Posts Tagged ‘MIT’
Dashboard-Mounted Smartphones Network Together to Watch for Red Light Patterns, Help Drivers Commute Efficiently
The idea stems from an already popular smartphone setup in which drivers perch their smartphones in dashboard brackets and use them as navigation devices. The MIT team built their SignalGuru app to take advantage of the camera on the other side of the phone by collecting stoplight data as cars drive around and feeding it back to a central system that then builds a larger picture of a city’s traffic flow.
The system could presumably be built out to incorporate all kinds of other useful data, the researchers say, like the locations of city buses, gas prices (via gas station signage), or where parking spaces are available. But the main idea is to build a realtime network of traffic signal timing. The app can then use GPS location to tell where a driver is, how quickly he or she is approaching a stoplight, and where that stoplight is in its green-yellow-red cycle.
From there, it does some simple math, telling the driver what speed to run to ensure he or she doesn’t have to make a complete stop at the upcoming light. That generally involves slowing down, but it doesn’t cost the driver any time overall, and the savings are in the gas tank. In tests, SignalGuru helped drivers shave 20 percent off their fuel consumption by cutting down on stopping and accelerating.
In those same tests SignalGuru was able to establish accuracy down to two-thirds of a second for signal changes. That could theoretically improve even more with more data--that is, with more people using the system. That’s both the key and the catch: SignalGuru is one of those things that only works with lots of data. So it might be difficult to get enough people participating in the system, but once it hit a certain tipping point it could become a very effective system that requires no additional roadway infrastructure to be installed.
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Make a High-Res 3-D Image of Just About Anything Anywhere, Using MIT’s New Gel
Described simply (you can get the more in-depth description via the video below), the system’s key component is a piece of transparent, synthetic rubber coated on one side with a metallic paint composed of very tiny particles. When the non-painted side is pressed against an object--even an object with very small features like the ink on a piece of paper (see image above)--the metallic paint deforms to capture those features.
Cameras set at various angles then capture that deformation from all sides, and computer-vision algorithms turn them into 3-D images. Contrast that with the usual method of obtaining a 3-D image with similar resolution--expensive and sensitive microscopes, vibration isolation tables, high-powered computers--and GelSight, as it is known, looks like a pretty big leap forward for both resolution and sheer simplicity.
GelSight also gets around a key problem with 3-D imaging. By translating an object’s most minuscule features--GelSight can measure features down to less than one micrometer in depth and roughly two micrometers across--through the gel to the metallic paint, it circumvents imaging problems introduced by the various optical properties of various materials (like, for instance, an opaque gel or a clear crystalline object, both of which interact with light differently than, say, a solid object that lets no light pass through).
Potential applications range from distinguishing moles from cancerous growths to quickly and cheaply inspecting manufactured goods to matching spent bullet casings to the firearms that fired them. See GelSight in action below.
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MIT’s Crash Cart for Frozen Software Lets You Escape the ‘Infinite Loop’
In all seriousness, the problem of “infinite loops” is beyond annoying. It saps productivity from software (and those using it). Infinite loops occur when a program gets stuck executing a single block of code over and over again (you probably know this as “freezing” or “f*&k!”). They often occur during functions where a program is trying to perform a task on many pieces of data in sequence, like when it searches for a word in a document for instance.
The problem occurs when the program, for whatever reason, doesn’t know when to stop repeating that operation, or executing that same segment of code repeatedly. Hence the term loop. And hence your frustration, as now your program won’t let you do anything else, including save your progress. So MIT researchers built a sort of crash cart for frozen programs that can shake them from an infinite loop, moving them along to the next logical block of code.
The program, appropriately named Jolt, recognizes infinite loops by examining the program’s use of memory. Say your program appears to be stalled. When you run Jolt, it takes a look at the program’s memory after each repetition of that loop. If there’s a change after each execution, your program is probably doing something useful. If not, it’s simply hung up in an infinite loop. Jolt then looks for the first instruction that follows the code the program is stuck on and forces the program to move ahead (for you programming types who are interested, there’s a much more thorough nuts-and-bolts description over at MIT News).
