From sensors to knowledge :
Communicate and decide
Communicate and decide
CNRS, UMR 6285
Title: RF fingerprinting based techniques for drones detection and classification
Post-doctoral position in signal processing
Keywords: RF fingerprinting, signal processing, drones, classification, identification, detection algorithms, compressed sampling
Laboratory: Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance, Lab-STICC / UMR CNRS 6285
Duration: 12 months
Starting date: september/october 2020
Applicant profile:
The applicant should have a PhD in signal processing with expertise in:
The applicant must be fluent in English.
Job description:
The use of drones has substantially grown over the past few years and finds its applications in various fields such as surveillance, search and rescue missions, infrastructure inspection, package delivery, etc. However, malicious use by some hobbyists or malevolent actors can present a threat to public safety and security, such as the overflight of certain risk areas (e.g., nuclear power plants). To respond to these threats, a market in the fight against drone is emerging and therefore the development of innovative solutions becomes necessary to guarantee the protection of sensitive infrastructure.
Several techniques have been proposed so far for drones detection and classification. Conventional radar-based techniques [1] are widely used to detect and identify drones, but generally fail to detect micro drones. Similarly, camera-based techniques [2] can be used but require good lighting conditions, high quality lens, and camera with ultra-high resolution for detecting drones at long distance. More recently, RF fingerprinting techniques have been proposed [3]. These techniques are based on the characteristics of the RF signal transmitted by the drone's radio controller or by the drone itself. In [3], the authors were interested in the drone body shifts during the navigation and in the body vibrations caused by the rotating propellers in order to characterize the RF transmitted signal and thus be able to detect its presence.
Under the supervision of two Lab-STICC researchers, the applicant mission will be to conduct research on the drones detection based on the RF signal signature. First, the work will consist of making a state-of-the-art on the "RF fingerprinting" algorithms which take into account the signatures provided by the radio
components defaults or by the signal distortion via the transmitter support platform and then analyzing the radio signals transmitted by the drones in order to highlight these radio signatures. In a second step, an intelligent processing of these radio characteristics will be proposed to best qualify the transmitted information. In addition, the applicant will implement an SDR (Software Defined Radio or Radio Software) type acquisition chain for the drones detection and classification based on the radio signatures. In order to test the proposed algorithms, the signal acquisition chain will be implemented on a SDR USRP type platform using the OpenSources GnuRadio suite and the processing or post-processing of signals for detection, classification and the characterization will be developed under the Matlab environment.
Supervision team:
The applicant will occupy a post-doctoral position within the SI3 team of the Lab-STICC at the University of Western Brittany. The proposed work takes place in the frame of a project with two industrial partners and an academic
partner, ENSTA Bretagne. The management team consists of two members: Roland Gautier, Senior Lecturer HDR and Roua Youssef, Lecturer. For all or part of the tasks, the applicant will collaborate closely with the researchers and engineers of the team as well as the industrial partners involved in the project.
Contact :
To apply, please send a detailed CV with a list of publications / communications as well as a reference letter supporting your skills in the subject by email to:
Roland Gautier : roland.gautier@univ-brest.fr
Roua Youssef :roua.youssef@univ-brest.fr
References
[1] M. Schmidt and M. Shear, “A drone, too small for radar to detect, rattles the white house,” 2015. [Online]. Available:https://www.nytimes.com/2015/01/27/us/white-house-drone.html.
[2] M. A. Ma’sum et al., “Simulation of Intelligent Unmanned Aerial Vehicle (UAV) for Military Surveillance,” 2013 Int’l. Conf. Advanced Computer Science and Inf. Systems, Sept. 2013, pp. 161–66.
[3] P. Nguyen, H. Truong, M. Ravindranathan, A. Nguyen, R. Han and T. Vu, “Matthan: Drone Presence Detection by Identifying Physical Signatures in the Drone’s RF Communication”, Proc. 15th Annual Int. Conf. on Mobile Systems, Applications, and Services, MobiSys 2017, pp.211-224.
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annonce en francais (349.31 ko)Duration : 16-18 months
Starting Date: 1.10.2020 (negotiable)
Location : Lab-STICC research lab, IMT Atlantique, Brest, France
IMT Atlantique is currently seeking a research associate (Post-Doc) for a 36 months project initiated by french Ministry of Education: “Transformation41: From the appropriation of digital tools to the transformation of practices”. IMT Atlantique is one of the most prestigious graduate engineering schools ("Grandes Ecoles") in France. It is a public institution, under the aegis of the Ministry for Industry and is a member of the Institut Mines-Telecom. The college trains future professionals for careers in industry, services and research.
