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Conference Paper

Fusion of Maagnetic Resonance and Ultrasound Images using Guided Filtering: Application to Endometriosis Surgery

Authors: El Bennioui Youssra, Halimi Abderrahim, Basarab Adrian and Tourneret Jean-Yves

In Proc. 32nd EUropean SIgnal Processing COnference (EUSIPCO), Lyon, France, August 26-30, 2024.

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This paper studies a new fusion method designed for magnetic resonance (MR) and ultrasound (US) images, with a specific focus on endometriosis diagnosis. The proposed method is based on guided filtering, leveraging the advantages of this technique to enhance the quality of fused images. The fused image is a weighted average of base and detail images from the MR and US images. The weights assigned to the US image account for the presence of speckle noise, a common challenge in US imaging whereas the weights assigned to the MR image allow the contrast of the fused image to be enhanced. The effectiveness of the method is evaluated using synthetic and phantom data, showing promising results. The image provided by the proposed fusion method holds potential for enhancing visualization and aiding decision-making in endometriosis surgery, offering a valuable contribution to the field of medical image fusion.

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Signal and image processing / Other

Detecting Abnormal Ship Trajectories using Functional Isolation Forests and Dynamic Time Warping

Authors: Mangé Valérian, Anezin Yoann, Tourneret Jean-Yves, Vincent François, Mirambell Laurent and Manzoni Vieira Fábio

In Proc. 32nd EUropean SIgnal Processing COnference (EUSIPCO), Lyon, France, August 26-30, 2024.

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This paper studies an algorithm allowing the isolation forest method to be adapted to time series associated with ship trajectories. This algorithm builds decision trees using different similarity measures between the ship trajectories of interest and the atoms of a dictionary constructed by the user. The similarity measure used to compare trajectories with potentially different lengths is based on dynamic time warping. Results obtained on synthetic data with an available ground truth yield promising results, when compared to the state-of-the-art.

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Signal and image processing / Localization and navigation

Track-to-Track AIS / Radar Association and Uncertainty Estimation by Coherent Point Drift

Authors: Mangé Valérian, Tourneret Jean-Yves, Vincent François, Mirambell Laurent and Manzoni Vieira Fábio

In Proc. 32nd EUropean SIgnal Processing COnference (EUSIPCO), Lyon, France, August 26-30, 2024.

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Multiple sensors, such as AIS and radar, are used to monitor nearby ships during maritime surveillance operations. The data from these sensors must be associated so as to accurately locate the targets and identify their behavior, while taking into account the presence of potential sensor biases. Several algorithms exist in the state-of-the-art to solve this association problem. However, few of them allow the sensor biases to be corrected. This paper adapts the coherent point drift method to the association of AIS and radar tracks while taking into account the radar uncertainty. The proposed adaptation is based on an expectation-maximization algorithm that jointly estimates the bias of the radar sensor with respect to the AIS sensor (in polar coordinates), the radar and AIS uncertainties and solves the association problem. The performance of this algorithm is evaluated using AIS and radar tracks obtained from numerous scenarios yielding promising results.

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Signal and image processing / Localization and navigation

Anomaly Detection Using Multiscale Signatures

Authors: Mignot Raphaël, Mangé Valérian, Usevich Konstantin, Clausel Marianne, Tourneret Jean-Yves and Vincent François

In Proc. 32nd EUropean SIgnal Processing COnference (EUSIPCO), Lyon, France, August 26-30, 2024.

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This paper analyzes multidimensional time series through the lens of their integrals of various moment orders, constituting their signatures, a novel tool for detecting anomalies in time series. The proposed anomaly detection (AD) method is compared using classical distance-based methods such as Local Outlier Factor (LOF) and One-Class Support Vector Machine (OCSVM). These methods are investigated using different similarity measures: distance on signature features, Euclidean distance and Dynamic TimeWarping (DTW). The combination of signature features with a specific segmentation of time series leads to a multi-scale analysis tool that is competitive with respect to the state-of-the-art results, while maintaining low computational costs thanks to a property of the signature features.

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Signal and image processing / Localization and navigation

Exploiting Redundant Measurements for Time Scale Generation in a Swarm of Nanosatellites

Authors: Mc Phee Hamish Scott, Tourneret Jean-Yves, Valat David, Delporte Jérôme, Gregoire Yoan and Paimblanc Philippe

In Proc. European Frequency and Time Forum (EFTF), Neufchâtel, Switzerland, June 25-27, 2024.

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The computation of a common reference time for a swarm of nanosatellites is restricted by the quality and availability of the timing measurements made with inter-satellite links. The presence of anomalies or absence of communication links is demonstrated to harm the stability of the time scale. The Least Squares (LS) estimator is introduced as a method of preprocessing measurement noise by using all available clock comparisons in the swarm. This estimator also provides filtered measurements when inter-satellite links are missing as long as each satellite maintains at least one link with another. Anomaly detection and removing corrupted satellite links are shown to be compatible with the LS estimator to mitigate the impact of anomalous measurements. When a satellite becomes completely isolated for some period of time, a correction at the beginning and the end of the isolation period are both detailed. The correction is simple and just requires resetting the weights of missing clocks and clocks being reintroduced. Continuity is shown to be maintained when a large portion of clocks are removed and later reintroduced at the same time.

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Signal and image processing / Localization and navigation

Talk

Managing Noisy and Missing Measurements in Time Scale Generation for a Swarm of Nanosatellites

Authors: Mc Phee Hamish Scott, Tourneret Jean-Yves, Valat David, Delporte Jérôme, Gregoire Yoan and Paimblanc Philippe

In Proc. European Frequency and Time Forum (EFTF), Neufchâtel, Switzerland, June 25-27, 2024.

