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Found 17 papers, 4 papers with code
An Efficient Approximate Method for Online Convolutional Dictionary Learning
Most existing convolutional dictionary learning (CDL) algorithms are based on batch learning, where the dictionary filters and the convolutional sparse representations are optimized in an alternating manner using a training dataset.
Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques
With the advances in multi-antenna technologies largely for radar and communications, there has been a great interest on beamformer design mostly relying on convex/nonconvex optimization.
Robust Adaptive Beamforming via Worst-Case SINR Maximization with Nonconvex Uncertainty Sets
Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint.
Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion
Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports.
Graph Neural Network Sensitivity Under Probabilistic Error Model
In this paper, we study the effect of a probabilistic graph error model on the performance of the GCNs.
Robust Adaptive Beamforming Maximizing the Worst-Case SINR over Distributional Uncertainty Sets for Random INC Matrix and Signal Steering Vector
The distributional uncertainty set for the steering vector consists of the constraints on the known first- and second-order moments.
DOA Estimation in Nonuniform Sensor Noise
The problem of direction-of-arrival (DOA) estimation in the presence of nonuniform sensor noise is considered and a novel algorithm is developed.
Efficient ADMM-based Algorithms for Convolutional Sparse Coding
Convolutional sparse coding improves on the standard sparse approximation by incorporating a global shift-invariant model.
Two-Dimensional DOA Estimation for L-shaped Nested Array via Tensor Modeling
To develop such approach, a higher-order tensor is constructed, whose factor matrices contain the sources azimuth and elevation information.
Transmit Beamspace DDMA Based Automotive MIMO Radar
The time division multiple access (TDMA) technique has been applied in automotive multiple-input multiple-output (MIMO) radar.
Enhanced Robust Adaptive Beamforming Designs for General-Rank Signal Model via an Induced Norm of Matrix Errors
In addition, a generalized RAB problem of maximizing the difference between an $l_p$-norm function and an $l_q$-norm function subject to the convex quadratic constraint is studied, and the actual array output SINR is further enhanced by properly selecting $p$ and $q$.
Coupled Feature Learning for Multimodal Medical Image Fusion
The resulting performance and execution times show the competitiveness of the proposed method in comparison with state-of-the-art medical image fusion methods.
DOA Estimation for Transmit Beamspace MIMO Radar via Tensor Decomposition with Vandermonde Factor Matrix
A computationally efficient tensor decomposition method is proposed to decompose the Vandermonde factor matrices.
Information Theory Signal Processing Information Theory
Joint DOD and DOA Estimation in Slow-Time MIMO Radar via PARAFAC Decomposition
We develop a new tensor model for slow-time multiple-input multiple output (MIMO) radar and apply it for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation.
Multi-Focus Image Fusion Using Sparse Representation and Coupled Dictionary Learning
In addition, to improve the fusion performance, we employ a coupled dictionary learning approach that enforces pairwise correlation between atoms of dictionaries learned to represent the focused and blurred feature spaces.
Image Fusion With Cosparse Analysis Operator
In our approach, we formulate the multi-focus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multi-focus images.
