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Taking the edge off quantization: projected back projection in dithered compressive sensing

Abstract: Quantized compressive sensing (QCS) deals with the problem of representing compressive signal measurements with finite precision representation, i.e., a mandatory process in any practical sensor design. To characterize the signal reconstruction quality in this framework, most of the existing theoretical analyses lie heavily on the quantization of sub-Gaussian random projections (e.
2018 IEEE
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A greedy blind calibration method for compressed sensing with unknown sensor gains

Abstract: The realisation of sensing modalities based on the principles of compressed sensing is often hindered by discrepancies between the mathematical model of its sensing operator, which is necessary during signal recovery, and its actual physical implementation, which can amply differ from the assumed model.
2017 Proceedings of IEEE International Symposium on Information Theory
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Compressive Hyperspectral Imaging using Coded Fourier Transform Interferometry

Abstract: Fourier Transform Interferometry (FTI) is a Hyperspectral (HS) imaging technique that is specially desirable in high spectral resolution applications, such as spectral microscopy in biology. The current resolution limit of FTI is actually due to the durability of biological elements when exposed to illuminating light.
2017 Proceedings of SPARS'17
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Fast Method to Fit a C1 Piecewise-Bézier Function to Manifold-Valued Data Points: How Suboptimal is the Curve Obtained on the Sphere S2?

Abstract: We propose an analysis of the quality of the fitting method proposed in Gousenbourger et al., 2017 (ESANN2017 proceedings). This method fits smooth paths to manifold-valued data points using C1 piecewise-Bézier functions.
2017 Geometric Science of Information. GSI 2017. Lecture Notes in Computer Science
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Piecewise-Bezier C1 smoothing on manifolds with application to wind field estimation

Abstract: We propose an algorithm for fitting C1 piecewise-Bezier curves to (possibly corrupted) data points on manifolds. The curve is chosen as a compromise between proximity to data points and regularity.
2017 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017)
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Rare Eclipses in Quantised Random Embeddings of Disjoint Convex Sets: a Matter of Consistency?

Abstract: We study the problem of verifying when two disjoint closed convex sets remain separable after the application of a quantised random embedding, as a means to ensure exact classification from the signatures produced by this non-linear dimensionality reduction.
2017 Proceedings of SPARS'17
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The Rare Eclipse Problem on Tiles: Quantised Embeddings of Disjoint Convex Sets

Abstract: Quantised random embeddings are an efficient dimensionality reduction technique which preserves the distances of low-complexity signals up to some controllable additive and multiplicative distortions. In this work, we instead focus on verifying when this technique preserves the separability of two disjoint closed convex sets, i.
2017 Proceedings of the 12th International Conference of Sampling Theory and Applications, Tallinn, 2017
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A Non-Convex Approach to Blind Calibration for Linear Random Sensing Models

Abstract: Performing blind calibration is highly important in modern sensing strategies, particularly when calibration aided by multiple, accurately designed training signals is infeasible or resource-consuming. We here address it as a naturally-formulated non-convex problem for a linear model with sub-Gaussian ran- dom sensing vectors in which both the sensor gains and the sig- nal are unknown.
2016 Proceedings of the third international Traveling Workshop on Interactions between Sparse models and Technology iTWIST'16
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A Non-Convex Approach to Blind Calibration from Linear Sub-Gaussian Random Measurements

Abstract: Blind calibration is a bilinear inverse problem arising in modern sensing strategies, whose solution becomes crucial when traditional calibration aided by multiple, accurately designed training signals is either infeasible or resource-consuming.
2016 Proceedings of the “37th WIC Symposium on Information Theory in the Benelux, 6th joint WIC/IEEE SP Symposium on Information Theory and Signal Processing in the Benelux”
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A non-convex blind calibration method for randomised sensing strategies

Abstract: The implementation of computational sensing strategies often faces calibration problems typically solved by means of multiple, accurately chosen training signals, an approach that can be resource-consuming and cumbersome. Conversely, blind calibration does not require any training, but corresponds to a bilinear inverse problem whose algorithmic solution is an open issue.
2016 Proceedings of International Workshop on “Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa)”, 2016
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