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
• 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.
• 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.
• 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
• KClist++: A Simple Algorithm for Finding k-Clique Densest Subgraphs in Large Graphs
  
  • Sun Bintao
  • Danisch Maximilien
  • Chan T-H Hubert
  • Sozio Mauro
  Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2020. The problem of finding densest subgraphs has received increasing attention in recent years finding applications in biology , finance, as well as social network analysis. The k-clique densest subgraph problem is a generalization of the densest subgraph problem, where the objective is to find a subgraph maximizing the ratio between the number of k-cliques in the subgraph and its number of nodes. It includes as a special case the problem of finding subgraphs with largest average number of triangles (k = 3), which plays an important role in social network analysis. Moreover, algorithms that deal with larger values of k can effectively find quasi-cliques. The densest subgraph problem can be solved in polynomial time with algorithms based on maximum flow, linear programming or a recent approach based on convex optimization. In particular, the latter approach can scale to graphs containing tens of billions of edges. While finding a densest subgraph in large graphs is no longer a bottleneck , the k-clique densest subgraph remains challenging even when k = 3. Our work aims at developing near-optimal and exact algorithms for the k-clique densest subgraph problem on large real-world graphs. We give a surprisingly simple procedure that can be employed to find the maximal k-clique densest subgraph in large-real world graphs. By leveraging appealing properties of existing results, we combine it with a recent approach for listing all k-cliques in a graph and a sampling scheme, obtaining the state-of-the-art approaches for the aforementioned problem. Our theoretical results are complemented with an extensive experimental evaluation showing the effectiveness of our approach in large real-world graphs.
• SENSITIVITY ANALYSIS FOR STOCHASTIC SIMULATORS USING DIFFERENTIAL ENTROPY
  
  • Azzi Soumaya
  • Sudret Bruno
  • Wiart Joe
  International Journal for Uncertainty Quantification, Begell House Publishers, 2020, 10 (1), pp.25-33. (10.1615/Int.J.UncertaintyQuantification.2020031610)
  DOI : 10.1615/Int.J.UncertaintyQuantification.2020031610
• Solving $x^{2^k+1}+x+a=0$ in $\mathbb{F}_{2^n}$ with $\gcd(n,k)=1$
  
  • Kim K. H.
  • Mesnager Sihem
  Finite Fields and Their Applications, Elsevier, 2020.
• Several classes of minimal linear codes with few weights from weakly regular plateaued function
  
  • Mesnager Sihem
  • Sinak A.
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020.
• A Fully Connected Neural Network to Mitigate 200G DP-16-QAM Transmission System Impairments
  
  • Catanese Clara
  • Ayassi Reda
  • Pincemin Erwan
  • Jaouën Yves
  , 2020, pp.SpTh3I.1. (10.1364/SPPCOM.2020.SpTh3I.1)
  DOI : 10.1364/SPPCOM.2020.SpTh3I.1
• Explicit Regularisation in Gaussian Noise Injections
  
  • Camuto Alexander
  • Willetts Matthew
  • Şimşekli Umut
  • Roberts Stephen
  • Holmes Chris
  , 2020. We study the regularisation induced in neural networks by Gaussian noise injections (GNIs). Though such injections have been extensively studied when applied to data, there have been few studies on understanding the regularising effect they induce when applied to network activations. Here we derive the explicit regulariser of GNIs, obtained by marginalising out the injected noise, and show that it penalises functions with high-frequency components in the Fourier domain; particularly in layers closer to a neural network's output. We show analytically and empirically that such regularisation produces calibrated classifiers with large classification margins.
• Advanced Optical Communications and Networking
  
  • Gallion Philippe
  Proceeding of International Conference on Optical & Wireless Technologies , OWT 2019, 2020, Éditeurs : Ghanshyam Singh, Manish Tiwari, Tawfik Ismail, Vijay Janyani.
• New Characterizations for the Multi-output Correlation- Immune Boolean Functions
  
  • Chai J.
  • Mesnager Sihem
  • Wang Z.
  Discrete Mathematics, Elsevier, 2020.
• Analyse de représentations spatiales de la musique par des opérateurs simples de morphologie mathématique
  
  • Lascabettes P.
  • Bloch Isabelle
  • Agon C.
  , 2020.
• Motion Correction for brain PET using a Real Time Motion Capture System
  
  • Chemli Y.
  • Tétrault M.-A.
  • Marin T.
  • Toussaint M.
  • Bloch Isabelle
  • El Fakhri G.
  • Normandin M.
  • Ouyang J.
  • Petibon Y.
  , 2020.
• MR based PET motion correction for irregular respiratory motion
  
  • Djebra Y.
  • Marin T.
  • Han P.
  • Chemli Y.
  • Bloch Isabelle
  • El Fakhri G.
  • Ouyang J.
  • Petibon Y.
  • Ma C.
  , 2020.
• A new parametrization for the Rician distribution
  
  • Nicolas Jean Marie
  • Tupin Florence
  IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2020. The Rician distribution is widely used in SAR imagery to model the backscattering of a strong target inside a resolution cell. Nevertheless the computation of the parameters of the Rice distribution remains a difficult task. In this paper, a new parametrization to model the Rice distribution is introduced. Thanks to the introduction of a new variable defined by the ratio of the target contribution to the speckle, the relationship between the coefficient of variation and this new parameter can be derived. An efficient numerical method is proposed to evaluate it from the coefficient of variation and a discussion on the variance of this estimator is led. A comparison with other methods of estimation showed that the proposed approach is a good compromise between the variance of the estimate and the computation time. At last, a link between permanent scatterers and Rice distributed targets is proposed through this new parametrization. (10.1109/LGRS.2019.2957240)
  DOI : 10.1109/LGRS.2019.2957240