That forced procedure may not restore the program to full functionality--for instance, Jolt (and its binary cousin Bolt) may not push the program to the correct next instruction--but ideally it will at least put the program in a state where you can save, quit, and relaunch. That beats retyping your term paper. After all, you stayed up all night just to get it finished.
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Harvard-MIT Team’s New Synthetic Vocal Cord Gel Gives Voice to the Voiceless
Rather than approaching the problem as a physiological one, they looked at the vocal cords as a mechanical issue. That is, they didn’t attack the scar tissue in the vocal cords but devised a fix for it. That fix came in the form of a material known as polyethylene glycol (PEG), which they chose because it is already FDA approved for other medical applications.
PEG is flexible, both literally an in terms of its manipulability. By playing with the molecular structure of PEG, the researchers were able to dial in on a variation that mimicked the viscoelasticity of human vocal cords. Known as PEG30, it moves very, very similarly to natural vocal tissues (you can see this in the video below). Further, it can restore vibration to vocal cords that have stiffened due to scarring--which is the ultimate goal here.
Their PEG30 gel, should it receive its own FDA approval, would be categorized as an injectable medical device rather than a drug, which could further speed it to market. If approved, it would have to be re-injected every six months or so because it breaks down over time. But it could restore voice to many who have lost their primary means of expression.
There’s a much more detailed and interesting background on this over at . Both Julie Andrews and Steven Tyler get mentions.
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MIT Demonstrates Smart Cars That Predict Each Others’ Moves to Avoid Collisions
To suss out the patterns in human driving, the team first had to break down the act of operating a moving vehicle into its most basic parts: accelerating and decelerating. Using onboard sensors, the computerized intelligent transportation system (ITS) first determines which state another vehicle is in. From there, there is a finite (but sill large) number of positions on the roadway the vehicle can be after any given duration, be it one second or ten.
It’s here that the human behavioral modeling comes into play. The computer assesses other factors (is it an intersection or an onramp?) and other data about where human drivers tend to accelerate or slow down. All this, filtered through an algorithm, gives the ITS a pretty good idea of where a vehicle might be immediately headed.
The ITS-equipped vehicle then quickly figures out the areas in which the two vehicles could theoretically collide (this is termed the “capture set”) decides what it thinks the other car is going to do, acting accordingly to avoid those “capture set” areas where the risk of collision is remarkably more pronounced.
To test the system, the MIT team built two miniature cars--one equipped with ITS, the other controlled by human drivers--and put them on circular, overlapping tracks. They then ran 100 trials, changing up the human driver to compensate for any particular driver’s style. The result: collision was avoided 97 times. Vehicles entered the “capture set” three times, and only one of these instances resulted in collision.
Not bad. Of course, all or this has to take place in an instant in the real world, and adding more cars and more variables (pedestrians and cyclists, for instance) compounds the challenges. But the work is important for reasons that go beyond the roadway. If we’re truly going to learn to live alongside our robots, we don’t just need to know what they are going to do next. To some degree, they need to be able to predict our next moves as well.
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Video: PR2 Learns to Bake Chocolate Chip Cookies From Scratch
The idea was to create a simple introductory project for PR2 and those students and grad students working on it, but baking turns out to be quite a trial for the robot. But it’s also been a valuable learning experience for all involved. In order to execute the chocolate chip cookie recipe--an relatively simple one to wrap the human mind around--engineers there had to program PR2 to follow a lengthy progression of tasks while integrating various aspects of robotics like computer vision, highly-controlled motions, and object detection.
As you’ll see, PR2 takes a moment to orient itself and locate everything on the table bu color and size using its laser scanner and stereo camera. I then sets about combining everything in sequence and--somewhat sloppily--gets to mixing. But don’t sweat the mess. A team of undergraduates is working to program PR2 to wipe down the table afterward.
