Passive WiFi On Microwatts

A lot of you use WiFi for your Internet of Things devices, but that pretty much rules out a battery-powered deployment because WiFi devices use a lot of juice. Until now. Researchers at the University of Washington have developed a passive WiFi implementation that uses only microwatts per device.

Working essentially like backscatter RFID tags do, each node has a WiFi antenna that can be switched to either reflect or absorb 2.4 GHz radiation. Your cell phone, or any other WiFi device, responds to this backscattered signal. All that’s missing is a nice steady signal to reflect.

passive_wifi-shot0008A single, plugged-in unit provides this carrier wave for multiple WiFi sensor nodes. And here’s the very clever part of the research: to keep the carrier from overwhelming the tiny modulated signal that’s coming from the devices, the plugged-in unit transmits off the desired frequency and the battery-powered units modulate that at just the right difference frequency so that the resulting (mixed) frequency is in the desired WiFi band.

If you’re a radio freak, you’ll recognize the WiFi node’s action being just like a frequency mixer. That’s what the researchers (slightly mysteriously) refer to as the splitting of the analog transmission stage from the digital. The plugged-in unit transmits the carrier, and the low-power nodes do the mixing. It’s like a traditional radio transmitter, but distributed. Very cool.

There’s a bunch more details to making this system work with consumer WiFi, as you’d imagine. The powered stations are responsible for insuring that there’s no collision, for instance. All of these details are very nicely explained in this paper (PDF). If you’re interested in doing something similar, you absolutely need to give it a read. This idea will surely work at lower frequencies, and we’re trying to think of a reason to use this distributed transmitter idea for our own purposes.

And in case you think that all of this RFID stuff is “not a hack”, we’ll remind you that (near-field) RFID tags have been made with just an ATtiny or with discrete logic chips. The remotely-powered backscatter idea expands the universe of applications.

Thanks [Ivan] for the tip!

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The Effects of Color on Material Properties of 3D Printed Components

The strength of object printed on filament-based 3D printers varies by the plastic used, the G-code used by the printer, the percent infill, and even the temperature the plastic was extruded at. Everything, it seems, has an effect on the strength of 3D printed parts, but does the color of filament have an effect on the stress and strain a plastic part it can withstand? [Joshua M. Pearce] set out to answer that question in one of his most recent papers.

The methods section of the paper is about what you would expect for someone investigating the strength of parts printed on a RepRap. A Lulzbot TAZ 4 was used, along with natural, white, black, silver, and blue 3mm PLA filament. All parts were printed at 190°C with a 60°C heated bed.

The printed parts demonstrated yet again that a RepRap can produce parts that are at least equal in material strength to those produced by a proprietary 3D printer. But what about a difference in the strength among different colors? While there wasn’t a significant variation in the Young’s modulus of parts printed in different colors, there was a significant variation of the crystallization of differently colored printed parts, with white PLA producing the largest percent crystallinity, followed by blue, grey, black, and finally natural PLA. This crystallinity of a printed part can affect the tensile properties of a printed part, but [Pearce] found the extrusion temperature also has a large effect on the percentage of crystallinity.

Before Google There Was the Chemical Rubber Company

Quick. What’s the difference in conductivity between silver and copper? Today, that’s easy to find out. You just ask Google (maybe even out loud if you have a phone handy). But it wasn’t that long ago that you needed another option. Before the Internet age, a big part of being “that guy” (or gal) was knowing where to go to find things. You had to be a master of the library’s reference section, know what might be in an encyclopedia or an almanac.

However if you were a hardcore math, science, or engineering geek you probably had, at least, one edition of CRC handbooks. Today, we usually think of CRC as cyclic redundancy check, but back then it was the Chemical Rubber Company.

The Chemical Rubber Company dates back to 1903 when brothers Arthur, Leo, and Emanuel Friedman were selling rubber lab aprons in Cleveland, Ohio (Arthur, apparently, had been in a similar business from 1900). In 1913, the brothers offered a short (116-page) booklet called the Rubber Handbook free with the purchase of a dozen aprons.

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Engauge Makes Graph Thieving a Cinch

We’ve seen ’em before: the charts and graphs in poorly photocopied ’80s datasheets, ancient research papers, or even our college prof’s chalkboard chicken scratch. Sadly, this marvelously plotted data is locked away in a poorly rendered png or textbook graphic. Fortunately, a team of programmers have come the rescue to give us the proper thieving tool to lift that data directly from the source itself, and that tool is Engauge.

