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Sending a password or secret code over airborne radio waves like WiFi or Bluetooth means anyone can eavesdrop, making those transmissions vulnerable to hackers who can attempt to break the encrypted code.Now, University of Washington computer scientists and electrical engineers have devised a way to send secure passwords through the human body—using benign, low-frequency transmissions generated by fingerprint sensors and touchpads on consumer devices."Fingerprint sensors have so far been used as an input device. What is cool is that we've shown for the first time that fingerprint sensors can be re-purposed to send out information that is confined to the body," said senior author Shyam Gollakota, UW assistant professor of computer science and engineering.These "on-body" transmissions offer a more secure way to transmit authenticating information between devices that touch parts of your body—such as a smart door lock or wearable medical device—and a phone or device that confirms your identity by asking you to type in a password.This new technique, which leverages the signals already generated by fingerprint sensors on smartphones and laptop touchpads to transmit data in new ways, is described in a paper presented in September at the 2016 Association for Computing Machinery's International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) in Germany."Let's say I want to open a door using an electronic smart lock," said co-lead author Merhdad Hessar, a UW electrical engineering doctoral student. "I can touch the doorknob and touch the fingerprint sensor on my phone and transmit my secret credentials through my body to open the door, without leaking that personal information over the air."The research team tested the technique on iPhone and other fingerprint sensors, as well as Lenovo laptop trackpads and the Adafruit capacitive touchpad. In tests with 10 different subjects, they were able to generate usable on-body transmissions on people of different heights, weights and body types. The system also worked when subjects were in motion—including while they walked and moved their arms."We showed that it works in different postures like standing, sitting and sleeping," said co-lead author Vikram Iyer, a UW electrical engineering doctoral student. "We can also get a strong signal throughout your body. The receivers can be anywhere—on your leg, chest, hands—and still work."The research team from the UW's Networks and Mobile Systems Lab systematically analyzed smartphone sensors to understand which of them generates low-frequency transmissions below 30 megahertz that travel well through the human body but don't propagate over the air.The researchers found that fingerprint sensors and touchpads generate signals in the 2 to 10 megahertz range and employ capacitive coupling to sense where your finger is in space, and to identify the ridges and valleys that form unique fingerprint patterns.Normally, sensors use these signals to receive input about your finger. But the UW engineers devised a way to use these signals as output that corresponds to data contained in a password or access code. When entered on a smartphone, data that authenticates your identity can travel securely through your body to a receiver embedded in a device that needs to confirm who you are.Their process employs a sequence of finger scans to encode and transmit data. Performing a finger scan correlates to a 1-bit of digital data and not performing the scan correlates to a 0-bit.The technology could also be useful for secure key transmissions to medical devices such as glucose monitors or insulin pumps, which seek to confirm someone's identity before sending or sharing data.The team achieved bit rates of 50 bits per second on laptop touchpads and 25 bits per second with fingerprint sensors—fast enough to send a simple password or numerical code through the body and to a receiver within seconds.This represents only a first step, the researchers say. Data can be transmitted through the body even faster if fingerprint sensor manufacturers provide more access to their software.Reference:HMR3400HMC6343HMR3000-D00-232HMR2300-D00-485 

