Smarter car algorithm shows radio interference risk

An experiment at the Massachussetts Institute of Technology has highlighted some of the hidden risks inherent in (supposedly) smart cars that will depend on radio-based Intelligent Transport Systems (ITS) for extra safety on the road.

In an ITS system, in-car computers communicate with each other over vehicle-to-vehicle (V2V) microwave radio links, while the cars also communicate with traffic lights and roadside speed sensors over a vehicle-to-infrastructure (V2I) radio signalling system (the infrastructure transmits information about cars that are too old to have ITS systems fitted). When two cars are approaching a junction and the V2V/V2I speed signals suggest they are going to crash, a warning can be sounded or a software algorithm can choose to make one of the cars brake, for instance.

I tried this out on the Millbrook test track in Bedfordshire, UK, in 2007: speeding towards a junction in a Saab my brakes were automatically applied to allow a speeding Opel to pass in front of me. It was by turns scary and impressive. But if it hadn't worked I'd have been toast.
But MIT engineer Domitilla Del Vecchio says such systems can be over-protective, taking braking action when there is no real threat. "It's tempting to treat every vehicle on the road as an agent that's playing against you," she says in an MIT research brief issued today.

So she and researcher Rajeev Verma set out to design an algorithm that doesn't over-react - and to test it with model vehicles in a lab. Their trick was simple: calculate not speed but acceleration and deceleration as cars approach a junction, allowing a much finer calculation of the risk. In 97 out of 100 circuits, the collision avoidance technology worked fine.

But in three cases, there were two near-misses and one collision. The reason? Nothing to do with the algorithm: it was due to delays in V2V and V2I radio communication. This highlights the risk of depending upon a complex safety system like ITS - especially a radio-based one which could easily be jammed or electromagnetically interfered with because of the wireless technologies which proliferate in our built environment.

There is only so much that researchers can do against a phenomenon as difficult to predict as radio interference.
The takehome message? ITS technology will doubtless do much to improve road safety - but sometimes it won't. It's never going to substitute for driver alertness.

Source New Scientist

Self-assembling Electronic Nano-components

Magnetic storage media such as hard drives have revolutionized the handling of information: We are used to dealing with huge quantities of magnetically stored data while relying on highly sensitive electronic components. And hope to further increase data capacities through ever smaller components. Together with experts from Grenoble and Strasbourg, researchers of KIT’s Institute of Nanotechnology (INT) have developed a nano-component based on a mechanism observed in nature. 

“Self-organization” of nano-devices: Magnetic molecules (green) arrange on a carbon nanotube (black) to build an electronic component.

What if the very tininess of a component prevented one from designing the necessary tools for its manufacture? One possibility could be to “teach” the individual parts to self-assemble into the desired product. For fabrication of an electronic nano-device, a team of INT researchers headed by Mario Ruben adopted a trick from nature: Synthetic adhesives were applied to magnetic molecules in such a way that the latter docked on to the proper positions on a nanotube without any intervention. In nature, green leaves grow through a similar self-organizing process without any impetus from subordinate mechanisms. The adoption of such principles to the manufacture of electronic components is a paradigm shift, a novelty.

The nano-switch was developed by a European team of scientists from Centre National de la Recherche Scientifique (CNRS) in Grenoble, Institut de Physique et Chimie des Matériaux at the University of Strasbourg, and KIT’s INT. It is one of the invention’s particular features that, unlike the conventional electronic components, the new component does not consist of materials such as metals, alloys or oxides but entirely of soft materials such as carbon nanotubes and molecules.

Terbium, the only magnetic metal atom that is used in the device, is embedded in organic material. Terbium reacts highly sensitively to external magnetic fields. Information as to how this atom aligns along such magnetic fields is efficiently passed on to the current flowing through the nanotube. The Grenoble CNRS research group headed by Dr. Wolfgang Wernsdorfer succeeded in electrically reading out the magnetism in the environment of the nano-component. The demonstrated possibility of addressing electrically single magnetic molecules opens a completely new world to spintronics, where memory, logic and possibly quantum logic may be integrated.

The function of the spintronic nano-device is described in the July issue of Nature Materials (DOI number: 10.1038/Nmat3050)for low temperatures of approximately one degree Kelvin, which is -272 degrees Celsius. Efforts are taken by the team of researchers to further increase the component’s working temperature in the near future.


