Florin Adrian Odagiu

Developed "NOW (That's what I call) Music," a Swift-based music streaming service with a rich, animation-driven interface. Established the architecture for the Swift version, coded the product, and coordinated activities with the Android developer, the back-end, and product teams. Extracted business logic from an older, C#/Xamarin-based version that had a large binary size, bad reviews, and a high crash rate-around 30%.

The Swift version ended up with a ~3 times smaller binary size, a 99.x% crash-free rate from the first release, according to Crashlytics, resulting in wider adoption and a more enthusiastic user response and 4.x stars.

Contributed to the AOPA ASI Flight Risk Evaluator, an iPhone app designed to assist light aircraft pilots with the process of preflight safety checks. Maintained and upgraded safety-related features such as a list of all continental US airports and runways, their ICAO designations, and their cardinal points orientation.

Extracted relevant weather information from the NOAA public API relating to cloud ceilings, prevailing wind direction, average speed, maximum gust speeds, precipitation, and extreme weather conditions, to name a few.

This information helps pilots determine the aircraft's technical characteristics, especially the maximum allowable perpendicular wind speed for a safe takeoff. This information is used to determine the angle between the runway and prevailing wind direction, decomposing the wind speed into normal and tangential components (using sin/cos values of said angle), and compare with the aircraft's specifications.

Too high a normal or tangential component would result in the flight deemed to be unsafe.

Contributed to GuidedKnowledge's wisdom-sharing platform designed to capture a professional's (music, sports) performance, distill it to a mathematical form, process and present it to an apprentice in an intuitive way, and highlight errors in a comprehensible manner while providing custom improvement strategies.

Raw data acquisition performed via analog-to-digital converters attached to musical instruments, feeding into mobile devices (iOS or Android tablets) with MEMS accelerometers strap-attached to the body of a performing athlete.

Various inputs fed (experimentally) into MATLAB or C-based libraries, performing relevant signal processing (FFT, sensor fusion) using ARM M4 and A5 architectures. Signal output to iOS or Android mobile devices for final business processing, display, gamification, social media integration, and cloud synchronization.