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Archive for the ‘robotics/AI’ category: Page 4

Oct 16, 2020

NASA-JPL team tests out DuAxel in Mojave Desert

Posted by in categories: robotics/AI, space

There’s rough terrain – then there are the craters and near-vertical cliffs on the Moon, Mars, and beyond. The DuAxel is a robot built for situations just like those. By creating two single-axle rovers that can combine into one with a central payload we could maximize versatility during future missions. See more details: go.nasa.gov/34QNo5T.

Oct 16, 2020

SpaceX targeting this weekend for Starlink launch from Kennedy Space Center

Posted by in categories: drones, internet, robotics/AI, satellites

SpaceX is targeting this weekend for its next Falcon 9 rocket launch from Kennedy Space Center, this time with another batch of Starlink internet satellites.

If schedules hold, teams will give the go-ahead for the 230-foot rocket to launch from pad 39A at 8:27 a.m. Sunday, the opening of an instantaneous window. It must launch at that time or delay to another day.

About eight minutes after liftoff, the rocket’s 162-foot first stage will target an autonomous landing on the Of Course I Still Love You drone ship in the Atlantic Ocean. SpaceX’s fleet of ships and the booster should return to Port Canaveral a few days later.

Continue reading “SpaceX targeting this weekend for Starlink launch from Kennedy Space Center” »

Oct 16, 2020

Artificial Intelligence Used to ‘Redefine’ Alzheimer’s Disease

Posted by in categories: biotech/medical, genetics, robotics/AI

Summary: New artificial intelligence technology will analyze clinical data, brain images, and genetic information from Alzheimer’s patients to look for new biomarkers associated with the neurodegenerative disease.

Source: University of Pennsylvania

As the search for successful Alzheimer’s disease drugs remains elusive, experts believe that identifying biomarkers — early biological signs of the disease — could be key to solving the treatment conundrum. However, the rapid collection of data from tens of thousands of Alzheimer’s patients far exceeds the scientific community’s ability to make sense of it.

Oct 16, 2020

How the Nervous System Mutes or Boosts Sensory Information to Make Behavioral Decisions

Posted by in category: robotics/AI

Summary: Researchers have identified a novel neural network in fruit flies that converts external stimuli of varying intensity into decisions about whether to act.

Source: University of Michigan

Fruit flies may be able to teach researchers a thing or two about artificial intelligence.

Oct 16, 2020

A radical new technique lets AI learn with practically no data

Posted by in category: robotics/AI

“Less than one”-shot learning can teach a model to identify more objects than the number of examples it is trained on.

Oct 16, 2020

China’s moon mission robots wake up for a 23rd lunar day as team snags major award

Posted by in categories: robotics/AI, space travel

China’s Chang’e 4 moon mission received a prestigious international award just as the two spacecraft that make up the project awoke for their 23rd lunar day.

Oct 16, 2020

Israeli robotic bee hive startup Beewise raises $10 million in series A funding

Posted by in category: robotics/AI

:oooooo.


The Israeli Beewise hopes to replace the old hives and make them smarter so that bees can be monitored remotely and treated without human contact.

Oct 16, 2020

Smart Prisons: Managing and Rehabilitating Prisoners with Psychology, Empathy and AI

Posted by in categories: law enforcement, policy, robotics/AI, virtual reality

Re-Imagining Prisons — with AI, VR, and Digitalization.


Ira Pastor, ideaXme life sciences ambassador, interviews Ms Pia Puolakka, Project Manager of the Smart Prison Project, under the Criminal Sanctions Agency, within Finland’s Central Administration Unit.

Continue reading “Smart Prisons: Managing and Rehabilitating Prisoners with Psychology, Empathy and AI” »

Oct 16, 2020

Roboticizing fabric

Posted by in categories: robotics/AI, wearables

Fabrics are key materials for a variety of applications that require flexibility, breathability, small storage footprint, and low weight. While fabrics are conventionally passive materials with static properties, emerging technologies have provided many flexible materials that can respond to external stimuli for actuation, structural control, and sensing. Here, we improve upon and process these responsive materials into functional fibers that we integrate into everyday fabrics and demonstrate as fabric-based robots that move, support loads, and allow closed-loop controls, all while retaining the desirable qualities of fabric. Robotic fabrics present a means to create smart adaptable clothing, self-deployable shelters, and lightweight shape-changing machinery.

Fabrics are ubiquitous materials that have conventionally been passive assemblies of interlacing, inactive fibers. However, the recent emergence of active fibers with actuation, sensing, and structural capabilities provides the opportunity to impart robotic function into fabric substrates. Here we present an implementation of robotic fabrics by integrating functional fibers into conventional fabrics using typical textile manufacturing techniques. We introduce a set of actuating and variable-stiffness fibers, as well as printable in-fabric sensors, which allows for robotic closed-loop control of everyday fabrics while remaining lightweight and maintaining breathability. Finally, we demonstrate the utility of robotic fabrics through their application to an active wearable tourniquet, a transforming and load-bearing deployable structure, and an untethered, self-stowing airfoil.

Oct 16, 2020

Holo-UNet: hologram-to-hologram neural network restoration for high fidelity low light quantitative phase imaging of live cells

Posted by in categories: biological, holograms, robotics/AI

Intensity shot noise in digital holograms distorts the quality of the phase images after phase retrieval, limiting the usefulness of quantitative phase microscopy (QPM) systems in long term live cell imaging. In this paper, we devise a hologram-to-hologram neural network, Holo-UNet, that restores high quality digital holograms under high shot noise conditions (sub-mW/cm2 intensities) at high acquisition rates (sub-milliseconds). In comparison to current phase recovery methods, Holo-UNet denoises the recorded hologram, and so prevents shot noise from propagating through the phase retrieval step that in turn adversely affects phase and intensity images. Holo-UNet was tested on 2 independent QPM systems without any adjustment to the hardware setting. In both cases, Holo-UNet outperformed existing phase recovery and block-matching techniques by ∼ 1.8 folds in phase fidelity as measured by SSIM. Holo-UNet is immediately applicable to a wide range of other high-speed interferometric phase imaging techniques. The network paves the way towards the expansion of high-speed low light QPM biological imaging with minimal dependence on hardware constraints.

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