Publications

2020

• Resilience by design & failures forecasting for a connected autonomous vehicle
  
  • Monteuuis Jean-Philippe
  , 2020. Autonomous vehicles with an automation level 5 will drive autonomously in any road scenarios such as highways, snowy roads, urban areas, or traffic jams. The integration of V2X communication, as a new source of perception for the vehicle could remove the limitations of local perception by communicating with an occluded pedestrian or by detecting in advance the presence of a vehicle under a heavy mist. However, this V2X communication may be a new source of attacks threatening the vehicle perception. Current countermeasures are not designed for all autonomous vehicles because these countermeasures require the driver assistance or work with a specific set of sensors. Therefore, the thesis aims to propose a generic failure resilient perception architecture for all types of connected and autonomous vehicles supporting different kinds of sensors. In this thesis, we propose a generic perception architecture named GPA with its failure resilient perception algorithm (FRPA). We propose a new threat analysis and risk assessment method named SARA that identifies and assess the risk of attacks targeting connected and automated vehicles with an automation level 5. To identify where and how these attacks occur, we propose an attacker and a security goal model for all automotive perception systems. We implemented two modules of our failures resilient perception algorithm (FRPA): a Machine Learning based Failure Classifier and a V2X-Sensor Correlation Module considering three kinds of source: camera, radar, and V2X. We highlighted several new attacks in the perception pipeline and raise the need for new security countermeasures such as the physical integrity of road infrastructures and trustworthy perception algorithms. Besides, our countermeasures based on machine learning and sensor correlation showed very accurate results to detect and classifies perception failures (over 90% accuracy score). Finally, the ideas developed in the thesis resulted in 10 filled patents and several publications. (10.70675/5bb4affaz6191z4303za083ze77ca86299b8)
  DOI : 10.70675/5bb4affaz6191z4303za083ze77ca86299b8
• Full-duplex for cellular networks : a stochastic geometry approach
  
  • Arrano Scharager Hernan
  , 2020. Full-duplex (FD) is a principle in which a transceiver can receive and transmit on the same time-frequency radio resource. The principle was long held as impractical due to the high self-interference that arises when simultaneously transmitting and receiving in the same resource block. When assuming perfect self-interference cancellation, FD can potentially double the spectral efficiency (SE) of a given point-to-point communication. In practice though, it is not possible to achieve the aforementioned characteristic. Moreover, under a cellular network context, not only the self-interference limits the performance, since additional co-channel interference is created by base stations (BSs) and users equipment (UEs). However, even with the higher interference dowlinks (DLs) still obtain higher SE performances, whereas uplinks (ULs) are generally critically degraded, when compared to half-duplex (HD). We focus our work in the study of alternatives that can help improve the impaired ULs in FD networks, while still trying to profit from the gains experienced by DLs.In this regard, we use stochastic geometry along the thesis as a means to characterize key performance indicators of cellular networks, such as: coverage probability, average SE and data rates. The thesis is divided into three major studies. Firstly, we propose a duplex-switching policy which enables BSs to operate in FD- or HD- depending on the UL and DL conditions. Secondly, we investigate the performance of hybrid HD/FD networks under a millimeter wave context. Finally, we propose a novel algorithm based on nonorthogonal multiple-access (NOMA) and successive interference cancellation (SIC), which allows BSs to coordinate on their respective transmission schemes to reduce the BS-to-BS interference. We demonstrate that the models presented in the thesis allow to balance the gains of one link over the other; reducing the UL degradation, while maintaining DL gains. In addition, we show that scenarios in which equipment is able to perform beamforming are ideal for FD deployments, since they directly reduce the cochannel interference. (10.70675/6cd908c0z3795z478az87a9z8bc7ae0a1431)
  DOI : 10.70675/6cd908c0z3795z478az87a9z8bc7ae0a1431
• Hardware / Software / Analog System Partitioning with SysML and SystemC-AMS
  
