Showing posts with label vision tech. Show all posts
Showing posts with label vision tech. Show all posts

Tuesday, September 20, 2022

Whatever Happened To Good Old Fashioned Robots


Twisted soft robots navigate mazes without human or computer guidance
May 2022, phys.org

Physical intelligence vs Computational intelligence, active matter, and the Internet of Everything.
Also, Translucent Rotini:

The soft robots are made of liquid crystal elastomers in the shape of a twisted ribbon, resembling translucent rotini. 

When you place the ribbon on a surface that is at least 55 degrees Celsius (131 degrees Fahrenheit), which is hotter than the ambient air, the portion of the ribbon touching the surface contracts, while the portion of the ribbon exposed to the air does not. This induces a rolling motion in the ribbon. And the warmer the surface, the faster it rolls.

"It's much like the robotic vacuums that many people use in their homes," Yin says. "Except the soft robot we've created draws energy from its environment and operates without any computer programming."

via North Carolina State University: Twisting for Soft Intelligent Autonomous Robot in Unstructured Environments, Proceedings of the National Academy of Sciences (2022). DOI: 10.1073/pnas.2200265119



A marsupial robotic system that combines a legged and an aerial robot
Jun 2022, phys.org

"Our idea comes from a very simple concept: the complementarity of walking and flying robots," De Petris explained.

via DARPA Subterranean Challenge and winning team CERBERUS of NTNU, UNR, ETH Zurich, UC Berkley, Oxford and Flyability: Paolo De Petris et al, Marsupial walking-and-flying robotic deployment for collaborative exploration of unknown environments. arXiv:2205.05477v1 [cs.RO], arxiv.org/abs/2205.05477


Robotic lightning bugs take flight
Jun 2022, phys.org

Electroluminescent soft artificial muscles for flying, insect-scale robots that communicate with each other. 

These researchers previously demonstrated a new fabrication technique to build soft actuators, or artificial muscles, that flap the wings of the robot. and are made by alternating ultrathin layers of elastomer and carbon nanotube electrode in a stack and then rolling it into a squishy cylinder. When a voltage is applied to that cylinder, the electrodes squeeze the elastomer, and the mechanical strain flaps the wing. Electroluminescent zinc sulfate particles into the elastomeric artificial muscles. 

via MIT: Suhan Kim et al, FireFly: An Insect-Scale Aerial Robot Powered by Electroluminescent Soft Artificial Muscles, IEEE Robotics and Automation Letters (2022). DOI: 10.1109/LRA.2022.3179486


Robotic arms connected directly to brain of partially paralyzed man allows him to feed himself
Jul 2022, phys.org

A person with very limited upper body mobility, who hasn't been able to use his fingers in about 30 years, has just fed himself dessert using his mind and some smart robotic hands.

The new paper outlines an innovative model for shared control that enables a human to maneuver a pair of robotic prostheses with minimal mental input. "This shared control approach is intended to leverage the intrinsic capabilities of the brain machine interface and the robotic system, creating a 'best of both worlds' environment where the user can personalize the behavior of a smart prosthesis,"

via Johns Hopkins Applied Physics Laboratory and the Department of Physical Medicine and Rehabilitation in the Johns Hopkins School of Medicine: Shared control of bimanual robotic limbs with a BMI for self-feeding, Frontiers in Neurorobotics (2022). DOI: 10.3389/fnbot.2022.918001


Extra 'eye' movements are the key to better self-driving cars
Jul 2022, phys.org
 
With the help of Levy patterns, also called a foraging behavior model:

When tested with shifted images that mimicked naturally altered visual input that would occur when the eyes move, performance dropped drastically to chance level. Classification improved significantly after training the network with shifted images, as long as the direction and size of the eye movements that resulted in the shift were also included. Adding the eye movements and their corresponding motor commands to the network model allowed the system to better cope with visual noise in the images. "This advancement will help avoid dangerous mistakes in machine vision,"

via RIKEN: Andrea Benucci et al, Motor-related signals support localization invariance for stable visual perception, PLOS Computational Biology (2022). DOI: 10.1371/journal.pcbi.1009928

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Tuesday, September 6, 2022

Neuromorphic Bodybots


Artificial neurons go quantum with photonic circuits
Mar 2022, phys.org

Good copy:
At the heart of all artificial intelligence applications are mathematical models called neural networks. These models are inspired by the biological structure of the human brain, made of interconnected nodes. Just like our brain learns by constantly rearranging the connections between neurons, neural networks can be mathematically trained by tuning their internal structure until they become capable of human-level tasks: recognizing our face, interpreting medical images for diagnosis, even driving our cars. Having integrated devices capable of performing the computations involved in neural networks quickly and efficiently has thus become a major research focus, both academic and industrial.

