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A Novel Multiplicative Phase Dithering Scheme for 1-bit Compressive Radar

Abstract: In this paper, we tackle the issue of implementing a dithering procedure for the 1-bit quantization of radar signals that is able to generate high-quality estimates while remaining a low-complexity and cost-efficient solution.

MROP: Modulated Rank-One Projections for compressive radio interferometric imaging

Abstract: The emerging generation of radio-interferometric (RI) arrays are set to form images of the sky with a new regime of sensitivity and resolution. This implies a significant increase in visibility data volumes, scaling as \(\mathcal{O}(Q^{2}B)\) for \(Q\) antennas and \(B\) short-time integration intervals (or batches), calling for efficient data dimensionality reduction techniques.

An alternating minimization algorithm with trajectory for direct exoplanet detection – The AMAT algorithm

Abstract: Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Standard PSF subtraction methods use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speckles, and rely on signal-to-noise ratio maps to detect the planet.

Compressive radio-interferometric sensing with random beamforming as rank-one signal covariance projections

Abstract: Radio-interferometry (RI) observes the sky at unprecedented angular resolutions, enabling the study of several far-away galactic objects such as galaxies and black holes. In RI, an array of antennas probes cosmic signals coming from the observed region of the sky.

Grid Hopping: Accelerating Direct Estimation Algorithms for Multistatic FMCW Radar

Abstract: In radars, sonars, or for sound source localization, sensor networks enable the estimation of parameters that cannot be unambiguously recovered by a single sensor. The estimation algorithms designed for this context are commonly divided into two categories: the two-step methods, separately estimating intermediate parameters in each sensor before combining them; and the single-step methods jointly processing all the received signals.

ADMM-inspired image reconstruction for terahertz off-axis digital holography

Abstract: Image reconstruction in off-axis terahertz digital holography is complicated due to the harsh recording conditions and the non-convexity form of the problem. In this paper, we propose an inverse problem-based reconstruction technique that jointly reconstructs the object field and the amplitude of the reference field.

Learning to Reconstruct Signals From Binary Measurements

Abstract: Recent advances in unsupervised learning have highlighted the possibility of learning to reconstruct signals from noisy and incomplete linear measurements alone. These methods play a key role in medical and scientific imaging and sensing, where ground truth data is often scarce or difficult to obtain.

Interferometric lensless imaging: rank-one projections of image frequencies with speckle illuminations

Abstract: Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of an interferometric matrix–a matrix encoding the spectral content of the sample image.

The Separation Capacity of Random Neural Networks

Abstract: Neural networks with random weights appear in a variety of machine learning applications, most prominently as the initialization of many deep learning algorithms and as a computationally cheap alternative to fully learned neural networks.

Asymmetric compressive learning guarantees with applications to quantized sketches

Abstract: The compressive learning framework reduces the computational cost of training on large-scale datasets. In a sketching phase, the data is first compressed to a lightweight sketch vector, obtained by mapping the data samples through a well-chosen feature map, and averaging those contributions.