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Computer chip will sniff your armpits and tell you when you have BO

New Scientist

It is an embarrassing problem we have all had to deal with. A run for the bus or a hot meeting room can leave you trying to check your armpit without anyone noticing. Luckily, AI is here to help. UK chip-maker Arm, better known for developing the hardware that powers most smartphones, is working on a new generation of smart chips that embed artificial intelligence inside devices. One of these chips is being taught to smell.

Printed Energy-Harvesting Device Aimed at Smart Packaging


A UK-based tech innovation center has completed development of a printed energy harvester that researchers say will help usher in the next generation of smart packaging. The Centre for Process Innovation (CPI) has developed the device -- which uses near-field communication (NFC) for power -- as part of the HaRFest project, launched more than a year ago to develop a low-cost energy-harvesting device that can be integrated into sensors, displays, and storage devices. The device works by drawing energy from a user's mobile telephone, and uses NFC to establish radio communication with another device or sensor by touching or being in close proximity to it. It's comprised of a printed antenna alongside printed passive and active components, including an array of tuning capacitors, according to CPI. The device can be tuned to resonant frequency in order to maximize harvested power output, researchers said.

Electronic Noses Sniff Success

AITopics Original Links

Several hundred years ago, village doctors in rural China diagnosed diabetes by the characteristically sweet smell of a patient's breath. Today hospitals use a battery of blood tests and laboratory analyses to make that same diagnosis, but doctors may soon be sniffing their patients' breath again. This time the doctors will have electronic noses small and cheap enough to carry in their pockets. This e-nose will be the culmination of decades of work at countless laboratories, where researchers have sought to create a tiny, cheap, automatic sniffer that would let wine bottles monitor the aging of their contents, allow meat packages to flag spoilage, and enable mailboxes to check for bombs. Imagine barroom coasters that double as Breathalyzers, bumper stickers that monitor car emissions. Until now, it's been just so much sci-fi.

Flexible Electronics? Researchers Plot a New Era for Production WSJD - Technology

Valley is rethinking one of its least glamorous and most ubiquitous building blocks, the circuit board, in a bet that flexible, form-fitting alternatives could reshape electronics and spur more manufacturing in the U.S. Backers envision ultrathin boards like skin patches that could analyze the sweat of soldiers and pilots, wrap around gas pipelines and act as leak detectors, or provide grids of flexible sensors able to detect stress on airplane wings. Such possibilities--the focus of a new manufacturing consortium here backed by the U.S. Department of Defense and others--require materials and production techniques that differ from conventional circuit boards, made of stiff plastic. In some cases, circuitry is imprinted on paper, plastic or other organic materials using processes akin to inkjet printing. The results, which can be as thin as temporary tattoos, can be tailored for extended contact with the skin or in large formats applied to walls or roofs. The concept of applying printing techniques to electronics has been around for more than a decade.

Mobile tool may be used to diagnose serious diseases


One method to detect small objects and related biomarkers is called plasmonic sensing, which involves shining light onto metal nanostructures to amplify the local electric field. The interaction between this amplified electric field and the molecule of interest can be measured, revealing important information about molecular concentration and kinetics. Although scientists have explored this type of sensing for decades, they have faced challenges when it comes to environments outside of laboratory settings that are limited in resources. This is because expensive and bulky instruments are needed for this work. The primary goal of machine learning is to "train" an algorithm with a large amount of data so that it can "learn" complex trends and statistics and in turn be used to predict outcomes with far more accuracy than a traditional model.