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Patent

Procédé de Cartographie d'un Réseau à Fil d'Attente IP, Basé sur l'Analyse Passive en un Point d'un Réseau

Author: Grislain Paul

n° FR2504199, April 18, 2025.

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Networking / Aeronautical communication systems and Space communication systems

Journal Paper

Robust Semiparametric Efficient Estimator for Time Delay and Doppler Estimation

Authors: Ortega Espluga Lorenzo and Fortunati Stefano

IEEE Signal Processing Letters, vol. 32, pp. 1855-1859, 2025.

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This letter explores time-delay and Doppler estimation in the presence of unknown heavy-tailed disturbance. Conventional methods for achieving optimal mean squared error performance rely on the maximum likelihood estimator (MLE), which is consistent and asymptotically efficient under the unrealistic assumption of a perfect a-priori knowledge of the noise distribution. However, in practical situations, the noise distribution is often unknown, and classical parametric estimation procedures are no longer able to guarantee the statistical efficiency. In this work, by relying on the semiparametric theory, we present an original rank-based and distribution-free $R$-estimator which have the remarkable property to be parametrically efficient, i.e. it attains the “classical” Cramér-Rao Bound, irrespective of the unknown noise distribution, provided that the latter belongs to the family of Complex Elliptically Simmetric (CES) distributions.

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Signal and image processing / Localization and navigation

Conference Paper

Estimating Instrument Spectral Response Functions Using Sparse Representations and Quadratic Envelopes

Authors: El Haouari Jihanne, Carlsson Marcus, Tourneret Jean-Yves, Wendt Herwig, Gaucel Jean-Michel and Pittet Christelle

In Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hyderabad, India, April 6-11, 2025.

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The estimation of high resolution spectrometer Instrument Spectral Response Functions (ISRFs) is crucial because an imperfect knowledge of these functions can induce errors in the measurements. The state-of-the-art for this problem currently relies on the use of parametric models, which frequently lack flexibility to accurately model real-world ISRFs. To address this limitation, this paper proposes and investigates the use of sparse representations for modeling and estimating ISRFs, where the ISRFs are decomposed in a fixed dictionary of atoms. To estimate the sparse coefficient vector, a novel sparsity inducing regularization of the problem based on quadratic envelopes is studied and compared to the classical LASSO estimator and to a greedy method based on the Orthogonal Matching Pursuit (OMP) algorithm. Results for simulated ISRFs from the MicroCarb mission indicate that the proposed spectral representations yield excellent ISRF estimates, and that the use of quadratic envelopes can yield significantly better precision than competing methods.

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Signal and image processing / Earth observation

Talk

Hybrid Approach for Predicting Fuel Cell Future Performance under Dynamic Load Profile and Variable Operating Conditions

Author: Pene Daouda

Seminar of TeSA, Toulouse, April, 2025.

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In this talk, we present a hybrid approach to predicting the future performance of a fuel cell. The idea is to combine the physical laws governing system dynamics with machine learning algorithms. The data used come from an aging campaign carried out under a dynamic load profile and variable operating conditions. The proposed approach is based on a simplified formulation of the well-known quasi-static model. Evolution laws are proposed for certain parameters related to activation and diffusion losses. The ohmic resistance, which determines the ohmic losses, is modeled by random forests. To estimate the evolution of other parameters over time, these methods are used in conjunction with an extended Kalman filter (EKF). To predict future performance, a long-term memory is used to learn the evolution of these parameters, estimated by the EKF.

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Signal and image processing / Other

Conference Paper

New Insights into Lower Bound for Lie Groups and their Applications

Authors: El Bouch Sara, Labsir Samy, Renaux Alexandre, Vilà-Valls Jordi and Chaumette Eric

In Proc. 59th annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, March 19-21, 2025.

This article presents a comprehensive review of recent advances in intrinsic Cramér-Rao bounds (ICRBs) for Lie groups (LGs), which play a pivotal role in addressing estimation problems involving parameters and/or observations constrained by geometric structures. The review encompasses both deterministic and Bayesian frameworks, with a detailed examination of their formulation, derivation, and theoretical foundations. Furthermore, we underscore significant theoretical contributions and extend the discussion to practical estimation challenges, offering insights into their applicability. Emphasis is placed on methodologies for validating these bounds, providing a robust framework for performance evaluation across a variety of estimation problems in engineering and applied sciences.

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Signal and image processing / Localization and navigation

Talk

Posterior Sampling with Diffusion Models: Methodological Insights and Applications to ECG Reconstruction

Author: Bedin Lisa

Seminar of TeSA, Toulouse, March, 2025.

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Diffusion models have emerged as a powerful tool in generative modeling, demonstrating remarkable capabilities in synthesizing high-fidelity data across various domains. These models transform an initial simple distribution into a more complex one through a denoising process, making them particularly effective for generating detailed and realistic data. In this seminar, we will explore how to integrate diffusion models within a mathematical framework to solve inverse problems, that is, to reconstruct data from partial observations. By leveraging diffusion models as prior knowledge of the data, we introduce a new approach that enables precise generation of conditional data under various noise and artifact conditions. We validate our approach through extensive experiments using various public image datasets, demonstrating its versatility and effectiveness. Furthermore, we demonstrate the practical implications of our method by applying it to reconstruct electrocardiograms (ECGs), where it enhances the quality and reliability of ECG signals, paving the way for broader applications in medical diagnostics and real-time health monitoring.

