Pitch 1 – Develop an API for Sensor

I was hoping to work with sensors to develop an interface/app that would be more useful for
individuals’ projects. Say there was this sensor that measured water quality that was inexpensive,
but pretty rudimentary and it doesn’t really have a good API already, I could make an app that
would work with that for individuals who are students or just wanting to do small experiments
with water qualities nearby their house or school. I would work with programming and
interfacing with the hardware sensor. I’m not entirely sure of the dataset I would use or need for
this. I could use one that is already present to work on different graphing techniques in the API.

Pitch 2 – Research into Feasibility and Practicality of Low-Cost Portable Air Quality Sensor and Network with Smartphone Connection

Similar to the sensor pitch I did before, this would be working with air sensors that could be put
into a compact container that would be attached to someone. The sensors would be built into an
Arduino, Charlie said he would show me some sensors and how to use the Arduino sometime
this week when he is back on campus. The air sensors would constantly read for different
dangerous compounds in the air and would notify the user through a smartphone app and track
different amounts in different places. The sensor would not track location, but the app on the
phone would because the sensor would probably be connected to the phone through Bluetooth or
some other possible wireless connection and therefore have to be near the phone to work. This
would be very useful for emergency responders and for maintenance people who work with
boilers and furnaces.

Pitch 3 – Land-Based Oil Spill Modeling

I was thinking about doing something within the realm of climate change simulations. Maybe
something along the lines of the damages of an oil pipeline bursting and then being carried
inland by a flood that was caused by excess rainfall due to climate change. Or perhaps a wildfire
simulation or smoke pollution simulation from said wildfires. This would just help to get an idea
of how we could mitigate these catastrophes while the risk is still present. It would require a lot
of machine learning and GIS information