Reuters briefly reports that Microsoft is planning to launch a smartwatch in the next few weeks, according to “sources close to the project”.
The report also claims that Microsoft’s smartwatch will passively track the heart rate and work across different mobile plaforms.
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[Christopher] has put together a Prank Stun Baton to annoy his friends. It delivers a slight shock to the person on the business end of the device. Oddly, it’s powered solely by static electricity, there is no battery here and the resulting injury is no worse than touching a door knob after scooting your socks around on some shag carpet.
The design is super simple and is effectively just a rudimentary capacitor. The main housing is a PVC pipe that acts as a dielectric in the ‘cap’ system. Two separate pieces of tin foil are wrapped around the inside and outside of the PVC pipe. These layers of tin foil provide a conductive path up to the a couple of screws stuck in the end of the baton. A ping-pong ball and some foam act as an insulator between the PVC and the screws.
To charge the baton it only has to be brought close to a source of static electricity, a tube TV will do the trick. Rubbing it with a piece of wool will also work. When this is done an electrostatic field is stored in the PVC between the two pieces of tin foil, one side takes on a positive charge and the other a negative charge creating an electric potential between the two screws at the end of the baton. When something (with a low-enough resistance) shorts the screws, the stored energy on the positive screw tries to go to the negative screw, shocking the unsuspecting victim.
Need something a little more powerful? You may want to check out this other stun baton.
Filed under: weapons hacks
[Raphael] has a motorcycle he’s constantly working on, and for him that means replacing the battery occasionally. Tired of the lead-acid batteries that have been used for 100 years now, he took a look at some of the alternatives, namely lithium and the much cooler supercapacitor option. A trip to the local electronics distributor, and [Raphael] had a new supercapacitor battery for his bike, and hopefully he’ll never need to buy another chunk of lead again.
The battery pack is built from six 2.7V, 350F caps, a few connectors, and a handful of diodes. These are lashed together with rubber bands to form a 16V, 58F capacitor that makes for a great stand-in for a chunk of lead or a potentially puffy lithium battery.
[Raphael] put up a walkthrough video of his battery pack where he shows off the enclosure – an old, empty lead acid cell. He also goes through the back current protection and his method of balancing the supercaps with a few diodes.
Filed under: transportation hacks
[Darell] recently purchased a fancy new bathroom scale. Unlike an average bathroom scale, this one came with a wireless digital display. The user stands on the scale and the base unit transmits the weight measurement to the display using infrared signals. The idea is that you can place the display in front of your face instead of having to look down at your feet. [Darell] realized that his experience with infrared communication would likely enable him to hack this bathroom scale to automatically track his weight to a spreadsheet stored online.
[Darell] started by hooking up a 38khz infrared receiver unit to a logic analyzer. Then he recorded the one-way communication from the scale to the display. His experience told him that the scale was likely using pulse distance coding to encode the data. The scale would start each bit with a 500ms pulse. Then it would follow-up with either another 500ms pulse, or a 1000ms pulse. Each combination represented either a 1 or a 0. The problem was, [Darell] didn’t know which was which. He also wasn’t sure in which order the bits were being transmitted. He modified a software plugin for his logic analyzer to display 1’s and 0’s on top of the waveform. He then made several configurable options so he could try the various representations of the data.
Next it was time to generate some known data. He put increasing amounts of weight on the scale and recorded the resulting data along with the actual reading on the display. Then he tried various combinations of display settings until he got what appeared to be hexadecimal numbers increasing in size. Then by comparing values, he was able to determine what each of the five bytes represented. He was even able to reconstruct the checksum function used to generate the checksum byte.
Finally, [Darell] used a Raspberry Pi to hook the scale up to the cloud. He wrote a Python script to monitor an infrared receiver for the appropriate data. The script also verifies the checksum to ensure the data is not corrupted. [Darell] added a small LED light to indicate when the reading has been saved to the Google Docs spreadsheet, so he can be sure his weight is being recorded properly.
Filed under: Raspberry Pi
Apple Pay is launching on Monday, October 20, and with it a brand new way to pay with your iPhone 6 and iPhone 6 Plus. Ahead of time, companies like American Express are trying to prepare their customers for what’s to come.
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[Daniel Whiteson and Michael Mulhearn], researchers at the University of California, have come up with a novel method of detecting ultra-high energy cosmic rays (UHECR) using smartphones. UHECR are defined as having energy greater than 1018eV. They are rare and very difficult to detect with current arrays. In order to examine enough air showers to detect UHECR, more surface area is needed. Current arrays, like the Pierre Auger Observatory and AGASA, cannot get much larger without dramatically increasing cost. A similar THP Quarterfinalist project is the construction of a low-cost cosmic ray observatory, where it was mentioned that more detection area is needed in order to obtain enough data to be useful.
[Daniel Whiteson and Michael Mulhearn] and colleagues noted that smartphone cameras with CMOS sensors can detect ionizing radiation, which means they also will pick up muons and high-energy photons from cosmic rays. The ubiquitous presence of smartphones makes their collective detection of air showers and UHECR an intriguing possibility. To make all this happen, [Whiteson and Mulhearn] created a smartphone app called CRAYFIS, short for Cosmic RAYs Found In Smartphones. The app turns an idle smartphone into a cosmic ray detector. When the screen goes to sleep and the camera is face-down, CRAYFIS starts taking data from the camera. If a cosmic ray hits the CMOS sensor, the image data is stored on the smartphone along with the arrival time and the phone’s geolocation. This information is uploaded to a central server via the phone’s WiFi. The user does not have to interact with the app beyond installing it. It’s worth noting that CRAYFIS will only capture when the phone is plugged in, so no worries about dead batteries.
