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Talk

Tensor Sparse Representation Learning for Single-Snapshot Compressive Spectral Video Reconstruction

Author: Leon Lopez Kareth

Seminar of TéSA, Toulouse, May 12, 2022.

An overview of Dark Matter theories and Zoom on the WIMP scenario

Author: Mimouni Kin

Seminar of TéSA, Toulouse, May 12, 2022.

Journal Paper

Clean-to-Composite Bound Ratio: A Multipath Criterion for GNSS Signal Design and Analysis

Authors: Lubeigt Corentin, Ortega Espluga Lorenzo, Vilà-Valls Jordi, Lestarquit Laurent and Chaumette Eric

IEEE Transactions on Aerospace and Electronic Systems, May, 2022.

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Multipath is one of the most challenging propagation conditions affecting Global Navigation Satellite Systems (GNSS), which must be mitigated in order to obtain reliable navigation information. In any case, the random multipath nature makes it difficult to anticipate and overcome. Therefore, for legacy GNSS signal performance assessment, modern GNSS signal design and future GNSS-based applications, robustness to multipath is a fundamental criterion. Different multipath metrics exist in the literature, such as the multipath error envelope, usually leading to analyses only valid for a dedicated receiver/signal combination and only providing information on the bias. This paper presents a general criterion to characterize the multipath robustness of a generic band-limited signal (e.g., GNSS or radar), considering the joint delay-Doppler and phase estimation. This criterion is based on the Cramr-Rao bound, which makes it universal, regardless the receiver architecture and the signal under analysis, and provides information on the actual achievable performance in terms of estimated time-delay (i.e., pseudo-range) and Doppler frequency variances.

Signal and image processing and Networking / Localization and navigation

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A novel image representation of GNSS correlation for deep learning multipath detection

Authors: Blais Antoine, Couellan Nicolas and Evgenii Munin

Array, online, April, 2022.

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This paper proposes a novel framework for multipath prediction in Global Navigation Satellite System (GNSS) signals. The method extends from dataset generation to deep learning inference through Convolutional Neural Network (CNN). The process starts at the output of the correlation stage of the GNSS receiver. Correlations of the received signal with a local replica over a (Doppler shift, propagation delay)-grid are mapped into grey scale 2D images. They depict the received information possibly contaminated by multipath propagation. The images feed a CNN for automatic feature construction and multipath pattern detection. The issue of unavailability of a large amount of supervised data required for CNN training has been overcome by the development of a synthetic data generator. It implements a well-established and documented theoretical model. A comparison of synthetic data with real samples is proposed. The complete framework is tested for various signal characteristics and algorithm parameters. The prediction accuracy does not fall below 93% for C/N0 ratio as low as 36 dBHz, corresponding to poor receiving conditions. In addition, the model turns out to be robust to the reduction of image resolution. Its performance is also measured and compared with an alternative Support Vector Machines (SVM) technique. The results show the undeniable superiority of the proposed CNN algorithm over the SVM benchmark.

Signal and image processing / Localization and navigation

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Talk

Equivariant Imaging: learning to solve inverse problems without ground truth

Author: Tachella Julian

Seminar of TeSA, Toulouse, March 15, 2022.

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In recent years, deep neural networks have obtained state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. Networks are generally trained with pairs of signals and associated measurements. However, in various imaging problems, we usually only have access to compressed measurements of the underlying signals, hindering this learning-based approach. Learning from measurement data only is impossible in general, as the compressed observations do not contain information outside the range of the forward sensing operator. In this talk, I will present a new learning framework, called Equivariant Imaging, which overcomes this limitation by exploiting the invariance to transformations (translations, rotations, etc.) present in natural signals. I will also discuss necessary and sufficient conditions for learning without ground truth. Our proposed learning strategy performs as well as fully supervised methods and can handle noisy data. I will show results on various inverse problems, including sparse-view X-ray computed tomography, accelerated magnetic resonance imaging and image inpainting.

Signal and image processing / Other

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Journal Paper

Generalized Frequency Estimator with Rational Combination of Three Spectrum Lines

Authors: Gigleux Benjamin, Vincent François and Chaumette Eric

IET Radar Sonar Navigation, pp. 1-9, March 2022.

