Tech Briefs Magazine - January 2023 - 35

creasing the electrical conductance of
protonic resistors enables analog machine
learning.
The conductance is controlled by the
movement of protons. To increase the
conductance, more protons are pushed
into a channel in the resistor, while to
decrease conductance protons are taken
out. This is accomplished using an electrolyte
(similar to that of a battery) that
conducts protons but blocks electrons.
To develop a super-fast and highly energy-efficient
programmable protonic
resistor, the researchers looked to different
materials for the electrolyte. While
other devices used organic compounds,
they focused on inorganic phosphosilicate
glass.
This illustration shows an analog deep learning processor powered by ultra-fast protonics. (Image:
Ella Maru Studio, Murat Onen)
sistors, which are measured in nanometers
(one nanometer is one billionth of a
meter), are arranged in an array, like a
chess board.
In the human brain, learning happens
due to the strengthening and weakening
of connections between neurons, called
synapses. Deep neural networks have
long adopted this strategy, where the
network weights are programmed
through training algorithms. In the case
of this new processor, increasing and deThese
programmable resistors vastly
increase the speed at which a neural network
is trained, while drastically reducing
the cost and energy to perform that
training. This could help scientists develop
deep learning models much more
quickly, which could then be applied in
uses like self-driving cars, fraud detection,
or medical image analysis.
For more information, contact Abby
Abazorius at abbya@mit.edu; 617-2532709.
Incorporating
Computer Vision and Uncertainty into AI
for Robotic Prosthetics
The software can be integrated with existing hardware to aid people using robotic
prosthetics or exoskeletons.
North Carolina State University, Raleigh, NC
R
esearchers have developed new software
that can enable people using
robotic prosthetics or exoskeletons to
walk in a safer, more natural manner on
different types of terrain. The new
framework incorporates computer vision
into prosthetic leg control and includes
robust artificial intelligence (AI)
algorithms that allow the software to better
account for uncertainty.
Lower-limb robotic prosthetics need
to execute different behaviors based on
the terrain users are walking on. The
new framework allows the AI in robotic
prostheses to predict the type of terrain
users will be stepping on, quantify the
uncertainties associated with that prediction,
and then incorporate that uncertainty
into its decision-making.
The researchers focused on distinguishing
among six different terrains
Tech Briefs, January 2023
Camera
Raspberry Pi
IMU
(a)
View from Glasses
(c)
View from Lower Limb
(d)
downstairs
grass
Battery
Camera
cement
tile
(b)
upstairs
brick
(e)
(f)
Imaging devices and environmental context. (a) On-glasses camera configuration using a Tobii Pro
Glasses 2 eye tracker. (b) Lower limb data acquisition device with a camera and an IMU chip. (c) and
(d) Example frames from the cameras for the two data acquisition configurations. (e) and (f) Example
images of the data collection environment and terrains considered in the experiments. (Image:
North Carolina State University)
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upstairs

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Tech Briefs Magazine - January 2023

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