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Signal and image processing / Localization and navigation

Conference Paper

Division Réeseau Equitable dans les Essaims de Nanosatellites

Authors: Akopyan Evelyne, Dhaou Riadh, Lochin Emmanuel, Pontet Bernard and Sombrin Jacques B.

In Proc. 9èmes Rencontres Francophones sur la Conception de Protocoles, l'Evaluation de Performance et l'Expérimentation des Réseaux de Communication (AlgoTel-CoRes), Saint-Briac-sur-Mer, France, May 27-31, 2024.

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Nous proposons de partitionner l’architecture d’un réseau ad-hoc mobile en plusieurs groupes, afin de re-distribuer équitablement la charge entre les membres du réseau. Notre étude porte sur un essaim de nanosatellites fonctionnant commue un télélescope spatial distribué, placé en orbite lunaire. Chaque nanosatellite de l’essaim collecte des données d’observation de l’espace, puis les échange avec les autres membres de l’essaim. Les données recueillies sont ensuite combinées localement afin de produire l’image globale observée par l’essaim. Cependant, un système fondé sur ce mode opératoire est particulièrement sensible aux pertes de paquets et aux pannes d’énergie. En effet, la transmission simultanée d’un important volume de données peut entraîner des problèmes de communication, notamment en surchargeant le canal radio ou en augmentant le risque de collisions, menant dans les deux cas à des pertes de paquets. La consommation énergétique totale de l’essaim est également proportionnelle au nombre de paquets transmis : il faut alors trouver une solution pour limiter le nombre de transmissions afin d’économiser l’énergie des nanosatellites. La principale contribution de ce papier est de proposer une approche basée sur la division équitable du réseau en plusieurs groupes de nanosatellites. Nous comparons les performances de trois algorithmes de division de graphe : Random Node Division (RND), Multiple Independent Random Walks (MIRW), et Forest Fire Division (FFD). Nos résultats montrent que MIRW obtient les meilleurs scores en termes d’équité, peu importe le nombre de groupes produit.

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Networking / Space communication systems

Division réseau équitable dans les essaims de nanosatellites

Authors: Akopyan Evelyne, Dhaou Riadh, Lochin Emmanuel, Pontet Bernard and Sombrin Jacques B.

In Proc. 26èmes Rencontres Francophones sur les Aspects Algorithmiques des Téléommunications (AlgoTel-CoRes), Saint-Briac-sur-Mer, France, May 27-31, 2024.

Nous proposons de partitionner l’architecture d’un réseau ad-hoc mobile en plusieurs groupes, afin de re-distribuer équitablement la charge entre les membres du réseau. Notre étude porte sur un essaim de nanosatellites fonctionnant comme un télescope spatial distribué, placé en orbite lunaire. Chaque nanosatellite de l’essaim collecte des données d’observation de l’espace, puis les échange avec les autres membres de l’essaim. Les données recueillies sont ensuite combinées localement afin de produire l’image globale observée par l’essaim. Cependant, un système fondé sur ce mode opératoire est particulièrement sensible aux pertes de paquets et aux pannes d’énergie. En effet, la transmission simultanée d’un important volume de données peut entraîner des problèmes de communication, notamment en surchargeant le canal radio ou en augmentant le risque de collisions, menant dans les deux cas `a des pertes de paquets. La consommation énergétique totale de l’essaim est également proportionnelle au nombre de paquets transmis : il faut alors trouver une solution pour limiter le nombre de transmissions afin d’économiser l’énergie des nanosatellites. La principale contribution de ce papier est de proposer une approche basée sur la division équitable du réseau en plusieurs groupes de nanosatellites. Nous comparons les performances de trois algorithmes de division de graphe : Random Node Division (RND), Multiple Independent Random Walks (MIRW), et Forest Fire Division (FFD). Nos résultats montrent que MIRW obtient les meilleurs scores en termes d’équité, peu importe le nombre de groupes produit.

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Networking / Space communication systems

Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations

Authors: De Boni Rovella Gastón, Benammar Meryem, Benaddi Tarik and Meric Hugo

In Proc. IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024), Stockholm, Sweden, May 5-8, 2024.

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In this work, we introduce a framework that enables the use of Syndrome-Based Neural Decoders (SBND) for highorder Bit-Interleaved Coded Modulations (BICM). To this end, we extend the previous results on SBND, for which the validity is limited to Binary Phase-Shift Keying (BPSK), by means of a theoretical channel modeling of the bit Log-Likelihood Ratio (bit-LLR) induced outputs.We implement the proposed SBND system for two polar codes (64, 32) and (128, 64), using a Recurrent Neural Network (RNN) and a Transformer-based architecture. Both implementations are compared in Bit Error Rate (BER) performance and computational complexity.

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Digital communications / Space communication systems and Other

On the Optimality of Support Vector Machines for Channel Decoding

Authors: De Boni Rovella Gastón, Benammar Meryem, Meric Hugo and Benaddi Tarik

In Proc. European Conference on Networks and Communications (EuCNC), Antwerp, Belgium, June 3-6, 2024.

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In this work, we investigate the construction of channel decoders based on machine learning solutions, and more specifically, Support Vector Machines (SVM). The channel decoding problem being a high-dimensional multiclass classification problem, previous attempts were made in the literature to construct SVM-based channel decoders. However, existing solutions suffer from a dimensionality curse, both in the number of SVMs involved –which are exponential in the block length–and in the training dataset size. In this work, we revisit SVMbased channel decoders by alleviating these limitations and prove that the suggested SVM construction can achieve optimal Bit Error Probability (BEP) by attaining the performance of the bit-Maximum A Posteriori (MAP) decoder in the Additive White Gaussian Noise (AWGN) channel.

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Digital communications / Space communication systems and Other

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