fern that [Clinton] planted did survive the 30-day experiment in the marsarium, it wasn’t doing too well. There’s an apparent lack of nitrogen in Martian soil which is crucial for plants to survive. Normally this is accomplished when another life form “fixes” nitrogen to the soil, but Mars probably doesn’t have any of that. Future experiments would need something that could do this for the other plants, but [Clinton] notes that he’ll need a larger marsarium for that. And, if you’re not interested in plants or Mars, there are some other interesting ramifications of nitrogen-fixing as well.
Continue reading “Growing Plants on Mars… on Earth”
Ziehl-Neelsen Sputum Smear Microscopy (ZN) is one of most common methods for diagnosing Tuberculosis. On the equipment side, it requires not much more than an optical microscope, although it still needs a trained professional to look through the glass, identify and count the number of bacteria in a sample. To provide reliable and effective Tuberculosis diagnostic to regions, where both equipment and trained personnel is in short supply, [Rodrigo Loza] and [khalilnallar] are developing an automated digital microscope based on computer vision and machine learning, their entry for the Hackaday Prize.
They started out gathering images of Tuberculosis bacteria from the internet and experimented with color threshold algorithms to detect dyed bacteria, as well as algorithms for counting individual and clusters of bacteria. This process alone can, according to the team, take a trained professional 30 minutes or more. A graphical interface highlights identified bacteria and reads the bacteria count.
[Rodrigo Loza] and [khalilnallar] are testing their device at the Dr. Roberto Galindo Teran hospital in Cobija, Bolivia. However, getting access to a lab environment is one thing, and being given access to a steady supply of fresh M. Tuberculosis samples is another. Unable to obtain samples, which they need to test their algorithms on live subjects, they turned to another front of their project: The hardware. In several iterations, they developed a low-cost, 3D-printable kit, which transforms a laboratory-grade optical microscope into an embedded CNC-controlled microscopy platform. Their kit comprises three stepper-motor-based axis for the X, Y and Z direction, as well as a webcam mount. An Intel Edison and a custom, Arduino compatible shield control the system to achieve features such as homing procedures, autofocus and bacteria detection.
The team is currently in the process of refining their bacteria detection pipeline, exploring the feasibility of semi-automated detection methods, machine learning and neural networks for classification of bacteria within the hardware constraints. The video below shows their latest update on the Z-axis of their microscope.
Continue reading “Hackaday Prize Entry: Automatic Digital Microscope”
Eddie is a surprisingly capable tiny balancing robot based around the Intel Edison from which it takes its name.
Eddie’s frame is 3D printed and comes in camera and top hat editions. The camera edition provides space for a webcam to be mounted, since the Edison has enough go power to do basic vision. The top hat edition just lets you 3D print a tiny top hat for the robot.
The electronics are based around the Edison board and Sparkfun’s set of, “Blocks” designed for it. This project needs the battery block, the H-Bridge block, the GPIO block, and the USB block along with a 9DOF block for balancing. It’s, somewhat unfortunately, not a cheap robot. The motors are Pololu all-metal gearmotors with hall-effect sensors acting as encoders.
We’re really impressed with [diabetemonster]’s design and documentation on the robot. Full source code is provided along with a very nice build guide to get the platform going fast.
There are a few videos of it in action, available after the break. They show it handling situation such as a load being placed on the robot and slopes as well as bonus features like dancing and remote control.
Continue reading “Eddie The Balance Bot”
[BrittLiv] and her boyfriend got in one too many fights about who set the alarm. It’s the only argument they seem to repeat. So, true to her nature as an engineer, she over-engineered. The result was this great puzzle alarm clock.
The time displayed on the front is not the current time. Since the argument was about alarm times in the first place, [BrittLiv] decided the most prominent number should be the next alarm. To hear the time a button (one of the dots in the colon) must be pressed on the front of the clock. To set the alarm, however, one must manually move the magnetized segments to the time you’d like to get up. Processing wise, for a clock, it’s carrying some heat. It runs on an Intel Edison, which it uses to synthesize a voice for the time, news, weather, and, presumably, tweets. It sounds great, check it out after the break.
All in all the clock looks great, and works well too. We hope it brought peace to [BrittLiv]’s household.
Continue reading “Puzzle Alarm Clock Gets Couple Up In The Morning”
California textiles artist and musician [push_reset] challenged herself to make a wearable, gesture-based synth without using flex-sensing resistors. In the end, she designed almost every bit of it from the ground up using conductive fabric, resistive paint, and 3-D printed parts.
A couple of fingers do double duty in this glove. Each of the four fingertips have a sensor made from polyurethane, conductive paint, and conductive fabric that is connected to wires using small rivets. These sensors trigger different samples on an Edison that are generated with Timbre.js. The index and middle fingers also have knuckle actuators made from 3-D printed pin-and-slot mechanisms that turn trimmer pots. Bending one knuckle changes the delay timing while the other manipulates a triangle wave.
On the back of the glove are two sensors made from conductive fabric. Touching one up and down the length will alter the reverb. Sliding up and down the other alters the frequency of a sine wave. [push_reset] has kindly provided everything necessary to re-create this build from the glove pattern to the STL files for the knuckle actuators. Check out a short demonstration of the glove after the break. If you love a parade, here’s a wearable synth that emulates a marching band.
Continue reading “Second Skin Synth Fits Like a Glove”
Synesthesia is a mix-up of sensory perception where stimulation of one sense leads to a stimulation of a second sense. This is the condition where Wednesdays can be blue, the best part of your favorite song can be orange, and six can be up and to the right of seventy-three. While you can’t teach yourself synesthesia – it’s something you’re born with – [Zachary] decided to emulate color to smell synesthesia with his most recent electronics project.
For his synesthesia mask, [Zach] is turning varying amounts of red, green, and blue found with a color sensor into scents. He’s doing this with an off-the-shelf color sensor, an Intel Edison, and a few servos and test tubes filled with essential oils. The color sensor is mounted on a ring, allowing [Zach] to pick which colors he wants to smell, and the scent helmet contains a small electronics box fitted with fans to blow the scent into his face.
There’s more than one type of synesthesia, and if you’re looking for something a little more painful, you can make objects feel loud with a tiny webcam that converts pixels into pulses of a small vibration motor.
Continue reading “Smell Colors With A Synesthesia Mask”