System Bits: July 30

A camera that sees around corners
Researchers at Stanford University developed a camera system that can detect moving objects around a corner, looking at single particles of light reflected on a wall.

“People talk about building a camera that can see as well as humans for applications such as autonomous cars and robots, but we want to build systems that go well beyond that,” said Gordon Wetzstein, an assistant professor of electrical engineering at Stanford. “We want to see things in 3D, around corners and beyond the visible light spectrum.”

The camera system, which the researchers are presenting at the SIGGRAPH 2019 conference on Aug. 1, builds upon previous around-the-corner cameras this team developed. It’s able to capture more light from a greater variety of surfaces, see wider and farther away and is fast enough to monitor out-of-sight movement for the first time. Someday, the researchers hope superhuman vision systems could help autonomous cars and robots operate even more safely than they would with human guidance.

Keeping their system practical is a high priority for these researchers. The hardware they chose, the scanning and image processing speeds, and the style of imaging are already common in autonomous car vision systems. Previous systems for viewing scenes outside a camera’s line of sight relied on objects that either reflect light evenly or strongly. But real-world objects, including shiny cars, fall outside these categories, so this system can handle light bouncing off a range of surfaces, including disco balls, books and intricately textured statues.

Objects – including books, a stuffed animal and a disco ball – in and around a bookshelf tested the system’s versatility in capturing light from different surfaces in a large-scale scene. (Image credit: David Lindell)

Central to their advance was a laser 10,000 times more powerful than what they were using a year ago. The laser scans a wall opposite the scene of interest and that light bounces off the wall, hits the objects in the scene, bounces back to the wall and to the camera sensors. By the time the laser light reaches the camera only specks remain, but the sensor captures each and every one, sending it along to a highly efficient algorithm, also developed by this team, that untangles these echoes of light to decipher the hidden tableau.

The system can scan at four frames per second. It can reconstruct a scene at speeds of 60 frames per second on a computer with a graphics processing unit, which enhances graphics processing capabilities.

To advance their algorithm, the team looked to other fields for inspiration. The researchers were particularly drawn to seismic imaging systems – which bounce sound waves off underground layers of Earth to learn what’s beneath the surface – and reconfigured their algorithm to likewise interpret bouncing light as waves emanating from the hidden objects. The result was the same high-speed and low memory usage with improvements in their abilities to see large scenes containing various materials.

“There are many ideas being used in other spaces – seismology, imaging with satellites, synthetic aperture radar – that are applicable to looking around corners,” said Matthew O’Toole, an assistant professor at Carnegie Mellon University who was previously a postdoctoral fellow in Wetzstein’s lab. “We’re trying to take a little bit from these fields and we’ll hopefully be able to give something back to them at some point.”

Team aims for a quantum Internet
A research team led by Osaka University demonstrated how information encoded in the circular polarization of a laser beam can be translated into the spin state of an electron in a quantum dot, each being a quantum bit and a quantum computer candidate. The achievement represents a major step towards a “quantum Internet,” in which future computers can rapidly and securely send and receive quantum information.

Quantum computers have the potential to vastly outperform current systems because they work in a fundamentally different way. Instead of processing discrete ones and zeros, quantum information, whether stored in electron spins or transmitted by laser photons, can be in a superposition of multiple states simultaneously. Moreover, the states of two or more objects can become entangled, so that the status of one cannot be completely described without this other. Handling entangled states allow quantum computers to evaluate many possibilities simultaneously, as well as transmit information from place to place immune from eavesdropping.

However, these entangled states can be very fragile, lasting only microseconds before losing coherence. To realize the goal of a quantum Internet, over which coherent light signals can relay quantum information, these signals must be able to interact with electron spins inside distant computers.

Researchers led by Osaka University used laser light to send quantum information to a quantum dot by altering the spin state of a single electron trapped there. While electrons don’t spin in the usual sense, they do have angular momentum, which can be flipped when absorbing circularly polarized laser light.

