Diverse Wi-Fi-based wireless applications have been proposed, ranging from daily activity recognition to vital sign monitoring. Despite their remarkable sensing accuracy, the high energy consumption and the requirement for customized hardware modification hinder the wide deployment of the existing sensing solutions. In this paper, we propose REHSense, an energy-efficient wireless sensing solution based on Radio-Frequency (RF) energy harvesting. Instead of relying on a power-hungry Wi-Fi receiver, REHSense leverages an RF energy harvester as the sensor and utilizes the voltage signals harvested from the ambient Wi-Fi signals to enable simultaneous context sensing and energy harvesting. We design and implement REHSense using a commercial-off-the-shelf (COTS) RF energy harvester. Extensive evaluation of three fine-grained wireless sensing tasks (i.e., respiration monitoring, human activity, and hand gesture recognition) shows that REHSense can achieve comparable sensing accuracy with conventional Wi-Fi-based solutions while adapting to different sensing environments, reducing the power consumption by 98.7% and harvesting up to 4.5mW of power from RF energy.

Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed assuming an ideal situation, while in reality, we can have measurement noise, DC offset, and variations in the voltage signal's amplitude and frequency. Building on the prior PQE classification works using deep learning, this paper proposes a deep-learning framework that leverages attention-enabled Transformers as a tool to accurately classify PQEs under the aforementioned considerations. The proposed framework can operate directly on the voltage signals with no need for a separate feature extraction or calculation phase. Our results show that the proposed framework outperforms recently proposed learning-based techniques. It can accurately classify PQEs under the aforementioned conditions with an accuracy varying between 99.81%$-$91.43% depending on the signal-to-noise ratio, DC offsets, and variations in the signal amplitude and frequency.

As power quality becomes a higher priority in the electric utility industry, the amount of disturbance event data continues to grow. Utilities do not have the required personnel to analyze each event by hand. This work presents an automated approach for analyzing power quality events recorded by digital fault recorders and power quality monitors operating within a power transmission system. The automated approach leverages rule-based analytics to examine the time and frequency domain characteristics of the voltage and current signals. Customizable thresholds are set to categorize each disturbance event. The events analyzed within this work include various faults, motor starting, and incipient instrument transformer failure. Analytics for fourteen different event types have been developed. The analytics were tested on 160 signal files and yielded an accuracy of ninety-nine percent. Continuous, nominal signal data analysis is performed using an approach coined as the cyclic histogram. The cyclic histogram process will be integrated into the digital fault recorders themselves to facilitate the detection of subtle signal variations that are too small to trigger a disturbance event and that can occur over hours or days. In addition to reducing memory requirements by a factor of 320, it is anticipated that cyclic histogram processing will aid in identifying incipient events and identifiers. This project is expected to save engineers time by automating the classification of disturbance events and increase the reliability of the transmission system by providing near real time detection and identification of disturbances as well as prevention of problems before they occur.

Is it feasible to build a computer with a sense of taste? In response to this question, IBM Research scientists developed HyperTaste, a chemical taste sensing tool. It performs analyses and detects the chemical composition of liquids using its "electronic tongue" status. "HyperTaste was inspired by advances in AI and machine learning to mimic human senses like sight and hearing for recognizing images and interpreting speech. We wanted to present a new lens for chemical sensing," explained Patrick Ruch from IBM Research, the coauthor of the study.

AI that can generate new flavor combinations from scratch is nothing novel, but what about models that can taste those flavors like a human? In a recently published paper ("A portable potentiometric electronic tongue leveraging smartphone and cloud platforms") and an accompanying blog post, researchers at IBM's Zurich-based R&D division detailed Hypertaste, an artificial tongue designed to fingerprint beverages and other liquids "less fit for ingestion." Such a system could be used to guarantee supply chain safety for food and drinks, proposed IBM research staff member Patrick Ruch, who noted that there's currently little to verify that packages contain what's on the label apart from conducting costly experiments. Suppliers acting in bad faith could insert lower-quality products into the supply chain, while counterfeiters could fake a real product by adding the few chemical compounds which are most likely to be tested for in a lab. "There are many substances out there that we would like to'taste' without actually putting them in our mouth. Consider a government agency interested in an on-the-fly water quality check of a lake or river at a remote location, a manufacturer wanting to verify the origin of raw materials, or a food producer trying to identify counterfeit wines or whiskeys," wrote Ruch.

The human sense of taste is the result of millennia of evolution. And its astoundingly good at letting us enjoy pleasant foods and beverages as well as warning us against ingesting harmful substances. Man-made sensors, on the other hand, have yet to approach the ease with which our taste buds recognize substances. This is a significant technological gap, as there are many substances out there that we would like to "taste" without actually putting them in our mouth. For the rapid and mobile fingerprinting of beverages and other liquids less fit for ingestion, our team at IBM Research is currently developing Hypertaste, an electronic, AI-assisted tongue that draws inspiration from the way humans taste things.