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The popular Discrete Fourier Transform (DFT) is known to be a sub‐optimal frequency estimation technique for a finite transform length. In order to approach the Cramer‐Rao Lower Bound (CRLB), many refinement techniques have been considered, but little considering both zero padding or tapering, also known as windowing or apodisation. In this paper, a frequency estimator with closed‐form combination of three DFT samples is generalized to zero padding and tapered data within the class of cosine windowing. Root Mean Squared Error (RMSE) is shown to approach the CRLB in the case of a single tone signal with additive white Gaussian noise. Compared to state‐of‐the‐art techniques, the proposed algorithm improves the frequency RMSE up to 1 dB when using significant zero‐padding lengths (K ≥ 2 N) and for small to moderate SNR, which is the most challenging case for practical radar applications.

Signal and image processing and Networking / Aeronautical communication systems, Localization and navigation and Space communication systems

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Talk

Signal Processing for GNSS-R

Author: Lubeigt Corentin

Seminar of TeSA, Toulouse, February 8, 2022.

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For more than three decades, Global Navigation Satellite System (GNSS) signals have been seen as signals of opportunity as in GNSS Reflectometry (GNSS-R). The study of the reflections from the ground of such signals can indeed lead to many features regarding the reflecting surface and the receiver's height. Due to the nature of the GNSS signal, that is, due to its wavelength, the distortion of the reflected signal may vary significantly depending on the reflecting surface and on the dynamic and height of the receiver. The latter does range from low earth orbit down to ground-based platforms. In this last case, the vicinity to the ground induces important interference between the direct and the reflected path which makes it difficult to process directly in order to obtain altimetry product. In this presentation, after a brief description of the main features of the GNSS-R problem, the feasibility of ground-based single antenna GNSS-R altimetry is studied.

Signal and image processing / Localization and navigation

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Improve Congestion Control mechanism with the help of Machine Learning

Author: Perrier Victor

Seminar of TeSA, Toulouse, February 8, 2022.

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TCP (Transmission Control Protocol) Congestion control mechanism is an essential part of internet communications: it manages how fast the information is sent between two end points. That mechanism aims to achieve a compromise between 3 goals. The first is to achieve the maximum throughput for each flows, the second goal is to reduce the latency between the server and the client, and the last goal is to achieve fairness between each flows. The compromise between these 3 goals is very hard to achieve with human heuristics and basic models because of the ever increasing complexity of internet topologies. We choose to investigate machine learning solution in order optimize the Congestion Control mechanism. In this presentation, the bases of congestion control and the impact of machine learning on that mechanism will be explained.

Networking / Space communication systems

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Journal Paper

Satellite Image Compression and Denoising With Neural Networks

Authors: Alves de Oliveira Vinicius, Chabert Marie, Oberlin Thomas, Poulliat Charly, Bruno Mickael, Latry Christophe, Carlavan Mikael, Henrot Simon, Falzon Frédéric and Camarero Roberto

IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, January, 2022.

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Earth observation through satellite images is crucial to help economic activities as well as to monitor the impact of human activities on ecosystems. Current satellite systems are subjected to strong computational complexity constraints. Thus, image compression is performed onboard with specifically tailored algorithms while image denoising is performed on the ground. In this letter, we intend to address satellite image compression and denoising with neural networks. The first proposed approach uses a single neural architecture for joint onboard compression and denoising. The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed by Alves de Oliveira et al. (2021). The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.

Signal and image processing / Earth observation

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Talk

Challenges in imaging and sensing in photon-starved regimes

Author: McLaughlin Stephen

Seminar of TeSA, Toulouse, December 8, 2021.

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How many photons per pixel do we need to construct an image? This apparently simple question is rather complicated to answer as it is dependent on what you want to use the image for. Computational imaging and sensing combines measurement and computational methods often when the measurement conditions are weak, few in number, or highly indirect (e.g. when the measurements are few in number, the information of interest is indirectly observed, or in challenging observation conditions). The recent surge in the development of sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low flux imaging and sensing.

Signal and image processing / Other

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Activity Report

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New co-president of the Scientific Committee

Riadh Dhaou (Toulouse INP) was elected to join the presidency of the Scientific Committee with Antoine Blais (ENAC)

Apply for a PhD NEXEYA-DGA

AI for maritim monitoring

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Apply for a PhD in Safran

Bayesian for motor monitoring

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