Séminaire ETIS-CELL - Khemmar Redouane
2026-04-02 10:00 - 11:30 (Europe/Paris) | Online
Orateur : Khemmar Redouane (ESIGELEC)
Titre : Contribution à la perception pour la smart mobilité
Résumé :
Les véhicules autonomes sont de plus en plus présents dans notre quotidien, ouvrant de nouvelles perspectives pour la smart mobilité. Un véhicule autonome doit comprendre 3 fonctions essentielles : perception, décision et actions. Plus le système est capable de percevoir son environnement, plus il prendra de meilleures décisions lui permettant, in fine, de déclencher des actions répondant aux exigences de sécurité, confort et d’énergie. La détection, localisation et tracking d’objets sont des tâches indispensables pour la perception. Depuis 2012, le deep learning est devenu un outil très puissant en raison de sa capacité à traiter de grandes quantités de données. L’apparition de nombreuses méthodes basées sur l’apprentissage profond a conduit à des progrès significatifs. Malgré cet engouement à l’IA, peu de méthodes se concentrent sur l’aspect temps-réel, essentiel pour les applications réelles et ce, en raison des coûts de calcul élevés. En plus, ces algorithmes présentent des lacunes évidentes dans les scènes complexes en partie à cause du manque de données vérité terrain comme pour la smart mobilité ferroviaire et la santé. En plus de la précision et la vitesse, les algorithmes de perception doivent prendre en compte la contrainte d’énergie liée aux systèmes embarqués. Mes travaux de recherche sont concernés par cette problématique et se concentrent donc sur la perception d’environnement pour deux domaines de la smart mobilité : routier/ferroviaire et robotique mobile/santé. L’objectif est d’atteindre un niveau d’analyse et de compréhension de scènes complexes permettant d’assurer une smart mobilité de très haut niveau de sécurité, de confort et d’énergie optimale. Cela repose sur deux briques essentielles et complémentaires : 1. Système fusion multicapteurs permettant d’enrichir davantage la perception avec des données hétérogènes, 2. Perception d’environnement basée IA permettant l’exploitation des données collectées pour une meilleure prédiction de l’ensemble des situations. Il s’agit donc du développement de plateformes génériques ouvertes pour expérimenter et valider des concepts technologiques et scientifiques du monde académique et industriel.
Short bio: Après une formation d’ingénieur en électronique, un DEA en informatique industrielle et d’un Master européen en traitement d’images à l’Université de Poitiers (2002), j’ai préparé au sein de l’Université de Strasbourg un doctorat en traitement d’images et vision par ordinateur (2005). J’ai obtenu par la suite une Habilitation à Diriger des Recherches (HDR) en vision par ordinateur, intelligence artificielle et smart mobilité à l’Université Rouen Normandie (2022). J’ai été moniteur au Centre d’Initiation à l’Enseignement Supérieur (CIES Alsace) pour une formation d’enseignant chercheur (2002-2005). J’ai occupé le poste d’Attaché Temporaire d’Enseignement et de Recherche (ATER) pendant 2 ans à l’Université de Strasbourg (2005-2007) et un PostDoc en gestion électronique des documents (GED) et dématérialisation au sein du groupe Jouve (2007-2008). Par la suite, j’ai occupé plusieurs postes en industrie en tant que chef de projet au sein des grands comptes industriels (JOUVE, THALES, ALTEN). Depuis 2009, je suis enseignant chercheur à l’ESIGELEC en systèmes embarqués, robotique mobile et smart mobilité. Mes travaux de recherche portent principalement sur la vision par ordinateur, robotique mobile, intelligence artificielle et smart mobilité.
