Lifeboat Foundation

Athletes with disabilities have been competing in a range of challenges that use assistive technology to overcome day-to-day practical challenges.

Bionic arms, powered exoskeletons, brain-controlled computer interfaces and supercharged wheelchairs all featured at the world’s first Cybathlon, near Zurich, Switzerland.

One of the races saw functional electrical stimulation (FES) used to activate the leg muscles of paralysed competitors to ride bikes.

It seems that biofeedback is a thing of future. By having brain activity feedback, you can train meditation, attention, improve sleep, control gadgets, artificial limbs, carts for impaired, and even computer I/O. Everything starts with proper biosensor and controller. Biological signals are very low voltage – microvolts. In order to distinguish them from noisy environment, a precision electronics is required. Brain activity signals are somewhat different from myograms or ECG, they can be analyzed as power spectrum that represent brain activity phases like Alpha, Beta, Theta. There has be a numerous modules developed to acquire brain signals. If you want low to develop sensors by yourself, you could grab a Neurosky platform which is a small size PCB with sensor and microcontroller interfaces.

With it you can read raw EEG signals with sampling 512Hz and do with them what you want. USART interface enables you to connect it yo Arduino or Raspberry Pi where you can calculate all sort of things and extract control signals. Of course you can read processed power spectrum as well to detect activities like attention, meditation and other activities. Eye blink detection is also an option. Great thing is that you can use this module to read ECG activity as well. Module incorporates AC noise filter which can be configured for 50HZ or 60Hz.

Continue reading “Affordable EEG biosensors to control physical things” »

In a development beneficial for both industry and environment, UC Santa Barbara researchers have created a high-quality coating for organic electronics that promises to decrease processing time as well as energy requirements.

“It’s faster, and it’s nontoxic,” said Kollbe Ahn, a research faculty member at UCSB’s Marine Science Institute and corresponding author of a paper published in Nano Letters (“Molecularly Smooth Self-Assembled Monolayer for High-Mobility Organic Field-Effect Transistors”).

Nice.

Scientists in Australia have developed a quantum bit that’s 10 times more stable than existing technologies, and the new record could vastly expand the kinds of calculations quantum computers can perform.

Continue reading “Australian scientists broke a new record for quantum computing stability” »

Great advancement around how to make QC available on small scale devices.

Researchers from the Institute for Quantum Computing (IQC) at the University of Waterloo led the development of a new extensible wiring technique capable of controlling superconducting quantum bits, representing a significant step towards to the realization of a scalable quantum computer.

“The quantum socket is a wiring method that uses three-dimensional wires based on spring-loaded pins to address individual qubits,” said Jeremy Béjanin, a PhD candidate with IQC and the Department of Physics and Astronomy at Waterloo. He and Thomas McConkey, PhD candidate from IQC and the Department of Electrical and Computer Engineering at Waterloo, are lead authors on the study that appears in the journal Physical Review Applied as an Editors’ Suggestion and is featured in Physics. “The technique connects classical electronics with quantum circuits, and is extendable far beyond current limits, from one to possibly a few thousand qubits.”

Continue reading “New 3D wiring technique brings scalable quantum computers closer to reality” »

Ruslan Salakhutdinov, a deep-learning expert at Carnegie Mellon, is exploring smart ways for computers to learn about the world.

Samsung announced today that it is rolling out 10nm technology for mass manufacturing, with hardware expected in early 2017.

In Brief:

• Using reservoir computing and backpropagation, researchers were able to push an analog computer past its own boundaries.
• By combining established technologies with new innovations, we can speed up development with tech having the ability to improve itself.

(Phys.org)—Over the past few decades, quantum effects have greatly improved many areas of information science, including computing, cryptography, and secure communication. More recently, research has suggested that quantum effects could offer similar advantages for the emerging field of quantum machine learning (a subfield of artificial intelligence), leading to more intelligent machines that learn quickly and efficiently by interacting with their environments.

In a new study published in Physical Review Letters, Vedran Dunjko and coauthors have added to this research, showing that quantum effects can likely offer significant benefits to machine learning.

“The progress in machine learning critically relies on processing power,” Dunjko, a physicist at the University of Innsbruck in Austria, told Phys.org. “Moreover, the type of underlying information processing that many aspects of machine learning rely upon is particularly amenable to quantum enhancements. As quantum technologies emerge, quantum machine learning will play an instrumental role in our society—including deepening our understanding of climate change, assisting in the development of new medicine and therapies, and also in settings relying on learning through interaction, which is vital in automated cars and smart factories.”

Continue reading “How quantum effects could improve artificial intelligence” »