Lifeboat Foundation

In the last few years, most of the data such as books, videos, pictures, medical and even the genetic information of humans are moving toward digital formats. Laptops, tablets, smartphones and wearable devices are the major source of this digital data transformation and are becoming the core part of our daily life. As a result of this transformation, we are becoming the soft target of various types of cybercrimes. Digital forensic investigation provides the way to recover lost or purposefully deleted or hidden files from a suspect’s device. However, current man power and government resources are not enough to investigate the cybercrimes. Unfortunately, existing digital investigation procedures and practices require huge interaction with humans; as a result it slows down the process with the pace digital crimes are committed. Machine learning (ML) is the branch of science that has governs from the field of AI. This advance technology uses the explicit programming to depict the human-like behaviour. Machine learning combined with automation in digital investigation process at different stages of investigation has significant potential to aid digital investigators. This chapter aims at providing the research in machine learning-based digital forensic investigation, identifies the gaps, addresses the challenges and open issues in this field.

“ “The monitoring of human health and well-being with the use of wearables is considered critical in the next generation of biomedical devices,” write the authors. “[But] most existing paper-based devices are designed for one-time use only, functioning under relatively intense capillary flow into the paper, which ceases upon saturation… [Our approach] can function as a key part of a platform for long-term sweat sampling and biomarker monitoring.””

Researchers have designed a paper-based wearable device that can monitor your sweat for 10-days at a time to detect important information about your health.

A team of researchers at Université de Sherbrooke with assistance from a group at Exonetik Inc., has created a wearable supernumerary robotic arm that adds functionality for common human tasks. In their paper published in IEEE Spectrum, the group describes their robotic arm, its abilities and their plans for expanding its functionality.

A supernumerary robotic device is of a type that adds functionality to an existing system. In this case, the team in Canada added a third arm and associated three-fingered hand to a human subject.

Continue reading “A supernumerary robotic arm adds functionality for carrying out common tasks” »

We’ve always had a soft spot for supernumerary robotic limbs here at The Verge, but this latest example of the genre is one of the most impressive we’ve seen to date. Designed by researchers at the Université de Sherbrooke in Canada, it’s a hydraulic arm that sits on the wearer’s hip and uses a three-fingered manipulator to carry out a range of tasks.

Researchers at Tufts University’s School of Engineering have developed biomaterial-based inks that respond to and quantify chemicals released from the body (e.g. in sweat and potentially other biofluids) or in the surrounding environment by changing color. The inks can be screen printed onto textiles such as clothes, shoes, or even face masks in complex patterns and at high resolution, providing a detailed map of human response or exposure. The advance in wearable sensing, reported in Advanced Materials, could simultaneously detect and quantify a wide range of biological conditions, molecules and, possibly, pathogens over the surface of the body using conventional garments and uniforms.

“The use of novel bioactive inks with the very common method of screen printing opens up promising opportunities for the mass-production of soft, wearable fabrics with large numbers of sensors that could be applied to detect a range of conditions,” said Fiorenzo Omenetto, corresponding author and the Frank C. Doble Professor of Engineering at Tufts’ School of Engineering. “The fabrics can end up in uniforms for the workplace, sports clothing, or even on furniture and architectural structures.”

Continue reading “Bioactive inks printed on wearable textiles can map conditions over the entire surface of the body” »

When it comes to monitoring electrical activity in the brain, patients typically have to lie very still inside a large magnetoencephalography (MEG) machine. That could be about to change, though, as scientists have developed a new version of a wearable helmet that does the same job.

Back in 2018, researchers at Britain’s University of Nottingham revealed the original version of their “MEG helmet.”

The 3D-printed device was fitted with multiple sensors that allowed it to read the tiny magnetic fields created by brain waves, just like a regular MEG machine. Unlike the case with one of those, however, wearers could move around as those readings were taking place.

A thin, iron-based generator uses waste heat to provide small amounts of power.

Researchers have found a way to convert heat energy into electricity with a nontoxic material. The material is mostly iron which is extremely cheap given its relative abundance. A generator based on this material could power small devices such as remote sensors or wearable devices. The material can be thin so it could be shaped into various forms.

There’s no such thing as a free lunch, or free energy. But if your energy demands are low enough, say for example in the case of a small sensor of some kind, then there is a way to harness heat energy to supply your power without wires or batteries. Research Associate Akito Sakai and group members from his laboratory at the University of Tokyo Institute for Solid State Physics and Department of Physics, led by Professor Satoru Nakatsuji, and from the Department of Applied Physics, led by Professor Ryotaro Arita, have taken steps towards this goal with their innovative iron-based thermoelectric material.

Continue reading “Researchers Have Found a New Way to Convert Waste Heat Into Electricity to Power Small Devices” »

More portable, fully wireless smart home setups. Lower power wearables. Batteryless smart devices. These could all be made possible thanks to a new ultra-low power Wi-Fi radio developed by electrical engineers at the University of California San Diego.

The device, which is housed in a chip smaller than a grain of rice, enables Internet of Things (IoT) devices to communicate with existing Wi-Fi networks using 5,000 times less power than today’s Wi-Fi radios. It consumes just 28 microwatts of power. And it does so while transmitting data at a rate of 2 megabits per second (a connection fast enough to stream music and most YouTube videos) over a range of up to 21 meters.

The team will present their work at the ISSCC 2020 conference Feb. 16 to 20 in San Francisco.

Roam Robotics is making robotic exoskeletons that are lightweight and affordable so that they can become a new category of consumer electronics. Traditional robotic exoskeletons can weigh between 30 to 60 pounds because they rely on high precision mechanical systems. They are big and bulky and cost as much as a luxury car, which significantly limits their usefulness and availability. Roam’s new robotic exoskeletons are so portable and inexpensive that they could quickly become a commonplace part of modern life.