About the lab
Created since 2008, the Lab-STICC is a multidisciplinary research laboratory in the field of Information and communication science and technology. Researchers work in a single structure within one central theme: “from sensor to knowledge”. The Lab-STICC is a research unit of the French national center for scientific research (CNRS) involving two universities (Université de Bretagne Occidentale, Brest; Université de Bretagne Sud, Lorient) and three graduate schools of engineering (IMT Atlantique, ENSTA Bretagne and ENIB). Under the title UMR 6285, it is attached to department INS2I and INSIS of the CNRS.
The Lab-STICC incorporates more than 566 people including 317 permanent staff, 206 PhD students and 43 non-permanent positions. The staffs are located over the different institutes on several geographical sites in Brittany: Brest, Lorient, Rennes, Quimper and Vannes. Over the last five years, Lab-STICC members have authored more than 700 refereed journal papers, 1800 conference communications and 40 patents. Our research leadership has involved strong academic and industrial collaborations with national and international research grants amounting to more than 30 M€. Lab-STICC members play an important role in the academic training (master, engineering degree) in the field of ICT in western Brittany. Several partner institutions are in charge of these courses. The Transformation41 is a project of a newly created team MOTEL (MOdels and Tools for Enhanced Learning).
Transformation41 Project
Transformation41 aims at proposing analytics tools to teachers in order to give insights of students’ digital practice, and to support them in transforming pedagogical practices.
The MOTEL team, in collaboration with another team of LIUM laboratory, Laval (France), will focus on proposing adapted dynamic Learning Analytics Dashboards (LAD) (Schwendimann et al. 2016). In previous work conducted during Hubble project, our two teams proposed an User centered approach for LAD Generation (Dabbebi et al. 2017), we aim to reuse and extend in this project. Current results include:
Our objective is to produce dedicated LADs to teachers in the project context, and to extend our models, and related tool and prototype in order to include dynamic evolution of the LAD to support the decision-making process.
Job description
The proposed work takes place in the frame of the Transformation41 project.
Within this project, the applicant will have in charge of:
To this purpose, the applicant will be part of a team of eight people, and work in close cooperation of the LIUM team, under the supervision of the project leader at IMT Atlantique.
The applicant should have
○ Adaptive Hypermedia
○ Dashboards generation
○ Software engineering and web development
For more information, please contact Jean-Marie Gilliot, jm.gilliot@imt-atlantique.fr, +33 (0)22 900 1539, or Sébastien Iksal, sebastien.iksal@univ-lemans.fr, +33 (0)24 359 4919
To apply, please send to Jean-Marie Gilliot (jm.gilliot@imt-atlantique.fr ):
- CV with list of publications, and referees
- short research statement
References
Dabbebi, I., Iksal, S., Gilliot, J. M., May, M., & Garlatti, S. Towards Adaptive Dashboards for Learning Analytic: An Approach for Conceptual Design and implementation. 9th International Conference on Computer Supported Education (CSEDU 2017), Apr 2017, Porto, Portugal. pp.120-131, 10.5220/0006325601200131ff. hal-01574127
Gilliot, J. M., Iksal, S., Medou, D., & Dabbebi, I. (2018, October). Conception participative de tableaux de bord d'apprentissage. IHM’18 : 30e Conférence Francophone sur l’Interaction Homme-Machine, Oct 2018, Brest, France. pp. 119-127. hal-01897914
Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., ... & Dillenbourg, P. (2016). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1), 30-41.
Durée: 16-18 mois
Date de début: 1.10.2020 (négociable)
Lieu: Lab-STICC, IMT Atlantique, Brest, France
IMT Atlantique recherche un chercheur associé (Post-Doc) pour un projet de 36 mois initié par le ministère de l'Éducation nationale: «Transformation41: De l'appropriation des outils numériques à la transformation des pratiques». IMT Atlantique est l'une des plus grandes écoles d'ingénieurs diplômées ("Grandes Ecoles") de France. Il s'agit d'un établissement public, placé sous l'égide du ministère de l'Industrie et membre de l'Institut Mines-Telecom. L’école forme de futurs professionnels à des carrières dans l'industrie, les services et la recherche, notamment dans le numérique.