when University of Wisconsin–Madison engineers announced in the journal Nature Communications that they had developed transparent sensors for use in imaging the brain, researchers around the world took notice. Then the requests came flooding in. “So many research groups started asking us for these devices that we couldn’t keep up,” says Zhenqiang (Jack) Ma, the Lynn H. Matthias Professor and Vilas Distinguished Achievement Professor in electrical and computer engineering at UW–Madison.Ma’s group is a world leader in developing revolutionary flexible electronic devices. The see-through, implantable micro-electrode arrays were light years beyond anything ever created.Although he and collaborator Justin Williams, the Vilas Distinguished Achievement Professor in biomedical engineering and neurological surgery at UW–Madison, patented the technology through the Wisconsin Alumni Research Foundation, they saw its potential for advancements in research.“That little step has already resulted in an explosion of research in this field,” says Williams. “We didn’t want to keep this technology in our lab. We wanted to share it and expand the boundaries of its applications.”As a result, in a paper published in the journal Nature Protocols, the researchers have described in great detail how to fabricate and use transparent graphene neural electrode arrays in applications in electrophysiology, fluorescent microscopy, optical coherence tomography, and optogenetics. “We described how to do these things so we can start working on the next generation,” says Ma.Now, not only are the UW–Madison researchers looking at ways to improve and build upon the technology, they also are seeking to expand its applications from neuroscience into areas such as research of stroke, epilepsy, Parkinson’s disease, cardiac conditions, and many others. And they hope other researchers do the same.“We didn’t want to keep this technology in our lab. We wanted to share it and expand the boundaries of its applications.”“This paper is a gateway for other groups to explore the huge potential from here,” says Ma. “Our technology demonstrates one of the key in vivo applications of graphene. We expect more revolutionary research will follow in this interdisciplinary field.”Funding for the initial research came from the Reliable Neural-Interface Technology program at the U.S. Defense Advanced Research Projects Agency. Other authors on the Nature Protocols paper include Dong-Wook Park, Sarah Brodnick, Jared Ness, Lisa Krugner-Higby, Solomon Mikael, Joseph Novello, Hyungsoo Kim, Dong-Hyun Baek, Jihye Bong, Kyle Swanson and Wendell Lake of UW–Madison; Farid Atry, Seth Frye and Ramin Pashaie of the University of Wisconsin-Milwaukee; Amelia Sandberg of Medtronic PLC Neuromodulation; Thomas Richner of the University of Washington; and Sanitta Thongpang of Mahidol University in Bangkok, Thailand.Reference:ISL29120IROZ-T7ADJD-J823TCS3200D-TR Like this Article? Register to receive updates here 

Inside a secretive AI nonprofit backed by Elon Musk and other Silicon Valley figures, a handful of robots designed to help out in warehouses are gradually learning how to do useful household chores.OpenAI, which was created to do basic AI research, is reprogramming robots developed by Fetch Robotics, a company that supplies warehouse automation hardware. Researchers at OpenAI are equipping the robots with software that lets them train themselves through trial and error.The effort reflects a bet that innovations in software and machine learning, rather than breakthroughs in hardware, are the way to give robotics remarkable new capabilities. Fetch makes a range of robots for warehouses, including systems that follow workers around a building, carrying items dropped into a basket. OpenAI is using a system that features a mobile base but also 3-D depth sensors, a 2-D laser scanner, and a robotic arm with seven degrees of freedom.In April, OpenAI recruited Pieter Abbeel, a professor at the University of California, Berkeley, and a leading expert on robot learning. Abbeel has shown how robots can use a machine-learning approach called deep reinforcement learning to acquire completely new skills that would be hard to program by hand, such as folding towels or retrieving items from a refrigerator. Google DeepMind, an AI subsidiary based in the U.K., uses this technique to get computers to play computer games at a superhuman level (see “Google’s AI Masters Space Invaders”).Abbeel’s robots learn tasks from scratch, using a neural network that receives sensor input and controls physical movement. The network adjusts its parameters automatically as it inches closer to its goal. A robot might try thousands of grips, for instance, in the process of learning how to hold a certain object.“If this goal can be achieved, then there will be economic and industrial benefits,” says Marc Deisenroth, an expert on reinforcement learning at Imperial College London. “Imagine a Roomba not only cleaning your floor but also doing the dishes, ironing the shirts, cleaning the windows, preparing breakfast.”Deisenroth says using off-the-shelf robots could drive costs down. “Currently, the software seems to be the bottleneck,” he adds. “However, independent of this, better hardware could also lead to substantial improvements.” Soft manipulators and elastic feet similar to a monkey’s feet are concepts that researchers have started working on, he says.Some manufacturers, including the Japanese company Fanuc, are testing reinforcement learning as a way to train industrial robots quickly in new tasks such as learning to grasp unfamiliar objects. When many robots work in parallel, the training time required is reduced accordingly. Robot researchers at Google are testing similar learning techniques.