Publications

2017

• Double MRF for water classification in SAR images by joint detection and reflectivity estimation
  
  • Lobry Sylvain
  • Denis Loïc
  • Tupin Florence
  • Fjortoft R.
  , 2017.
• Similarity criterion for SAR tomography over dense urban area
  
  • Rambour Clément
  • Denis Loïc
  • Tupin Florence
  • Nicolas Jean Marie
  • Oriot Hélène
  • Ferro-Famil Laurent
  • Deledalle Charles-Alban
  , 2017. (10.1109/IGARSS.2017.8127315)
  DOI : 10.1109/IGARSS.2017.8127315
• A Complex Sprectrum Based SAR Image Resampling Method With Restricted Target Sidelobes and Statistics Preservation
  
  • Abergel Rémy
  • Ladjal Saïd
  • Tupin Florence
  • Nicolas Jean-Marie
  , 2017. The aim of this work is to present a resampling scheme for SAR images that preserves spatial resolution and produces statistically accurate images at the same time. Indeed, SAR images are, for reasons due to their acquisition process, well sampled signals according to the Shannon sampling theory. In the presence of strong responses, that we will refer to as targets, a sinc-like function centered at the target is smeared over the entire image and is particularly visible in the range of tens of pixels surrounding the target. To mitigate this phenomenon, the usual solution is to apply an apodization window in the Fourier domain so as to change the cardinal sine impulse response into a much rapidly decaying one. This approach has two major drawbacks. It reduces the resolution of the image and introduces inaccurate statistical dependency between pixels. We propose to resample the image in an adaptive and robust way so that the target smear is canceled and the new sampled image is completely faithful to the underlying signal. (10.1109/IGARSS.2017.8128214)
  DOI : 10.1109/IGARSS.2017.8128214
• System-Level Design for Communication-Centric Task Farm Applications
  
  • Genius Daniela
  • Apvrille Ludovic
  , 2017. —Massively parallel applications such as telecommunication and video streaming have the par-ticularity that a large proportion of the time is spent on accessing communication channels between the tasks, due to contention on the on-chip interconnect. Moreover, the analysis of a given task deployment is often fastidious. Thus, we propose to extend an existing easy-to-use System-level Design methodology to task farm applications. The contribution first concerns adding relevant SysML modeling elements to take into account application code, hardware platforms and deployment constraints. Secondly, new modeling elements – including access techniques to communication channels – must be given a semantics in order to transform models into a well-defined SystemC virtual prototyping MPSoC platform. A telecommunication application serves as an example.
• Challenging 3D Head Tracking and Evaluation Using Unconstrained Test Data Set
  
  • Ababsa Fakhreddine
  • Tran Ngoc-Trung
  • Charbit Maurice
  , 2017, pp.205--210. 3D face tracking using one monocular camera is an important topic, since it is useful in many domains such as: video surveillance system, human machine interaction, biometrics, etc. In this paper, we propose a new 3D face tracking which is robust to large head rotations. Underlying cascaded regression approach for 2D landmark detection, we build an extension in context of 3D pose tracking. To better work with out-of-plane issues, we extend the training dataset by including a new set of synthetic images. For evaluation, we propose to use a new recording system to capture automatically face pose ground-truth, and create a new test dataset, named U3PT (Unconstrained 3D Pose Tracking). Theperformance of our method along with the state-of-the-art methods are carried out to analyze advantage as well as limitations need to be improved in the future. (10.1109/iV.2017.40)
  DOI : 10.1109/iV.2017.40
• Amélioration d'attaques par canaux auxiliaires sur la cryptographie asymétrique
  
  • Dugardin Margaux
  , 2017. Depuis les années 90, les attaques par canaux auxiliaires ont remis en cause le niveau de sécurité des algorithmes cryptographiques sur des composants embarqués. En effet, tout composant électronique produit des émanations physiques, telles que le rayonnement électromagnétique, la consommation de courant ou encore le temps d’exécution du calcul. Or il se trouve que ces émanations portent de l’information sur l’évolution de l’état interne. On parle donc de canal auxiliaire, car celui-ci permet à un attaquant avisé de retrouver des secrets cachés dans le composant par l’analyse de la « fuite » involontaire. Cette thèse présente d’une part deux nouvelles attaques ciblant la multiplication modulaire permettant d’attaquer des algorithmes cryptographiques protégés et d’autre part une démonstration formelle du niveau de sécurité d’une contre-mesure. La première attaque vise la multiplication scalaire sur les courbes elliptiques implémentée de façon régulière avec un masquage du scalaire. Cette attaque utilise une unique acquisition sur le composant visé et quelques acquisitions sur un composant similaire pour retrouver le scalaire entier. Une fuite horizontale durant la multiplication de grands nombres a été découverte et permet la détection et la correction d’erreurs afin de retrouver tous les bits du scalaire. La seconde attaque exploite une fuite due à la soustraction conditionnelle finale dans la multiplication modulaire de Montgomery. Une étude statistique de ces soustractions permet de remonter à l’enchaînement des multiplications ce qui met en échec un algorithme régulier dont les données d’entrée sont inconnues et masquées. Pour finir, nous avons prouvé formellement le niveau de sécurité de la contre-mesure contre les attaques par fautes du premier ordre nommée extension modulaire appliquée aux courbes elliptiques.
• Learning-based Adaptive Tone Mapping for Keypoint Detection
  