Teams link: https://teams.microsoft.com/meet/382497063309?p=QKk3yAxhEeTcXWt1Ch
Meeting ID: 382 497 063 309
Passcode: [REDACTED]

Séminaire ETIS-CELL - Sylvain Colomer
2026-04-02 16:00 - 17:30 | Online
Orateur : Sylvain Colomer
Title: Visual perception across scales: from bio-inspired navigation to large-scale scene understanding
Abstract:
Visual perception, or computer vision, is a central component of intelligent and robotic systems. It plays a key role in a wide range of applications, from robotic navigation to remote sensing, scene understanding, medical imaging, or large-scale visual data analysis. Although visual data is very rich in information, it is also highly complex, as it is subject to many challenges such as illumination variations, weather conditions, viewpoint and scale changes, as well as domain shifts or noise. Learning efficient and meaningful representations from such data is therefore a major challenge for intelligent systems, especially under constraints related to performance, computational cost, and energy efficiency. In this context, my research focuses on understanding how visual representations can be learned and exploited across different scales and application domains. In particular, I investigate how perception models can be designed to remain robust and adaptable when moving from local, embodied settings to large-scale visual understanding tasks. During my PhD, I developed bio-inspired approaches for visual navigation and localization, leveraging principles from neuroscience to design robust and adaptive models for decision-making in autonomous systems. These models aim to bridge perception and action through structured visual representations, with a particular emphasis on efficiency through the use of simple, optimized monocular vision systems and lightweight computational models. In addition, part of this work focused on deploying these models on FPGA platforms, with the goal of designing efficient hardware implementations and exploring circuit architectures adapted to bio-inspired and neural computation. More recently, my work has explored large-scale scene understanding in the context of remote sensing, using deep learning techniques for tasks such as instance segmentation and analysis of hyperspectral aerial imagery, in particular for large-scale forest monitoring in Canada. This setting introduces new challenges related to scale, variability, and data complexity, and raises questions about the deployment of such systems directly on drones to enable efficient and adaptive forest monitoring. In this talk, I will highlight the connections between these research directions and show how they contribute to the design of more general, robust, and efficient visual perception systems. I will finally discuss perspectives towards adaptive, energy-efficient, and deployable AI models, capable of bridging embodied perception and large-scale visual understanding in real-world applications.
Bio: Sylvain Colomer is a researcher in computer vision and robotic navigation. His work focuses on designing robust and efficient visual models for real-world applications, from autonomous navigation to large-scale remote sensing. He obtained his PhD from CY Cergy Paris University, where he developed bio-inspired approaches for visual navigation on embedded systems. He then worked as a postdoctoral researcher at the University of Toronto on deep learning methods for hyperspectral image analysis and forest monitoring. His research aims to bridge embodied perception and large-scale visual understanding. His current research focuses on designing robust and efficient visual perception models that can operate in real-world conditions, from resource-constrained embedded systems to large-scale data analysis, with an emphasis on multimodality, generalization, and deployment.
Teams link: https://teams.microsoft.com/meet/37168326511389?p=dQSREX67PUox8rsHTo
Meeting ID: 371 683 265 113 89
Passcode: [REDACTED]

Séminaire ETIS-CELL - Julien Le Kernec
2026-04-02 16:00 - 17:30 | ENSEA, salle 331, 6 avenue du Ponceau, Cergy, 95000, France
Orateur : Julien Le Kernec, Senior Lecturer, HDR, University of Glasgow.
Title: Radar sensing in assisted living: an overview
Abstract: In this keynote, I will discuss the place of radar for assisted living. First, the context of assisted living and the urgency to address the problem will be described. The second part will give an overview of existing sensing modalities for assisted living and explain why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that help improve performances in classification, especially with deep learning with a reflection on lessons learned from it. Finally, I’ll conclude with open challenges and future developments.
Bio: Dr Julien Le Kernec is currently a Senior Lecturer with the School of Engineering in the Autonomous Systems & Connectivity Group, University of Glasgow, he is also an adjunct Associate Professor at the University Cergy-Pontoise, France, in the ETIS (Information and Signal Processing group). Previous to this, he held a post-doctoral position with the Kuang-Chi Institute of Advanced Technology, Shenzhen, China, from 2011 to 2012 and he was a Lecturer at the Department of Electrical and Electronic Engineering at the University of Nottingham Ningbo China, from 2012 to 2016. Dr Le Kernec received his B.Eng. and M.Eng. degrees in Electronic Engineering from the Cork Institute of Technology, Ireland, in 2004 and 2006, respectively, and his Ph.D. degree in Electronic Engineering from the University Pierre and Marie Curie, France, in 2011. In 2022, he received “Habilitation a Diriger des Recherches” from University Cergy-Pontoise, France.