“Moving away from having to program robots by hand by endowing robots to learn autonomously is a key element for the future of robotics,” says Jens Kober, an expert on robot learning at Delft University of Technology in the Netherlands. Kober says having robots share the information they have learned will be crucial.While robots such as those made by Fetch are finding their way into many factories and warehouses, domestic robot helpers remain the stuff of science fiction. Performing seemingly simple tasks like washing dishes or folding laundry in a messy home setting is incredibly hard for a machine. A robot programmed the conventional way can easily be thrown off by an unfamiliar object or a slight variation in lighting.OpenAI confirmed that it is working with the robots from Fetch, but it declined to comment further. Melonee Wise, the company’s founder, couldn’t be reached for comment (see “Innovators Under 35: Melonee Wise”).OpenAI was created by Musk and a handful of well-known (and well-heeled) Silicon Valley entrepreneurs, including investor Peter Thiel, Y Combinator president Sam Altman, and the incubator’s cofounder Jessica Livingston. The nonprofit’s backers have committed $1 billion in funding to the project, and it is being led by Ilya Sutskever, a prominent AI researcher who left Google to join the project, and Greg Brockman, an early employee at the high-profile digital payment company Stripe.While OpenAI has committed to making the technology it develops publicly available, it could certainly benefit companies backed by Musk and Thiel, as well as those emerging from Y Combinator. 

Thermal Solution Resources, LLC (TSR) introduced the world's smallest wireless LED lamp – the intelliSSL MR16 – which can be controlled remotely from IOS, Android or Windows smartphones, tablets or PCs. The intelliSSL lamp incorporates an ultra-compact driver architecture with a powerful antenna system, designed to maximize reception strength, and uses the ultra-low-power JN5168 wireless microcontroller from NXP Semiconductors N.V.. The TSR intelliSSL wireless MR16 lamp will be featured in the NXP booth this week at LIGHTFAIR (no. 2453) and the LEDdynamics booth (no. 100), as well as the ZigBee Alliance booth (no. 3251) as part of a ZigBee Light Link network. The patent-pending intelliSSL design provides strong wireless reception, eliminating signal interference even under high temperatures, while conforming to a standard ANSI format – 1.98 inches in diameter and 1.98 inches in length – with a GU 5.3 base. A GU 10 base version is planned for release later this year. Compliant with the IEEE 802.15.4 standard for wireless communication, TSR's new wireless MR16 LED can be used with a wide range of network software stacks, including ZigBee Light Link, ZigBee Home Automation and JenNet-IP. The intelliSSL smart MR16 LED is ideal for commercial, architectural and outdoor lighting installations, as well as general lighting applications. "Wireless lighting control is becoming a strategic investment for commercial and residential buildings, with significant ROI in terms of energy savings and total cost of ownership, as well as safety and security," said Mikhail Sagal, president of Thermal Solution Resources. "With the world's first wireless MR16 LED, we've opened a host of new use cases for wireless smart lighting. The flexible, ultra-compact design of the intelliSSL lamp enables wireless LED replacement lamps to be integrated in nearly any type of lighting installation, as well as security systems and devices such as motion sensors, cameras and automated shade controls." The intelliSSL smart MR16 LED lamp is made with TSR's moduLED design, which eliminates thermal bottlenecks and inefficient assembly steps, while strengthening wireless range and data accuracy. TSR, a design and manufacturing firm for LED lighting headquartered in Rhode Island, offers unique expertise and IP in thermal management, LED lighting design, manufacturing and wireless integration. "Wireless smart lighting has been embraced enthusiastically by tech-savvy consumers. Now with the first wirelessly-enabled MR16 from TSR, we expect to see broader adoption, also by businesses, for commercial and architectural lighting installations," said Marcel Walgering, general manager, smart home and energy product line, NXP Semiconductors. "As part of a connected home, smart building or intelligent city network, smart LEDs have the potential to significantly change the way we manage lighting, security and energy consumption on a grand scale."  

