Lifeboat Foundation

Discarded electronic devices, such as cell phones, are a fast-growing source of waste. One way to mitigate the problem could be to use components that are made with renewable resources and that are easy to dispose of responsibly. Now, researchers reporting in ACS Applied Materials & Interfaces have created a prototype circuit board that is made of a sheet paper with fully integrated electrical components, and that can be burned or left to degrade.

Most small electronic devices contain circuit boards that are made from glass fibers, resins and metal wiring. These boards are not easy to recycle and are relatively bulky, making them undesirable for use in point-of-care medical devices, environmental monitors or personal wearable devices.

One alternative is to use paper-based circuit boards, which should be easier to dispose of, less expensive and more flexible. However, current options require specialized paper, or they simply have traditional metal circuitry components mounted onto a sheet of paper. Instead, Choi and colleagues wanted to develop circuitry that would be simple to manufacture and that had all the electronic components fully integrated into the sheet.

A team of Penn State engineers has created a stretchy, wearable synaptic transistor that could turn robotics and wearable devices smarter. The device developed by the team works like neurons in the brain, sending signals to some cells and inhibiting others to enhance and weaken the devices’ memories.

The research was led by Cunjiang Yu, Dorothy Quiggle Career Development Associate Professor of Engineering Science and Mechanics and associate professor of biomedical engineering and of materials science and engineering.

The research was published in Nature Electronics.

Robotics and wearable devices might soon get a little smarter with the addition of a stretchy, wearable synaptic transistor developed by Penn State engineers. The device works like neurons in the brain to send signals to some cells and inhibit others in order to enhance and weaken the devices’ memories.

Led by Cunjiang Yu, Dorothy Quiggle Career Development Associate Professor of Engineering Science and Mechanics and associate professor of biomedical engineering and of materials science and engineering, the team designed the synaptic transistor to be integrated in robots or wearables and use artificial intelligence to optimize functions. The details were published Sept. 29 in Nature Electronics.

“Mirroring the human brain, robots and wearable devices using the synaptic transistor can use its artificial neurons to ‘learn’ and adapt their behaviors,” Yu said. “For example, if we burn our hand on a stove, it hurts, and we know to avoid touching it next time. The same results will be possible for devices that use the synaptic transistor, as the artificial intelligence is able to ‘learn’ and adapt to its environment.”

The existing jacket can perform one logical operation per second, compared to the more than a billion operations per second typical of a home computer, says Preston. In practice, this means the jacket can only execute short command sequences. Due to the speed of the logic, along with some other engineering challenges, Zhang says he thinks it’ll take five to 10 years for these textile-based robots to reach commercial maturity.

In the future, Preston’s team plans to do away with the carbon dioxide canister, which is impractical. (You have to refill it like you would a SodaStream.) Instead, his team wants to just use ambient air to pump up the jacket. As a separate project, the team has already developed a foam insole for a shoe that pumps the surrounding air into a bladder worn around the waist when the wearer takes a step. They plan to integrate a similar design into the jacket.

Preston also envisions clothing that senses and responds to the wearer’s needs. For example, a sensor on a future garment could detect when the wearer is beginning to lift their arm and inflate without any button-pressing. “Based on some stimulus from the environment and the current state, the logic system can allow the wearable robot to choose what to do,” he says. We’ll be waiting for this fashion trend to blow up.

Imperial researchers have embedded new low-cost sensors that monitor breathing, heart rate, and ammonia into t-shirts and face masks.

Potential applications range from monitoring exercise, sleep, and stress to diagnosing and monitoring disease through breath and vital signs.

Spun from a new Imperial-developed cotton-based conductive thread called PECOTEX, the sensors cost little to manufacture. Just $0.15 produces a meter of thread to seamlessly integrate more than ten sensors into clothing, and PECOTEX is compatible with industry-standard computerized embroidery machines.

Wearable tech has seen an explosion of creativity and applications in the last decade; especially with circuit components getting smaller and cheaper, and batteries getting better and better. Whereas taking phone calls on your wrist was impressive just a few years ago, now, you can experiment with deauthentication attacks on WiFi networks just from this watch: the DSTIKE Deauther Watch SE.

Based on the ESP8266 WiFi microcontroller, this watch is the latest generation of a project to give you a wearable interface for pen testing local WiFi networks. The watch only works on 2.4GHz networks, due to the restrictions of the ESP8266. It comes pre-flashed with the latest ESP8266 Deauther firmware, which is an open-source project! The watch supports four main functions: a deauther attack, which disconnects all local 2.4GHz networks; deauther beacon, used for creating fake networks; deauther probe, to confuse any nearby WiFi trackers; and packet monitoring, which lets you display local WiFi traffic. As you can see, there’s a lot to appreciate in this slick and discreet package.

This watch (and its prior iterations) are made and sold by Travis Lin. Much like the seller emphasizes on the product page, this device is meant for educational purposes, and should be only tested on devices and networks you own. But if this has your curiosity piqued, put on your red hat and check out the wearable devices and other security goodies they have for sale!

You need to wait till 2023 to get them though.

Lenovo has unveiled its T1 Glasses at its Tech Life 2022 event and promises to place a full HD video-watching experience right inside your pockets, a company press release stated.

Mobile computing devices have exploded in the past few years as gaming has become more intense, and various video streaming platforms have gathered steam. The computing power of smartphones and tablets has increased manifold. Whether you want to ambush other people in an online shooting game or sit back and watch a documentary in high-definition, a device in your pocket can help you do that with ease.

Choosing interesting dissertation topics in ML is the first choice of Master’s and Doctorate scholars nowadays. Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. Selecting and working on a dissertation topic in machine learning is not an easy task as machine learning uses statistical algorithms to make computers work in a certain way without being explicitly programmed. The main aim of machine learning is to create intelligent machines which can think and work like human beings. This article features the top 10 ML dissertations for Ph.D. students to try in 2022.

Text Mining and Text Classification: Text mining is an AI technology that uses NLP to transform the free text in documents and databases into normalized, structured data suitable for analysis or to drive ML algorithms. This is one of the best research and thesis topics for ML projects.

Recognition of Everyday Activities through Wearable Sensors and Machine Learning: The goal of the research detailed in this dissertation is to explore and develop accurate and quantifiable sensing and machine learning techniques for eventual real-time health monitoring by wearable device systems.

00:00 Intro.
02:44 Kernel Flow brain interface.
08:03 Seeing my brain activity.
12:42 Reversing aging-Project Blueprint.
18:18 Overcoming depression.
26:42 Starting Kernel.
34:40 Why non-invasive?
36:43 Comparison to Tesla/ Neuralink.
43:52 Elon considered joining Kernel?
44:52 Kernel hiring.
46:17 Participate in the studies.

Participate & experience Kernel Flow: https://www.kernel.com/participate.
Information: Kernel Flow: https://www.kernel.com/flow.
Kernel Careers: https://jobs.lever.co/kernel-2
Neura Pod Episode about Kernel & Bryan Johnson: https://youtu.be/c0VFiEhDg6I
Bryan Johnson LinkedIn: https://www.linkedin.com/in/bryanrjohnson/
Bryan Johnson Personal Page: https://www.bryanjohnson.co/
Blueprint Website: https://blueprint.bryanjohnson.co/

Continue reading “Elon Musk’s NEURALINK vs Bryan Johnson’s KERNEL (No Surgery)” »