Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.

This thesis details the design process of a variable gain amplifier (VGA) based circuit which maintains a consistent output power over a wide range of input power signals. This effect is achieved by using power detection circuitry to adjust the gain of the VGA based on the current input power so that it is amplifier to a set power level. The paper details the theory behind this solutions as well as the design process which includes both simulations and physical testing of the actual circuit. It also analyses results of these tests and gives suggestions as to what could be done to further improve the design. The VGA based constant output power solution was designed as a section of a larger circuit which was developed as part of a senior capstone project, which is also briefly described in the paper.

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.

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.