One of the major game changers in the field was the discovery of the memristor, made in 2008. This device changes its resistance depending on a memory of the past current, hence the name memory-resistor, or memristor. Immediately after its discovery, scientists realized that (among many other applications) the peculiar behavior of memristors was surprisingly similar to that of neural synapses. The memristor has thus become a fundamental building block of neuromorphic architectures.

via University of Vienna: Michele Spagnolo, Experimental photonic quantum memristor, Nature Photonics (2022). DOI: 10.1038/s41566-022-00973-5

Image credit: Topological Defects, Oleg Lavrentovich at Kent State University, 2006 [link]


Neuromorphic simulations can yield computational advantages relevant to many applications
Mar 2022, phys.org

via Sandia National Laboratories: J. Darby Smith et al, Neuromorphic scaling advantages for energy-efficient random walk computations, Nature Electronics (2022). DOI: 10.1038/s41928-021-00705-7


Study highlights the potential of neuromorphic architectures to perform random walk computations
Apr 2022, phys.org

via Sandia National Laboratories: Neuromorphic scaling advantages for energy-efficient random walk computations. Nature Electronics(2022). DOI: 10.1038/s41928-021-00705-7.


How to build brain-inspired neural networks based on light
Apr 2022, phys.org

via Eindhoven University of Technology: Bin Shi et al, Deep Neural Network Through an InP SOA-Based Photonic Integrated Cross-Connect, IEEE Journal of Selected Topics in Quantum Electronics (2019). DOI: 10.1109/JSTQE.2019.2945548


Neuromorphic memory device simulates neurons and synapses
May 2022, phys.org

via The Korea Advanced Institute of Science and Technology KAIST: Sang Hyun Sung et al, Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse, Nature Communications (2022). DOI: 10.1038/s41467-022-30432-2

Topological matter - Nature - Jul 2016

Demonstrating significant energy savings using neuromorphic hardware
May 2022, phys.org

The "Loihi" chip can get up to sixteen times more energy-efficiency than non-neuromorphic hardware.

These chips are chasing something the brain already does naturally, and much more efficiently than our conventional chips, because our brain stores information as something called "internal variables" which are from the used neurons in a network getting fatigued, and then just measuring which ones in the network are fatigued, to know which ones were just activated. Neurons are storing memory simply by not working, and that's about as energy efficient as you can get. 

via Graz University of Technology's Institute of Theoretical Computer Science and Intel Labs, and supported by The Human Brain Project: Arjun Rao et al, A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware, Nature Machine Intelligence (2022). DOI: 10.1038/s42256-022-00480-w


Ultrafast 'camera' captures hidden behavior of potential 'neuromorphic' material
May 2022, phys.org

"Vanadium dioxide is one of the rare, amazing materials that has emerged as a promising candidate for neuro-mimetic bio-inspired devices" 

via Brookhaven National Laboratory: Junjie Li et al, Direct Detection of V-V Atom Dimerization and Rotation Dynamic Pathways upon Ultrafast Photoexcitation in VO2, Physical Review X (2022). DOI: 10.1103/PhysRevX.12.021032


A neuromorphic computing architecture that can run some deep neural networks more efficiently
Jun 2022, phys.org

In their experiments, Maass and his colleagues showed that the tendency of many biological neurons to rest after spiking could be replicated in neuromorphic hardware and used as a "computational trick" to solve time series processing tasks more efficiently. In these tasks, new information needs to be combined with information gathered in the recent past (e.g., sentences from a story that the network processed beforehand).

"We showed that the network just needs to check which neurons are currently most tired, i.e., reluctant to fire, since these are the ones that were active in the recent past," Maass said. "Using this strategy, a clever network can reconstruct based on what information was recently processed. Thus, 'laziness' can have advantages in computing."

via Graz University of Technology and Intel and funded by the Human Brain Project: Arjun Rao et al, A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware, Nature Machine Intelligence (2022). DOI: 10.1038/s42256-022-00480-w

Topological Solitons - Soft Matter Publishing - 2020

A chip that can classify nearly 2 billion images per second
Jun 2022, phys.org

Optical Deep Neural Network:
"Our chip processes information through what we call 'computation-by-propagation,' meaning that unlike clock-based systems, computations occur as light propagates through the chip," says Aflatouni. "We are also skipping the step of converting optical signals to electrical signals because our chip can read and process optical signals directly, and both of these changes make our chip a significantly faster technology."

