This project implements an integrated hardware-software architecture to enable robust acoustic sensing. The deployed setup pairs the microphone with a Blues Cygnet microcontroller board, which digitizes and streams audio data to a PC via USB. The wireless Blues Notecard operates by connecting to local Wi-Fi, enabling real-time environmental sound capture and automatic cloud uploads for centralized processing. Acoustic features, such as Mel-Frequency Energy (MFE) coefficients, are extracted from the raw signals to train machine learning models that are capable of distinguishing gunshots from ambient noise.

Heavy Menstrual Bleeding (HMB), or Menorrhagia, is a menstrual condition defined as greater than 80 mL of blood loss per cycle. HMB can be an indicator for many serious conditions, such as endometriosis, reproductive cancer, and anemia. The most used methods to measure menstrual output volume are inaccurate and subjective. Furthermore, the volume threshold for HMB does not consider the unique characteristics between people who menstruate. This means clinicians need more insightful information about a person who menstruates menstrual output to assist in patient care. We are developing an imaging-based app solution that will utilize a machine learning model to offer an accurate and objective way to measure the menstrual blood output during a period, while also using a personalized approach to establish individual baseline blood loss values in order to predict abnormal menstrual bleeding.

An analysis of the 2024 NFL Kickoff Rule Change that focuses on the rule’s impact on the increased number of long field goal attempts in recent years

This project explores the mathematical foundations and perceptual effects of Frequency Modulation (FM) synthesis—a powerful technique used in sound design. Inspired by the classic Yamaha DX7 synthesizer, we reconstruct FM sound generation from first principles, using computational tools to analyze how modulation parameters affect the resulting timbre. By applying the Short-Time Fourier Transform (STFT), we break down audio signals into evolving spectral components, visualizing how simple waveforms combine to create rich, dynamic textures. We also introduce an interactive interface for real-time exploration of spectral features and a novel method for reconstructing sounds from their frequency components. Together, these tools provide fresh insights into FM synthesis and how complex audio structures can emerge from simple mathematical processes. This work bridges applied math, signal processing, and musical acoustics—advancing both our understanding and creative potential in sound design.