Robot's in-hand eye maps surroundings, determines hand's location

Researchers at Carnegie Mellon University's Robotics Institute have shown that a camera attached to a robot's hand can rapidly create a 3-D model of its environment and also locate the hand within that 3-D world.

Doing so with imprecise cameras and wobbly arms in real-time is tough, but the CMU team found they could improve the accuracy of the map by incorporating the arm itself as a sensor, using the angle of its joints to better determine the pose of the camera. This would be important for a number of applications, including inspection tasks, said Matthew Klingensmith, a Ph.D. student in robotics.
The researchers will present their findings on May 17 at the IEEE International Conference on Robotics and Automation in Stockholm, Sweden. Siddhartha Srinivasa, associate professor of robotics, and Michael Kaess, assistant research professor of robotics, joined Klingensmith in the study.
Placing a camera or other sensor in the hand of a robot has become feasible as sensors have grown smaller and more power-efficient, Srinivasa said. That's important, he explained, because robots "usually have heads that consist of a stick with a camera on it." They can't bend over like a person could to get a better view of a work space.
But an eye in the hand isn't much good if the robot can't see its hand and doesn't know where its hand is relative to objects in its environment. It's a problem shared with mobile robots that must operate in an unknown environment. A popular solution for mobile robots is called simultaneous localization and mapping, or SLAM, in which the robot pieces together input from sensors such as cameras, laser radars and wheel odometry to create a 3-D map of the new environment and to figure out where the robot is within that 3-D world.
"There are several algorithms available to build these detailed worlds, but they require accurate sensors and a ridiculous amount of computation," Srinivasa said.
Those algorithms often assume that little is known about the pose of the sensors, as might be the case if the camera was handheld, Klingensmith said. But if the camera is mounted on a robot arm, he added, the geometry of the arm will constrain how it can move.
"Automatically tracking the joint angles enables the system to produce a high-quality map even if the camera is moving very fast or if some of the sensor data is missing or misleading," Klingensmith said.
The researchers demonstrated their Articulated Robot Motion for SLAM (ARM-SLAM) using a small depth camera attached to a lightweight manipulator arm, the Kinova Mico. In using it to build a 3-D model of a bookshelf, they found that it produced reconstructions equivalent or better to other mapping techniques.
"We still have much to do to improve this approach, but we believe it has huge potential for robot manipulation," Srinivasa said. Toyota, the U.S. Office of Naval Research and the National Science Foundation supported this research.

New method for making green LEDs enhances their efficiency and brightness

A new method of cubic phase synthesis: Hexagonal-to-cubic phase transformation. The scale bars represent 100 nm in all images. (a) Cross sectional and (b) Top-view SEM images of cubic GaN grown on U-grooved Si(100). (c) Cross sectional and (d) Top-view EBSD images of cubic GaN grown on U-grooved Si(100), showing cubic GaN in blue, and hexagonal GaN in red. Credit: University of Illinois

Read more at: http://phys.org/news/2016-07-method-green-efficiency-brightness.html#jCp
Researchers at the University of Illinois at Urbana Champaign have developed a new method for making brighter and more efficient green light-emitting diodes (LEDs). Using an industry-standard semiconductor growth technique, they have created gallium nitride (GaN) cubic crystals grown on a silicon substrate that are capable of producing powerful green light for advanced solid-state lighting.

"This work is very revolutionary as it paves the way for novel green wavelength emitters that can target advanced solid-state lighting on a scalable CMOS-silicon platform by exploiting the new material, cubic gallium nitride," said Can Bayram, an assistant professor of electrical and computer engineering at Illinois who first began investigating this material while at IBM T.J. Watson Research Center several years ago.

"The union of solid-state lighting with sensing (e.g. detection) and networking (e.g. communication) to enable smart (i.e. responsive and adaptive) visible lighting, is further poised to revolutionize how we utilize light. And CMOS-compatible LEDs can facilitate fast, efficient, low-power, and multi-functional technology solutions with less of a footprint and at an ever more affordable device price point for these applications."

