Structure-Aware Sparse Bayesian Learning-Based Channel Estimation for Intelligent Reflecting Surface-Aided MIMO

This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve...

Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction

Objective. Machine Learning methods can learn how to reconstruct magnetic resonance images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity,...

Learning a preconditioner to accelerate compressed sensing reconstructions in MRI

Purpose: To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions. Methods: A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images....

Towards a general framework for fast and feasible k-space trajectories for MRI based on projection methods

The design of feasible trajectories to traverse the k-space for sampling in magnetic resonance imaging (MRI) is important while considering ways to reduce the scan time. Over the recent years, non-Cartesian trajectories have been observed to result in benign artifacts and being less sensitive to motion. In this paper, we propose a generalized...

Coding Mask Design for Single Sensor Ultrasound Imaging

We study the design of a coding mask for pulse-echo ultrasound imaging. We are interested in the scenario of a single receiving transducer with an aberrating layer, or ‘mask,’ in front of the transducer's receive surface, with a separate co-located transmit transducer. The mask encodes spatial measurements into a single output signal, containing...

Active Semi-Supervised Learning for Diffusions on Graphs

Diffusion-based semi-supervised learning on graphs consists of diffusing labeled information of a few nodes to infer the labels on the remaining ones. The performance of these methods heavily relies on the initial labeled set, which is either generated randomly or using heuristics. The first sometimes leads to unsatisfactory results because...

K-Space Trajectory Design for Reduced MRI Scan Time

The development of compressed sensing (CS) techniques for magnetic resonance imaging (MRI) is enabling a speedup of MRI scanning. To increase the incoherence in the sampling, a random selection of points on the k-space is deployed and a continuous trajectory is obtained by solving a traveling salesman problem (TSP) through these points. A...

Accelerating compressed sensing in parallel imaging reconstructions using an efficient circulant preconditioner for cartesian trajectories

Purpose: Design of a preconditioner for fast and efficient parallel imaging (PI) and compressed sensing (CS) reconstructions for Cartesian trajectories. Theory: PI and CS reconstructions become time consuming when the problem size or the number of coils is large, due to the large linear system of equations that has to be solved in l<sub>1<...

Heat source layout optimization for two-dimensional heat conduction using iterative reweighted L1-norm convex minimization

Optimization of heat source distribution in two dimensional heat conduction for electronic cooling problem is considered. Convex optimization is applied to this problem for the first time by reformulating the objective function and the non-convex constraints. Mathematical analysis is performed to describe the heat source equation and the...

Bound on the estimation grid size for sparse reconstruction in direction of arrival estimation

A bound for sparse reconstruction involving both the signal-to-noise ratio (SNR) and the estimation grid size is presented. The bound is illustrated for the case of a uniform linear array (ULA). By reducing the number of possible sparse vectors present in the feasible set of a constrained ℓ1-norm minimization problem, ambiguities in the...

Compressive Sampling for Wireless Communications

Wireless communications is undergoing massive development in all forms of its manifestations. In the field of short-range communications, technologies like ultra- wideband (UWB) systems are promising very high data rates, fine timing resolution and coexistence with other physical layer standards. Along with these benefits, the promise of low...

Compressive Sensing and Fast Simulations: Applications to Radar Detection

In most modern high-resolution multi-channel radar systems one of the major problems to deal with is the huge amount of data to be acquired, processed and/or stored. But why do we need all these data? According to the well known Nyquist-Shannon sampling theorem, real signals have to be sampled at at least twice the signal bandwidth to prevent...