3 docs tagged with "Self-Supervised Learning"

Author: Yufeng Luo, MCS Research Aide, University of Wyoming, Summer 2024

By processing data directly at the collection source, edge computing networks like Sage/Waggle offer unique advantages over using centralized servers for ecological monitoring tasks. Audio in particular is useful for tracking metrics like noise pollution from human development or population statistics for vocal wildlife in the area (birds, insects, frogs, etc.). Further analysis can provide deeper insights into the overall health of the ecosystems where nodes are deployed.

Clouds have long fascinated humans because of their complex and diverse nature. To gain a deeper understanding of these atmospheric phenomena, a multidisciplinary team of computer scientists, meteorologists, and machine learning experts from Northwestern-Argonne Institute of Science and Engineering (NAISE) collaborated on the National Science Foundation (NSF)-supported Sage project. The project's goal was to develop new edge computing technologies that would allow scientists to collect and analyze large amounts of data from advanced sensors in real time.