Typically, GaN forms in one of two crystal structures: hexagonal or cubic. Hexagonal GaN is thermodynamically stable and is by far the more conventional form of the semiconductor. However, hexagonal GaN is prone to a phenomenon known as polarization, where an internal electric field separates the negatively charged electrons and positively charged holes, preventing them from combining, which, in turn, diminishes the light output efficiency.

Until now, the only way researchers were able to make cubic GaN was to use molecular beam epitaxy, a very expensive and slow crystal growth method when compared to the widely used metal-organic chemical vapor deposition (MOCVD) method that Bayram used.

Bayram and his graduate student Richard Liu made the cubic GaN by using lithography and isotropic etching to create a U-shaped groove on Si (100). This non-conducting layer essentially served as a boundary that shapes the hexagonal material into cubic form.

"Our cubic GaN does not have an internal electric field that separates the charge carriers—the holes and electrons," explained Liu. "So, they can overlap and when that happens, the electrons and holes combine faster to produce light."

Ultimately, Bayram and Liu believe their cubic GaN method may lead to LEDs free from the "droop" phenomenon that has plagued the LED industry for years. For green, blue, or ultra-violet LEDs, their light-emission efficiency declines as more current is injected, which is characterized as "droop."

"Our work suggests polarization plays an important role in the droop, pushing the electrons and holes away from each other, particularly under low-injection current densities," said Liu, who was the first author of the paper, "Maximizing Cubic Phase Gallium Nitride Surface Coverage on Nano-patterned Silicon (100)", appearing Applied Physics Letters.

Having better performing green LEDs will open up new avenues for LEDs in general solid-state lighting. For example, these LEDs will provide energy savings by generating white light through a color mixing approach. Other advanced applications include ultra-parallel LED connectivity through phosphor-free green LEDs, underwater communications, and biotechnology such as optogenetics and migraine treatment.

Enhanced green LEDs aren't the only application for Bayram's cubic GaN, which could someday replace silicon to make power electronic devices found in laptop power adapters and electronic substations, and it could replace mercury lamps to make ultra-violet LEDs that disinfect water.

Source:phys.org

World’s first 1,000-core processor runs on AA batteries

The CPU is 100 times more power-efficient than modern laptops

A team of engineering students at the University of California, Davis has developed a new processor with 1,000 CPU cores. The chip is so power-efficient that it can run on a single AA battery, and is said to be the most energy-efficient many-core processor created to date.

Presented at the 2016 Symposium on VLSI Technology and Circuits in Honolulu earlier this month, the KiloCore, as it’s called, can execute 115 billion instructions per second while dissipating just 0.7 watts. The cores inside the processor can be clocked to a maximum of 1.78 GHz, and individually shut down when not in use. The cores also transfer data among each other instead of relying on a shared cache of memory, which is typical of today’s commercial processors. 

The KiloCore is 100 times more power-efficient than modern laptops. Image source: University of California, Davis.

The KiloCore is 100 times more powerful than today’s laptops, despite being built on old 32-nm CMOS processor tech from IBM. But the question is: what would you use a chip with 1,000 cores for? Well, nothing out of the ordinary — you could use it for encryption, decryption, video processing, and plenty of scientific tasks that may come to mind.

Unfortunately, we aren’t about to see mass production of this processor. This is mainly due to the fact that the university had IBM manufacture the chip on the ancient 32-nm process. And, most of today’s similar applications are designed to run more efficiently on a lower number of highly-clocked threads rather than splitting the work across slower cores.

However, this does raise the possibility of many-core processors finding their ways into a variety of mobile devices. Though they’re not universally helpful, they could save lots of time when your laptop or smartphone would otherwise slow down.

Still, it's difficult to disagree that a creation such as this is impressive.

Source: PCWorld

By Nicole DiGiose

Samsung’s Galaxy S7 -- A Tale of Two Image Sensors

TechInsights discusses which wafer bonding technology hints at a possible future for stacked dies.

