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DTSTART;TZID=Europe/Paris:20260331T103000
DTEND;TZID=Europe/Paris:20260331T120000
DTSTAMP:20260417T130508
CREATED:20260326T073216Z
LAST-MODIFIED:20260326T073216Z
UID:9973-1774953000-1774958400@www.etis-lab.fr
SUMMARY:Séminaire ETIS-ICI - Samar Chebbi
DESCRIPTION:Orateur : Dr. Samar Chebbi will present the following work. \nTitle: From MIMO-NOMA Optimization to EMF Mapping: Towards Sustainable and Energy-Efficient Wireless Networks\nAbstract:\nThe presentation will first introduce the context of next-generation wireless networks and the main challenges related to massive connectivity\, interference management\, and resource allocation. I will then present my doctoral work on MIMO-NOMA systems\, focusing on user clustering and power allocation strategies based on optimization techniques\, including metaheuristic approaches. In a second part\, I will present my current research at Télécom Paris on electromagnetic field (EMF) exposure mapping\, where machine learning methods are used to model and predict exposure levels from heterogeneous data sources combining measurements and simulations. Finally\, I will discuss the perspective of sustainable wireless networks\, highlighting the need to jointly address performance\, energy efficiency\, and EMF exposure\, and outlining future research directions at the intersection of optimization\, physical modeling\, and artificial intelligence. \n 
URL:https://www.etis-lab.fr/event/seminaire-etis-ici-samar-chebbi/
CATEGORIES:ETIS,Seminar
ORGANIZER;CN="Ke Feng":MAILTO:ke.feng@ensea.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260331T140000
DTEND;TZID=Europe/Paris:20260331T153000
DTSTAMP:20260417T130508
CREATED:20260326T071413Z
LAST-MODIFIED:20260326T071413Z
UID:9962-1774965600-1774971000@www.etis-lab.fr
SUMMARY:Séminaire ETIS-CELL - Aurélique Saulquin
DESCRIPTION:Orateur : Aurélique Saulquin (CRIStAL\, Université de Lille) \nTitle: Spiking Neural Network emulation on FPGA for low-power AI\nAbstract:\nAI\, and particularly ANNs have become central to modern computing with remarkable performances for complex task resolution. However\, deploying ANNs on embedded systems remains challenging due to their complexity. Neuromorphic computing\, and especially Spiking\nNeural Networks (SNNs) offers an alternative solution\, drawing inspiration from the brain.\nAlthough promising\, neuromorphic chips are limited and poorly reconfigurable\, making FPGAs an interesting target for research and embedded low-power classification tasks.\nIn this context\, we develop ModNEF\, an open-source modular FPGA architecture for SNN inference. Modnef is based on the interconnection of independent modules\, offering high implementation flexibility and control. ModNEF was validated with standard neuromorphic\ndatasets and with a use case for sperm whale detection in the Mediterranean Sea. \nTeam link: https://teams.microsoft.com/meet/31942320814023?p=pSkW9xXckmBVCDlmqr\nMeeting ID: 319 423 208 140 23\nPasscode: zJ9Yp2wz
URL:https://www.etis-lab.fr/event/seminaire-etis-cell-aurelique-saulquin/
LOCATION:Online
CATEGORIES:ETIS,Seminar
ORGANIZER;CN="St%C3%A9phane Zuckerman":MAILTO:stephane.zuckerman@etis-lab.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260331T150000
DTEND;TZID=Europe/Paris:20260331T163000
DTSTAMP:20260417T130508
CREATED:20260319T142019Z
LAST-MODIFIED:20260320T192254Z
UID:9944-1774969200-1774974600@www.etis-lab.fr
SUMMARY:Séminaire ETIS-DATA&AI - Intégration de métadonnées de nœuds dans des modèles graphiques gaussiens contraints par le Laplacien
DESCRIPTION:Titre :\nIntégration de métadonnées de nœuds dans des modèles graphiques gaussiens contraints par le Laplacien \nRésumé :\nNous nous intéressons à l’apprentissage de graphes dans le cadre des modèles graphiques gaussiens (GGM). Dans ce contexte\, les matrices de données sont souvent accompagnées de métadonnées auxiliaires (par exemple\, des descriptions textuelles associées à chaque nœud)\, qui sont généralement ignorées dans les méthodes traditionnelles d’estimation de graphes.\nPour combler cette lacune\, nous proposons une approche d’apprentissage de graphe fondée sur des GGM contraints par le Laplacien\, exploitant conjointement les signaux des nœuds et ces métadonnées. La formulation obtenue conduit à un problème d’optimisation pour lequel nous développons un algorithme de majoration-minimisation (MM) efficace\, avec des mises à jour sous forme fermée à chaque itération. Des résultats expérimentaux sur des données financières réelles montrent que la méthode proposée améliore significativement les performances de regroupement de graphes\, illustrant ainsi l’intérêt de la fusion de ces deux sources d’information. \nIntervenant :\nJianhua WANG\, CNAM / Université Paris-Nanterre \nSéminaire en ligne\, lien à venir.