Researchers are making an attempt to steer us closer to comfortable touch mechanisms for operating mobile devices. A promising sign that they are on to something is evident in iSkin. A team from Max Planck Institute for Informatics, Saarland University, Carnegie Mellon, CNRS LTCI, Telecom-ParisTech and Aalto University have authored a paper describing their work in "iSkin: Flexible, Stretchable and Visually Customizable On-Body Touch Sensors for Mobile Computing." They envision a digital life where one can use a comfortable, light wearable such as finger strap, arm sticker and even keyboard extensions on rollout paper attached to the wrist device for mobile touch input. A video shows iSkin in action. In one scene, it is an arm sticker to control a music player; the person presses various places on the sticker to play a song or adjust the volume. Another scene shows somebody using a finger overlay to accept an incoming call. The video also shows a keyboard extension that can be rolled out on demand for a smartwatch text entry. iSkin is flexible and stretchable; it can detect touch input with two levels of pressure, even when stretched by 30 percent or when bent with a radius of 0.5 cm, they said. It can be of different shapes and sizes for different parts of the body—-such as fingers, forearm, or ear. A key feature is its construction of biocompatible materials. That requirement was not taken lightly. The authors in the paper said "iSkin should be non-toxic, and easily cleanable, washable, or replaceable in order to limit the accumulation of pathogens such as bacteria. Moreover, the properties of skin vary greatly." As "wrinkliness, oiliness, and distribution of receptors, sweat glands, and hair follicles" vary across body locations and individuals, they said this presented a requirement for materials and adhesives compatible with natural skin and exhibiting a high variability of form factors. What they came up with is "based on advances in electronic skin (e-skin) and soft-matter electronics, an active research field in robotics and material science." The sensor can be thought of as a sandwich composed of multiple layers. They wrote that "iSkin is made of multiple layers of thin, flexible and stretchable silicone. The base material is polydimethylsiloxane (PDMS), an easy-to-process silicone-based organic polymer. PDMS is fully transparent, elastic, and a highly biocompatible material." A Reuters report by Matthew Stock on Monday had further comments on the materials and the research from co-developer Martin Weigel, who said the technology was initially coming from robotics :where it's used to give robots kind of a feeling similar to the human body, to human skin. However, we are the first to look into how we can use it on the body to control mobile devices; so as a kind of second-skin which nicely conforms to your body." Weigel said carbon particles inside the silicone make it conductive so they can use it for electronics. The Reuters report added that the stickers are attached to the body using a medical-grade adhesive, easily peeled off after use without hurting the skin. In their paper, they said that study results showed the sensor remained functional under typical and extreme deformations occurring on the human body; also it accurately sensed touch input when worn on various body locations. Right now there are no signs that you will find iSkin sensors in the marketplace; this is a proof of concept, said the authors, of on-skin touch sensing "that bears some promise over rigid sensors and computer vision based solutions." Related products: CY8CMBR2044-24LKXI  

If you want to save on your monthly electric bill and reduce your greenhouse gas emissions at the same time, you might buy a new, energy-efficient refrigerator. Or water heater. Or clothes dryer. But if you can only replace one of these, which will give you the biggest payback?You could try to figure that out by comparing the energy-use labels from your existing appliances with those of the models you might purchase—if you still have your old labels. Even then, the numbers may differ significantly from your actual usage, depending on factors such as age, condition, and your local climate. But soon, there could be a much easier way to figure out exactly how much power is being used by every appliance, lighting fixture, and device in your home, with pinpoint accuracy and at low cost, thanks to devices and software developed by researchers at MIT.The team's findings, developed over several years of intensive research, are described in a series of papers, including one published this week in the IEEE Sensors Journal, in a paper by MIT Professor of Electrical Engineering Steven Leeb and recent graduates David Lawrence MEng '16 and John Donnal PhD '16. Another paper from the team, which also includes as co-author James Paris PhD '13, is still in press.While many groups have worked on developing devices to monitor electricity use, the new MIT system has some key advantages over other approaches. First, it involves no complex installation: No wires need to be disconnected, and the placement of the postage-stamp-sized vibration sensors over the incoming power line does not require any particular precision—the system is designed to be self-calibrating. Second, because it samples data