Smart Chopsticks Function as Food-Safety Sensor

Chinese tech company Baidu is developing smart chopsticks that can act as a sensor telling users whether the food they’re eating is unsafe. The Baidu Kuaisou can detect issues such as the use of unsanitary cooking oil, a prevalent concern when eating Chinese street food. Kuaisou can also measure metrics such as food temperature, food nutrients, and product expiration dates. The device connects to computers that analyze sensor data via Wi-Fi and Bluetooth. Baidu is still testing the Kuaisou. A price tag for the chopsticks hasn’t yet been announced, and the company said the product isn’t yet ready for release commercially. (BBC)(TIME)(Business Insider)(The Wall Street Journal)

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Smart Bra Aids Overeaters

Microsoft Research scientists have created a high-tech bra designed to monitor users to curb stress-related overeating. The undergarment is laden with sensors that record heart rate, respiration, skin conductance – the electrical conductance of the skin, which varies with moisture levels and indicates how much a person is perspiring -- and the individual’s movement, which are indicative of stress and other emotions that can lead to overeating. The bra feeds the data to a smartphone application, which sends prompts to users asking them to visit social media sites or write positive e-mails as simple ways of elevating their mood. The researchers say a bra is an ideal garment for such technology because it is close enough to the heart to take meaningful readings; however, the batteries in the undergarment had to be changed every four hours, which they say is prompting them to explore alternatives. They presented their findings at the Conference on Affective Computing and Intelligent Interaction (ACII 2013) held in September. (SlashDot)(Care2)(Mashable)(Discovery News)(Microsoft Research)

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Smart search engines for news videos

Jan. 7, 2013 — Searching for video recordings regularly pushes search engines to their limit. The truth of the matter is that purely automatic algorithms are not enough; user knowledge has to be harnessed, too. Now, researchers are making automated engines smarter.

Anyone who has visited one of the big online video portals or TV broadcasters’ media libraries to search for a video clip is already familiar with the search engines tasked with seeking out and flagging video footage. However, these engines have their weaknesses. Their results are based on automatic search algorithms that often go by text-based information alone. Although they can be used to locate and identify videos, a comparison of individual sequences is still very difficult. To make search engines even smarter, the Fraunhofer Institute for Digital Media Technology IDMT in Ilmenau has developed a piece of software called “NewsHistory” that will now make full use of user knowledge as well. Researchers will be presenting an initial demonstration version of the smart video search engine at the CeBIT trade fair in Hannover.

Technology learns from users

“NewsHistory provides users with search algorithms, a data model and a web-based user interface so that they can locate identical sequences within various news videos,” explains Patrick Aichroth from Fraunhofer IDMT. He is responsible for coordinating the institute’s R&D work within the EU’s CUbRIK project. Here, researchers are harnessing user knowledge to optimize and extend the capabilities of automated analysis techniques. “The search engine learns from each individual user, allowing it to keep improving search results. Not only does this improve the quality of results, but the resources needed to undertake the analysis are also cut down,” Aichroth continues.

NewsHistory allows each user to add additional information to the results generated by the search engine, including production and broadcast date, sources and keywords for videos. It is also possible to rate the results. Finally, the user’s search itself is a source of information, providing data that is incorporated into the search engine; the metadata of a newly uploaded video, for instance, passes into the database.

“Comparing digital video data online or within video databases is very complex,” explains Christian Weigel from the Audio-Visual Systems research group at the IDMT. “Videos that share the same content have for the most part been edited, meaning that they are scaled and encoded in a variety of formats. Also, search engines are often unable to distinguish images cropped from a larger picture, lower thirds or the zoom shots so popular with US news channels.”

The demonstration version being presented at CeBIT will investigate how a selection of TV channels have made use of film footage, changed its form and broadcast it. The user interface displays commonalities and appraises them in graphic form. The search itself is conducted either by inputting text or by directly uploading individual video sequences. The researchers’ aim is to make the software sufficiently robust that it could also be used in the future to compare the multimedia content found on big online media portals. The scientists do not imagine archivists or journalists will be the only users. “NewsHistory is of particular interest to media and market researchers, say if they want to assess the televised political duels coming up this year,” concludes Weigel.

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Self-sculpting sand: Heaps of 'smart sand’ could assume any shape, form new tools or duplicatie broken parts

ScienceDaily (Apr. 2, 2012) — Imagine that you have a big box of sand in which you bury a tiny model of a footstool. A few seconds later, you reach into the box and pull out a full-size footstool: The sand has assembled itself into a large-scale replica of the model.