Samsung’s S7 smartphones are offered with several unique builds depending on the country of use. For example, the US models sport the Qualcomm Snapdragon 820 processor and the European versions that we at TechInsights bought contained the Samsung Exynos 8 Octa processor. Both of these processors were fabbed by Samsung using its 14 nm Low Power Plus (LPP) process. Other chips in the phones were dual sourced as well, including their 12 megapixel CMOS image sensors. We knew that Sony was one vendor and Samsung the other, but how was the split done and what technologies were used?

Figure 1 shows the US (SM-G930A) and European (SM-G930F) smartphones with their cover plates removed to expose their circuit boards and camera modules. The layout of the two circuit boards is quite similar but we note small differences in the metal housings used by the two image sensors. We originally thought this to be a marker for the Sony and Samsung variants, but this turned out not to be the case.

Figure 1 Samsung S7 with cover removed SM-G930A US model left, SM-G930F European model right (Source: TechInsights)

Figure 2 shows the camera modules from the US sourced Smartphone (left) and the European model (right). The US model has ‘SONY’ printed on its flex ribbon and our examination of the die confirms it to be the Sony IMX260 12 megapixel backside illuminated CMOS image sensor. Our first sample of the European phone’s module revealed it to be the Samsung S5L2L1 backside CMOS image sensor, but our remaining six European S7 phones housed the Sony image sensors. This was a surprise as we had assumed that Samsung would do a geographic split for the image sensors, much like they did for the Qualcomm Snapdragon and Exynos processors.

Figure 2: CMOS Image Sensor Modules (Source: TechInsights) SM-G930A US model left, SM-G930F European model right

Figures 3 and 4 are die photographs of the Sony IMX260 and Samsung S5k2L1SX 12 Mp backside illuminated (BSI) CMOS image sensors (CIS), respectively that were removed from the two phones. We have removed the organic microlenses and color filters that cover the two dies so that we can get a better view of the pixel array size, and in the case of the Samsung die, the layout of through silicon vias (TSVs) that are used to connect the CIS die to an underlying control ASIC.

The two dies are the same size and their array sizes are essentially the same size as well. No surprise here as the two dies use what appear to be the same optical housings in the European versions of the phones.

Sony had used TSVs in their earlier CMOS image sensors and we had expected the same for the IMX260. But we don’t see them, as they have been replaced by a direct wafer bonding process that we will discuss later.

The Samsung CMOS image sensor has arrays of TSVs along its perimeter and these are used to make the electrical connections to the underlying ASIC.

Figure 3: Sony IMX260 CMOS Image Sensor (Source: TechInsights)

Figure 4: Samsung S5k2L1SX CMOS Image Sensor (Source: TechInsights)

Produced by Kevin Gibb, Product Line Manager, TechInsights

Flexible wearable electronic skin patch offers new way to monitor alcohol levels

Engineers at the University of California San Diego have developed a flexible wearable sensor that can accurately measure a person's blood alcohol level from sweat and transmit the data wirelessly to a laptop, smartphone or other mobile device. The device can be worn on the skin and could be used by doctors and police officers for continuous, non-invasive and real-time monitoring of blood alcohol content.

The device consists of a temporary tattoo—which sticks to the skin, induces sweat and electro chemically detects the alcohol level—and a portable flexible electronic circuit board, which is connected to the tattoo by a magnet and can communicate the information to a mobile device via Bluetooth. The work, led by nano engineering professor Joseph Wang and electrical engineering professor Patrick Mercier, both at UC San Diego, was published recently in the journal ACS Sensors.

"Lots of accidents on the road are caused by drunk driving. This technology provides an accurate, convenient and quick way to monitor alcohol consumption to help prevent people from driving while intoxicated," Wang said. The device could be integrated with a car's alcohol ignition interlocks, or friends could use it to check up on each other before handing over the car keys, he added.

"When you're out at a party or at a bar, this sensor could send alerts to your phone to let you know how much you've been drinking," said Jayoung Kim, a materials science and engineering PhD student in Wang's group and one of the paper's co-first authors.

Blood alcohol concentration is the most accurate indicator of a person's alcohol level, but measuring it requires pricking a finger. Breathalyzers, which are the most commonly used devices to indirectly estimate blood alcohol concentration, are non-invasive, but they can give false readouts. For example, the alcohol level detected in a person's breath right after taking a drink would typically appear higher than that person's actual blood alcohol concentration. A person could also fool a breathalyzer into detecting a lower alcohol level by using mouthwash.