URL:https://www.etis-lab.fr/event/seminaire-dataai-integration-de-metadonnees-de-noeuds-dans-des-modeles-graphiques-gaussiens-contraints-par-le-laplacien/
LOCATION:Online
CATEGORIES:ETIS,Seminar
ORGANIZER;CN="Vassiis Christophides":MAILTO:vassilis.christophides@ensea.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260401T133000
DTEND;TZID=Europe/Paris:20260401T150000
DTSTAMP:20260417T130508
CREATED:20260326T072044Z
LAST-MODIFIED:20260326T072110Z
UID:9964-1775050200-1775055600@www.etis-lab.fr
SUMMARY:Séminaire ETIS-CELL - Pascal Cotret
DESCRIPTION:Orateur : Pascal Cotret (ENSTA Brest) \nTitle: Contributions to software-hardware border security\nAbstract:\nThis seminar provides an overview of some research activities\, which examines security at various levels in both software and hardware. First\, I will discuss work in which hardware supports software security (code monitoring on ARM\, securing JIT compiled code). In the second part\, the contributions presented involve hardware accelerators for security challenges: here\, there is a strong algorithm-architecture fit to optimize the use of the FPGA resources to solve security problems. Finally\, in the last section\, we will focus on security at the microarchitecture level of embedded systems\, and more specifically on cache memories in the context of using a TEE (Trusted Execution Environment) on a RISC-V processor. \nShort bio: Pascal Cotret is an associate professor at ENSTA on the Brest campus. He earned his Ph.D. from the University of South Brittany in 2012\, served as a lecturer and researcher at CentraleSupélec Rennes from 2014 to 2017\, and then worked for two years at Thales SIX GTS before joining ENSTA in 2019. His expertise focuses on security at the software/hardware interface and embedded systems (heterogeneous architectures\, reconfigurable components such as FPGAs). He is also interested in the algorithm-architecture matching of security mechanisms. \nTeams Link: https://teams.microsoft.com/meet/34941367702216?p=vvHuTzMkM1BarALE08\nMeeting ID: 349 413 677 022 16\nPasscode: CM9ew7MP
URL:https://www.etis-lab.fr/event/semianire-etis-cell-pascal-cotret/
LOCATION:Online
CATEGORIES:ETIS,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.etis-lab.fr/wp-content/uploads/2026/03/pascal_cotret.jpg
ORGANIZER;CN="St%C3%A9phane Zuckerman":MAILTO:stephane.zuckerman@etis-lab.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260401T140000
DTEND;TZID=Europe/Paris:20260401T153000
DTSTAMP:20260417T130508
CREATED:20260326T073603Z
LAST-MODIFIED:20260326T073746Z
UID:9975-1775052000-1775057400@www.etis-lab.fr
SUMMARY:Séminaire ETIS-ICI - Adrien Berthelot
DESCRIPTION:Orateur : Dr. Adrien Berthelot (Univ. Bristol) \nTitre: Empreinte environnementale du numérique : comprendre et évaluer\nRésumé :\nDes préoccupations autour de la consommation d’électricité du numérique pendant la crise énergétique de 2022\, en passant par les controverses autour de l’empreinte carbone de ce secteur\, et sans oublier les tragiques évènements au Congo\, l’industrie du numérique pose des problèmes aussi divers et complexes que son écosystème. Les impacts environnementaux du numérique sont de diverses natures : contribution au réchauffement climatique\, consommation d’eau\, extraction des métaux et des énergies fossiles. De plus\, ces impacts sont répartis de manière hétérogène entre les différentes phases de cycle de vie des équipements et les différentes parties des services numériques\, des