His research interests include radar system design, software-defined radio/radar, signal processing, and health applications. Dr Le Kernec has over 130 publications in journals (IEEE sensors, IEEE Signal processing Magazine, IEEE Journal of Biomedical and Health Informatics, Nature Scientific Reports), Conferences (IET internation radar conference, IEEE radarcon,..), book chapters, patents and databases.
Teams link: https://teams.microsoft.com/meet/335139198265625?p=Gym3TZyH3Fd4cQMDaB
Meeting ID: 335 139 198 265 625
Passcode: [REDACTED]

Séminaire ETIS-ICI - Sotiris Skaperas
2026-04-09 10:30 - 12:00 | ENSEA, salle 331, 6 avenue du Ponceau, Cergy, 95000, France
Title: Efficient and robust learning approaches for dynamic wireless and edge systems.
Orateur : Dr. Sotiris Skaperas, ETIS.
Abstract: Next-generation communication and computing infrastructures operate under dynamic conditions, resource constraints, and increasing demands for reliability and security. In this talk, I will present recent contributions on learning-based methods that address these challenges across wireless and edge environments. The presentation will cover anomaly detection in wireless mesh networks, efficient and robust learning in dynamic edge systems, and physical-layer authentication under adversarial conditions. Overall, these works explore how intelligent methods can improve reliability, efficiency, and security in dynamic wireless and edge systems.
Bio: Sotiris Skaperas is postdoctoral researcher at ETIS UMR 8051, CY Cergy Paris University, ENSEA, CNRS and he is currently working on the EU SNS JU ROBUST-6G project. His research focuses on 6G wireless systems, IoT, and edge-cloud environments, with an emphasis on statistical modeling and machine learning for anomaly detection, resource management, and wireless physical-layer security.

ETIS-ICI Seminar - E. Veronica Belmega
2026-04-14 10:30 - 12:00 | ENSEA, salle 331, 6 avenue du Ponceau, Cergy, 95000, France
We are glad to announce the upcoming ETIS-ICI seminar by Prof. E. Veronica Belmega, which will take place on Tuesday, 10:30am, April 14th at D331 ENSEA.
It is also accessible by the following link https://webconf.numerique.gouv.fr/ETISICI2026
Title: Research overview and two recent contributions
Abstract: After a brief research activity overview, the presentation will focus on two recent contributions. 1) Beam coherence time prediction in mobile high frequency (0.1THz) communications exploiting deep learning, in collaboration with Irched Chafaa and Giacomo Bacci at Univ. of Pisa; 2) Defence against false data injection attacks in the power grid exploiting a two-player zero-sum game in collaboration with Sajjad Maleki (PhD co-tutelle CYU Cergy Paris Univ. and Univ. of Warwick) and Subhash Lakshminarayana at Univ. of Warwick.
Bio: E. Veronica Belmega is a Professor at Univ. Gustave Eiffel – ESIEE Paris and LIGM laboratory, Marne-la-Vallée, France, since May 2022. Previously, she was an Associate Professor (MCF HDR) with ENSEA graduate school and ETIS laboratory, Cergy-Pontoise, France, where she is currently an Associate Researcher. Her main research interests lie in convex and online optimization, game theory and machine learning applied to resource optimization and security of wireless communication and smart grid networks.

Séminaire ETIS-ICI - Laura Luzzi
2026-04-15 10:30 - 12:00 | ENSEA, salle 331, 6 avenue du Ponceau, Cergy, 95000, France
Title: Covert communication over additive noise channels
(based on joint work with Cécile Bouette, Ligong Wang and Matthieu Bloch)
Abstract: In the first part of the talk, I will give a brief overview of my research in the areas of physical layer security and post-quantum cryptography.
Then, I will focus on some recent results in the setting of covert communication in physical layer security. In this scenario, a transmitter and a receiver wish to communicate reliably while preventing an eavesdropper from detecting the fact that a communication is ongoing. It is known that the channel capacity under this constraint is zero, and the amount of information that can be sent reliably and covertly scales like the square root of the number of channel uses. We study the scaling constant of the square root law, or “covert capacity”, for a general class of memoryless additive noise channels, and show that it is upper bounded by the square root of the varentropy of the noise. Under some additional assumptions, we show that this upper bound is tight.
