Block-sparse signal recovery via general total variation regularized sparse Bayesian learning
One of the main challenges in block-sparse signal recovery, as encountered in, eg, multi-
antenna mmWave channel models, is block-patterned estimation without knowledge of …
antenna mmWave channel models, is block-patterned estimation without knowledge of …
Sparsity is better with stability: Combining accuracy and stability for model selection in brain decoding
L Baldassarre, M Pontil… - Frontiers in neuroscience, 2017 - frontiersin.org
Structured sparse methods have received significant attention in neuroimaging. These
methods allow the incorporation of domain knowledge through additional spatial and …
methods allow the incorporation of domain knowledge through additional spatial and …
SPITFIR (e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos
Modern fluorescent microscopy imaging is still limited by the optical aberrations and the
photon budget available in the specimen. A direct consequence is the necessity to develop …
photon budget available in the specimen. A direct consequence is the necessity to develop …
Euler elastica regularized logistic regression for whole-brain decoding of fMRI data
C Zhang, L Yao, S Song, X Wen… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Objective: Multivariate pattern analysis methods have been widely applied to functional
magnetic resonance imaging (fMRI) data to decode brain states. Due to the “high features …
magnetic resonance imaging (fMRI) data to decode brain states. Due to the “high features …
Towards Model-based Synergistic Learning for Robust Next-Generation MIMO Systems
A Sant - 2024 - search.proquest.com
As the demand for high-speed, reliable wireless communication among interconnected
devices rises, the need for robust next-generation wireless MIMO systems becomes crucial …
devices rises, the need for robust next-generation wireless MIMO systems becomes crucial …
SPITFIR (e): A supermaneuverable algorithm for restoring 2D-3D fluorescence images and videos, and background subtraction
While fluorescent microscopy imaging has become the spearhead of modern biology as it is
able to generate long-term videos depicting 4D nanoscale cell behaviors, it is still limited by …
able to generate long-term videos depicting 4D nanoscale cell behaviors, it is still limited by …
Local region sparse learning for image-on-scalar regression
Identification of regions of interest (ROI) associated with certain disease has a great impact
on public health. Imposing sparsity of pixel values and extracting active regions …
on public health. Imposing sparsity of pixel values and extracting active regions …
Sturm: Sparse tubal-regularized multilinear regression for fmri
While functional magnetic resonance imaging (fMRI) is important for healthcare/
neuroscience applications, it is challenging to classify or interpret due to its multi …
neuroscience applications, it is challenging to classify or interpret due to its multi …
Significant anatomy detection through sparse classification: a comparative study
We present a comparative study for discriminative anatomy detection in high dimensional
neuroimaging data. While most studies solve this problem using mass univariate …
neuroimaging data. While most studies solve this problem using mass univariate …
Stable anatomy detection in multimodal imaging through sparse group regularization: a comparative study of iron accumulation in the aging brain
M Pietrosanu, L Zhang, P Seres, A Elkady… - Frontiers in human …, 2021 - frontiersin.org
Multimodal neuroimaging provides a rich source of data for identifying brain regions
associated with disease progression and aging. However, present studies still typically …
associated with disease progression and aging. However, present studies still typically …