Project

# Title Team Members TA Documents Sponsor
35 Acoustic Motion Tracking
Hojin Chun
Sean Nachnani
Yuchen He TA design_document0.pdf
final_paper0.pdf
other0.pdf
proposal0.pdf
Group Members:
Sean Nachnani (nachnan2)
Kevin Chun (hchun8)

General Description:
The project idea is to use sound rather than video as a means of motion recognition. Current smart devices are limited to only using natural language processing to interpret a user's needs. We want to expand upon this further and allow devices to perform commands using simple gestures.
The current idea is to create a 4-input microphone array with an ADC that allows for at least a 48khz sample rate, and use a speaker that can reproduce sounds up to at least 24khz. We will start off by sending pseudo-random pulses across a large bandwidth and correlating the sent signal with the received input from the microphones. Given time we will switch to using FMCW (Frequency Modulated Continuous Waveform) radar as a basis for this approach. This will allow us to achieve accurate distance and velocity measurements, and potentially transmit in the inaudible range.
I have spent the last month prototyping this device using a raspberry Pi and a speaker array. I've gotten the pseudo random pulse approach to work, coding all the signal processing in Python, mainly with the PyAudio and SciPy libraries. The prototype's speaker array is currently sampling at 44.1khz and using a speaker that can play up to 20khz. I was able to achieve accurate measurements within the range of a normal living room (about the size of a smaller classroom in eceb).
We plan on building the microphone array using 4 MEMS microphones and appropriate ADCs to sample up to 48khz. This will allow us to play sounds up to 24khz, which will give us enough bandwidth to get accurate measurements. We'll also use a micro controller (most likely a raspberry pi) to sample from these microphones and perform the DSP needed. This system will be designed to be plugged into a regular power outlet.

Related Research Papers:
CAT: High-Precision Acoustic Motion Tracking http://www.cs.utexas.edu/~wmao/resources/papers/cat.pdf
FingerIO: Using Active Sonar for Fine-Grained Finger Tracking https://fingerio.cs.washington.edu/fingerio.pdf

Propeller-less Multi-rotor

Ignacio Aguirre Panadero, Bree Peng, Leo Yamamae

Propeller-less Multi-rotor

Featured Project

Our project explored the every-expanding field of drones. We wanted to solve a problem with the dangers of plastic propellers as well as explore new method of propulsion for drones.

Our design uses a centrifugal fan design inspired by Samm Shepard's "This is NOT a Propeller" video where he created a centrifugal fan for a radio controlled plane. We were able to design a fan that has a peak output of 550g per fan that is safe when crashing and when the impeller inside damaged.

The chassis and fans are made of laser-cut polystyrene and is powered using brushless motors typically used for radio-controlled helicopters.

The drone uses an Arduino DUE with a custom shield and a PCB to control the system via Electronic Speed Controllers. The drone also has a feedback loop that will try to level the drone using a MPU6050.

We were able to prove that this method of drone propulsion is possible and is safer than using hard plastic propellers.

Project Videos