Summer research opportunities
The Summer Undergraduate Research in Engineering (SURE) and Summer Research Opportunity Program (SROP) gives undergraduates a chance to participate in summer research in their field. Students who’ve begun or completed their third year can apply to the program.
NAME Project #1
Marine Engineering Systems for Autonomous Ship Operation
Faculty Mentor: Professor Timothy J. McCoy, email@example.com
Desired skills/experience: familiarity with Matlab/Simulink, LabView, Solidworks or Autocad is helpful but not required. An understanding of marine engineering systems to include motors/generators, variable frequency drives, diesel engines, control systems instrumentation, pumps, valves, is helpful but not required. Specific Role for students will depend on their skills, experience and interests.
Project Description: We are currently building a small scale hardware lab with representative shipboard propulsion, power generation, electrical system, fuel system and cooling system for an autonomous ship. The goal of the research is to investigate different approaches to both component technologies and system architectures in order to improve marine system reliability and enable autonomous operation. The effort includes developing computer simulations of the lab scale hardware and running experiments in the lab to demonstrate techniques for improving overall system reliability. We are also investigating the impacts on overall mission performance of component failures by introducing failures into the lab system and evaluating the ability of the remaining equipment to meet various missions
Research mode: In Lab, Remote and Hybrid are all possible depending upon specific tasks and student interests.
NAME Project #2
Machine Learning for Shipwreck Detection from Sonar Imagery: Dataset and Benchmarking Tools
Faculty Mentor: Katie Skinner; firstname.lastname@example.org
Project Description: Machine learning has demonstrated great success in automating detection of objects of interest in imagery collected on land. However, extending this success to data collected in marine environments is a challenging task due to lack of available datasets and open source tools for marine applications. The goal of this project is to develop software and benchmarking tools to enable machine learning for automated detection of shipwrecks from sonar imagery. The main goals are (i) development and deployment of an interactive application to visualize and label sidescan sonar imagery, (ii) development of a benchmark dataset from robotically-gathered sonar imagery, and (iii) development of evaluation tools for testing state-of-the-art methods for automated detection of shipwrecks from sidescan sonar imagery. This work will leverage sidescan sonar data collected from large area robotic surveys in Thunder Bay National Marine Sanctuary.
Research mode: Hybrid (occasional in lab work required)