Recent research has shown that blood alcohol concentration can also be estimated by measuring alcohol levels in what's called insensible sweat—perspiration that happens before it's perceived as moisture on the skin. But this measurement can be up to two hours behind the actual blood alcohol reading. On the other hand, the alcohol level in sensible sweat—the sweat that's typically seen—is a better real-time indicator of the blood alcohol concentration, but so far the systems that can measure this are neither portable nor fit for wearing on the body.

Now, UC San Diego researchers have developed an alcohol sensor that's wearable, portable and could accurately monitor alcohol level in sweat within 15 minutes.

"What's also innovative about this technology is that the wearer doesn't need to be exercising or sweating already. The user can put on the patch and within a few minutes get a reading that's well correlated to his or her blood alcohol concentration. Such a device hasn't been available until now," Mercier said.

How it works

Wang and Mercier, the director and co-director, respectively, of the UC San Diego Center for Wearable Sensors, collaborated to develop the device. Wang's group fabricated the tattoo, equipped with screen-printed electrodes and a small hydrogel patch containing pilocarpine, a drug that passes through the skin and induces sweat.

Mercier's group developed the printed flexible electronic circuit board that powers the tattoo and can communicate wirelessly with a mobile device. His team also developed the magnetic connector that attaches the electronic circuit board to the tattoo, as well as the device's phone app.

"This device can use a Bluetooth connection, which is something a breathalyzer can't do. We've found a way to make the electronics portable and wireless, which are important for practical, real-life use," said Somayeh Imani, an electrical engineering PhD student in Mercier's lab and a co-first author on the paper.

The tattoo works first by releasing pilocarpine to induce sweat. Then, the sweat comes into contact with an electrode coated with alcohol oxidase, an enzyme that selectively reacts with alcohol to generate hydrogen peroxide, which is electrochemically detected. That information is sent to the electronic circuit board as electrical signals. The data are communicated wirelessly to a mobile device.

Putting the tattoo to the test

Researchers tested the alcohol sensor on 9 healthy volunteers who wore the tattoo on their arms before and after consuming an alcoholic beverage (either a bottle of beer or glass of red wine). The readouts accurately reflected the wearers' blood alcohol concentrations.

The device also gave accurate readouts even after repeated bending and shaking. This shows that the sensor won't be affected by the wearer's movements, researchers said.

As a next step, the team is developing a device that could continuously monitor alcohol levels for 24 hours.

Provided by: University of California - San Diego

THE GREATEST ELECTRONICS BOOK EVER WRITTEN?

Getting Started in Electronics, by Forrest M. Mims, III. is a spectacular introduction to the world of electronics.  This book is not new - the truth is that it has changed little since it’s first release in 1983.  Despite this, twenty-five years later, there is really nothing else like it.  This book is suitable for beginners of any age yet it comprehensively describes the technical theory and practical use of electronic devices like resistors, capacitors, inductors, diodes, transistors, FETs (including the now-rare JFET), and LEDs, as well as circuits like amplifiers, oscillators, and logic gates.  There is even a graphical introduction to device physics (semiconductor materials, doping, electrons and holes) and semiconductor fabrication!  This is kind of stuff they teach third-year students in university ECE classes, written in a way that is understandable to a child in third grade!

The entire book is formatted like an engineering notebook with handwritten notes on every page. The illustrations are fun and make the book friendly and accessible. Here is an excerpt from the chapter on diodes:

My father gave me this book when I was six or seven years old along with a 25 watt soldering iron from Radio Shack.  I am convinced that this book, together with a Science Fair 160-in-ONE kit, is what caused me to pursue a career in Electrical Engineering.  I still enjoy leafing through its pages and proving to myself that I can understand how each circuit works.

Forrest Mims himself is an interesting individual and has led a prolific career as a writer and amateur scientist.  He is an active member of the Society for Amateur Scientists (SAS) and edits the Citizen Scientist.
The book even includes a handy guide to help you learn How to Solder!