That may sound like a scene from a Harry Potter novel, but it's the vision animating a research project at the Distributed Robotics Laboratory (DRL) at MIT's Computer Science and Artificial Intelligence Laboratory. At the IEEE International Conference on Robotics and Automation in May DRL researchers will present a paper describing algorithms that could enable such "smart sand." They also describe experiments in which they tested the algorithms on somewhat larger particles -- cubes about 10 millimeters to an edge, with rudimentary microprocessors inside and very unusual magnets on four of their sides.

Unlike many other approaches to reconfigurable robots, smart sand uses a subtractive method, akin to stone carving, rather than an additive method, akin to snapping LEGO blocks together. A heap of smart sand would be analogous to the rough block of stone that a sculptor begins with. The individual grains would pass messages back and forth and selectively attach to each other to form a three-dimensional object; the grains not necessary to build that object would simply fall away. When the object had served its purpose, it would be returned to the heap. Its constituent grains would detach from each other, becoming free to participate in the formation of a new shape.

Distributed intelligence

Algorithmically, the main challenge in developing smart sand is that the individual grains would have very few computational resources. "How do you develop efficient algorithms that do not waste any information at the level of communication and at the level of storage?" asks Daniela Rus, a professor of computer science and engineering at MIT and a co-author on the new paper, together with her student Kyle Gilpin. If every grain could simply store a digital map of the object to be assembled, "then I can come up with an algorithm in a very easy way," Rus says. "But we would like to solve the problem without that requirement, because that requirement is simply unrealistic when you're talking about modules at this scale." Furthermore, Rus says, from one run to the next, the grains in the heap will be jumbled together in a completely different way. "We'd like to not have to know ahead of time what our block looks like," Rus says.

Conveying shape information to the heap with a simple physical model -- such as the tiny footstool -- helps address both of these problems. To get a sense of how the researchers' algorithm works, it's probably easiest to consider the two-dimensional case. Picture each grain of sand as a square in a two-dimensional grid. Now imagine that some of the squares -- say, in the shape of a footstool -- are missing. That's where the physical model is embedded.

According to Gilpin-author on the new paper, the grains first pass messages to each other to determine which have missing neighbors. (In the grid model, each square could have eight neighbors.) Grains with missing neighbors are in one of two places: the perimeter of the heap or the perimeter of the embedded shape.

Once the grains surrounding the embedded shape identify themselves, they simply pass messages to other grains a fixed distance away, which in turn identify themselves as defining the perimeter of the duplicate. If the duplicate is supposed to be 10 times the size of the original, each square surrounding the embedded shape will map to 10 squares of the duplicate's perimeter. Once the perimeter of the duplicate is established, the grains outside it can disconnect from their neighbors.

Rapid prototyping

The same algorithm can be varied to produce multiple, similarly sized copies of a sample shape, or to produce a single, large copy of a large object. "Say the tire rod in your car has sheared," Gilpin says. "You could duct tape it back together, put it into your system and get a new one."

The cubes -- or "smart pebbles" -- that Gilpin and Rus built to test their algorithm enact the simplified, two-dimensional version of the system. Four faces of each cube are studded with so-called electropermanent magnets, materials that can be magnetized or demagnetized with a single electric pulse. Unlike permanent magnets, they can be turned on and off; unlike electromagnets, they don't require a constant current to maintain their magnetism. The pebbles use the magnets not only to connect to each other but also to communicate and to share power. Each pebble also has a tiny microprocessor, which can store just 32 kilobytes of program code and has only two kilobytes of working memory.

The pebbles have magnets on only four faces, Gilpin explains, because, with the addition of the microprocessor and circuitry to regulate power, "there just wasn't room for two more magnets." But Gilpin and Rus performed computer simulations showing that their algorithm would work with a three-dimensional block of cubes, too, by treating each layer of the block as its own two-dimensional grid. The cubes discarded from the final shape would simply disconnect from the cubes above and below them as well as those next to them.

True smart sand, of course, would require grains much smaller than 10-millimeter cubes. But according to Robert Wood, an associate professor of electrical engineering at Harvard University, that's not an insurmountable obstacle. "Take the core functionalities of their pebbles," says Wood, who directs Harvard's Microrobotics Laboratory. "They have the ability to latch onto their neighbors; they have the ability to talk to their neighbors; they have the ability to do some computation. Those are all things that are certainly feasible to think about doing in smaller packages."

"It would take quite a lot of engineering to do that, of course," Wood cautions. "That's a well-posed but very difficult set of engineering challenges that they could continue to address in the future."

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The above story is reprinted from materials provided by Massachusetts Institute of Technology. The original article was written by Larry Hardesty, MIT News Office.

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