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The World’s Biggest Space Experiment Launches Tomorrow, Ready to Find Dark Matter and Alternate Universes
The ultra-sensitive Alpha Magnetic Spectrometer will hunt for the nature of matter
Soaking up cosmic rays from its permanent perch on the International Space Station, the AMS is designed to study the universe’s deepest secrets — what happened to all the antimatter, and what, in the name of all creation, is dark matter?
“The nature of dark materials is the great mystery of our time,” said Peter Fisher, an MIT physicist involved in the project.
The AMS traveled a long and circuitous path to reach Friday’s launch. The , nearly two decades in the making, involves some 600 researchers at 60 institutions across 16 countries. It cost somewhere between $1.5 and $2 billion — apparently, . It was almost canceled entirely when NASA dropped it from the launch manifest after the Columbia disaster, but scientists, most notably Nobel laureate and principal investigator Samuel Ting, convinced NASA to put it back on the schedule.
The AMS is Ting’s brainchild — some would even argue his — and if it works as planned, detecting the telltale signs of dark matter, it could potentially win him another Nobel.
The AMS is kind of like an orbiting version of the particle detectors in the Large Hadron Collider. At its heart is a powerful cryogenically cooled permanent magnet that bends incoming particles, in this case from cosmic rays, beams of high-energy materials belched toward Earth from dying stars, black holes and other cosmic phenomena. The way the particles bend in the magnetic field reveals their charge.
The 7-ton AMS canister also contains trackers to measure incoming particles’ energy and velocity, which will tell physicists exactly what they’re looking at.
AMS was built at CERN and tested inside the LHC, which helped calibrate its instruments. It was already detecting cosmic particles while being prepared for launch, Ting said .
The system is so sensitive that it can detect one single anti-nucleus in a sea of billions of atomic nuclei. It can measure particles with energies of 100 million TeV — to put that in perspective, the LHC, often called the world’s biggest science experiment, sends particles zooming around at a comparably trivial 7 trillion electron volts and measures their collisions.
The atmosphere strips these ultra-high-energy cosmic particles of some of their qualities, so physicists have long been angling for a space-based detector. The AMS is technically called AMS-02, because an earlier version flew on the shuttle Discovery in 1998. Incidentally, that was also the last mission to the Russian outpost Mir.
That mission, which lasted just 10 days, detected some very bizarre signatures — including a possible “strangelet,” an elementary particle made up of strange quarks as well as up and down quarks. The standard model of particles and forces says there are six flavors of quarks (the building blocks of protons and neutrons), but as far as scientists can tell, everything is made of just two — the up and down flavors. If these strangelet particles exist in any sort of abundance in the cosmos, AMS will see them.
Along with unmasking strangelets, the AMS will look for signatures of primordial antimatter, if any of it persists in the universe. This could help solve the question of why everything exists.
From a purely mathematical point of view, nothing should — antimatter and normal matter should have annihilated each other in the first moments after the Big Bang. But they didn't, and the universe was left with a preponderance of matter over antimatter, and therefore something rather than nothing. Some recent studies at ground-based particle detectors have shed some light on , but the AMS will take better measurements. It will be able to detect anti-helium or anti-hydrogen — so far only — which could be evidence for antimatter galaxies, or even parallel universes made of antimatter.
The AMS will also sniff out the weak signatures of dark matter, which is six times more abundant than the “normal” matter we can see. AMS is sensitive enough to detect new classes of weakly interacting massive particles (WIMPs), and signals in the background positron, anti-proton, or gamma ray flux that could show dark matter is present.
Such lofty goals are a fitting finale for the space shuttle, which helped scientists discover dark matter in the first place, through its delivery of the Hubble Space Telescope.
While all these bizarre possibilities are exciting, in an interview with , Ting said he hoped the experiment would go beyond even his wildest dreams.
“To my collaborators and me, the most exciting objective of AMS is to probe the unknown, to search for phenomena that exist in Nature but yet we have not the tools or the imagination to find them,” he said.