Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Machine Learning-Based Signal Degradation Models For Attenuated Underwater Optical Communication Oam Beams, Patrick L. Neary, Abbie T. Watnik, K. Peter Judd, James R. Lindle, Nicholas S. Flann
Machine Learning-Based Signal Degradation Models For Attenuated Underwater Optical Communication Oam Beams, Patrick L. Neary, Abbie T. Watnik, K. Peter Judd, James R. Lindle, Nicholas S. Flann
Computer Science Faculty and Staff Publications
Signal attenuation in underwater communications is a problem that degrades classification performance. Several novel CNN-based (SMART) models are developed to capture the physics of the attenuation process. One model is built and trained using automatic differentiation and another uses the radon cumulative distribution transform. These models are inserted in the classifier training pipeline. It is shown that including these attenuation models in classifier training significantly improves classification performance when the trained model is tested with environmentally attenuated images. The improved classification accuracy will be important in future OAM underwater optical communication applications.
A Robust Structured Tracker Using Local Deep Features, Mohammadreza Javanmardi, Amir Hossein Farzaneh, Xiaojun Qi
A Robust Structured Tracker Using Local Deep Features, Mohammadreza Javanmardi, Amir Hossein Farzaneh, Xiaojun Qi
Computer Science Faculty and Staff Publications
Deep features extracted from convolutional neural networks have been recently utilized in visual tracking to obtain a generic and semantic representation of target candidates. In this paper, we propose a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization …