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- Creators: Barrett, The Honors College
- Resource Type: Text
Speedsolving, the art of solving twisty puzzles like the Rubik's Cube as fast as possible, has recently benefitted from the arrival of smartcubes which have special hardware for tracking the cube's face turns and transmitting them via Bluetooth. However, due to their embedded electronics, existing smartcubes cannot be used in competition, reducing their utility in personal speedcubing practice. This thesis proposes a sound-based design for tracking the face turns of a standard, non-smart speedcube consisting of an audio processing receiver in software and a small physical speaker configured as a transmitter. Special attention has been given to ensuring that installing the transmitter requires only a reversible centercap replacement on the original cube. This allows the cube to benefit from smartcube features during practice, while still maintaining compliance with competition regulations. Within a controlled test environment, the software receiver perfectly detected a variety of transmitted move sequences. Furthermore, all components required for the physical transmitter were demonstrated to fit within the centercap of a Gans 356 speedcube.
In this research, I surveyed existing methods of characterizing Epilepsy from Electroencephalogram (EEG) data, including the Random Forest algorithm, which was claimed by many researchers to be the most effective at detecting epileptic seizures [7]. I observed that although many papers claimed a detection of >99% using Random Forest, it was not specified “when” the detection was declared within the 23.6 second interval of the seizure event. In this research, I created a time-series procedure to detect the seizure as early as possible within the 23.6 second epileptic seizure window and found that the detection is effective (> 92%) as early as the first few seconds of the epileptic episode. I intend to use this research as a stepping stone towards my upcoming Masters thesis research where I plan to expand the time-series detection mechanism to the pre-ictal stage, which will require a different dataset.