Lifeboat Foundation

Sony is bringing machine intelligence to its image sensors. The electronics and entertainment giant announced this week a sensor that applies artificial intelligence while processing imagery without the need for extrema hardware or assistance.

Players in the photography industry have been focusing on increasingly greater numbers of pixels for ever-sharper enlargement and reproduction capabilities and on increasingly compressed devices for lighter weight and greater portability.

Continue reading “Sony unveils first built-in AI image sensors” »

Researchers at Zhejiang University of Technology, Tianjin University, Nanjing Institute of Technology and Ritsumeikan University have recently created a soft robotic finger that integrates a self-powered curvature sensor using multi-material 3D printing technology. The new robotic finger, presented in a paper published in Elsevier’s Nano Energy journal, is made of several materials, including a stretchable electrode, polydimethylsiloxane (PDMS), AgilusBlack, VeroWhite and FLX9060.

“Soft robots have the potential to bridge the gap between machines and humans, but it is important for them to ensure a safe interaction between humans, objects and the environment,” Mengying Xie, co-author of the paper, told TechXplore. “Embedded soft sensors are critical for the development of controllable soft robots that can fulfill their full potential in practical applications.”

In their previous research, part of the research team working at Ritsumeikan University developed a fully multi-material 3D printed gripper with variable stiffness that could achieve robust grasping of objects. In this new study, Xie, Zhu and their colleagues drew inspiration from this previous work and set out to create a 3D-printed soft finger with sensing capabilities that could monitor its bending movements.

From navigation to remote banking, mobile device users rely on a variety of applications to streamline daily tasks, communicate, and dramatically increase productivity. While exceedingly useful, the ecosystem of third-party applications utilizes a number of sensors – microphones, GPS, pedometers, cameras – and user interactions to collect data used to enable functionality. Troves of sensitive personal data about users are accessible to these applications and as defense and commercial mobile device users become increasingly reliant on the technology, there are growing concerns around the challenge this creates for preserving user privacy.

Under DARPA’s Brandeis program, a team of researchers led by Two Six Labs and Raytheon BBN Technologies have developed a platform called Privacy Enhancements for Android (PE for Android) to explore more expressive concepts in regulating access to private information on mobile devices. PE for Android seeks to create an extensible privacy system that abstracts away the details of various privacy-preserving technologies, allowing application developers to utilize state-of-the-art privacy techniques, such as secure multi-party computation and differential privacy, without knowledge of their underlying esoteric technologies. Importantly, PE for Android allows mobile device users to take ownership of their private information by presenting them with more intuitive controls and permission enforcement options.

The researchers behind PE for Android today released a white paper detailing the platform’s capabilities and functionality, and published an open source release of its code to GitHub. In open sourcing PE for Android, the researchers aim to make it easier for the open-source Android community and researchers to employ enhanced privacy-preserving technologies within Android apps while also encouraging them to help address the platform’s current limitations and build upon its initial efforts.

Graphene has already proven itself to be a weird and wonderful material in many different ways, but its properties get even more unusual and exotic when it’s twisted – and two new studies have given scientists a much closer look at this intriguing phenomenon.

When two sheets of graphene are put together at slightly different angles, the resulting material becomes either very effective at conducting electricity, or very effective at blocking it. It’s known as ‘magic-angle’ twisted graphene, and knowing more about how and why this happens could lead to advances in high-temperature superconductors and quantum computing.

Now for the first time, scientists have mapped out a twisted graphene structure in its entirety, and at a very high resolution. They’ve also been able to get ‘graphene twistronics’ working with four layers of graphene as well as just two.

Even as dramatic social change has been imposed by COVID-19, the kinds of fraud attacks companies experience and the biometric authentication technologies they use to prevent them have remained basically the same. What has changed is that online volumes of traffic, transactions and authentications have reached levels they were expected to years in the future, BehavioSec VP of Products Jordan Blake told Biometric Update in an interview.

As a result, he says, “timelines are getting advanced.”

Demand is coming from new verticals, according to Blake, as numerous people begin using the online channel to interact with many organizations they never have dealt with that way before.

This is a guest post by Shahrokh Shahidzadeh, CEO at Acceptto

These past two months have been among the most extraordinary times any of us can remember. The COVID-19 (CV-19) impact is all around us, indiscriminately impacting all of our lives, our work, the economy and for sure we are on the onset of a new normal that we are learning how to deal with daily.

There are always two stages of dealing with a change of this magnitude. First, we react immediately, thinking about what we must do differently now. Soon, we will begin to think in the longer term, reacting to and planning for permanent changes that result from the CV-19 pandemic.

With the biggest launch in nearly a decade two weeks away, it’s been a two-horse race in the new “space race”.

UCSC researchers developed a deep-learning framework called Morpheus to perform pixel-level morphological classifications of objects in astronomical images.

Researchers at UC Santa Cruz have developed a powerful new computer program called Morpheus that can analyze astronomical image data pixel by pixel to identify and classify all of the galaxies and stars in large data sets from astronomy surveys.

Morpheus is a deep-learning framework that incorporates a variety of artificial intelligence technologies developed for applications such as image and speech recognition. Brant Robertson, a professor of astronomy and astrophysics who leads the Computational Astrophysics Research Group at UC Santa Cruz, said the rapidly increasing size of astronomy data sets has made it essential to automate some of the tasks traditionally done by astronomers.

In the near future, we will have manned drones going at blistering speeds and doing crazy aerobatic stunts.