Are you a maker? A genius at computer algorithms? Can you whip up a mobile app before breakfast? Instead of building the next ethically questionable social media platform, how would you like to use your powers for good? Consider a research project in the Structural Ecology Lab!
There are many opportunities for research and development projects in which you apply your skills to help protect the natural world.
Here is a short list of some possible research areas:
- Automated species ID using machine learning
- Fish tracking and identification from underwater cameras
- Flying insects from laser transit signals
- Bees from microscope wing images
- Augmented reality for hidden environmental processes
- Ocean temperature and currents
- Tree benefits
Below is a little more information about some of the projects. If you are interested in one of these, or have your own brilliant idea, please get in touch.
Dynamic fish tracking and identification
Given a video of fish swimming in an aquarium, develop a system to both track and identify the individual fish in real time, so as to create a dynamic, live overlay of labels on the video. This would involve putting together a number of existing technologies, as separate good (mainly neural-network-based) systems exist now for tracking and image identification. Suitable for a student with experience in machine learning, especially CNNs. Potential commercial applications.
Fast ID of bee species
Bees are identified from preserved specimens under a microscope. Obtaining a clear image of a bee’s wing is a challenge: generally the wing is detached from the body and sandwiched under a cover slip, which is fiddly and time-consuming. The resulting images often have glare spots, which are noise for the ID algorithm. So the design challenge is this: is there a specially shaped platform (in microscope terms, a ‘stage’) that would allow a high-quality image of a bee wing (or really any kind of insect wing) to be taken quickly without detachment?
Augmented reality for hidden environmental processes
Take existing environmental datasets and figure out how to represent normally invisible environmental variables and processes using location-based AR. Example 1: show sea surface temperature anomalies as an overlay on the actual ocean. Example 2: show gas exchange around a tree, for example as visible particles as in the ‘carbon tree’ simulation (from hiilipuu.fi). Requires someone with skills in (or interested in learning) Apple’s ARKit or Google’s ARCore.