from scipy.io import wavfile from IPython.display import Audio import numpy from scipy.fft import fft from scipy.signal import welch import matplotlib.pyplot as plt from IPython.display import HTML plt.rcParams["figure.figsize"] = (20,3) %matplotlib inline
The centerpiece of the Cornell M.Eng. program is the professional project, in which students apply theory to a real problem, with the guidance from faculty, and often in collaboration with other students. This page describes the projects of my past advisees that have graduated.
Authors: Mingyang Feng and Yingjia Zhang
Abstract: This project is a collaboration with the Herbert F. Johnson Museum of Art on campus. Art museum staff must regularly gather temperature and humidity measurements from throughout the museum. These measurements inform maintenance schedules and display locations for sensitive artwork. Light exposure could also inform these schedules, but the museum does not presently measure it with the same regularity. To help museum staff gather measurements, we developed an IoT system which allows them to remotely monitor the real-time environmental conditions throughout the museum. In particular, the system measures temperature, humidity, ambient light, and ultraviolet light. Each node in the IoT system is composed of multiple deployable sensors controlled by a low-cost and low-power microcontroller named NodeMCU. The IoT system gather data from these sensors at a programmable rate. All data are aggregated in a remote database and displayed on a personal website for users to access via the internet. A one-month test has been performed in the museum to verify the system works as per the requirements.
Report: Download here
Authors: Zifu Qin
Abstract: In some circumstances, human ears are better than eyes at recognizing patterns. Researchers have sonified complex datasets, including DNA sequences, to use their ears to find patterns hidden from their eyes. By sonifying the well-known chaotic system – “Lorenz System”, this project qualitatively investigates the ear's response to a sonified chaotic system. To sonify the Lorenz System, the team built a microcontroller-based chaotic synthesizer which could generate sounds with chaotic patterns. In this project, the source of chaos was from modulated Lorenz Attractors which were mapped to the output frequencies of a Direct Digital Synthesis sine-wave synthesizer. The RP2040 microcontroller was used to implement the algorithm and send the generated digital frequency signals to a digital to analog converter to make sounds. This project was an exploratory experiment of sonifying a chaotic system. We found that the sonified chaotic system sounded vaguely natural and organic.
Report: Download here
samplerate2, data2 = wavfile.read('./one.wav') Audio(data2[:,0], rate=samplerate2)