The concept behind non-line-of-sight (NLOS) imaging seems fairly easy to grasp: a laser bounces photons off a surface that illuminate objects that are within in sight of that surface, but not of the imaging equipment. The photons that are then reflected or refracted by the hidden object make their way back to the laser’s location, where they are captured and processed to form an image. Essentially this allows one to use any surface as a mirror to look around corners.
Main disadvantage with this method has been the low resolution and high susceptibility to noise. This led a team at Stanford University to experiment with ways to improve this. As detailed in an interview by Tech Briefs with graduate student [David Lindell], a major improvement came from an ultra-fast shutter solution that blocks out most of the photons that return from the wall that is being illuminated, preventing the photons reflected by the object from getting drowned out by this noise.
The key to getting the imaging quality desired, including with glossy and otherwise hard to image objects, was this f-k migration algorithm. As explained in the video that is embedded after the break, they took a look at what methods are used in the field of seismology, where vibrations are used to image what is inside the Earth’s crust, as well as synthetic aperture radar and similar. The resulting algorithm uses a sequence of Fourier transformation, spectrum resampling and interpolation, and the inverse Fourier transform to process the received data into a usable image.
This is not a new topic; we covered a simple implementation of this all the way back in 2011, as well as a project by UK researchers in 2015. This new research shows obvious improvements, making this kind of technology ever more viable for practical applications.
Continue reading “Looking Around Corners With F-K Migration”
Building a marble run has long been on my project list, but now I’m going to have to revise that plan. In addition to building an interesting track for the orbs to traverse, [Jack Atherton] added custom sound effects triggered by the marble.
I ran into [Jack] at Stanford University’s Center for Computer Research in Music and Acoustics booth at Maker Faire. That’s a mouthful, so they usually go with the acronym CCRMA. In addition to his project there were numerous others on display and all have a brief write-up for your enjoyment.
[Jack] calls his project Leap the Dips which is the same name as the roller coaster the track was modeled after. This is the first I’ve heard of laying out a rolling ball sculpture track by following an amusement park ride, but it makes a lot of sense since the engineering for keeping the ball rolling has already been done. After bending the heavy gauge wire [Jack] secured it in place with lead-free solder and a blowtorch.
As mentioned, the project didn’t stop there. He added four piezo elements which are monitored by an Arduino board. Each is at a particularly extreme dip in the track which makes it easy to detect the marble rolling past. The USB connection to the computer allows the Arduino to trigger a MaxMSP patch to play back the sound effects.
For the demonstration, Faire goers wear headphones while letting the balls roll, but in the video below [Jack] let me plug in directly to the headphone port on his Macbook. It’s a bit weird, since there no background sound of the Faire during this part, but it was the only way I could get a reasonable recording of the audio. I love the effect, and think it would be really fun packaging this as a standalone using the Teensy Audio library and audio adapter hardware.
Continue reading “Ball Run Gets Custom Sound Effects”
What it is:
Some would argue that replicating the human brain in silicon is impossible. However, the folks over at Brains in Silicon of Stanford University might disagree. They’ve created a circuit board capable of simulating one million neurons and up to 6 billion synapses in real-time. Yes, that’s billion with a “B”. They call their new type of computer The Neurogrid.
The Neurogrid board boasts 16 of their Neurocore chips, with each one holding 256 x 256 “neurons”. It attempts to function like a brain by using analog signals for computations and digital signals for communication. “Soft-wires” can run between the silicon neurons, mimicking the brain’s synapses.
Be sure to stick around after the break, where we discuss the limitations of the Neurogrid, along with a video from its creators.
Continue reading “The Neurogrid – What It Is And What It Is Not”
It’s Christmas time. You have a string of 50 individually addressable RGB LEDs, what would you do? Well, [Barney] decided to try something different. He’s made a Christmas tree that reflects Twitter’s current sentiments about the holiday.
Wait, what? We admit, it’s a kind of weird concept, but the software behind it is pretty cool. As it turns out Stanford University’s Natural Language Processing Group released the source code for their sentiment analyzer. Unlike a normal sentiment analyzer which assigns points to positive words and negative points for negative words, this one actually uses a deep learning model which builds up a representation of entire sentences based on their structure — only problem? It was designed and trained to analyze movie reviews, not Christmas tweets.
Regardless, it still does the trick (kind of), but, it’s pretty slow. [Barney] has his fastest computer running four instances of the analyzer, which pulls Christmas tweets that have been sorted by the Twitter API — it then analyzes them, assigns the sentiment, and places them in a second queue. He’s using beanstalkd for the queuing and a Raspberry Pi to control the lights. The result is a pretty light display whose colors represent the sentiments of incoming tweets — it’s hard to say if it’s actually successful in reflecting the opinion of the tweets, but it’s a pretty cool concept.
Stick around after the break to see the Christmas Tweet Analyzing Tree in action — say that 5 times fast!
Continue reading “Christmas Tree Analyzes Your Tweets”