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Jonathan Ligo
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The CCSDS 123.0-B-2 “Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression” Standard: A comprehensive review
M Hernández-Cabronero, AB Kiely, M Klimesh, I Blanes, J Ligo, E Magli, ...
IEEE Geoscience and Remote Sensing Magazine 9 (4), 102-119, 2021
282021
A Controlled Sensing Approach to Graph Classification
JG Ligo, GK Atia, VV Veeravalli
IEEE Transactions on Signal Processing 62 (24), 6468-6480, 2014
192014
The new CCSDS standard for low-complexity lossless and near-lossless multispectral and hyperspectral image compression
AB Kiely, M Klimesh, I Blanes, J Ligo, E Magli, N Aranki, M Burl, ...
International Workshop on On-Board Payload Data Compression, 2018
182018
MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression
M Kim, X Zhang, JG Ligo, F Farnoud, VV Veeravalli, O Milenkovic
BMC bioinformatics 17, 1-13, 2016
172016
Rate analysis for detection of sparse mixtures
JG Ligo, GV Moustakides, VV Veeravalli
2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016
62016
MetaPar: Metagenomic Sequence Assembly via Iterative Reclassification
M Kim, JG Ligo, A Emad, F Farnoud, O Milenkovic, VV Veeravalli
Global Conference on Signal and Information Processing (GlobalSIP), 2013 …, 2013
42013
MCUIUC—A new framework for metagenomic read compression
JG Ligo, M Kim, A Emad, O Milenkovic, VV Veeravalli
2013 IEEE Information Theory Workshop (ITW), 1-5, 2013
32013
Sparse Gaussian mixture detection: Low complexity, high performance tests via quantization
JG Ligo, GV Moustakides, VV Veeravalli
2017 IEEE International Symposium on Information Theory (ISIT), 1277-1281, 2017
22017
Detection of Sparse Mixtures: The Finite Alphabet Case
JG Ligo, GV Moustakides, VV Veeravalli
Asilomar Conference on Signals, Systems, and Computers 50, 5, 2016
22016
Detecting sparse mixtures: Rate of decay of error probability
JG Ligo, GV Moustakides, VV Veeravalli
arXiv preprint arXiv:1509.07566, 2015
22015
Appendix F Diver Detection in Real Time: Using Embedded Convolutional Neural Network-based Algorithms on Forward Looking Sonar Data
BP McNelly, F Mohammad, C Albert, JG Ligo
ADVANCES IN AUTONOMOUS UNDERWATER VEHICLE TECHNOLOGIES FOR ENHANCED HARBOR …, 2023
2023
Detection of sparse mixtures: fundamental limits and algorithms
JG Ligo
University of Illinois at Urbana-Champaign, 2017
2017
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