  • Genius Daniela
  • Apvrille Ludovic
  , 2020. Model-driven approaches for designing software and hardware parts of embedded systems are generally limited to their digital parts. On the other hand, virtual prototyping and co-simulation have emerged as a promising research topic, but target the modeling levels when partitioning has already been performed. This paper presents a model-driven platform for the partitioning of analog/mixed-signal systems. keywords: virtual prototyping, embedded systems , analog/mixed signal, design space exploration
• A New Network Configuration Management Architecture for Future Aircraft Systems
  
  • Delmas Thibault
  • Iannone Luigi
  • Garcia Jean-Pierre
  • Monsuez Bruno
  , 2020. Aircraft systems are evolving and being enhanced thanks to new design paradigms leveraging on recent technology advances in embedded systems. However, the Integrated Modular Avionics (IMA) model, used in current avionics, has shown important limitations to accommodate such evolution. These new paradigms demand for much more global system modularity than what IMA is able to offer. Such system evolution has as well an impact on the underlying different networks present on aircrafts. In this context, it is mandatory to investigate the kind and the breadth of adaptation networks need in order to cope with new requirements. To this end, this paper we firstly investigate the current aircrafts network configuration and management procedures. It appears that they lack the features, more specifically, the configuration management features, necessary to support these new use cases. We then look at proposals trying to fulfil this features gap. Each of them, while providing parts of the answers, also come with trade-off or insufficiencies that prevent them from fully answering to the new needs. Then, a new network configuration architecture able to cope with the newly defined configuration management requirements is provided. A comparison to the other approaches is presented, so to highlight how the proposed architecture better fulfils long-term evolution requirements while being less complex and more suitable for current configuration procedures than the other proposal. Finally, a simulation of the configuration architecture is done to provide insights on the new proposed features.
• Towards Formal Verification of Autonomous Driving Supervisor Functions
  
  • Assioua Yasmine
  • Ameur-Boulifa Rabéa
  • Guitton-Ouhamou Patricia
  , 2020. In the software development lifecycle, errors and flaws can be introduced in the different phases and lead to failures. Establishing a set of functional requirements helps producing safe software. However, ensuring that the (being) developed software is compliant with those requirements is a challenging task due to the lack of automatic and formal means to lead this verification. In this paper, we present our approach that aims at analysing a collection of automotive requirements by using formal methods. The proposed approach for formal verification is evaluated by the application to supervisor functions of the autonomous driving (AD) system, the system in charge of self-driving.
• Invited Lecture on Continous-Variable Quantum Key Distribution
  
  • Alleaume Romain
  , 2020.
• Engineering Railway Systems with an Architecture-Centric Process Supported by AADL and ALISA: an Experience Report
  
  • Crisafulli Paolo
  • Blouin Dominique
  • Caron Françoise
  • Maxim Cristian
  , 2020. The increasing automation of transportation systems has contributed to the emergence of the so-called Cyber-Physical Systems (CPS), which are computation-based systems in which computing devices, sensors, actuators and networks collaborate to monitor and control physical entities via feedback loops. To cope with the increasing complexity of such systems, engineering teams require model-based tools, because they can provide early virtual integration and verification, reuse of existing models, requirements traceability and support of an incremental development process. However, these benefits can only be earned if the chosen modelling languages are expressive enough to capture all aspects necessary to perform the virtual verifications with the required confidence degree.
• Prédiction conformelle profonde pour des modèles robustes
  
  • Messoudi Soundouss
  • Rousseau Sylvain
  • Destercke Sébastien
  , 2020, RNTI-E-36, pp.301-308. Les réseaux profonds, comme d'autres modèles, peuvent associer une confiance élevée à des prédictions peu fiables. Rendre ces modèles robustes et fiables est donc essentiel, surtout pour les décisions critiques. Ce papier montre expérimentalement que la prédiction conformelle, et plus particulièrement l'ap-proche de [Hechtlinger et al. (2018)], apporte une solution convaincante à ce défi. La prédiction conformelle fournit un ensemble de classes couvrant la vraie classe avec avec une fréquence choisie au préalable par l'utilisateur. Dans le cas où l'exemple à prédire est atypique, la prédiction conformelle prédira l'en-semble vide. Les expériences menées montrent le bon comportement de l'ap-proche conformelle, en particulier lorsque les données sont bruitées.
• Innovative ATMEGA8 Microcontroler Static Authentication Based on SRAM PUF
  
