Hi and welcome!
I primarily focus on Geospatial concepts and applications through integrative, hands-on experimentation: applying what I learn to real-world use cases through algorithmic, data-driven experiments.
With a less structured approach, I explore diverse topics across Computer Science and other fields in what I call a Playground for curious learning.
A C++ performance demonstration with a Python 3 geoprocessing phase. It extracts coordinate data from a California Open Data GeoJSON file and applies a Mercator projection to measure the runtime difference between calculating distances via Great Circle Arc vs. Euclidean lines.
A C++ raster-model application demonstrating a spatial filtering use case for environmental public health data. It applies a 3x3 smoothing mean filter to a raster grid of Air Quality Index (AQI) values to reduce sensor noise and computes its gradient magnitude to highlight areas of rapid AQI change.
Ever wondered how someone experiencing CVD perceives colors? This is exactly what my final Design Challenge for UCR's Overview of Bioengineering course aims to explain.
It's an Arduino-based system that reads RGB intensities from light reflected off of a given object. It then, in real time, transforms the readings computationally based on a user-selected vision mode, toggled through a push button. The transformed RGB values are sent to an LCD screen and an RGB LED to simulate perceived color. The computations I implemented are grounded in research which I link to in the document below.
It is important to note that the simulated perceived color is an approximation, limited by factors such as sensor characteristics and LED output, and may require calibration to better approximate the intended perceptual transformation.
This challenge turned into an interdisciplinary engineering experience, during which I even learned how to solder one of the components I needed to buy, namely the RGB TCS34725 sensor! Thank you Dr. Grover and UCR's Creat'R Lab Makerspace for the soldering and circuitry help!