The most significant risk that could jeopardize this project is collecting enough data of moving humans with our own radar to train the machine learning architecture. On Friday, we met to test the radar. Angie brought the newer radar in hopes of getting the green board to work. However, it refused to work with her computer. We will instead meet tomorrow to test the older radar on moving humans. Angie will visualize the corresponding Doppler effect on her computer. Not only will this prepare us for the interim demo, it will also ensure that the collected radar data is suited for our machine learning architecture. After this assurance, Angie will be able to send Linsey the radar data, so that she can start training the architecture on our data as soon as possible.
Another risk in general is integration timeline. We spoke about this as a team and assured ourselves that we have enough time to integrate components. Ayesha and Linsey have already initiated integration of the web application frontend and the machine learning architecture. Ayesha installed the Django REST API that will connect the two components, and together they’ll migrate their code to the same location for integration.
There were no system design changes. Just to clarify, we plan on using the older radar for our project, because it works much better with our computers, and at this point in the project, we need something reliable.
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