BIOL-CT01

Preferred major field of study or minimum required skills

Major in environmental science, CS, EE, IE, ME Preferable (not required)

Skills: Problem solving, Programming: C/C++, Python Autodesk Eagle and Inventor Linux Embedded devices: Raspberry Pi, Arduino Small scale construction in aluminum, wood, and plastic Ecosystem or environmental science

Good written, oral communication and presentation skills

Capacity to work in an interdisciplinary team

Scholarly significance/intellectual merit

Low-cost time-lapse digital image capture and analysis has transformed plant to landscape phenological studies but camera HW and approaches to image analysis have been shown to be limiting and/or problematic. This research project will use a novel multispectral digital image acquisition and analysis system developed in our lab. The system acquires RGB & NIR imagery as well as ancillary environmental data. Data is collected, preprocessed, and either stored or streamed to the cloud. Our approach suggests that it will be possible to simplify and enrich traditional approaches to ecological data collection and analysis. However, we have yet to optimize and fully test the system over a range of ecosystems. Participants will be involved in aspects of the project including HW design and optimization; testing; and analysis. Mentors will include postdocs, technicians, and students with expertise in data and ecosystem science, EE, CS, and ecoinformatics. Students will complete an independent studies project and produce a publication/presentation suitable for submission to a scientific meeting.

Research question(s)

1. How can hardware/software operation be designed to optimize data capture and power consumption?
2. What quality assurance and quality control procedures need to be implemented to improve system robustness, reliability, and data quality?
3. What mounting system will best suit different research applications?
4. What environmental phenomenon can be detected using digital imaging and analysis approaches?

Methods/techniques/instruments to be learned/utilized

Understand and apply good practices for Linux on ARM architectures (RaspberryPi) Programming complex solution and GUIs using Python, C/C++, ShellScripting, HTML, and JS. Knowledge of networks interfaces communications. Design, modification, testing, and production of PCBs, 3D printing, and SMD. Using MCU (Arduino), for interfacing with analog/digital student will learn to problem solving in SW/HW.