The goal of CRAYFIS is to have a minimum of one million smartphones running the app, with a density of 1000 smartphones per square kilometer. As an incentive, anyone whose smartphone data is used in a future scientific paper will be listed as an author. There are CRAYFIS app versions for Android and iOS platforms according to the site. CRAYFIS is still in beta, so the apps aren’t publicly available. Head over to the site to join up!
Filed under: phone hacks
This week, [Chris] tips the scales but ultimately fails. He’s on the road, hacking through the Great White North and improvising from a poorly-lit echo chamber that happens to have a vise.
Knowing nothing about firearms (do you believe that?), he decided to build a BB cannon out of pure scrap. Several kinds of sparks fly, starting with a Hitachi drill-as-lathe and ending with a tiny cupcake sparkler. [Chris] proceeds to bore out some redi-rod by eyeballing it and offers helpful tips for course correction should you attempt same. Have centered the cavity, he drills out a tiny hole for a fuse.
His first fuse is of the crushed up match head paste variety. It burns kind of slowly and does not launch the BB. Naturally, Plan B is to make napalm glue to adhere Pyrodex pistol powder to paper. As you might imagine, it worked quite well. The wadding was singed, but still no joy. After packing her full of propellant, it still didn’t explode and merely burned out the blowhole. So, what gives? Insufficient barrel length? Should have used bamboo instead of redi-rod? Didn’t want it badly enough? Give us your fodder below.
Fail of the Week is a Hackaday column which runs every Thursday. Help keep the fun rolling by writing about your past failures and sending us a link to the story — or sending in links to fail write ups you find in your Internet travels.
Filed under: Fail of the Week, Hackaday Columns
I needed identical issue.
Your job is to make a circuit that will illuminate a light bulb when it hears the song “Mary Had a Little Lamb”. So you breadboard a mic, op amp, your favorite microcontroller (and an ADC if needed) and get to work. You will sample the incoming data and compare it to a known template. When you get a match, you light the light. The first step is to make the template. But what to make the template of?
“Hey boss, what style of the song do you want to trigger the light? Is it children singing, piano, what?”
Your boss responds:
“I want the light to shine whenever any version of the song occurs. It could be singing, keyboard, guitar, any musical instrument or voice in any key. And I want it to work even if there’s a lot of ambient noise in the background.”
Uh oh. Your job just got a lot harder. Is it even possible? How do you make templates of every possible version of the song? Stumped, you talk to your friend about your dilemma over lunch, who just so happens to be [Jeff Hawkins] – a guy whose already put a great deal of thought into this very problem.
“Well, the brain solves your puzzle easily.” [Hawkins] says coolly. “Your brain can recall the memory of that song no matter if it’s vocal, instrumental in any key or pitch. And it can pick it out from a lot of noise.”
“Yea, but how does it do that though!” you ask. “The pattern’s of electrical signals entering the brain have to be completely different for different versions of the song, just like the patterns from my ADC. How does the brain store the countless number of templates required to ID the song?”
“Well…” [Hawkins] chuckles. “The brain does not store templates like that”. The brain only remembers the parts of the song that doesn’t change, or are invariant. The brain forms what we call invariant representations of real world data.”
Eureka! Your riddle has been solved. You need to construct an algorithm that stores only the parts of the song that doesn’t change. These parts will be the same in all versions – vocal or instrumental in any key. It will be these invariant, unchanging parts of the song that you will look for to trigger the light. But how do you implement this in silicon?
Some organizations have taken Hawkins’ ideas and stealthily run with them, with schemes already underway at companies like IBM and federal organizations like DARPA to implement his ideas in silicon…
Indeed, companies are already working to implement [Jeff Hawkin's] theory of intelligence into their own systems. It’s a complicated theory, which is laid out in his book – On Intelligence. Forming invariant representations (IR) is only the beginning, and we will discuss other parts of the theory in later articles. But for now, we will concentrate on how one would go about forming IR’s of real world data in silicone. We simply cannot move forward with the theory until this core component is understood. The problem is nobody seems to know how to do this. Or if they do, they’re not talking This is where you come in!
Consider this image. Let us pretend these are serial signals coming off multiple ADCs. On the other end of the circuit would be different versions of our song, with A – E representing those different versions. Because the data is constantly changing, we sample 4 signals at the same time for each version, which are numbered 1 – 4.
Immediately, we see a common pattern in all versions at times T4, T5 and T6. If we can somehow set our microcontroller to listen to the these times, we can detect all versions of the song. Further, we can see another pattern between the versions at times T1, T2 and T3. This type of analysis can be used to distinguish between the different versions. Both patterns are invariant representations of the song – a common, unchanging pattern hidden in the mist of a constantly changing environment.
This is a hypothetical example of course. In the real world, the signals would vary wildly. The key is to find the part that does not. Can you do it? How would you create an invariant representation of a real world event?
Filed under: Ask Hackaday, Hackaday Columns
Gizmodo has obtained what appears to be full marketing materials for two new Fitbit activity trackers — the Charge and Charge HR — ahead of each device’s respective release. Based on the leak, it appears that the two new wearables will have some interesting features like caller ID and heart rate monitoring that could be paired with an iPhone.
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