Project

# Title Team Members TA Documents Sponsor
35 Acoustic Motion Tracking
Hojin Chun
Sean Nachnani
Yuchen He TA design_review
final_paper
other
proposal
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

Cypress Robot Kit

Todd Nguyen, Byung Joo Park, Alvin Wu

Cypress Robot Kit

Featured Project

Cypress is looking to develop a robotic kit with the purpose of interesting the maker community in the PSOC and its potential. We will be developing a shield that will attach to a PSoC board that will interface to our motors and sensors. To make the shield, we will design our own PCB that will mount on the PSoC directly. The end product will be a remote controlled rover-like robot (through bluetooth) with sensors to achieve line following and obstacle avoidance.

The modules that we will implement:

- Motor Control: H-bridge and PWM control

- Bluetooth Control: Serial communication with PSoC BLE Module, and phone application

- Line Following System: IR sensors

- Obstacle Avoidance System: Ultrasonic sensor

Cypress wishes to use as many off-the-shelf products as possible in order to achieve a “kit-able” design for hobbyists. Building the robot will be a plug-and-play experience so that users can focus on exploring the capabilities of the PSoC.

Our robot will offer three modes which can be toggled through the app: a line following mode, an obstacle-avoiding mode, and a manual-control mode. In the manual-control mode, one will be able to control the motors with the app. In autonomous modes, the robot will be controlled based off of the input from the sensors.