Earbuds to Monitor Brain, Body

The diagnostic goals are to monitor interactions between the earbud readouts of brainwaves, vital signs, and chemical biomarkers in real-world settings by correlating electrical brainwave patterns with the body's chemical biomarkers. The therapeutic goal is to foster improved brain function by monitoring the brain/body followed with appropriate theraputic feedback through the earbuds to improve the patient's condition.

Fabrication process of the prototype earbud showing the printed sensors (middle) bonded with the flexible circuit board (top), and assembled to the outside of earbuds in the optimal sensor assembly area (bottom) with double-sided medical tape. FULL TEXT

Department of Energy Accelerates Emerging Technologies

The Pacific Northwest National Laboratory aims to use computational chemistry expertise to advance the next generation of molecular modeling capabilities. FULL TEXT

Nanophotonics Flattens Smartphone Camera Bump

FULL TEXT

Optical Vectors Beam Multi-Bits

Optical communications—the backbone of the internet—use modulated lasers to transmit over a hundred different colored channels (using wavelength-division multiplexing) each with a hundred gigabits per second of speed. Multi-bit encoding modes—instead of just 1s and 0s—have been proven in the lab to increase throughput even further, but all suffer from errors introduced by noise in the channels, especially over free-space channels such as to satellites, submarines or even through living cells, according to researchers at the University of the Witwatersrand (Johannesburg, South Africa). Now Wits’ researchers have demonstrated a mode-division multiplexing (MDM) scheme using a quantitative measure for a vector (polarized) beam’s purity, its vector-quality factor (technically, its polarization separability) as the information carrier that can encode not just 1s and 0s, but countless modes of vector light that are virtually unaffected by channel noise.

Automated Evolution Tackles Tough Tasks

Deep neural networks (DNNs) using reinforcement learning are getting harder to come by, due to sensitivity to the initial hyper-parameters chosen (such as the width and depth of the DNN, as well as other application-specific initial conditions). However, these limitations have recently been overcome by combining RL with evolutionary computation (EC), which maintains a population of learning agents, each with unique initial conditions, that together "evolve" an optimal solution. FULL TEXT

Keeping Hackers Off the Electrical Grid

FULL TEXT

MXenes Shield Against Radar, RF, Stealth

FULL TEXT

Holy Grail of Batteries Obsolete Lithium Ion

Rechargeable batteries have become the lifeblood of electronics, enabling the mobile revolution. Unfortunately, today's rechargeable batteries incorporate flammable liquid cores. That could change soon, however by switching to rechargeable batteries that have solid cores with nothing to spill, nothing to catch on fire, nothing to potentially explode. FULL TEXT