Engauge is an open source software tool that enables to convert pictures of plots into the numerical representation of their data. While some of us might still be tracing graphs by hand, Engauge enables us to simply define reference points on the graph, and a clever image-processing algorithm extracts the curve for us automatically! Sure, there’s a little fine-tuning to determine what counts as data, but the net result is an all-in-one software tool that eats pictures and produces data–no intermediate steps required!

Engauge has been helping scientists and engineers preserve ancient data logs for years now, but it’s a tool that’s still fresh today when we’re recording from an analog o’scope or lifting those xs and ys off a textbook. In a world that’s increasingly digital, we’ve got the Engague developers to thank for arming us with the right tool for the job. All that said, If graph-thieving isn’t your thing, try spline-thieving to go from camera to CAD.

Engauge is a little lacking in the demo-video department, but we dug up a quickie on YouTube.

Thanks for the tip, [Jason]!

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University Peristaltic Pump Has Hacker Heritage

A team at [Vanderbilt University] have been hacking together their own peristaltic pumps. Peristaltic pumps are used to deliver precise volumes of fluid for research, medical and industrial applications. They’re even occasionally used to dose fish tanks.

pumpThey work by squeezing the fluid in a flexible tube with a series of rollers (check out the awesome gif from Wikipedia to the right). We’ve seen 3D printed peristaltic pumps before, and cheap pumps have been appearing on eBay. But this build is designed to be lab grade, and while the cheap eBay devices can deliver ~20ml/min this one can deliver flow rates in the microliter/min range. It also has a significant cost advantage over commercial research grade pumps which typically cost thousands of dollars, each of these pumps costs only fifty bucks.

The pump has a clear hacker heritage, using an Arduino Uno, Adafruit Motor shield, and 3D printed mechanical parts. So it’s particularly awesome that they’ve also made their design files and Arduino code freely available!

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UAV Coaxial Copter Uses Unique Drive Mechanism

Personal UAV’s are becoming ubiquitous these days, but there is still much room for improvement. Researchers at [Modlab] understand this, and they’ve come up with a very unique method of controlling pitch, yaw, and roll for a coaxial ‘copter using only the two drive motors.

In order to control all of these variables with only two motors, you generally need a mechanism that adjusts the pitch of the propeller blades. Usually this is done by mounting a couple of tiny servos to the ‘copter. The servos are hooked up to the propellers with mechanical linkages so the pitch of the propellers can be adjusted on the fly. This works fine but it’s costly, complicated, and adds weight to the vehicle.

[Modlab’s] system does away with the linkages and extra servos. They are able to control the pitch of their propellers using just the two drive motors. The propellers are connected to the motors using a custom 3D printed rotor hub. This hub is specifically designed to couple blade lead-and-lag oscillations to a change in blade pitch. Rather than drive the motors with a constant amount of torque, [Modlab] adds a sinusoidal component in phase with the current speed of the motor. This allows the system to adjust the pitch of the blades multiple times per rotation, even at these high speeds.

Be sure to watch the demonstration video below. Continue reading “UAV Coaxial Copter Uses Unique Drive Mechanism”

Inexpensive Robot Tracking System is Swarm Ready

RobotWebcam

[Ladvien] has figured an inexpensive way to control a robot from a remote PC with a static webcam. Inspired by swarming robot videos such as those from the UPENN Grasp lab, [Ladvien] wanted to build his own static camera based system. He’s also managed to create one of the more eclectic Instructables we’ve seen. You don’t often find pseudo code for robot suicide mixed in with the project instructions.

Fixed cameras are used in many motion capture systems, such as the Vicon system used by numerous film, game, and animation studios. Vicon and similar systems cost tens of thousands of dollars. This was a bit outside [Ladvien’s] budget. He set about building his own system from scratch. The first step was the hardest – obtaining permission from his wife to screw a webcam into the ceiling. With that problem overcome, [Ladvien] brought openCV and python to bear. He created Overlord, his webcam vision and control system. A vision system with nothing to control would be rather boring, so [Ladvien] created DotMuncher, Overlord’s radio controlled robot slave.

The basic processing system is rather simple. DotMuncher carries a magnetometer on board, which it uses to send heading information to Overlord. Overlord is pre-calibrated with an offset from magnetic north to “video game north” (toward the top of the screen). Overlord then uses openCV’s color detection to find DotMuncher in the current scene.
Overlord finally generates a virtual “Dot” on screen, and directs DotMuncher to drive over to it. When the robot gets to the dot, it is considered munched, and a new dot is generated.

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