“Importantly, this action allowed us to read the state of the electron after applying the laser light to confirm that it was in the correct spin state,” says first author Takafumi Fujita. “Our readout method used the Pauli exclusion principle, which prohibits two electrons from occupying the exact same state. On the tiny quantum dot, there is only enough space for the electron to pass the so-called Pauli spin blockade if it has the correct spin.”

Quantum information transfer has already been used for cryptographic purposes. “The transfer of superposition states or entangled states allows for completely secure quantum key distribution,” senior author Akira Oiwa says. “This is because any attempt to intercept the signal automatically destroys the superposition, making it impossible to listen in without being detected.”

The rapid optical manipulation of individual spins is a promising method for producing a quantum nanoscale general computing platform. An exciting possibility is that future computers may be able to leverage this method for many other applications, including optimization and chemical simulations.

Producing gridlock by hacking connected cars
Researchers at the Georgia Institute of Technology, working with Multiscale Systems, applied physics in a new study to simulate what it would take for future hackers to wreak exactly this widespread havoc by randomly stranding these cars. The researchers want to expand the current discussion on automotive cybersecurity, which mainly focuses on hacks that could crash one car or run over one pedestrian, to include potential mass mayhem.

They warn that even with increasingly tighter cyber defenses, the amount of data breached has soared in the past four years, but objects becoming hackable can convert the rising cyber threat into a potential physical menace.

“Unlike most of the data breaches we hear about, hacked cars have physical consequences,” said Peter Yunker, who co-led the study and is an assistant professor in Georgia Tech’s School of Physics.

It may not be that hard for state, terroristic, or mischievous actors to commandeer parts of the Internet of Things, including cars.

“With cars, one of the worrying things is that currently there is effectively one central computing system, and a lot runs through it. You don’t necessarily have separate systems to run your car and run your satellite radio. If you can get into one, you may be able to get into the other,” said Jesse Silverberg of Multiscale Systems, who co-led the study with Yunker.

In simulations of hacking Internet-connected cars, the researchers froze traffic in Manhattan nearly solid, and it would not even take that to wreak havoc. Here are their results, and the numbers are conservative for reasons mentioned below.

“Randomly stalling 20% of cars during rush hour would mean total traffic freeze. At 20%, the city has been broken up into small islands, where you may be able to inch around a few blocks, but no one would be able to move across town,” said David Yanni, a graduate research assistant in Yunker’s lab.

Not all cars on the road would have to be connected, just enough for hackers to stall 20% of all cars on the road. For example, if 40% of all cars on the road were connected, hacking half would suffice.

Hacking 10% of all cars at rush hour would debilitate traffic enough to prevent emergency vehicles from expediently cutting through traffic that is inching along citywide. The same thing would happen with a 20% hack during intermediate daytime traffic.

For the city to be safe, hacking damage would have to be below that. In other cities, things could be worse.

“Manhattan has a nice grid, and that makes traffic more efficient. Looking at cities without large grids like Atlanta, Boston, or Los Angeles, and we think hackers could do worse harm because a grid makes you more robust with redundancies to get to the same places down many different routes,” Yunker said.

The researchers left out factors that would likely worsen hacking damage, thus a real-world hack may require stalling even fewer cars to shut down Manhattan.

“I want to emphasize that we only considered static situations – if roads are blocked or not blocked. In many cases, blocked roads spill over traffic into other roads, which we also did not include. If we were to factor in these other things, the number of cars you’d have to stall would likely drop down significantly,” Yunker said.

The researchers also did not factor in ensuing public panic nor car occupants becoming pedestrians that would further block streets or cause accidents. Nor did they consider hacks that would target cars at locations that maximize trouble.

They also stress that they are not cybersecurity experts, nor are they saying anything about the likelihood of someone carrying out such a hack. They simply want to give security experts a calculable idea of the scale of a hack that would shut a city down.

The researchers do have some general ideas of how to reduce the potential damage.

“Split up the digital network influencing the cars to make it impossible to access too many cars through one network,” said lead author Skanka Vivek, a postdoctoral researcher in Yunker’s lab. “If you could also make sure that cars next to each other can’t be hacked at the same time; that would decrease the risk of them blocking off traffic together.”