Ukash Nakarmi
タイトル引用先
BCS: Compressive sensing for binary sparse signals
U Nakarmi, N Rahnavard
MILCOM 2012-2012 IEEE Military Communications Conference, 1-5, 2012
312012
A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI
U Nakarmi, Y Wang, J Lyu, D Liang, L Ying
IEEE transactions on medical imaging 36 (11), 2297-2307, 2017
182017
Joint wideband spectrum sensing in frequency overlapping cognitive radio networks using distributed compressive sensing
U Nakarmi, N Rahnavard
2011-MILCOM 2011 Military Communications Conference, 1035-1040, 2011
112011
Dynamic magnetic resonance imaging using compressed sensing with self-learned nonlinear dictionary (NL-D)
U Nakarmi, Y Wang, J Lyu, L Ying
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 331-334, 2015
102015
Accelerating dynamic magnetic resonance imaging by nonlinear sparse coding
U Nakarmi, Y Zhou, J Lyu, K Slavakis, L Ying
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 510-513, 2016
62016
M-MRI: A manifold-based framework to highly accelerated dynamic magnetic resonance imaging
U Nakarmi, K Slavakis, J Lyu, L Ying
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 19-22, 2017
52017
KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction
J Lyu, U Nakarmi, D Liang, J Sheng, L Ying
IEEE transactions on medical imaging 38 (1), 312-321, 2018
22018
MLS: Joint manifold-learning and sparsity-aware framework for highly accelerated dynamic magnetic resonance imaging
U Nakarmi, K Slavakis, L Ying
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018
22018
Bi-Linear modeling of manifold-data geometry for Dynamic-MRI recovery
K Slavakis, GN Shetty, A Bose, U Nakarmi, L Ying
2017 IEEE 7th International Workshop on Computational Advances in Multi …, 2017
12017
Calibration-free Parallel Imaging Using Randomly Undersampled Multichannel Blind Deconvolution (MALBEC)
J Lyu, U Nakarmi, Y Zhou, C Zhang, L Ying
ISMRM Annual Meeting and Exhibition, 2017, 2017
12017
Highly accelerated cardiac cine parallel MRI using low-rank matrix completion and partial separability model
J Lyu, U Nakarmi, C Zhang, L Ying
Compressive Sensing V: From Diverse Modalities to Big Data Analytics 9857 …, 2016
12016
Highly accelerated cardiac cine parallel MRI using low-rank matrix completion and partial separability model
F Ahmad, J Lyu, U Nakarmi, C Zhang, L Ying
SPIE Proceedings 9857, 2016
12016
Compressive Spectrum Sensing for Cognitive Radio Networks
U Nakarmi
Oklahoma State University, 2011
12011
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery
GN Shetty, K Slavakis, A Bose, U Nakarmi, G Scutari, L Ying
IEEE transactions on medical imaging, 2019
2019
Kernel and Manifold Framework for Magnetic Resonance Imaging
U Nakarmi
State University of New York at Buffalo, 2018
2018
Effect of CNN Layers on MR Image Reconstruction with Deep Learning
H Li, C Zhang, U Nakarmi, P Huang, R Liu, D Liang, S Wei, Y Zhou, ...
ISMRM Workshop on machine learning 2018, 2018
2018
MR Knee Image Reconstruction using Very Deep Convolutional Neural Networks
H Li, C Zhang, U Nakarmi, P Huang, R Liu, D Liang, S Wei, B Shen, ...
ISMRM Workshop on machine learning 2018, 2018
2018
Accelerating T2 Mapping Using a Self-trained Kernel PCA Model
C Zhang, U Nakarmi, H Li, Y Zhou, D Liang, L Ying
ISMRM Annual Meeting and Exhibition, 2018, 2018
2018
MLS: Self-learned Joint Manifold Geometry and Sparsity aware Framework for Highly Accelerated Cardiac Cine Imaging
U Nakarmi, K Slavakis, H Li, C Zhang, P Huang, S Gaire, L Ying
ISMRM Annual Meeting and Exhibition, 2018
2018
Beyond Low-Rank and Sparsity: A Manifold driven Framework for Highly Accelerated Dynamic Magnetic Resonance Imaging
U Nakarmi, K Slavakis, J Lyu, C Zhang, L Ying
ISMRM Annual Meeting and Exhibition, 2017
2017
現在システムで処理を実行できません。しばらくしてからもう一度お試しください。
論文 1–20