Self-powered artificial synapse combines sensing and memory in flexible electronics
Neuromorphic devices, which are designed to emulate aspects of biological neural networks, are promising candidates for low-power, intelligent sensing technologies, including wearable applications.
Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta.
To make human-robot interactions safer and more fruitful, robots should be capable of sensing their environment. In a recent study, researchers developed a novel robotic link with tactile and proximity sensing capabilities. Additionally, they created a simulation and learning framework that can be employed to train the robotic link to…
Joint embedding (JE) architectures have emerged as a promising avenue for acquiring transferable data representations. A key obstacle to using JE methods, however, is the inherent challenge of evaluating learned representations without access to a downstream task, and an annotated dataset. Without efficient and reliable evaluation, it is difficult to…