Publications

2020

• 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.
• 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.
• 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.
• 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.
• 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
• Invited Lecture on Continous-Variable Quantum Key Distribution
  
  • Alleaume Romain
  , 2020.
• 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.
• 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
• 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
• 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
• Groove2Groove: One-Shot Music Style Transfer with Supervision from Synthetic Data
  
  • Cífka Ondřej
  • Şimşekli Umut
  • Richard Gael
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, 28, pp.2638-2650. Style transfer is the process of changing the style of an image, video, audio clip or musical piece so as to match the style of a given example. Even though the task has interesting practical applications within the music industry, it has so far received little attention from the audio and music processing community. In this paper, we present Groove2Groove, a one-shot style transfer method for symbolic music, focusing on the case of accompaniment styles in popular music and jazz. We propose an encoder-decoder neural network for the task, along with a synthetic data generation scheme to supply it with parallel training examples. This synthetic parallel data allows us to tackle the style transfer problem using end-to-end supervised learning, employing powerful techniques used in natural language processing. We experimentally demonstrate the performance of the model on style transfer using existing and newly proposed metrics, and also explore the possibility of style interpolation. (10.1109/TASLP.2020.3019642)
  DOI : 10.1109/TASLP.2020.3019642
• Optical noise of dual-state lasing quantum dot lasers
  
  • Zhou Yueguang
  • Duan Jianan
  • Grillot Frederic
  • Wang Cheng
  IEEE Journal of Quantum Electronics, Institute of Electrical and Electronics Engineers, 2020, pp.1-1. (10.1109/JQE.2020.3026090)
  DOI : 10.1109/JQE.2020.3026090
• Simulation Framework for Misbehavior Detection in Vehicular Networks
  
  • Kamel Joseph
  • Raashid Ansari Mohammad
  • Petit Jonathan
  • Kaiser Arnaud
  • Ben Jemaa Ines
  • Urien Pascal
  IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers, 2020, 69 (6), pp.6631-6643. Cooperative Intelligent Transport Systems (C-ITS) is an ongoing technology that will change our driving experience in the near future. In such systems, vehicles and RoadSide Unit (RSU) cooperate by broadcasting V2X messages over the vehicular network. Safety applications use these data to detect and avoid dangerous situations on time. MisBehavior Detection (MBD) in C-ITS is an active research topic which consists of monitoring data semantics of the exchanged Vehicle-to-X communication (V2X) messages to detect and identify potential misbehaving entities. The detection process consists of performing plausibility and consistency checks on the received V2X messages. If an anomaly is detected, the entity may report it by sending a Misbehavior Report (MBR) to the Misbehavior Authority (MA). The MA will then investigate the event and decide to revoke the sender or not. In this paper, we present a MisBehavior Detection (MBD) simulation framework that enables the research community to develop, test, and compare MBD algorithms. We also demonstrate its capabilities by running example scenarios and discuss their results. Framework For Misbehavior Detection (F 2 MD) is open source and available for free on our github. (10.1109/TVT.2020.2984878)
  DOI : 10.1109/TVT.2020.2984878
• Lattice Codes for C-RAN Based Sectored Cellular Networks
  
  • Gelincik Samet
  • Rekaya-Ben Othman Ghaya
  , 2020.
• CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer
  
  • Kips R.
  • Perrot M.
  • Gori P.
  • Bloch Isabelle
  , 2020.
• La mathématique migrante
  
  • Zayana Karim
  • Jadiba Sami
  • Kraiem Selma
  • Fayala Abdelwahid
  Les Cahiers Pédagogiques, 2020, 558. La route de l'inconnu(e) : histoire de quelques concepts mathématiques à travers les grandes migrations en méditerranée
• Processing Simple Geometric Attributes with Autoencoders
  
  • Newson Alasdair
  • Almansa Andrés
  • Gousseau Yann
  • Ladjal Saïd
  Journal of Mathematical Imaging and Vision, Springer Verlag, 2020, 62 (3), pp.293-312. Image synthesis is a core problem in modern deep learning, and many recent architectures such as autoencoders and Generative Adversarial networks produce spectacular results on highly complex data, such as images of faces or landscapes. While these results open up a wide range of new, advanced synthesis applications, there is also a severe lack of theoretical understanding of how these networks work. This results in a wide range of practical problems, such as difficulties in training, the tendency to sample images with little or no variability, and generalisation problems. In this paper, we propose to analyse the ability of the simplest generative network, the autoencoder, to encode and decode two simple geometric attributes : size and position. We believe that, in order to understand more complicated tasks, it is necessary to first understand how these networks process simple attributes. For the first property, we analyse the case of images of centred disks with variable radii. We explain how the autoencoder projects these images to and from a latent space of smallest possible dimension, a scalar. In particular, we describe a closed-form solution to the decoding training problem in a network without biases, and show that during training, the network indeed finds this solution. We then investigate the best regularisation approaches which yield networks that generalise well. For the second property, position, we look at the encoding and decoding of Dirac delta functions, also known as `one-hot' vectors. We describe a hand-crafted filter that achieves encoding perfectly, and show that the network naturally finds this filter during training. We also show experimentally that the decoding can be achieved if the dataset is sampled in an appropriate manner. (10.1007/s10851-019-00924-w)
  DOI : 10.1007/s10851-019-00924-w
• Recent results and problems on constructions of linear codes from cryptographic functions
  
  • Li N.
  • Mesnager Sihem
  Journal of Cryptography and Communications- Discrete Structures, Boolean Functions, and Sequences, 2020.
• Constructions of self-orthogonal codes from hulls of BCH codes and their parameters
  
  • Du Z.
  • Li C.
  • Mesnager Sihem
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020.
• Epitaxial quantum dot lasers on silicon with high thermal stability and strong resistance to optical feedback
  
  • Huang H.
  • Duan J.
  • Dong B.
  • Norman J.
  • Jung D.
  • Bowers J. E
  • Grillot F.
  APL Photonics, AIP Publishing LLC, 2020, 5 (1), pp.016103. (10.1063/1.5120029)
  DOI : 10.1063/1.5120029
• Artifical Intelligence and Pattern Recognition, Vision, Learning
  
  • Bloch Isabelle
  • Clouard Régis
  • Revenu Marinette
  • Sigaud Olivier
  , 2020, III, pp.337-364.
• An Experimental Study of State-of-the-Art Entity Alignment Approaches
  
  • Zhao Xiang
  • Zeng Weixin
  • Tang Jiuyang
  • Wang​ Wei
  • Suchanek Fabian
  IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2020. Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e.g., question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different settings (i.e., datasets, information used as input, etc.). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories. Next, we judiciously evaluate these solutions on a wide range of use cases, based on their effectiveness, efficiency and robustness. Finally, we construct a new EA dataset to mirror the real-life challenges of alignment, which were largely overlooked by existing literature. This study strives to provide a clear picture of the strengths and weaknesses of current EA approaches, so as to inspire quality follow-up research. (10.1109/TKDE.2020.3018741)
  DOI : 10.1109/TKDE.2020.3018741
• Precoding in Massive MU-MIMO Systems Based on New CSI Accuracy Indicator Reporting
  
  • Askri Aymen
  • Rekaya-Ben Othman Ghaya
  , 2020.