Source KIT

Einstein's and Fourier's ideas as keys to new humanlike computer vision

WEST LAFAYETTE, Ind. - Two new techniques for computer-vision technology mimic how humans perceive three-dimensional shapes by instantly recognizing objects no matter how they are twisted or bent, an advance that could help machines see more like people.

The techniques, called heat mapping and heat distribution, apply mathematical methods to enable machines to perceive three-dimensional objects, said Karthik Ramani, Purdue University's Donald W. Feddersen Professor of Mechanical Engineering.

This graphic illustrates a new computer-vision technology that builds on the basic physics and mathematical equations related to how heat diffuses over surfaces. The technique mimics how humans perceive three-dimensional shapes by instantly recognizing objects no matter how they are twisted or bent, an advance that could help machines see more like people. Here, a "heat mean signature" of a human hand model is used to perceive the six segments of the overall shape and define the fingertips.

"Humans can easily perceive 3-D shapes, but it's not so easy for a computer," he said. "We can easily separate an object like a hand into its segments - the palm and five fingers - a difficult operation for computers."

Both of the techniques build on the basic physics and mathematical equations related to how heat diffuses over surfaces. 

"Albert Einstein made contributions to diffusion, and 18th century physicist Jean Baptiste Joseph Fourier developed Fourier's law, used to derive the heat equation," Ramani said. "We are standing on the shoulders of giants in creating the algorithms for these new approaches using the heat equation."

As heat diffuses over a surface it follows and captures the precise contours of a shape. The system takes advantage of this "intelligence of heat," simulating heat flowing from one point to another and in the process characterizing the shape of an object, he said.

Findings will be detailed in two papers being presented during the IEEE Computer Vision and Pattern Recognition conference on June 21-23 in Colorado Springs. The paper was written by Ramani, Purdue doctoral students Yi Fang and Mengtian Sun, and Minhyong Kim, a professor of pure mathematics at the University College London.

A major limitation of existing methods is that they require "prior information" about a shape in order for it to be analyzed.

Researchers developing a new machine-vision technique tested their method on certain complex shapes, including the human form or a centaur – a mythical half-human, half-horse creature. The heat mapping allows a computer to recognize the objects no matter how the figures are bent or twisted and is able to ignore "noise" introduced by imperfect laser scanning or other erroneous data. 

"For example, in order to do segmentation you have to tell the computer ahead of time how many segments the object has," Ramani said. "You have to tell it that you are expecting, say, 10 segments or 12 segments."

The new methods mimic the human ability to properly perceive objects because they don't require a preconceived idea of how many segments exist.

"We are trying to come as close as possible to human segmentation," Ramani said. "A hot area right now is unsupervised machine learning. This means a machine, such as a robot, can perceive and learn without having any previous training. We are able to estimate the segmentation instead of giving a predefined number of segments." 

The work is funded partially by the National Science Foundation. A patent on the technology is pending.

The methods have many potential applications, including a 3-D search engine to find mechanical parts such as automotive components in a database; robot vision and navigation; 3-D medical imaging; military drones; multimedia gaming; creating and manipulating animated characters in film production; helping 3-D cameras to understand human gestures for interactive games; contributing to progress of areas in science and engineering related to pattern recognition; machine learning; and computer vision.

The heat-mapping method works by first breaking an object into a mesh of triangles, the simplest shape that can characterize surfaces, and then calculating the flow of heat over the meshed object. The method does not involve actually tracking heat; it simulates the flow of heat using well-established mathematical principles, Ramani said.

Heat mapping allows a computer to recognize an object, such as a hand or a nose, no matter how the fingers are bent or the nose is deformed and is able to ignore "noise" introduced by imperfect laser scanning or other erroneous data.

"No matter how you move the fingers or deform the palm, a person can still see that it's a hand," Ramani said. "But for a computer to say it's still a hand is going to be hard. You need a framework - a consistent, robust algorithm that will work no matter if you perturb the nose and put noise in it or if it's your nose or mine."

The method accurately simulates how heat flows on the object while revealing its structure and distinguishing unique points needed for segmentation by computing the "heat mean signature." Knowing the heat mean signature allows a computer to determine the center of each segment, assign a "weight" to specific segments and then define the overall shape of the object.

"Being able to assign a weight to segments is critical because certain points are more important than others in terms of understanding a shape," Ramani said. "The tip of the nose is more important than other points on the nose, for example, to properly perceive the shape of the nose or face, and the tips of the fingers are more important than many other points for perceiving a hand."

In temperature distribution, heat flow is used to determine a signature, or histogram, of the entire object.