very quickly, the sensors can pick up enough detailed information about spikes and patterns in the voltage and current that the system can, thanks to dedicated software, tell the difference between every different kind of light, motor, and other device in the home and show exactly which ones go on and off, at what times.Own your own dataPerhaps most significantly, the system is designed so that all of the detailed information stays right inside the user's own home, eliminating concerns about privacy that potential users may have when considering power-monitoring systems. The detailed analysis, including the potential for specialized analysis based on an individual user's specific needs or interests, can be provided by customized apps that can be developed using the MIT team's system.Tests of the system have showed its potential to save energy and greenhouse emissions—and even to improve safety. One installation at a military base used for training exercises revealed that large tents were being heated all day during winter months, even though they were unoccupied for most of the daytime hours—a significant waste of money and fuel (which, in a combat setting, could be an important logistical concern). Another test installation, in a home, found an anomalous voltage pattern that revealed a wiring flaw that caused some copper plumbing pipes to carry a potentially dangerous live voltage."For a long time, the premise has been that if we could get access to better information [about energy use], we would be able to create some significant savings," Leeb says. He and his students have been tackling the problem for more than 10 years and bit by bit have found ways to circumvent the daunting problems involved in achieving this basic task.First was the ability to monitor changes in voltage and current without cutting the main incoming power line to a home or business (an expensive process requiring a licensed electrician) or plugging every appliance into a special monitoring device. Other groups have attempted to use wireless sensors to pick up the very faint magnetic and electric fields near a wire, but such systems have required a complex alignment process since the fields in some places can cancel each other out. The MIT team solved the problem by using an array of five sensors, each slightly offset from the others, and a calibration system that tracks the readings from each sensor and figures out which one is positioned to give the strongest signal.Interpreting the data flowThe next trick was in figuring out how to analyze the reams of data flowing in from the high-speed sensors, in order to tease out which bits correspond to current and voltage, and how that information could be used to identify "signatures" of specific appliances. This is possible because every motor or device has distinctive characteristics as to exactly how fast and how much the voltage varies, or spikes, at the moment the device switches on, or as it operates. After extensive testing in the lab, in homes, at the Fort Devens Army base outside Boston, and aboard the U.S. Coast Guard cutter Spencer, the team was able to develop a catalog of such signatures, to identify each kind of electrical load.And finally, given the prodigious amount of raw data generated by the system, the team had to figure out how to extract the useful information and display it in a way that would make it easy for people to make decisions about energy investments. They developed an interface that allows users to "zoom in" on specific time segments, revealing enough data to tell when a refrigerator turns on or off, or goes into its defrost cycle, or how often a water heater is switching on and off during the day."A bunch of major players have gotten into, and out of, this field," says Leeb, including giants like Google and Microsoft. But now, he says, the MIT team has solved the key issues and come up with a practical and very powerful system. One of the major insights they had was that keeping most of the data within the home and sending only small subsets out into the cloud for processing solved two problems at once: It eliminated the privacy concerns of using such a system, and it eliminated the huge bandwidth and data transmission costs that would be required if the raw data was sent to a central facility.Once the system is developed into a commercial product, Leeb says, it should cost only about $25 to $30 per home. "We're trying to lower the barriers to installation," says co-author John Donnal, and this noncontact sensor is simple enough for most home users to install on their own. "It just goes on with a zip tie," he says.William Singleton, an engineer at the U.S. Army Fort Devens Base Camp Integration Laboratory, who was not involved in this research, says this work "is an excellent example of how theoretical scientific and mathematical principles can be brought to bear on real world, practical, problem-solving applications." By using the MIT team's sensing system, he says, "significant potential savings in fuel, water, and equipment maintenance can be realized. This will provide increased options for the battlefield commander in accomplishing his mission, reduce the overall base camp logistics footprint, and ultimately save lives of warfighters involved in base camp sustainment and resupply."