Tag: ECE

A Transfer Learning Framework for Creating Subject-Specific Musculoskeletal Models of the Hand

This project utilizes machine learning methods to examine how subject-specific differences influence hand function and create subject-specific computer models from easy to obtain clinical data. Completion of this project will critically advance the ability to efficiently create subject-specific models of the hand and understand the biomechanical mechanism underlying hand force production.

SiTS: Hyperspectral Signals in the Soil

In this effort, we will build hyperspectral cameras (which also collect information outside of the visible range collected by standard color cameras) that will be inexpensive, have an automated mechanism to move up and down minirhizotron tubes, and are paired with automated algorithms to process and understand the collected data.

MRA: Disentangling Cross-scale Influences on Tree Species, Traits and Diversity from Individual Trees to Continental Scales

Trees are essential to ecosystems. They store carbon, reduce erosion, and serve as habitat for other species. The factors influencing trees, and the spatial scales at which they are managed, range from an individual tree to entire continents. Since there are approximately three trillion trees in the world collecting data on every tree over large areas is impossible using traditional methods. Therefore, it is necessary to use new technology to measure and describe individual trees over large geographic areas. This research will address this fundamental challenge by combining high resolution remote sensing data with field data on trees.

Improved System Assessment of Aflatoxin Risk Utilizing Novel Data and Sensing

Vulnerability of agroecosystems to aflatoxin is a major problem worldwide, and in the U.S., represents an economic threat from high costs associated with testing and lost trade when outbreaks occur – events predicted to become increasingly common with climate variability. Our team includes engineers, agronomists, breeders, physiologists, and data scientists, utilizing an integrated systems approach, employing modeling, new sensing technologies, and data mining for assessing risk at vulnerability points.