Soumik Sarkar
Soumik Sarkar
Associate Professor, Iowa State University
Verified email at - Homepage
Cited by
Cited by
Machine learning for high-throughput stress phenotyping in plants
A Singh, B Ganapathysubramanian, AK Singh, S Sarkar
Trends in plant science 21 (2), 110-124, 2016
LLNet: A deep autoencoder approach to natural low-light image enhancement
KG Lore, A Akintayo, S Sarkar
Pattern Recognition 61, 650-662, 2017
An explainable deep machine vision framework for plant stress phenotyping
S Ghosal, D Blystone, AK Singh, B Ganapathysubramanian, A Singh, ...
Proceedings of the National Academy of Sciences 115 (18), 4613-4618, 2018
Deep learning for plant stress phenotyping: trends and future perspectives
AK Singh, B Ganapathysubramanian, S Sarkar, A Singh
Trends in plant science 23 (10), 883-898, 2018
Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns
C Rao, A Ray, S Sarkar, M Yasar
Signal, Image and Video Processing 3 (2), 101-114, 2009
A real-time phenotyping framework using machine learning for plant stress severity rating in soybean
HS Naik, J Zhang, A Lofquist, T Assefa, S Sarkar, D Ackerman, A Singh, ...
Plant methods 13 (1), 1-12, 2017
Plant disease identification using explainable 3D deep learning on hyperspectral images
K Nagasubramanian, S Jones, AK Singh, S Sarkar, A Singh, ...
Plant methods 15 (1), 1-10, 2019
Collaborative deep learning in fixed topology networks
Z Jiang, A Balu, C Hegde, S Sarkar
arXiv preprint arXiv:1706.07880, 2017
Data-driven fault detection in aircraft engines with noisy sensor measurements
S Sarkar, X Jin, A Ray
Journal of Engineering for Gas Turbines and Power 133 (8), 2011
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems
T Han, C Liu, L Wu, S Sarkar, D Jiang
Mechanical Systems and Signal Processing 117, 170-187, 2019
A weakly supervised deep learning framework for sorghum head detection and counting
S Ghosal, B Zheng, SC Chapman, AB Potgieter, DR Jordan, X Wang, ...
Plant Phenomics 2019, 2019
Computer vision and machine learning for robust phenotyping in genome-wide studies
J Zhang, HS Naik, T Assefa, S Sarkar, RVC Reddy, A Singh, ...
Scientific Reports 7 (1), 1-11, 2017
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems
K Nagasubramanian, S Jones, S Sarkar, AK Singh, A Singh, ...
Plant methods 14 (1), 1-13, 2018
Fault detection and isolation in aircraft gas turbine engines. Part 1: Underlying concept
S Gupta, A Ray, S Sarkar, M Yasar
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of …, 2008
Traffic congestion detection from camera images using deep convolution neural networks
P Chakraborty, YO Adu-Gyamfi, S Poddar, V Ahsani, A Sharma, S Sarkar
Transportation Research Record 2672 (45), 222-231, 2018
Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network
C Liu, A Akintayo, Z Jiang, GP Henze, S Sarkar
Applied Energy 211, 1106-1122, 2018
An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed cps
C Liu, S Ghosal, Z Jiang, S Sarkar
2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS …, 2016
Prognostics of combustion instabilities from hi-speed flame video using a deep convolutional selective autoencoder
A Akintayo, KG Lore, S Sarkar, S Sarkar
International Journal of Prognostics and Health Management 7 (4), 2016
Learning localized features in 3D CAD models for manufacturability analysis of drilled holes
S Ghadai, A Balu, S Sarkar, A Krishnamurthy
Computer Aided Geometric Design 62, 263-275, 2018
Early detection of combustion instability from hi-speed flame images via deep learning and symbolic time series analysis
S Sarkar, KG Lore, S Sarkar, V Ramanan, SR Chakravarthy, S Phoha, ...
Annual Conference of the PHM Society 7 (1), 2015
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