2

Subsampling strategies for compressive Hadamard single-pixel imaging

Abstract: Single-pixel imaging (SPI) is a sensing technique that uses a single-pixel detector to acquire spatially resolved measurements of a scene. Compressive SPI reduces acquisition time by measuring only a fraction of the Hadamard patterns, with the sampling strategy strongly impacting the reconstruction quality.

Random features for Grassmannian kernel approximation with bounded rank-one projections

Abstract: We propose a family of random feature maps for scalable kernel machines defined over low-dimensional subspaces in high dimensions, i.e. over the Grassmannian manifold. This is typically useful in a machine learning context when data classes or clusters are well represented by the span of a few data points.

Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging

Abstract: Learning based methods are now ubiquitous for solving inverse problems, but their deployment in real-world applications is often hindered by the lack of ground truth references for training. Recent self-supervised learning strategies offer a promising alternative, avoiding the need for ground truth.

Initial radiometric calibration of the HRIEUV telescope of the EUI instrument on board Solar Orbiter

Abstract: The \(\textrm{HRI}_\textrm{EUV}\) telescope was calibrated on ground at the Physikalisch-Technische Bundesanstalt (PTB), Germany’s national metrology institute, using the Metrology Light Source (MLS) synchrotron in April 2017 during the calibration campaign of the Extreme Ultraviolet Imager (EUI) instrument onboard the Solar Orbiter mission.

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.

DiffeRT2d: A Differentiable Ray Tracing Python Framework for Radio Propagation

Abstract: Ray Tracing (RT) is arguably one of the most prevalent methodologies in the field of radio propagation modeling. However, access to RT software is often constrained by its closed-source nature, licensing costs, or the requirement of high-performance computing resources.