Engineers improve electrochemical sensing by incorporating machine learning
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…
Designed artifacts are ontological, shaping, and at times limiting, what becomes possible or imaginable. One path toward mitigating such foreclosures is giving people power over how systems are designed and built. Despite decades of scholarship around systems that enable such authorship, these systems are often evaluated on whether or not…
Researchers at the USC Viterbi School of Engineering and School of Advanced Computing have developed artificial neurons that replicate the complex electrochemical behavior of biological brain cells.