  • Urien Pascal
  , 2020, pp.1-2. (10.1109/CCNC46108.2020.9045502)
  DOI : 10.1109/CCNC46108.2020.9045502
• High Security Bare Metal Bluetooth Blockchain Payment Terminal For Trusted Ethereum Transaction
  
  • Urien Pascal
  , 2020, pp.1-2. (10.1109/CCNC46108.2020.9045146)
  DOI : 10.1109/CCNC46108.2020.9045146
• On the Effect of Aging on Digital Sensors
  
  • Anik Md Toufiq Hasan
  • Guilley Sylvain
  • Danger Jean-Luc
  • Karimi Naghmeh
  , 2020, pp.189-194. (10.1109/VLSID49098.2020.00050)
  DOI : 10.1109/VLSID49098.2020.00050
• Static Data-Flow Analysis of UML/SysML Functional Views for Signal and Image Processing Applications
  
  • Enrici Andrea
  • Apvrille Ludovic
  • Pacalet Renaud
  • Pham Minh Hiep
  , 2020, pp.101-126. (10.1007/978-3-030-37873-8_5)
  DOI : 10.1007/978-3-030-37873-8_5
• Nonparametric imputation by data depth
  
  • Mozharovskyi Pavlo
  • Josse Julie
  • Husson François
  Journal of the American Statistical Association, Taylor & Francis, 2020, 115 (529), pp.241-253. The presented methodology for single imputation of missing values borrows the idea from data depth --- a measure of centrality defined for an arbitrary point of the space with respect to a probability distribution or a data cloud. This consists in iterative maximization of the depth of each observation with missing values, and can be employed with any properly defined statistical depth function. On each single iteration, imputation is narrowed down to optimization of quadratic, linear, or quasiconcave function being solved analytically, by linear programming, or the Nelder-Mead method, respectively. Being able to grasp the underlying data topology, the procedure is distribution free, allows to impute close to the data, preserves prediction possibilities different to local imputation methods (k-nearest neighbors, random forest), and has attractive robustness and asymptotic properties under elliptical symmetry. It is shown that its particular case --- when using Mahalanobis depth --- has direct connection to well known treatments for multivariate normal model, such as iterated regression or regularized PCA. The methodology is extended to the multiple imputation for data stemming from an elliptically symmetric distribution. Simulation and real data studies positively contrast the procedure with existing popular alternatives. The method has been implemented as an R-package. (10.1080/01621459.2018.1543123)
  DOI : 10.1080/01621459.2018.1543123
• Tunable All-Optical Modulation and Building Blocks for Optical Neurons at Mid-Infrared Wavelength
  
  • Spitz O
  • Wu J
  • Herdt A
  • Carras M
  • Maisons G
  • Elsässer W
  • Wong C.-W
  • Grillot F
  , 2020. Quantum cascade lasers (QCLs) under optical feedback can output several non-linear dynamics whose properties depend on the reinjected light polarization. We demonstrate all-optical modulation, thresholding and excitability in QCLs, to experimentally build basic optical neurons.
• Conveying Emotions Through Device-Initiated Touch
  
  • Teyssier Marc
  • Bailly Gilles
  • Pelachaud Catherine I
  • Lecolinet Eric
  IEEE Transactions on Affective Computing, Institute of Electrical and Electronics Engineers, 2020, pp.1-1. Humans have the ability to convey an array of emotions through complex and rich touch gestures. However, it is not clear how these touch gestures can be reproduced through interactive systems and devices in a remote mediated communication context. In this paper, we explore the design space of device-initiated touch for conveying emotions with an interactive system reproducing a collection of human touch characteristics. For this purpose, we control a robotic arm to touch the forearm of participants with different force, velocity and amplitude characteristics to simulate human touch. In view of adding touch as an emotional modality in human-machine interaction, we have conducted two studies. After designing the touch device, we explore touch in a context-free setup and then in a controlled context defined by textual scenarios and emotional facial expressions of a virtual agent. Our results suggest that certain combinations of touch characteristics are associated with the perception of different degrees of valence and of arousal. Moreover, in the case of non-congruent mixed signals (touch, facial expression, textual scenario) not conveying a priori the same emotion, the message conveyed by touch seems to prevail over the ones displayed by the visual and textual signals. (10.1109/TAFFC.2020.3008693)
  DOI : 10.1109/TAFFC.2020.3008693
• Power efficient all-fiberized 12-core erbium/ytterbium doped optical amplifier
  