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Signal and image processing / Other

Radars météorologiques - Vue d’ensemble et perspectives

Author: Lubeigt Corentin

Seminar of TeSA, Toulouse, February 24, 2025.

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Cette présentation a pour but d’introduire le radar météorologique et de présenter son fonctionnement global.

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Signal and image processing / Earth observation

Cooperative Positioning using Pseudorange Measurements: Solvability and Conservative Algorithms

Authors: Cros Colin, Amblard Pierre-Olivier, Prieur Christophe and Da Rocha Jean-François

Seminar of TeSA, Toulouse, January 30, 2025.

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In this talk, Colin Cros will focus on the problem of cooperative positioning in the context of GNSS (Global Navigation Satellite Systems). The presentation is divided into two parts. The first examines the solvability of the problem from a theoretical point of view, where the specificity comes from the type of measurements made: pseudo-distances. The approach adopted is based on a study of the measurement graph and the theory of rigidity. The second part deals with practical aspects, presenting how to integrate a cooperative measurement into a Kalman-type navigation filter. The difficulty arises from the lack of knowledge of the correlations between the agents' errors, which means that so-called conservative filters have to be used. This presentation is based on my doctoral thesis, which is available at: https://theses.fr/2024GRALT032

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Signal and image processing / Localization and navigation

Conference Paper

Investigation on New Fuzzing Techniques to Address Navigation System Testing

Authors: Haag Nina, Ouzeau Christophe, Fejri Lotfi, Bartolone Patrick, Blais Antoine and Prun Daniel

In Proc. IEEE International Technical Meeting (ITM), Long Beach, California-USA, January 27-30, 2025.

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Fuzz testing is a method used in software testing that involves inputting random or unexpected data into a system to identify vulnerabilities. Unlike deterministic methods, which test performance under controlled and predictable conditions, fuzz testing introduces variability to uncover hidden issues. This variability simulates real-world scenarios, uncovering weaknesses that might otherwise remain unnoticed. For instance, fuzz testing can effectively reveal how GNSS receivers respond to rapid signal fluctuations and other anomalous behaviors, situations often overlooked by standard tests. Unlike traditional methods that rely on predefined inputs, Collins Aerospace works on a new fuzz testing framework for GNSS, which employs advanced techniques such as automated input generation and real-time response monitoring. This approach not only facilitates a comprehensive assessment of receiver resilience but also allows for the dynamic adaptation of test scenarios in real-time, ensuring that a wide range of operational conditions is explored. The navigation equipment minimum testing procedures must be defined and need scenarios definitions as well as test steps and pass/fail criteria to provide minimum guidance to manufacturers for future equipment certification. The limitations of current testing methods further highlight the necessity of adopting fuzz testing. These methods predominantly rely on deterministic approaches, which do not effectively simulate the unpredictable nature of real-world signal degradation or complex interference scenarios posed by advanced spoofing techniques. As technology advances, the techniques utilized by malevolent actors likewise evolve, emphasizing the necessity for adaptive testing methodologies capable of responding to these changes. By introducing randomness and variability, fuzz testing plays a critical role in bolstering the reliability and operational integrity of GNSS systems by rigorously assessing their ability to withstand both known and unknown threats. The anticipated results from this fuzz testing framework are expected to identify vulnerabilities and enhance the resilience of GNSS receivers, suggesting that fuzz testing can play a transformative role in GNSS validation.

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Digital communications / Localization and navigation

Journal Paper

Exponential Families, Rényi Divergence and the Almost Sure Cauchy Functional Equation

Authors: Letac Gérard and Piccioni Mauro

Journal of Theoretical Probability, January, 2025.

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If P1, . . . , Pn and Q1, . . . , Qn are probability measures on Rd and P1 ∗ · · · ∗ Pn and Q1 ∗ · · · ∗ Qn are their respective convolutions, the Rényi divergence Dλ of order λ ∈ (0, 1] satisfies Dλ(P1 ∗ · · · ∗ Pn||Q1 ∗ · · · ∗ Qn) ≤ ni=1 Dλ(Pi ||Qi ). When Pi belongs to the natural exponential family generated by Qi , with the same natural parameter θ for any i = 1, . . . , n, the equality sign holds. The present note tackles the inverse problem, namely “does the equality Dλ(P1 ∗ · · · ∗ Pn||Q1 ∗ · · · ∗ Qn) = ni=1 Dλ(Pi ||Qi ) imply that Pi belongs to the natural exponential family generated by Qi for every i = 1, . . . , n?” The answer is not always positive and depends on the set of solutions of a generalization of the celebrated Cauchy functional equation. We discuss in particular the case P1 = · · · = Pn = P and Q1 = · · · = Qn = Q, with n = 2 and n = ∞, the latter meaning that the equality holds for all n. Our analysis is mainly devoted to P and Q concentrated on non-negative integers, and P and Q with densities with respect to the Lebesgue measure. The results cover the Kullback– Leibler divergence (KL), this being the Rényi divergence for λ = 1. We also show that the only f -divergences such that Df (P∗2||Q∗2) = 2Df (P||Q), for P and Q in the same exponential family, are mixtures of KL divergence and its dual.

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Signal and image processing / Other

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