À propos du laboratoire
Créé depuis 2008, le Lab-STICC est un laboratoire de recherche multidisciplinaire dans le domaine des sciences et technologies de l'information et de la communication. Les chercheurs travaillent dans une structure unique autour d'un thème central: «du capteur à la connaissance». Le Lab-STICC est une unité de recherche du Centre national de la recherche scientifique (CNRS - UMR 6285 ) regroupant deux universités (Université de Bretagne Occidentale, Brest; Université de Bretagne Sud, Lorient) et trois écoles d'ingénieurs diplômées (IMT Atlantique, ENSTA Bretagne et ENIB). Il est rattaché aux services INS2I et INSIS du CNRS.
Le Lab-STICC regroupe plus de 566 personnes dont 317 permanents, 206 doctorants et 43 postes non permanents. Les personnels sont répartis sur les différents instituts sur plusieurs sites géographiques en Bretagne: Brest, Lorient, Rennes, Quimper et Vannes. Au cours des cinq dernières années, les membres du Lab-STICC ont rédigé plus de 700 articles de revues à comité de lecture, 1800 communications de conférence et 40 brevets. Notre leadership dans la recherche a impliqué de solides collaborations académiques et industrielles avec des subventions de recherche nationales et internationales d'un montant supérieur à 30 M €. Les membres du Lab-STICC jouent un rôle important dans la formation académique (master, diplôme d'ingénieur) dans le domaine des TIC en Bretagne occidentale. Plusieurs institutions partenaires sont en charge de ces cours. Le projet Transformation41 est rattaché à la nouvelle équipe MOTEL (MOdels and Tools for Enhanced Learning).
Projet Transformation41
Le projet Transformation41 vise à proposer des outils d'analyse aux enseignants afin de leur donner des retours sur la pratique numérique de leurs élèves et de les accompagner dans la transformation de leurs pratiques pédagogiques.
L'équipe MOTEL, en collaboration avec le laboratoire LIUM, Laval (France), se concentrera sur la proposition de tableaux de bord d'apprentissage (TBA) dynamiques adaptés (Schwendimann et al.2016). Dans les travaux antérieurs menés lors du projet Hubble, nos deux équipes ont proposé une approche centrée sur l'utilisateur pour la génération de tels tableaux (Dabbebi et al.2017). Nous visons à réutiliser et étendre ces résultats dans ce projet. Les résultats actuels comprennent:
Notre objectif est de produire des tableaux dédiés aux enseignants dans le contexte du projet, et d'étendre nos modèles et les outils et prototypes associés afin d'inclure une évolution dynamique des tableaux de bord d’apprentissage pour soutenir le processus de prise de décision.
Description du poste
Les travaux proposés se déroulent dans le cadre du projet Transformation41.
Dans le cadre de ce projet, le demandeur aura en charge:
A cet effet, le candidat fera partie d'une équipe de huit personnes, et travaillera en étroite collaboration avec l'équipe du LIUM, sous la supervision du chef de projet à IMT Atlantique.
Compétences demandées
○ Hypermédia adaptatif
○ Génération de tableaux de bord
○ Génie logiciel et développement web
Pour plus d'informations, contactez Jean-Marie Gilliot, jm.gilliot@imt-atlantique.fr, +33 (0) 22 900 1539, ou Sébastien Iksal, sebastien.iksal@univ-lemans.fr, +33 (0) 24 359 4919
Pour postuler, merci d'envoyer à Jean-Marie Gilliot (jm.gilliot@imt-atlantique.fr):
Références
Dabbebi, I., Iksal, S., Gilliot, J. M., May, M., & Garlatti, S. Towards Adaptive Dashboards for Learning Analytic: An Approach for Conceptual Design and implementation. 9th International Conference on Computer Supported Education (CSEDU 2017), Apr 2017, Porto, Portugal. pp.120-131, 10.5220/0006325601200131ff. hal-01574127
Gilliot, J. M., Iksal, S., Medou, D., & Dabbebi, I. (2018, October). Conception participative de tableaux de bord d'apprentissage. IHM’18 : 30e Conférence Francophone sur l’Interaction Homme-Machine, Oct 2018, Brest, France. pp. 119-127. hal-01897914
Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., ... & Dillenbourg, P. (2016). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1), 30-41.
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