Team SimVolts showed and demonstrated a working prototype in the Final Design Review to Sponsor and IPPD staff. SimVolts is aware that the accomplishment is expandable, and the team is turning-in a final report with all specifications for future reference.
SimVolts is very grateful to IPPD and CAE team for the opportunity to work on this project. It has been a great learning experience!
During Prototype Evaluation Day, SimVolts was able to demonstrate a prototype demo in progress and testing plans.
SimVolts did a live demo of its working API system that will be integrated with the above Graphical User Interface (GUI). A live demo of the GUI was done were the audience was able to see how the GUI calls a local API system.
GUI system is not done yet, but SimVolts intend to have it done within two weeks.
SimVolts received many feedbacks. It was said that the team needs to work on the Elevator Pitch. But, we also had positive feedbacks, it was said that the team had improved a lot and that SimVolts is close to a functioning Prototype. The team-wide participation was reflected and fair.
Future plans include to finish our prototype, and add final touches to the project. In the meantime, the hard work continues!
SimVolts is very close to its first functioning prototype. This is good news for the team as we get closer to Prototype Inspection Day.
SimVolts is running multiple test cases to ensure that the Application Process Interface system works properly as expected. This API system will be interfacing with a Graphical User Interface soon to be integrated.
During a sponsor meeting SimVolts and CAE members agreed to reduced the scope of work of the project. Computer vision side is not longer a priority and SimVolts will only work on it if the new scope tasks are completed.
This new scope is more doable and it aims to fit more within the time limits of IPPD program.
On the other hand the computer back-end system stays the same and SimVolts has created DC circuit models intended to be used as LRU Boxes.
SimVolts is making good progress in the computer vision side of the project. The team ran multiple neural network trainings on the jetson nano. The training is able to detect two different pins on the same cannon plug.