Page 30 - Nature Electronics News Today

A nightmarish robot with Venus flytraps for hands that can trap objects in its jaw-like leaves — and then pick them up — has been developed by scientists. Engineers from Singapore used tiny remote-controlled electrodes to stimulate severed leaves of the iconic carnivorous plants into closing on command. While the project may seem straight out of the mad scientists playbook, integrating soft and flexible plant matter into robots could have sensible practical applications. It would allow robo...

Scientists Give Robot Actual Venus Flytraps for Hands Twitter 0 comments Even though America is fast becoming the land of the electric vehicle, there’s one kind of fuel that’ll never go out of fashion: nightmare fuel. And here, to top you off, is a robot-Venus flytrap hybrid that looks like the offspring of Wall-E and the Demogorgon. Or maybe Audrey II from Little Shop of Horrors and a Martian rover. Science News reported on the Frankenstein robot. (And yes, we know Frankenstein was the s...

Research delivers improved stretchable electronics A new sensor design, from the Pritzker School of Molecular Engineering (PME) at the University of Chicago, looks to address the key problem associated with stretchable electronics, in that changes in shape can affect the data produced and means that sensors cannot collect and process signals accurately. The human body can send out a host of signals - chemicals, electrical pulses, mechanical shifts - that can provide a we...

Edge learning breakthrough CEA-Leti scientists have demonstrated a machine-learning technique that exploits the “non-ideal” traits of resistive-RAM (RRAM) devices, and in the process overcoming barriers to developing RRAM-based edge-learning systems. In a paper published in the January issue of Nature Electronics, the research team demonstrated how RRAM, or memristor, technology can be used to create intelligent systems that learn locally at the edge, independe...

GRENOBLE, France – Jan. 19, 2021 – CEA-Leti scientists have demonstrated a machine-learning technique exploiting what have been previously considered as “non-ideal” traits of resistive-RAM (RRAM) devices, overcoming barriers to developing RRAM-based edge-learning systems. Reported in a paper published in the January issue of Nature Electronics titled, “In-situ learning using intrinsic memristor variability via Markov chain Monte…

By Tom Abate Smartwatches and other battery-powered electronics would be even smarter if they could run AI algorithms. But efforts to build AI-capable chips for mobile devices have so far hit a wall – the so-called “memory wall” that separates data processing and memory chips that must work together to meet the massive and continually growing computational demands imposed by AI. Hardware and software innovations give eight chips the illusion that they’re one mega-chip working together t...

 E-Mail Smartwatches and other battery-powered electronics would be even smarter if they could run AI algorithms. But efforts to build AI-capable chips for mobile devices have so far hit a wall - the so-called "memory wall" that separates data processing and memory chips that must work together to meet the massive and continually growing computational demands imposed by AI. "Transactions between processors and memory can consume 95 percent of the energy needed to do machine learning and AI, an...

The device is in the form of an armband and uses brain signaling to make gestures Although similar inventions already exist, this invention has a lower power budget In the near future, making prosthetic hand movements won’t necessarily require any form of physical effort – all it would take is sending signals from the brain, triggered by thoughts. Engineers at the University of California (UC) collaborated to develop a device that uses electrical signals to make hand signals. This system c...

This device can recognise hand gestures using wearable biosensors Updated: Updated: December 30, 2020 11:07 IST The algorithm has been taught to recognise 21 unique hand gestures, including thumbs-up, fist, flat hand, holding up individual fingers and counting numbers. Share Article This device can recognise hand gestures using wearable biosensors. | Picture by special arrangement.   The algorithm has been taught to recognise 21 unique hand gestures, including thumbs-up, fist, flat hand, hold...

A new device can recognize hand gestures based on electrical signals it detects in the forearm. Imagine typing on a computer without a keyboard, playing a video game without a controller, or driving a car without a wheel. That’s the goal researchers envision for the system, which couples wearable biosensors with artificial intelligence (AI) and could one day control prosthetics or to interact with almost any type of electronic device. “Prosthetics are one important application of this techn...