Soumik Sarkar
Soumik Sarkar
Associate Professor, Iowa State University
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Citata da
Citata da
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
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
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
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
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), 23, 2017
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
Collaborative deep learning in fixed topology networks
Z Jiang, A Balu, C Hegde, S Sarkar
Advances in Neural Information Processing Systems, 5904-5914, 2017
Fault detection and isolation in aircraft gas turbine engines. Part 2: validation on a simulation test bed
S Sarkar, M Yasar, S Gupta, A Ray, K Mukherjee
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of …, 2008
Ntire 2020 challenge on spectral reconstruction from an rgb image
B Arad, R Timofte, O Ben-Shahar, YT Lin, GD Finlayson
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
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
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
Anomaly detection in nuclear power plants via symbolic dynamic filtering
X Jin, Y Guo, S Sarkar, A Ray, RM Edwards
IEEE Transactions on Nuclear Science 58 (1), 277-288, 2010
Sensor fusion for fault detection and classification in distributed physical processes
S Sarkar, S Sarkar, N Virani, A Ray, M Yasar
Frontiers in Robotics and AI 1, 16, 2014
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 (023), 1-14, 2016
Generalization of Hilbert transform for symbolic analysis of noisy signals
S Sarkar, K Mukherjee, A Ray
Signal Processing 89 (6), 1245-1251, 2009
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
Multi-sensor information fusion for fault detection in aircraft gas turbine engines
S Sarkar, S Sarkar, K Mukherjee, A Ray, A Srivastav
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of …, 2013
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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