  • Mélin Gilles
  • Kerampran Romain
  • Monteville Achille
  • Bordais Sylvain
  • Robin Thierry
  • Landais David
  • Lebreton Aurélien
  • Jaouën Yves
  • Taunay Thierry
  , 2020, pp.M4C.2. (10.1364/OFC.2020.M4C.2)
  DOI : 10.1364/OFC.2020.M4C.2
• Raman-free fibered photon-pair source
  
  • Cordier Martin
  • Delaye Philippe
  • Gérôme Frédéric
  • Benabid Fetah
  • Zaquine Isabelle
  Scientific Reports, Nature Publishing Group, 2020, 10, pp.1650. Raman-scattering noise in silica has been the key obstacle toward the realisation of high quality fiber-based photon-pair sources. Here, we experimentally demonstrate how to get past this limitation by dispersion tailoring a xenon-filled hollow-core photonic crystal fiber. The source operates at room temperature, and is designed to generate Raman-free photon-pairs at useful wavelength ranges, with idler in the telecom, and signal in the visible range. We achieve a coincidence-to-accidentals ratio as high as 2740 combined with an ultra low heralded second order coherence g(2)H(0)=0.002, indicating a very high signal to noise ratio and a negligible multi-photon emission probability. Moreover, by gas-pressure tuning, we demonstrate the control of photon frequencies over a range as large as 13 THz, covering S-C and L telecom band for the idler photon. This work demonstrates that hollow-core photonic crystal fiber is an excellent platform to design high quality photon-pair sources, and could play a driving role in the emerging quantum technology. (10.1038/s41598-020-58229-7)
  DOI : 10.1038/s41598-020-58229-7
• Spiking Neural Networks and online learning: An overview and perspectives
  
  • Lobo Jesus
  • del Ser Javier
  • Bifet Albert
  • Kasabov Nikola
  Neural Networks, Elsevier, 2020, 121, pp.88-100. Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they often turn into evolving environments where a change may affect the input data distribution. Such a change causes that predictive models trained over these stream data become obsolete and do not adapt suitably to new distributions. Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores. Unfortunately, most off-the-shelf classification models need to be retrained if they are used in changing environments, and fail to scale properly. Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks. Besides, some specific flavors of Spiking Neural Networks can overcome the necessity of retraining after a drift occurs. This work intends to merge both fields by serving as a comprehensive overview, motivating further developments that embrace Spiking Neural Networks for online learning scenarios, and being a friendly entry point for non-experts. (10.1016/j.neunet.2019.09.004)
  DOI : 10.1016/j.neunet.2019.09.004
• On the decoding of Barnes-Wall lattices
  
  • Corlay Vincent
  • Boutros Joseph
  • Ciblat Philippe
  • Brunel Loïc
  , 2020. We present new efficient recursive decoders for the Barnes-Wall lattices based on their squaring construction. The analysis of the new decoders reveals a quasi-quadratic complexity in the lattice dimension. The error rate is shown to be close to the universal lower bound in dimensions 64 and 128.
• Introduction of 3D Modeling and Peripheral Nerve Tractography in the Management of Pelvic Tumors
  
  • Goulin Jeanne
  • Meignan Pierre
  • Blanc Thomas
  • Delmonte Alessandro
  • Peyrot Quoc
  • Berteloot Laureline
  • Boddaert Nathalie
  • Bloch Isabelle
  • Sarnacki Sabine
  , 2020.
• Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling
  