  • Rana Aakanksha A
  • Valenzise Giuseppe
  • Dufaux Frederic
  , 2017. The goal of tone mapping operators (TMOs) has traditionally been to display high dynamic range (HDR) pictures in a perceptually favorable way. However, when tone-mapped images are to be used for computer vision tasks such as keypoint detection, these design approaches are suboptimal. In this paper, we propose a new learning-based adaptive tone mapping framework which aims at enhancing keypoint stability under drastic illumination variations. To this end, we design a pixel-wise adaptive TMO which is modulated based on a model derived by Support Vector Regression (SVR) using local higher order characteristics. To circumvent the difficulty to train SVR in this context, we further propose a simple detection similarity-maximization model to generate appropriate training samples using multiple images undergoing illumination transformations. We evaluate the performance of our proposed framework in terms of keypoint repeatability for state-of-the-art keypoint detectors. Experimental results show that our proposed learning-based adaptive TMO yields higher keypoint stability when compared to existing perceptually-driven state-of-the-art TMOs. (10.1109/icme.2017.8019394)
  DOI : 10.1109/icme.2017.8019394
• A Circuit-Based Approach to Efficient Enumeration
  
  • Amarilli Antoine
  • Bourhis Pierre
  • Jachiet Louis
  • Mengel Stefan
  , 2017, pp.1-15. We study the problem of enumerating the satisfying valuations of a circuit while bounding the delay, i.e., the time needed to compute each successive valuation. We focus on the class of structured d-DNNF circuits originally introduced in knowledge compilation, a sub-area of artificial intelligence. We propose an algorithm for these circuits that enumerates valuations with linear preprocessing and delay linear in the Hamming weight of each valuation. Moreover, valuations of constant Hamming weight can be enumerated with linear preprocessing and constant delay. Our results yield a framework for efficient enumeration that applies to all problems whose solutions can be compiled to structured d-DNNFs. In particular, we use it to recapture classical results in database theory, for factorized database representations and for MSO evaluation. This gives an independent proof of constant-delay enumeration for MSO formulae with first-order free variables on bounded-treewidth structures. (10.4230/LIPIcs.ICALP.2017.111)
  DOI : 10.4230/LIPIcs.ICALP.2017.111
• Distributed event-triggered control for multi-agent formation stabilization
  
  • Viel Christophe
  • Bertrand Sylvain
  • Kieffer Michel
  • Piet-Lahanier Hélène
  , 2017. This paper addesses the problem of formation control in multi-agent systems (MAS) and adopts an event-triggered strategy to reduce the number of communications between agents. For that purpose, to evaluate its control input, each agent maintains estimators of the states of the other agents. Communication is triggered when the discrepancy between the actual state of an agent and its estimate reaches some threshold. The impact of additive state perturbations is studied. A condition for the convergence of the MAS to a stable formation is studied. Simulations show the effectiveness of the proposed approach.
• Sparse Stochastic Bandits
  
  • Kwon Joon
  • Perchet Vianney
  • Vernade Claire
  , 2017. In the classical multi-armed bandit problem, d arms are available to the decision maker who pulls them sequentially in order to maximize his cumulative reward. Guarantees can be obtained on a relative quantity called regret, which scales linearly with d (or with sqrt(d) in the minimax sense). We here consider the sparse case of this classical problem in the sense that only a small number of arms, namely s < d, have a positive expected reward. We are able to leverage this additional assumption to provide an algorithm whose regret scales with s instead of d. Moreover, we prove that this algorithm is optimal by providing a matching lower bound - at least for a wide and pertinent range of parameters that we determine - and by evaluating its performance on simulated data.
• Large-scale operator-valued kernel regression
  