Thankfully, it turns out that this fantastic book is still in print. The groovy green cover is gone (a mistake, in my opinion), but the contents have not changed.  This is fantastic news for anyone interested in learning about electronics.  These also make great Christmas presents – I bought one for my brother last year and he loved it!
In my opinion, this is probably the greatest introductory book about electronics ever written. I’d be curious to hear if anyone has any other favorites – leave a comment if you do!

Oxbotica’s New Autonomous Vehicle Software Learns As It Goes

A spin-out company from the University of Oxford called Oxbotica has developed a new software system for making regular cars into driverless vehicles.

The system, called Selenium, can ingest data from visual cameras, laser scanners, or radar systems. It then uses a series of algorithms to establish where the "it" is, what surrounds it, and how to move. “It takes any vehicle and makes it into an autonomous vehicle,” explains Paul Newman, a professor at the University of Oxford and cofounder of Oxbotica. That sounds ambitious, but he’s being serious: the team plans for the software to be used to control not just autonomous cars, but warehouse robots, forklifts, and self-driving public transport vehicles.

Most systems being developed by other manufacturers rely on building a system that is robust enough to handle driving from the moment they are first switched on. Tesla’s Autopilot, for example, makes use of onboard cameras and image analysis to control the car on highways. Butits reliability has come into question after a series of recent crashes.

Oxbotica’s software gradually acquires data about the routes along which a vehicle is driven and learns how to react by analyzing the way its human driver acts. “When you buy your autonomous car and drive off the (lot), it will know nothing,” says Ingmar Posner, an associate professor at Oxford and another of Oxbotica’s cofounders. “But at some point it will decide that it knows where it is, that its perception system has been trained by the way you’ve been driving, and it can then offer autonomy.”

The company explains that the software provides two primary functions: localize the vehicle in space, and perceive what’s happening around it. Based on those two feeds, a central planner can determine how the car should move. Both localization and perception systems rely on sensors dotted about the vehicle, the choice of which depends on application: Newman suggests that a warehouse forklift may use just use cheap cameras, while a car could make use of all kinds of sensors.

Selenium can compare on-the-fly sensor readings with those stored away in prior maps from previous journeys in similar conditions. “If you take it out in the snow and it’s not seen it before, it keeps the ideas of snowy-ness around for the next time,” Newman says. Then it can identify image features—such as details on buildings or placement of street furniture—to localize the vehicle in the wider world. Meanwhile laser data, due to its high resolution, can be used to more accurately localize the car, especially in low-visibility conditions when cameras can falter.

The team initially teaches Selenium to recognize, say, cars and humans by providing it with a labeled training set from which it can learn. But over time it also learns from the driver. “If a human’s driving and they pass straight through what the car thought was a human, the software can learn from that,” Posner says. The system uses similar prior knowledge and continual learning to work out, for instance, which parts of a surface it can safely drive on or how traffic signals are changing.

The result is a vehicle that can gain a deep understanding of the routes it drives regularly. That, Posner says, means that the software isn’t simply trying to do a mediocre job wherever it’s placed—instead, it does an excellent job where it’s learned to drive. I took a spin in a modified Renault Twizy that had been fitted with lasers, cameras, and a large computer powered by Selenium. It felt much like being driven by a confident human driver, with smooth but assertive acceleration, braking, and steering—though there were no hazards on the simple loop we drove.

Oxbotica’s software is set to be tested in two real-world settings in the near future: in the self-driving public transport vehicles of the GATEway project in Greenwich, London, and the LUTZ Pathfinder driverless pods being tested in Milton Keynes, U.K. When pressed, Newman explained that the company is already working with auto manufacturers, though he wasn’t able to say which ones, nor say when the technology might be rolled out in cars.

Despite recent investigations into Tesla’s autonomous systems casting somewhat of a shadow over self-driving car technology, those working in the sector are clearly pressing ahead with their work. Oxbotica isn’t alone in launching software: Nissan also announced its new ProPilot driver aid this week, too. The two systems are very different, but their arrivals suggest that the race to achieve automotive autonomy shows no signs of slowing.