by Michigan Tech IT
On Friday, August 13, 2021, at the conclusion of Summer Track B, Michigan Tech IT will be reverting remote access computer labs to in-person only labs in preparation for face-to-face instruction this fall.
Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems, has co-authored a paper recently published in The Journal of the Acoustical Society of America, Volume 50, Issue 1.
The paper is titled, “Recurrent networks for direction-of-arrival identification of an acoustic source in a shallow water channel using a vector sensor.” Havens’s co-authors are Steven Whitaker (EE graduate student), Andrew Barnard (ME-EM/GLRC), and George D, Anderson, US Naval Undersea Warfare Center (NUWC)-Newport.
The work described in the paper was funded by the United States Naval Undersea Warfare Center and Naval Engineering Education Consortium (NEEC) (Grant No. N00174-19-1-0004) and the Office of Naval Research (ONR) (Grant No. N00014-20-1-2793). This is Contribution No. 76 of the Great Lakes Research Center at Michigan Technological University.
Conventional direction-of-arrival (DOA) estimation algorithms for shallow water environments usually contain high amounts of error due to the presence of many acoustic reflective surfaces and scattering fields. Utilizing data from a single acoustic vector sensor, the magnitude and DOA of an acoustic signature can be estimated; as such, DOA algorithms are used to reduce the error in these estimations.
Three experiments were conducted using a moving boat as an acoustic target in a waterway in Houghton, Michigan. The shallow and narrow waterway is a complex and non-linear environment for DOA estimation. This paper compares minimizing DOA errors using conventional and machine learning algorithms. The conventional algorithm uses frequency-masking averaging, and the machine learning algorithms incorporate two recurrent neural network architectures, one shallow and one deep network.
Results show that the deep neural network models the shallow water environment better than the shallow neural network, and both networks are superior in performance to the frequency-masking average method.
The College of Computing is pleased to welcome new Department of Computer Science faculty members Dr. Dukka KC and Dr. Xinyu Lei.
Dr. KC, associate professor, comes to Michigan Tech from Wichita State University (WSU); he was also on the faculty at North Carolina A&T University. His expertise is in applied deep learning and bioinformatics.
Dr. Xinyu Lei, assistant professor, joins Michigan Tech directly following his PhD completion at Michigan State University. Dr. Lei’s speciality is cybersecurity.
Dennis Livesay, Dave House Dean of Computing, has known Dr. KC for more than 20 years. “Most recently, we worked together at WSU,” Livesay says. “Dukka built the WSU Data Science programs and a number of large interdisciplinary research teams, including a high-profile disaster resiliency effort that enables formation of research clusters to address some of today’s most pressing challenges.”
“Michigan Tech and the College’s research successes are drawing world-class faculty to our campus, and creating exciting new learning and research opportunities for our students,” Livesay notes.
Dean Livesay, who joined MTU earlier this year, is pursuing a steep growth trajectory for the College. Hiring additional faculty bolsters both student success and research capacity, two of Livesay’s four strategic areas of emphasis in the College’s growth initiative, “Forward Together.” Additional strategic initiatives are growing diversity and inclusion and expanding partnerships with industry.
College of Computing Dean Dennis Livesay will resume in-person open drop-in office hours every Friday from 2:00 to 3:00 p.m., beginning Friday, August 24, 2021, through the spring 2022 semester, while classes are in session.
All faculty, staff, and students who wish to chat with Dr. Livesay are invited to “drop in.” Appointments are not needed.
Dean Livesay’s office is in Rekhi Hall, Room 223. Email the dean at firstname.lastname@example.org.