  • Brault Romain
  , 2017. Many problems in Machine Learning can be cast into vector-valued approximation. Operator-Valued Kernels and vector-valued Reproducing Kernel Hilbert Spaces provide a theoretical and practical framework to address that issue, extending nicely the well-known setting of scalar-valued kernels. However large scale applications are usually not affordable with these tools that require an important computational power along with a large memory capacity. In this thesis, we propose and study scalable methods to perform regression with Operator-Valued Kernels. To achieve this goal, we extend Random Fourier Features, an approximation technique originally introduced for scalar-valued kernels, to Operator-Valued Kernels. The idea is to take advantage of an approximated operator-valued feature map in order to come up with a linear model in a finite-dimensional space. This thesis is structured as follows. First we develop a general framework devoted to the approximation of shift-invariant MErcer kernels on Locally Compact Abelian groups and study their properties along with the complexity of the algorithms based on them. Second we show theoretical guarantees by bounding the error due to the approximation, with high probability. Third, we study various applications of Operator Random Fourier Features (ORFF) to different tasks of Machine learning such as multi-class classification, multi-task learning, time serie modelling, functionnal regression and anomaly detection. We also compare the proposed framework with other state of the art methods. Fourth, we conclude by drawing short-term and mid-term perspectives of this work.
• Every dog has its day: A comparative study of clustering algorithms in VANETs
  
  • Zhang Jun
  • Ren Mengying
  • Labiod Houda
  • Khoukhi Lyes
  , 2017, pp.383-389.
• Pre-Coded NRZ and Electrical Duo-Binary Transmission in C and O-band at Data Bit Rates up to 25 Gbit/s
  
  • Konopacki Justine
  • Le Guyader Bertrand
  • Genay Naveena
  • Anet Neto Luiz
  • Chanclou Philippe
  • Erasme Didier
  , 2017. In this paper we present real-time transmission performances up to 25 Gbit/s for optical access network. Our solution is based at transceiver side on pre-coded NRZ and electrical duo-binary modulations using limited electrical bandwidth DML in C and O band. At the receiver side, an electrical duo-binary receiver based on an 8 GHz APD photodiode combined with an online duo-binary to binary converter is employed.
• Implementation of an UAV Guidance, Navigation and Control System based on the CAN data bus: Validation using a Hardware In the Loop Simulation
  
  • Louali Rabah
  • Gacem Hind
  • Elouardi Abdelhafid
  • Bouaziz Samir
  , 2017. (10.1109/AIM.2017.8014217)
  DOI : 10.1109/AIM.2017.8014217
• Impact of traffic serving order on mixed-line-rate optical network performances
  
  • Chouman Hussein
  • Lourdiane Mounia
  • Ware Cédric
  , 2017 (Th.A3.5). The forecasted traffic growth makes the transmission capacity in backbone optical networks a scarce resource. Enabling the use of high-spectral-efficiency modulation formats (HSE-MFs) such as DQPSK or PM-QPSK for 40 or 100 Gbps respectively is one of the suggested on-going researches. However, some lightpaths may not require the highest rate and this call for mixed-line rate (MLR) networks. In MLR networks xQPSK formats' reachability decreases due to cross-phase-modulation (XPM) induced by neighbouring OOK channels. Therefore, to increase MLR networks spectral efficiency it should be able to use HSE-MF for high capacity demands. However, the choice of the MF to establish a lightpath is dependent on the length to satisfy the Bit-error-rate (BER) constraint. For that, we should route the high capacity demand in the shortest physical routes and vice versa. Thus, in this work we demonstrate the importance of traffic serving ordering based either on demand capacity or physical route in the static phase of the network design. We use the Core-Topology of the Pan-European network with a static traffic matrix of Log-Normal distribution. Serving traffic in the increasing order of the demands' shortest-path shows the best performance in term of blocking and resources utilization (10.1109/ICTON.2017.8025137)
  DOI : 10.1109/ICTON.2017.8025137
• WDM slot sharing of colored optical packets for latency improvement and Class of Service differentiation
  
  • Amar Djamel
  • Lepers Catherine
  • Gillet Franck
  • Lourdiane Mounia
  • Ware Cédric
  • Chiaroni Dominique
  , 2017 (Th.A3.4). The optical packet switching technology has been identified as a key technology to offer data rate and modulation format transparency, fine switching granularity and efficient bandwidth utilization. The ANR N-GREEN project proposes a novel over-dimensioned switch/router node with colored optical packet concept. In this work, we propose a new WDM slot sharing approach that significantly improves N-GREEN node-level latency, and show that it is quite effective for Class of Service differentiation in the optical domain (10.1109/ICTON.2017.8025136)
  DOI : 10.1109/ICTON.2017.8025136
• Asymptotic properties of quasi-maximum likelihood estimators in observation-driven time series models
  