  • Clémençon Stéphan
  • Vogel Robin
  • Achab Mastane
  • Tillier Charles
  , 2020. We consider statistical learning problems, when the distribution P of the training observations Z 1 ,. .. , Z n differs from the distribution P involved in the risk one seeks to minimize (referred to as the test distribution) but is still defined on the same measurable space as P and dominates it. In the unrealistic case where the likelihood ratio Φ(z) = dP/dP (z) is known, one may straightforwardly extends the Empirical Risk Minimization (ERM) approach to this specific transfer learning setup using the same idea as that behind Importance Sampling, by minimizing a weighted version of the empirical risk functional computed from the 'biased' training data Z i with weights Φ(Z i). Although the importance function Φ(z) is generally unknown in practice, we show that, in various situations frequently encountered in practice, it takes a simple form and can be directly estimated from the Z i 's and some auxiliary information on the statistical population P. By means of linearization techniques, we then prove that the generalization capacity of the approach aforementioned is preserved when plugging the resulting estimates of the Φ(Z i)'s into the weighted empirical risk. Beyond these theoretical guarantees, numerical results provide strong empirical evidence of the relevance of the approach promoted in this article.
• A lightweight ECC-based authentication scheme for Internet of Things (IoT)
  
  • Hammi Badis
  • Fayad Achraf
  • Khatoun Rida
  • Zeadally Sherali
  IEEE Systems Journal, IEEE, 2020. (10.1109/JSYST.2020.2970167)
  DOI : 10.1109/JSYST.2020.2970167
• Real-Time Deformation with Coupled Cages and Skeletons
  
  • Corda F
  • Thiery J M
  • Livesu M
  • Puppo E
  • Boubekeur T
  • Scateni R
  Computer Graphics Forum, Wiley, 2020. Skeleton-based and cage-based deformation techniques represent the two most popular approaches to control real-time deformations of digital shapes and are, to a vast extent, complementary to one another. Despite their complementary roles, high-end modelling packages do not allow for seamless integration of such control structures, thus inducing a considerable burden on the user to maintain them synchronized. In this paper, we propose a framework that seamlessly combines rigging skeletons and deformation cages, granting artists with a real-time deformation system that operates using any smooth combination of the two approaches. By coupling the deformation spaces of cages and skeletons, we access a much larger space, containing poses that are impossible to obtain by acting solely on a skeleton or a cage. Our method is oblivious to the specific techniques used to perform skinning and cage-based deformation, securing it compatible with pre-existing tools. We demonstrate the usefulness of our hybrid approach on a variety of examples. (10.1111/cgf.13900)
  DOI : 10.1111/cgf.13900
• Minimal linear codes from characteristic functions
  
  • Mesnager Sihem
  • Qi Y.
  • Ru H.
  • Tang C.
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020.
• Discrete and stochastic coalitional storage games
  
  • Kiedanski Diego
  • Orda Ariel
  • Kofman Daniel
  , 2020. To achieve a fully decarbonized power grid, a massive deployment of renewable energy resources will be needed, but because of the intermittent nature of their generation, their full potential will not be unleashed unless demand side flexibility plays a bigger role than today. Introducing energy storage at the residential level enables increasing load flexibility, as it allows end-customers to easily change their consumption profile and adapt to the grid requirements. As of today, energy storage for residential consumers represents a considerable investment that is not guaranteed to be profitable. Shared investment models in which a group of consumers jointly acquires energy storage have been proposed in the literature to increase the attractiveness of these devices. Such models naturally employ concepts of cooperative game theory. In this paper, we extend the state-of-the-art cooperative game for modeling the shared investment in storage by adding two crucial extensions: stochasticity of the load and discreetness of the storage device capacity. As our goal is to increase storage capacity in the grid, the number of devices that would be acquired by a group of players that cooperate according to our proposed scheme is compared to the number of devices that would be bought by consumers acting individually. Under the same criteria of customer profitability , simulations using real data reveal that our proposed scheme can increase the deployed storage capacity between 100% and 250%. (10.1145/3396851.3397729)
  DOI : 10.1145/3396851.3397729