"A histogram is a two-dimensional mapping of a three-dimensional shape," Ramani said. "So, no matter how a dog bends or twists, it gives you the same signature."

The temperature distribution technique also uses a triangle mesh to perceive 3-D shapes. Both techniques, which could be combined in the same system, require modest computer power and recognize shapes quickly, he said.

"It's very efficient and very compact because you're just using a two-dimensional histogram," Ramani said. "Heat propagation in a mesh happens very fast because the mathematics of matrix computations can be done very quickly and well."

The researchers tested their method on certain complex shapes, including hands, the human form or a centaur, a mythical half-human, half-horse creature. 

Source Purdue University

Gender-spotting tool could have rumbled fake blogger

Software that guesses a writer's gender could have prevented the world being duped into believing a blog that opposed the Syrian government and was striking out for gay rights was written by a young lesbian living in the country.

It turned out the author of the blog, "Gay Girl in Damascus", was a man – something the online gender checker would have picked up on. When New Scientist fed the text of the last blog post into the software, it said that the author was 63.2 per cent likely to be male.

Developed by Na Cheng and colleagues at the Stevens Institute of Technology in Hoboken, New Jersey, the ever-improving software could soon be revealing the gender of online writers – whether they are blogging, emailing, writing on Facebook or tweeting. The team say the software could help protect children from grooming by predators who conceal their gender online.

The fake blog highlights the problem of people masking their identity online. The truth about Amina Abdullah only emerged when the blogger disappeared, supposedly snatched by militiamen.

Online contacts realised that none of them had ever met Amina, and it turned out her blog photo had been stolen from a Facebook page. Then a 40-year-old American, Tom MacMaster living in Edinburgh, UK, confessed that he had been writing the blog all along.

Gender analysis

To determine the gender of a writer or blogger, Cheng and her colleagues Rajarathnam Chandramouli and Koduvayur Subbalakshmi wrote software that allows users to either upload a text file or paste in a paragraph of 50 words or more for gender analysis.

After a few moments, the program spits out a gender judgement: male, female or neutral. The neutral option points to how much of the text has been stripped of any indication of gender. This is something particularly prevalent in scientific texts, the researchers say.

To write their program, the team first turned to vast tranches of bylined text from a Reuters news archive and the massive email database of the bankrupt energy firm Enron. They trawled these documents for "psycho-linguistic" factors that had been identified by previous research groups, such as specific words and punctuation styles.

In total they found 545 of these factors, says Chandramouli, which they then honed down to 157 gender-significant ones. These included differences in punctuation style or paragraph lengths between men and women.

Other gender-significant factors included the use of words that indicate the mood or sentiment of the author and the degree to which they use "emotionally intensive adverbs and affective adjectives such as really, charming or lovely" which were used more often by women, says Chandramouli. Men were more likely to use the word "I", for example, whereas women used questions marks more often.

Bayesian algorithms

Finally, the software combined these cues using a Bayesian algorithm, which guesses gender based on the balance of probabilities suggested by the telltale factors. The work will appear in an upcoming edition of the journal Digital Investigation.

It doesn't always work, however. When the software is fed text, its judgement on a male or female writer is only accurate 85 per cent of the time – but that will improve as more people use it. That's because users get the chance to tell the system when it has guessed incorrectly, helping the algorithm learn. The next version will analyse tweets and Facebook updates.

Bernie Hogan, a specialist in social network technology at the Oxford Internet Institute in the UK, thinks there is a useful role for such technology. "Being able to provide some extra cues as to the gender of a writer is a good thing – it can only help."

Even a "neutral" decision might indicate that someone is trying to write in a gender voice that does not come naturally to them, he says. "It could be quite telling."

Testing the gender software

What did the gender identifier make of three well-known authors? We fed it some sample text to find out.
V. S. Naipaul, a winner of the Nobel prize for literature, claims he can tell a woman's writing by reading just two paragraphs of text, and controversially thinks female authors are no match for his writing. The software's verdict on this extract from his book The Enigma of Arrival: 88.4 per cent male.
Mary Evans was a female novelist who famously wrote under the male nom de plume George Eliot. The software has the measure of her, though. Its analysis of the writer's gender from the first paragraphs of Middlemarch: 94.6 per cent female.

More than 14,000 of Sarah Palin's emails were released by the state of Alaska last week after a lengthy campaign by various media organisations to obtain access to them. One email from the archive was put through the system, but the software got it wrong: 70.77 per cent male.

Source New Scientist