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CERN Tests First Artificial Retina Capable of Looking For High Energy Particles
KentuckyFC writes: Pattern recognition is one of the few areas where humans regularly outperform even the most powerful computers. Our extraordinary ability is a result of the way our bodies process visual information. But surprisingly, our brains only do part of the work. The most basic pattern recognition—edge detection, line detection and the detection of certain shapes—is performed by the complex circuitry of neurons in the retina. Now particle physicists are copying this trick to hunt for new particles. A team at CERN has built and tested an artificial retina capable of identifying particle tracks in the debris from particle collisions. The retina can do this at the same rate the LHC smashes particles together: about 800 million collisions per second. In other words, it can sift through the data in real time. The team says the retina outperforms any other particle-detecting device by a factor of 400v.
What are US scientists up to? Apart from teaching creationism, of course. lol
"The team says the retina outperforms any other particle-detecting device by a factor of 400v." 400v! What the hell is a factor of 400 volts?
So, to summarize the paper
http://arxiv.org/pdf/1409.1565... :
They have developed an algorithm for quickly giving a rough interpretation of the raw data stream coming out from the detector, i.e. converting the information that "value pixel A =12, value of pixel B = 43, ..." into useful physics data like "a particle with momentum vector P and charge Q was probably created 2 m from the collision point". This algorithm is special in that it can be implemented on an FPGA, and is somehow inspired by the retina of our eyes. Because it can run on an FPGA, it has the potential to be much faster, and can handle much larger data fluxes than current algorithms.
This is needed, because in a few years, we will upgrade the LHC such that it produces many more collisions per second, i.e. the data rates will be much higher. We do this to get more statistics, which may uncover rare physics processes (such as was done for the Higgs boson). Not all of this data deluge can be written to disk (or even downloaded from the detector hardware), so we use a trigger which decides which collisions are interesting enough to read out and store. This trigger works by downloading *part* of the data to a computing cluster that sits in the next room (yes, it does run on Linux), quickly reconstructing the event, and sending the "READ" signal to the rest of the detector if it fits certain criteria indicating that (for example) a heavy particle was created. If the data rate goes up, so must the processing speed, or else we will run out of buffers on the detector.
Reading the abstract, it is clear that what they did was to do image analysis using an algorithm (albeit in FPGA) modeled on what happens in the retina. Other than the speed advantage, there is nothing special about this that makes it an artificial retina. If you take a picture with a cellphone and do edge detection using software, is that an artificial retina? I would argue no more or less than what is described here.
TFS makes it sound like the image detectors are actually doing edge detection like the retina. The image sensors (CCD or CMOS or whatever) is doing no such thing. The image sensors are providing raw images that are being analyzed using edge detection algorithms using an FPGA.
There are VLSI implementations of retina-like processing, i.e. center excite, surround inhibit, that can do edge detection/enhancement, but this ain't it.