This project aims to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes an investigation of computational topology and contrastive learning for manifold learning.
The goal of this work is to translate streams of data from individual sensors into a shared-manifold space for joint understanding and processing.
The goal of this work is to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes investigation of computational topology for manifold learning, data summarization, and intrinsic dimensionality estimation. In practice, for a given application, processing chains are generally developed for a particular sensor or set of sensors.
The goal of this work is to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes investigation of computational topology for manifold learning, data summarization, and intrinsic dimensionality estimation. In practice, for a given application, processing chains are generally developed for a particular sensor or set of sensors.
The goal of this work is to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes investigation of computational topology for manifold learning, data summarization, and intrinsic dimensionality estimation. In practice, for a given application, processing chains are generally developed for a particular sensor or set of sensors.
Researchers in UF’s Department of Electrical and Computer Engineering, along with collaborators from UF/IFAS, are developing AI systems for the purpose of“for the purpose of more accurately measuring ecosystem services and how they are impacted by various land management practices.