In the last part of the talk, we consider the asymptotic limits of covert communication over an i.i.d. Gaussian channel when we allow a positive average error probability ε. In this case, the strong converse does not hold, and the scaling constant C_ε depends on ε. We derive upper and lower bounds for C_ε and show that allowing a small positive error probability enables the transmission of additional covert information.
Bio: Laura Luzzi is an Associate Professor (MCF HDR) at ENSEA, Cergy-Pontoise, France, and a researcher at ETIS (UMR 8051, CY Cergy Paris Université, ENSEA, CNRS). She is currently a visiting researcher with Project COSMIQ, Centre Inria de Paris. Her research interests include physical layer security and post-quantum cryptography.

ETIS seminar: Claudia Paris
2026-05-11 13:30 - 15:00 | Online
Title: Integrating multimodal satellite data, ground-based field photos and AI to go beyond traditional mapping.
Abstract
Increasingly frequent extreme weather events and climate change pose growing threats to human well-being and ecosystem health. To monitor and assess environmental change and land degradation (e.g., deforestation, desertification, urban expansion), satellite data can be leveraged for the regular production of land-cover maps that capture dynamics across the Earth’s surface. These data have been widely used to generate consistent, large-scale land-cover information over time, enabling monitoring at regional to global scales.
With rapid advancements in Earth Observation technologies and computational capabilities, there is now an opportunity to move beyond traditional land-cover mapping toward more comprehensive and actionable characterizations of landscape dynamics. In particular, the increasing availability of crowdsourced, geo-tagged field photos introduces an additional and complementary data source. When integrated with satellite imagery, these data enable richer, multi-dimensional representations of land systems.
This integration allows information to be captured at multiple levels of semantic detail, which is essential for understanding complex and heterogeneous landscapes. While satellite image time series provide valuable temporal information on land-cover dynamics, ground-level photos offer fine-grained spatial detail and contextual insights into local conditions, such as vegetation structure, land management practices, disturbance events, and small-scale landscape features that are difficult to observe from space.
Recent advances in Artificial Intelligence (AI), particularly in visual-language models, self-supervised learning, and multimodal approaches, create new opportunities to effectively combine satellite and ground-based data. The integration of geo-tagged field photos, satellite image time series, and AI methods enables more detailed and scalable interpretation of land-cover patterns and processes, supporting improved detection and understanding of environmental changes that were previously difficult to capture.
Bio
Claudia Paris is a Senior Assistant Professor (UD1) in the Faculty of Geoinformation and Earth Observation Sciences (ITC) at the University of Twente, Enschede, the Netherlands. She received the “Laurea” (B.S.), the “Laurea Specialistica” (M.S.) (summa cum laude) degrees in Telecommunication Engineering and the Ph.D. in Information and Communication Technology from the University of Trento, Italy, in 2010, 2012, 2016, respectively. She accomplished the Honors Master Program in Research within the Master’s Degree in Telecommunication Engineering in 2012. Claudia Paris’ research encompasses image processing, signal processing, pattern recognition, machine learning, and deep learning, specifically applied to remote sensing image analysis. She focuses on designing innovative and automated workflows for the analysis and classification of large-scale Earth Observation (EO) data for various applications (e.g., forest/agricultural mapping and monitoring) by leveraging high-performance computing (HPC) and cloud computing platforms (Google Earth Engine). Her main research interests focus on the classification and fusion of multisource remote sensing data, multitemporal image analysis, domain adaptation methods, and land cover map updates. She has been conducting research on these topics in the framework of national and international projects. She is a member of the scientific and programme committee of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) and the SPIE International Symposium on Remote Conferences, respectively, and is also a referee for several international journals. Dr. Paris was twice the recipient of the prestigious Symposium Prize Paper Award (exceptional paper in terms of content and impact on the Geoscience & Remote Sensing Society) at the 2016 IEEE IGARSS (Beijing, China, 2016) and at the 2017 IEEE IGARSS (Fort Worth, TX, USA, 2017). She also won the IEEE Geoscience and Remote Sensing Society 2022 Letters Prize Paper Award (exceptional paper in terms of content and impact on the GRS-Society).
Place: online (link to be shared)