"When current computer chips process electrical signals they often run them through a Graphics Processing Unit, or GPU, which takes up space and energy," says Ashtiani. "Our chip does not need to store the information, eliminating the need for a large memory unit."

"A movie usually plays between 24 and 120 frames per second. This chip will be able to process nearly 2 billion frames per second! For problems that require light speed computations, we now have a solution, but many of the applications may not be fathomable right now."
You heard the man. Fathom away.

via University of Pennsylvania: Farshid Ashtiani et al, An on-chip photonic deep neural network for image classification, Nature (2022). DOI: 10.1038/s41586-022-04714-0


New hardware offers faster computation for artificial intelligence, with much less energy
Jul 2022, phys.org

Massive:
Practical inorganic material in the fabrication process enables devices to run 1 million times faster than previous versions, which is also 1 million times faster than the synapses in the human brain.

Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial "neurons" and "synapses" that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

"Analog deep learning" - computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.

"Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn't damage anything, thanks to the small size and low mass of protons. It is almost like teleporting."

via MIT's Department of Electrical Engineering and Computer Science: Murat Onen et al, Nanosecond protonic programmable resistors for analog deep learning, Science (2022). DOI: 10.1126/science.abp8064


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Wednesday, January 13, 2021

Face Rec and Image Tech


Deepfake used to attack activist couple shows new disinformation frontier
Jul 2020, Reuters
Experts in deceptive imagery used state-of-the-art forensic analysis programs to determine that Taylor’s profile photo is a hyper-realistic forgery - a “deepfake.”
Something about the Ventriloquist's Dummy and the dangers of impersonating an influential leader.

Image credt: Oliver Taylor Deepfake, Reuters, Jul 2020


Image cloaking tool thwarts facial recognition programs
Aug 2020. phys.org

Reverse camoufaluge? Fill the pool with dirty data; it's a simple approach. This technique is named Fawkes after the V for Vendetta guy, also known as the Anonymous Mask from the online shadow activist group circa 2010.

"What we are doing is using the cloaked photo in essence like a Trojan Horse, to corrupt unauthorized models to learn the wrong thing about what makes you look like you and not someone else," Fawkes co-creator Ben Zhao, a computer science professor at the University of Chicago, said.

"Our original goal was to serve as a preventative measure for Internet users to inoculate themselves against the possibility of some third-party, unauthorized model," the team recently stated in a FAQ sheet.


Deepfake detection tool unveiled by Microsoft
Sep 2020, BBC News
"The only really widespread use we've seen so far is in non-consensual pornography against women," commented Nina Schick, author of the book Deep Fakes and the Infocalypse.

"But synthetic media is expected to become ubiquitous in about three to five years, so we need to develop these tools going forward

Synthetic media and fingerprinted news - Microsoft has teamed up with the BBC, among other media organisations, to support Project Origin, an initiative to "mark" online content in a way that makes it possible to spot automatically any manipulation of the material.

New photon-counting camera captures 3-D images with record speed and resolution
Apr 2020, phys.org

24,000 fps:
The camera's speed makes it possible to measure the time a photon hits the sensor very precisely. This information can be used to calculate how long it takes individual photons to travel the distance from a source to the camera, known as time-of-flight. Combining time-of-flight information with the ability to capture a million pixels simultaneously enables extremely high-speed reconstruction of 3-D images.

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Friday, July 10, 2020

Sending Mixed Signals


Deep-learning system detects human presence by harvesting RF signals
Feb 2020, phys.org
The presence of humans in a room or in other indoor environments can alter the propagation of RF signals in several ways. By pre-processing RF channel measurements, the researchers were able to create 'images' summarizing the signals, which could in turn be analyzed to detect the presence of humans in a given environment. 
They then trained a CNN on a large amount of data containing both magnitude and phase information, two key properties of RF signals. Over time, the deep learning algorithm learned to distinguish when an environment is populated by humans and when it is free from them by analyzing what is known as channel state information (CSI). 
"Exploiting the ubiquity of ambient RF signals such as WiFi, Bluetooth or cellular signals for situational awareness information provides added value to existing RF infrastructure," Chen said. "Occupancy detection, for example, is an application where RF sensing can be a low-cost and infrastructure-free alternative or complement to existing approaches."

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