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Category: robotics/AI – Page 1,844

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.

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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.

Criminal Sanctions Agency: https://www.rikosseuraamus.fi/en/index/topical/pressreleases…tices.html

Ira Pastor Comments

In 2018, according to the World Prison Population List, which gives details of the number of prisoners held in 223 prison systems in independent countries and dependent territories around the globe, there were close to 11 million people are held in penal institutions, either as pre-trial detainees/remand prisoners or having been convicted and sentenced. About 50% of them were represented by prison populations in the U.S., China, Brazil, Russia and India.

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

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.

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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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Seven performers selected to pursue novel USV concepts and enabling technologies.

DARPA has awarded seven contracts for work on Phase 1 of the NOMARS program, which seeks to simultaneously explore two competing objectives related to unmanned surface vessels (USV) ship design: the maximization of seaframe performance when human constraints are removed; and achieving sufficient vessel maintenance and logistics functionality for long endurance operations with no human crew onboard. NOMARS aims to disrupt conventional naval architecture designs through creative trade space explorations that optimize useable onboard room considering a variety of constraints. This should pave the way for more capable, affordable small warships that can be procured and maintained in large numbers.

Autonomous Surface Vehicles, LLC, Gibbs & Cox Inc., and Serco Inc. received Phase 1 Track A awards, and will work toward developing novel NOMARS demonstrator conceptual designs. These awards will focus on maximizing vessel performance gain across new design criteria, with potential considerations to include: unusual hull forms, low freeboard, minimizing air-filled volumes, innovative materials, repurposing or eliminating “human space” exploring distributed system designs, and developing architectures optimized for depot-maintenance.

Barnstorm Research Corporation and TDI Technologies, Inc. received Phase 1 Track B awards, and will develop robust approaches to ship health-monitoring via novel Self-Adaptive Health Management (SAHM) architectures, which will be pivotal to achieving NOMARS at-sea endurance and reliability objectives. InMar Technologies and Siemens Corporation also received Phase 1 Track B awards; the former will develop new techniques for morphing hull structures to maximize performance, while the latter will implement toolsets previously developed through the DARPA TRADES program to design optimized material structures for novel NOMARS ship concepts.

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