ordinateurs individuels aux immenses infrastructures de communications et de calculs. Il y a derrière le plus commun des usages du numérique\, comme naviguer sur un site internet\, une complexité d’attribution des couts environnementaux et par extension des leviers de réductions de ces “couts” environnementaux. \nDans cette présentation\, je vous propose d’explorer l’empreinte du numérique au travers de la notion transversale de service numérique. Loin d’une métrique unique\, nous verrons comment percevoir ces impacts au travers d’analyse de cycle de vie (ACV) de services numériques. Sur la base de cette évaluation des impacts directs\, nous détaillerons les leviers possibles pour réduire l’empreinte environnementale du numérique. Nous évoquerons les problèmes posés par l’utilisation de métrique unique\, de performance ou d’efficacité. Nous verrons alors que ces leviers sont aussi divers que les problèmes posés par le numérique\, et nécessitent\, là aussi\, une approche multicritère. \nThe talk is in French and slides are in English.
URL:https://www.etis-lab.fr/event/semianire-etis-ici-adrien-berthelot/
LOCATION:Online
CATEGORIES:ETIS,Seminar
ATTACH;FMTTYPE=image/webp:https://www.etis-lab.fr/wp-content/uploads/2026/03/Adrien_Berthelot-e1774510615155.webp
ORGANIZER;CN="Ke Feng":MAILTO:ke.feng@ensea.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260402T100000
DTEND;TZID=Europe/Paris:20260402T113000
DTSTAMP:20260417T130508
CREATED:20260326T072532Z
LAST-MODIFIED:20260326T072532Z
UID:9968-1775124000-1775129400@www.etis-lab.fr
SUMMARY:Séminaire ETIS-CELL - Khemmar Redouane
DESCRIPTION:Orateur : Khemmar Redouane (ESIGELEC) \nTitre : Contribution à la perception pour la smart mobilité\nRésumé :\nLes 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. \nShort 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. \nTeams link: https://teams.microsoft.com/meet/382497063309?p=QKk3yAxhEeTcXWt1Ch\nMeeting ID: 382 497 063 309\nPasscode: vH39fR9F
URL:https://www.etis-lab.fr/event/seminaire-etis-cell-khemmar-redouane/
LOCATION:Online
CATEGORIES:ETIS,Seminar
ATTACH;FMTTYPE=image/png:https://www.etis-lab.fr/wp-content/uploads/2026/03/Redouane_Khemmar.png
ORGANIZER;CN="St%C3%A9phane Zuckerman":MAILTO:stephane.zuckerman@etis-lab.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260402T160000
DTEND;TZID=Europe/Paris:20260402T173000
DTSTAMP:20260417T130508
CREATED:20260326T072830Z
LAST-MODIFIED:20260326T072855Z
UID:9971-1775145600-1775151000@www.etis-lab.fr
SUMMARY:Séminaire ETIS-CELL - Sylvain Colomer
DESCRIPTION:Orateur : Sylvain Colomer \nTitle: Visual perception across scales: from bio-inspired navigation to large-scale scene understanding\nAbstract:\nVisual 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. \nBio: 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. \nTeams link: https://teams.microsoft.com/meet/37168326511389?p=dQSREX67PUox8rsHTo\nMeeting ID: 371 683 265 113 89\nPasscode: Ry6ZC3bU
URL:https://www.etis-lab.fr/event/seminaire-etis-cell-sylvain-colomer/
LOCATION:Online
CATEGORIES:ETIS,Seminar
ORGANIZER;CN="St%C3%A9phane Zuckerman":MAILTO:stephane.zuckerman@etis-lab.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260402T160000
DTEND;TZID=Europe/Paris:20260402T173000
DTSTAMP:20260417T130508
CREATED:20260401T052928Z
LAST-MODIFIED:20260401T052928Z
UID:10037-1775145600-1775151000@www.etis-lab.fr
SUMMARY:Séminaire ETIS-CELL - Julien Le Kernec
