40 citations · 40 across the 4 of their papers we have counts for
4 papers
Estimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning
Toros Arikan, Luca M. Chackalackal, Fatima Ahsan +4
Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution…
MazeNet: An Accurate, Fast, and Scalable Deep Learning Solution for Steiner Minimum Trees
Gabriel Díaz Ramos, Toros Arikan, Richard G. Baraniuk
The Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem, which seeks the shortest interconnection of a given number of terminals in a rectilinear plane while avoidi…
Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach
Amir Weiss, Toros Arikan, Gregory W. Wornell
Direct localization (DLOC) methods, which use the observed data to localize a source at an unknown position in a one-step procedure, generally outperform their indirect two-step co…
A Semi-Blind Method for Localization of Underwater Acoustic Sources
Amir Weiss, Toros Arikan, Hari Vishnu +3
Underwater acoustic localization has traditionally been challenging due to the presence of unknown environmental structure and dynamic conditions. The problem is richer still when…