Non-invasive monitoring of rare wildlife populations
This project is to develop non-invasive techniques to monitor wildlife populations. Since it is costly and difficult to survey wildlife habitat, the project aims to explore cost-effective and efficient methods to detect and assess wildlife in a relative large area. A wireless sensor network will be build up to sample and analyze rare species without directly handling animals. Different sensors such as camera, force sensors, sound sensors etc will be deployed and sensory data will be collected and analyzed. Machine learning algorithms will be explored to process the sensory data evaluate the wildlife habitat.
Two hours every week
Data mining, machine learning knowledge prefered
- Job Opening ID
- Fall 2021 and Winter 2022
- Work could be done by someone not coming to campus (e.g., online or non-local student)
- What majors can apply?
- Biology (MS)
- Computer Science and Information Systems (MS/Certificate)
- Mechanical Engineering (MSE)
- Faculty Name
- Ming Li