DESCRIPTION:Orateur : Julien Le Kernec\, Senior Lecturer\, HDR\, University of Glasgow. \nTitle: Radar sensing in assisted living: an overview\nAbstract: 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. \nBio: 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.\nHis 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. \nTeams link: https://teams.microsoft.com/meet/335139198265625?p=Gym3TZyH3Fd4cQMDaB\nMeeting ID: 335 139 198 265 625\nPasscode: ZV98iz9b
URL:https://www.etis-lab.fr/event/seminaire-etis-cell-julien-le-kernec/
LOCATION:ENSEA\, salle 331\, 6 avenue du Ponceau\, Cergy\, 95000\, France
CATEGORIES:ETIS,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.etis-lab.fr/wp-content/uploads/2026/04/julien_le_kernec.jpg
ORGANIZER;CN="St%C3%A9phane Zuckerman":MAILTO:stephane.zuckerman@etis-lab.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260409T103000
DTEND;TZID=Europe/Paris:20260409T120000
DTSTAMP:20260417T130508
CREATED:20260331T091052Z
LAST-MODIFIED:20260331T091904Z
UID:10010-1775730600-1775736000@www.etis-lab.fr
SUMMARY:Séminaire ETIS-ICI - Sotiris Skaperas
DESCRIPTION:Title: Efficient and robust learning approaches for dynamic wireless and edge systems.\nOrateur : Dr. Sotiris Skaperas\, ETIS. \nAbstract: 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. \nBio: 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. \n 
URL:https://www.etis-lab.fr/event/seminaire-etis-ici-sotiris-skaperas/
LOCATION:ENSEA\, salle 331\, 6 avenue du Ponceau\, Cergy\, 95000\, France
CATEGORIES:ETIS,Seminar
ORGANIZER;CN="Ke Feng":MAILTO:ke.feng@ensea.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260414T103000
DTEND;TZID=Europe/Paris:20260414T120000
DTSTAMP:20260417T130508
CREATED:20260320T192141Z
LAST-MODIFIED:20260320T192141Z
UID:9952-1776162600-1776168000@www.etis-lab.fr
SUMMARY:ETIS-ICI Seminar - E. Veronica Belmega
DESCRIPTION: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.\nIt is also accessible by the following link https://webconf.numerique.gouv.fr/ETISICI2026 \nTitle: Research overview and two recent contributions \nAbstract: 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. \nBio: 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.
URL:https://www.etis-lab.fr/event/etis-ici-seminar-e-veronica-belmega/
LOCATION:ENSEA\, salle 331\, 6 avenue du Ponceau\, Cergy\, 95000\, France
CATEGORIES:ETIS,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.etis-lab.fr/wp-content/uploads/2023/12/veronica_belmega.jpg
ORGANIZER;CN="Ke Feng":MAILTO:ke.feng@ensea.fr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260415T103000
DTEND;TZID=Europe/Paris:20260415T120000
DTSTAMP:20260417T130508
CREATED:20260331T091500Z
LAST-MODIFIED:20260331T091640Z
UID:10013-1776249000-1776254400@www.etis-lab.fr
SUMMARY:Séminaire ETIS-ICI - Laura Luzzi
DESCRIPTION:Title: Covert communication over additive noise channels\n(based on joint work with Cécile Bouette\, Ligong Wang and Matthieu Bloch) \nAbstract: 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.\nThen\, 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.\nIn 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. \nBio: 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.
URL:https://www.etis-lab.fr/event/seminaire-etis-ici-laura-luzzi/
LOCATION:ENSEA\, salle 331\, 6 avenue du Ponceau\, Cergy\, 95000\, France
CATEGORIES:ETIS,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.etis-lab.fr/wp-content/uploads/2022/01/LAURA_LUZZI_300.jpg
ORGANIZER;CN="Ke Feng":MAILTO:ke.feng@ensea.fr
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