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compressed sensing

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Showing 751–800 of 992 papers

TitleStatusHype
Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints—0
Accelerated First-Order Optimization under Nonlinear Constraints—0
Accelerated MRI With Deep Linear Convolutional Transform Learning—0
Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations—0
Accelerated Sparse Recovery Under Structured Measurements—0
Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction—0
Accelerating CS in Parallel Imaging Reconstructions Using an Efficient and Effective Circulant Preconditioner—0
A Coded Compressed Sensing Scheme for Uncoordinated Multiple Access—0
A coherence parameter characterizing generative compressed sensing with Fourier measurements—0
A Compressed Sensing Approach for Distribution Matching—0
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals—0
A Context-Aware Readout System for Sparse Touch Sensing Array Using Ultra-low-power Always-on Event Detection—0
A Guaranteed Convergence Analysis for the Projected Fast Iterative Soft-Thresholding Algorithm in Parallel MRI—0
A convergence proof of the split Bregman method for regularized least-squares problems—0
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces—0
Active Sensing of Social Networks—0
Activity Detection for Massive Random Access using Covariance-based Matching Pursuit—0
Adaptive and Fast Combined Waveform-Beamforming Design for mmWave Automotive Joint Communication-Radar—0
Adaptive-CS-Net: FastMRI with Adaptive Intelligence—0
Adaptive Hierarchical Sensing for the Efficient Sampling of Sparse and Compressible Signals—0
Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing—0
Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstruction—0
A Data-driven Approach for Rapid Detection of Aeroelastic Modes from Flutter Flight Test Based on Limited Sensor Measurements—0
A Deep Error Correction Network for Compressed Sensing MRI—0
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction—0
A detail-enhanced sampling strategy in Hadamard single-pixel imaging—0
A divide-and-conquer approach for sparse recovery of high dimensional signals—0
A Divide-and-Conquer Approach to Compressed Sensing MRI—0
Advances in Electron Microscopy with Deep Learning—0
Adversarial Reconfigurable Intelligent Surface Against Physical Layer Key Generation—0
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion—0
A Fast Method for Lasso and Logistic Lasso—0
A fast nonconvex Compressed Sensing algorithm for highly low-sampled MR images reconstruction—0
Aggregated Network for Massive MIMO CSI Feedback—0
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization—0
A Homotopy Coordinate Descent Optimization Method for l_0-Norm Regularized Least Square Problem—0
A hybrid approach to seismic deblending: when physics meets self-supervision—0
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis—0
Algorithms for Learning Sparse Additive Models with Interactions in High Dimensions—0
Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee—0
A Low-rank Projected Proximal Gradient Method for Spectral Compressed Sensing—0
A Machine Learning Based Framework for the Smart Healthcare Monitoring—0
Isotropic multichannel total variation framework for joint reconstruction of multicontrast parallel MRI—0
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction—0
Analog Compressed Sensing for Sparse Frequency Shift Keying Modulation Schemes—0
Phase Transitions in Frequency Agile Radar Using Compressed Sensing—0
Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings—0
Towards Designing Optimal Sensing Matrices for Generalized Linear Inverse Problems—0
Analysis of Sensing Spectral for Signal Recovery under a Generalized Linear Model—0
Analysis of The Ratio of _1 and _2 Norms in Compressed Sensing—0
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