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Going Below and Beyond, Off-the-Grid Velocity Estimation from 1-bit Radar Measurements

Abstract: In this paper we propose to bridge the gap between using extremely low resolution 1-bit measurements and estimating targets’ parameters, such as their velocities, that exist in a continuum, i.

Sparse Factorization-based Detection of Off-the-Grid Moving targets using FMCW radars

Abstract: In this paper, we investigate the application of continuous sparse signal reconstruction algorithms for the estimation of the ranges and speeds of multiple moving targets using an FMCW radar. Conventionally, to be reconstructed, continuous sparse signals are approximated by a discrete representation.

Compressive Learning of Generative Networks

Abstract: Generative networks implicitly approximate complex densities from their sampling with impressive accuracy. However, because of the enormous scale of modern datasets, this training process is often computationally expensive. We cast generative network training into the recent framework of compressive learning: we reduce the computational burden of large-scale datasets by first harshly compressing them in a single pass as a single sketch vector.

Factorization over interpolation: A fast continuous orthogonal matching pursuit

Abstract: We propose a fast greedy algorithm to compute sparse representations of signals from continuous dictionaries that are factorizable, i.e., with atoms that can be separated as a product of sub-atoms.

Keep the phase! Signal recovery in phase-only compressive sensing

Abstract: We demonstrate that a sparse signal can be estimated from the phase of complex random measurements, in a “phase-only compressive sensing” (PO-CS) scenario. With high probability and up to a global unknown amplitude, we can perfectly recover such a signal if the sensing matrix is a complex Gaussian random matrix and the number of measurements is large compared to the signal sparsity.

Morphological components analysis for circumstellar disks imaging

Abstract: Recent developments in astronomical observations enable direct imaging of circumstellar disks. Precise characterization of such extended structure is essential to our understanding of stellar systems. However, the faint intensity of the circumstellar disks compared to the brightness of the host star compels astronomers to use tailored observation strategies, in addition to state-of-the-art optical devices.

One Bit to Rule Them All: Binarizing the Reconstruction in 1-bit Compressive Sensing

Abstract: This work focuses on the reconstruction of sparse signals from their 1-bit measurements. The context is the one of 1-bit compressive sensing where the measurements amount to quantizing (dithered) random projections.

When compressive learning fails: blame the decoder or the sketch?

Abstract: In compressive learning, a mixture model (a set of centroids or a Gaussian mixture) is learned from a sketch vector, that serves as a highly compressed representation of the dataset.

An Analog-to-Information VGA Image Sensor Architecture for Support Vector Machine on Compressive Measurements

Abstract: This work presents a compact VGA (480 × 640) CMOS Image Sensor (CIS) architecture with dedicated end-of-column Compressive Sensing (CS) scheme allowing embedded object recognition. The architecture takes advantage of a low-footprint pseudo-random data mixing circuit and a first order incremental Sigma-Delta (ΣΔ) Analog to Digital Converter (ADC) to extract compressed features.