Ryan Williams (GLRC) is the PI on a project that has received a $398,843 research and development grant from the U.S. Army Construction Engineering Research Laboratory.
The project is titled “Robotic Platform Soil and Terrain Characterization for Close to Real Time GO/NOGO Maps.”
Thomas Oommen (GMES/GLRC/ICC-DataS) and Jeremy Bos (ECE/GLRC/ICC-DataS) are co-PIs on this potential two-year project.
Dr. Snehamoy Chatterjee (GMES/ICC-DataS) is the principal investigator (PI) on a project that has received a $288,343 research and development contract from the Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health (NIOSH).
The project is titled “Mine Health and Safety Big Data Analysis and Text Mining by Machine Learning Algorithms.”
Aref Majdara (ECE/ICC) is a co-PI on this potential two-year project.
Timothy Schulz (ECE/ICC-DataS) is the principal investigator (PI) on a project that has received a $30,000 research and development grant from the University of Arizona.
The project is titled “Multiscale Phase Retrieval.”
by Office of the Provost and Senior Vice President for Academic Affair
Snehamoy Chatterjee (GMES/ICC-DataS) has been appointed to a endowed faculty fellow position in the Department of Geological and Mining Engineering and Sciences (GMES).
Witte Family Endowed Faculty Fellow in Mining Engineering
Dr. Chatterjee, associate professor in GMES, has been appointed the new Witte Family Endowed Faculty Fellow in Mining Engineering, a position created to retain and attract highly qualified faculty who are at the top of their profession, inspire students to think beyond the classroom material, and integrate their research into the classroom.
Chatterjee was instrumental in developing GMES’s new interdisciplinary program in mining engineering and now teaches several key courses for this program. He continuously updates his courses to adopt new teaching and technological approaches and incorporates research in his instruction. He is always looking out for students’ best interests by seeking ways for them to participate in research and design projects in order to enhance their learning and professional development.
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.
Abstract
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.
Citation: The Journal of the Acoustical Society of America 150, 111 (2021); https://doi.org/10.1121/10.0005536Steven Whitaker1,b), Andrew Barnard2, George D. Anderson3, and Timothy C. Havens4