The University of Texas at Austin's Department of Electrical and Computer Engineering in the Cockrell School of Engineering (Texas ECE) announced the winners of the Spring 2016 Senior Design program while celebrating graduates at the 2016 Graduate Reception on Monday, May 9, 2016 in the Texas Union Ballroom.
Upper division undergraduate students in Texas ECE complete a required capstone design course sequence. In the courses, students solve open-ended problems submitted by industrial collaborators. Students work in small groups over two semesters to identify an opportunity, define the problem, analyze competing needs and requirements, perform prior art and patent searches, develop alternative designs, carry out cost analyses, and select and implement a design solution.
Companies partner with faculty and student teams, providing funding, equipment, industry expertise, and mentoring as the team works toward a successful outcome to their design problem.
Texas ECE is deeply appreciative of our industrial collaborators, whose expertise and support makes these projects successful and meaningful. Industry project sponsors for the Spring 2016 projects include Boeing, Dell, Dun and Bradstreet, National Instruments, Pedernales Electric Cooperative, Tenaris, and Texas Instruments. The fall 2016 Senior Design project submission timeline is mid-summer. To learn more on how to sponsor a project for fall 2016, please contact Jennifer Campbell.
First, second and third place winners were selected in both the Honors/Entrepreneurial and Non-Honors categories. Winners were announced by Prof. Gary Hallock, who taught the course. Prof. Mark McDermott taught the Entrepreneurial section of the course.
Flood Early Warning System
Team: Ryan Chow, Tu-an Nguyen, Peter Schaeffer, Wallace Tran, Derek Young, Wen Zhang
Faculty Advisor: Bill Bard
This project has the potential of making a major impact in efforts to detect floods and save lives. The system is low cost compared to what currently exists, potentially allowing much wider use. In fact, many flood prone areas are simply checked manually, further putting lives at risk. This team developed a new, innovative system based on an accurate low cost water depth sensor, RS-232 radios, an MSP430 microcontroller, and a waterproof housing. The entire system is solar powered, with an integral battery that allows the low power electronics to function in cloudy weather and at night. The City of Austin is excited about this new system, and future work will include its implementation in Austin.
Sound Shield – A Dynamic Noise-Masking System
Team: Yeong Foong Choo, Jun Qi Lau, Dung K. Le, Mark Leatherman, Sung Hyun Park, Negin Mohammad Raoof, Brandon Williams
Faculty Advisors: Brian Evans, Gregory Allen
Sound Shield, an adaptive noise masking software solution, aims to fill a shortcoming in the noise-conditioner market by generating sound masks that respond to environmental noise in real time. It achieves this by performing a fast Fourier transform on the noise signal and then finding its power spectral density. Knowing the power in each frequency band of the noise, Sound Shield can then synthesize a custom mask whose spectrum actively tracks and covers up the changing noise levels. This means that when noise is present, Sound Shield will tailor its mask to cover up frequencies most prevalent in the noise, and when noise is not as noticeable, Sound Shield will play out a “white” mask at a low volume. This offers a much more efficient and effective noise-conditioning experience— the user never needs to stop what they are doing to make adjustments to the mask; they can simply let the software run in the background while they work or relax.
Advanced Imaging System for Automated Insert Inspection
Team: William Headrick, Zahra Jianpanah, Abhinav Kallur, Simon Kumets, Tiffany Tso, Demetry Zozulya
Faculty Advisor: John Pearce
Project Partner: Tenaris
This team designed and constructed an advanced insert inspection system to automatically determine when a cutting tool insert needs replacement. The insert, or cutting tool, is used to thread steel couplings used to connect pipes in the oil and gas industry. In past operation, a worn insert is discarded at the operator’s discretion. This is quite costly, as typically discarded inserts are still usable since manual inspection is inefficient and unreliable. Or, in some cases worn inserts are used, resulting in defective couplings. This team designed an insert inspection system to automate the process. The innovative design includes advanced image processing. Components of the system include a camera, interface hardware and software, image processing, and an indicator/interface to alert technicians when replacement is required. Tenaris will soon begin field trials of their system.
National Instruments myRIO Logic Analyzer
Team: Albert Block, Hai Doan, Jaehun Kang, Zuhair Parvez, Jan-Michael Yatar
Project Partner: National Instruments
Faculty Advisor: Ananth Dodabalapur
Conventional logic analyzers are often expensive and hard to use. This team created a logic analyzer based on the NI myRIO, a low cost embedded hardware device. The myRIO includes a fast FPGA and an ARM dual-core microcontroller. The team’s logic analyzer operates up to 100 megasamples per second, and can collect 30K samples. In addition to basic logic waveform monitoring, their device can decode several bus protocols, including SPI and I2C. National Instruments intends to make this project available on their website as a downloadable myRIO VI. This team exceeded their design goals with additional functionality, and their logic analyzer has already been put to use at UT’s Microelectronic Research Center.
Soil Sensing and Data Aggregation System
Team: Brendan Deems, Michael Hodges, Tom Li, Sam Moran, Gabrielle Palma, Diana Ruth
Project Partner: Texas Instruments
Faculty Advisor: Nan Sun
This team created a cost effective system for soil sensing and data aggregation. Requirements included low power, mesh network implementation, and user friendliness. A node network was devised, making use of a 6LowWPAN network. The nodes are scattered throughout the field, and monitor the local soil moisture content and temperature. Sensor nodes are powered with a coin cell, achieving 21 months of battery life when readings are taken every 10 seconds. The Contiki OS serves as the framework for the mesh network, forming a low power IOT protocol. Data is formatted and viewable as a web page, and stored in a database.
Mobile Multi-Payload Data Analytics Platform
Team: Eijae Cho, Brandon D’Souza, Vannara Houth, Cary Kuo, My Lai, Jacob Walsh
Project Partner: Boeing
Faculty Advisor: Edward Yu
This project created a small aerial platform capable of streaming data analytics. The team designed a plug-and-play sensor architecture to transmit data to a ground station for data processing. The project lays a foundation for Boeing’s future developments in mobile multi-payload data analytics platforms. The software is highly modular, allowing customization for a given mission. Such missions might include search and rescue, moving or erratic vehicle recognition, detection of specific pedestrians, crop surveillance, or geographic heat and pressure mapping. The hardware includes multiple sensors, two cameras, and other data processing hardware integrated into a portable enclosure.