  • Douc Randal
  • Fokianos Konstantinos
  • Moulines Éric
  Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2017, 11 (2), pp.2707 - 2740. We study a general class of quasi-maximum likelihood estimators for observation-driven time series models. Our main focus is on models related to the exponential family of distributions like Poisson based models for count time series or duration models. However, the proposed approach is more general and covers a variety of time series models including the ordinary GARCH model which has been studied extensively in the literature. We provide general conditions under which quasi-maximum likelihood estimators can be analyzed for this class of time series models and we prove that these estimators are consistent and asymptotically normally distributed regardless of the true data generating process. We illustrate our results using classical examples of quasi-maximum likelihood estimation including standard GARCH models, duration models, Poisson type autoregressions and ARMA models with GARCH errors. Our contribution unifies the existing theory and gives conditions for proving consistency and asymptotic normality in a variety of situations (10.1214/17-EJS1299)
  DOI : 10.1214/17-EJS1299
• Practical Realization of a Transformation Optics based Dielectric Superstrate for Patch Antenna using 3D printing
  
  • Joshi Chetan
  • Lepage A. C.
  • Begaud Xavier
  • Piau Gérard-Pascal
  , 2017.
• FUSION OF SAR AND OPTICAL REMOTE SENSING DATA - CHALLENGES AND RECENT TRENDS
  
  • Schmitt Michael
  • Tupin Florence
  • Zhu Xiao Xiang
  , 2017.
• Beat gesture prediction using prosodic features
  
  • Jain Varun
  • Clavel Chloé
  • Pelachaud Catherine
  , 2017. In this work we present a machine learning approach to gesture prediction using prosodic features. We use conditional random fields to predict the presence of beat gestures using the following prosodic features: pitch, pitch-derivatives, intensity and absence or presence of syllable nuclei. These features are calculated over overlapping sliding windows big enough to average out the high frequency variations associated with pitch and intensity at the syllable level. We found that the results improve remarkably when the classification is treated as a multi-class problem as opposed to a binary problem with the two classes: presence and absence of gesture.
• Nonlinear Integrated Photonics
  
  • Grillot Frédéric
  , 2017.
• SMART : Règles d’associations temporelles de signaux sociaux pour la synthèse d’un Agent Conversationnel Animé avec une attitude spécifique
  
  • Bailly Kévin
  • Clavel Chloé
  • Janssoone Thomas
  • Richard Gael
  Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, Lavoisier, 2017. <p>Afin d'améliorer l'interaction entre des Humains et des Agents Conversationnels Animés (ACA), l'un des enjeux majeurs du domaine est de générer des agents crédibles socialement. Dans cet article, nous présentons une méthode, intitulée SMART pour Social Multimodal Association Rules with Timing, capable de trouver automatiquement des associations temporelles entre l'utilisation de signaux sociaux (mouvements de tête, expressions faciales, prosodie ...) issues de vidéos d'interactions d'Humains exprimant différents états affectifs (comportement, attitude, émotions, ... ). Notre système est basé sur un algorithme de fouille de séquences qui lui permet de trouver des règles d'associations temporelles entre des signaux sociaux extraits automatiquement de flux audio-vidéo. SMART va également analyser le lien de ces règles avec chaque état affectif pour ne conserver que celles qui sont pertinentes. Finalement, SMART va les enrichir afin d'assurer une animation facile d'un ACA pour qu'il exprime l'état voulu.</p> <p> </p> <p>Dans ce papier, nous formalisons donc l'implémentation de SMART et nous justifions son intérêt par plusieurs études. Dans un premier temps, nous montrons que les règles calculées sont bien en accord avec la littérature en psychologie et sociologie. Ensuite, nous présentons les résultats d'évaluations perceptives que nous avons conduites suite à des études de corpus proposant l'expression d'attitudes sociales marquées.</p>
• A Tour of Patch-based Methods and their Applications in Remote Sensing
  
  • Tupin Florence
  • Deledalle Charles-Alban
  , 2017.
• PROCESS FOR MONOVALENT ONE-TO-ONE EXTRACTION OF KEYS FROM THE PROPAGATION CHANNEL
  
  • Molière Renaud
  • Kameni Ngassa Christiane L.
  • Delaveau François
  • Lemenager Claude
  • Sibille Alain
  • Mazloum Taghrid
  • Shapira Nir
  , 2017.
• Évènements rares contrôlables dans un laser à semi-conducteurs sous injection optique
  
  • Schires Kevin
  • Grillot Frédéric
  , 2017.