Publications

2017

• Balanced Fair Resource Sharing in Computer Clusters
  
  • Bonald Thomas
  • Comte Céline
  Performance Evaluation, Elsevier, 2017. We represent a computer cluster as a multi-server queue with some arbitrary graph of compatibilities between jobs and servers. Each server processes its jobs sequentially in FCFS order. The service rate of a job at any given time is the sum of the service rates of all servers processing this job. We show that the corresponding queue is quasi-reversible and use this property to design a scheduling algorithm achieving balanced fair sharing of the computing resources. (10.1016/j.peva.2017.08.006)
  DOI : 10.1016/j.peva.2017.08.006
• Convergence to multi-resource fairness under end-to-end window control
  
  • Bonald Thomas
  • Roberts James
  • Vitale Christian
  , 2017. The paper relates to multi-resource sharing between flows with heterogeneous requirements as arises in networks with wireless links or software routers implementing network function virtualization. Bottleneck max fairness (BMF) is a sharing objective in this context with good performance. The paper shows that BMF results when local fairness is imposed at each resource while flow rates are controlled by an end-to-end window. We analytically prove convergence to BMF under a fluid model when flows share a network limited to 2 resources while numerical results confirm BMF convergence for larger networks. Simulation results illustrate the impact of packetized transmission.
• Acoustic Features for Environmental Sound Analysis
  
  • Serizel Romain
  • Bisot Victor
  • Essid Slim
  • Richard Gael
  , 2017, pp.71-101. Most of the time it is nearly impossible to differentiate between particular type of sound events from a waveform only. Therefore, frequency domain and time-frequency domain representations have been used for years providing representations of the sound signals that are more inline with the human perception. However, these representations are usually too generic and often fail to describe specific content that is present in a sound recording. A lot of work have been devoted to design features that could allow extracting such specific information leading to a wide variety of hand-crafted features. During the past years, owing to the increasing availability of medium scale and large scale sound datasets, an alternative approach to feature extraction has become popular, the so-called feature learning. Finally, processing the amount of data that is at hand nowadays can quickly become overwhelming. It is therefore of paramount importance to be able to reduce the size of the dataset in the feature space. The general processing chain to convert an sound signal to a feature vector that can be efficiently exploited by a classifier and the relation to features used for speech and music processing are described is this chapter. (10.1007/978-3-319-63450-0_4)
  DOI : 10.1007/978-3-319-63450-0_4
• Towards a framework for the levels and aspects of selfaware computing systems
  
  • Lewis Peter
  • Bellman Kirstie
  • Landauer Chris
  • Esterle Lukas
  • Glette Kyrre
  • Diaconescu Ada
  • Giese Holger
  , 2017, pp.51-85.
• Goal-oriented Holonic Systems
  
  • Diaconescu Ada
  , 2017, pp.209-258.
• Using modular extension to provably protect Edwards curves against fault attacks
  
  • Dugardin Margaux
  • Guilley Sylvain
  • Moreau Martin
  • Najm Zakaria
  • Rauzy Pablo
  Journal of Cryptographic Engineering, Springer, 2017, vol. 7, nb. 4.
• Dispositif échantillonneur- bloqueur de signal électrique
  
  • Meyer A.
  • Louis B.
  • Corbière Rémi
  • Petit V.
  • Desgreys Patricia
  • Petit H.
  , 2017.
• On metric convexity, the discrete Hahn-Banach theorem, separating systems and sets of points forming only acute angles
  
  • Randriambololona Hugues
  Int. J. of Information and Coding Theory, 2017, 4 (2/3), pp.159-169.
• A top-down engineering curriculum and application to a French "grande école
  
  • Chinchilla Raphael
  • Rodriguez G.
  IEEE Transactions on Education, Institute of Electrical and Electronics Engineers, 2017.
• Safe and Secure Support for Public Safety Networks
  
  • Apvrille Ludovic
  • Li Letitia W.
  , 2017, pp.185 - 210. <p>As explained by Tanzi et al. in the first volume of this book, communicating and autonomous devices will surely have a role to play in the future Public Safety Networks. The “communicating” feature comes from the fact that the information should be delivered in a fast way to rescuers. The “autonomous” characteristic comes from the fact that rescuers should not have to concern themselves about these objects: they should perform their mission autonomously so as not to delay the intervention of the rescuers, but rather to assist them efficiently and reliably.</p> (10.1016/B978-1-78548-053-9.50009-3)
  DOI : 10.1016/B978-1-78548-053-9.50009-3
• Signal and quantum noise in optical communications and in cryptography
  
  • Gallion Philippe
  • Mendieta F J
  • Jiang Shifeng
  , 2017.
• Exploring structure for long-term tracking of multiple objects in sports videos
  
  • Morimitsu Henrique
  • Bloch Isabelle
  • Cesar R. M.
  Computer Vision and Image Understanding, Elsevier, 2017, 159, pp.89-104. In this paper we propose a novel approach for exploring structural relations to track multiple objects that may undergo long-term occlusion and abrupt motion. We use a model-free approach that relies only on annotations given in the first frame of the video to track all the objects online, i.e. without knowledge from future frames. We initialize a probabilistic Attributed Relational Graph (ARG) from the first frame, which is incrementally updated along the video. Instead of using structural information only to evaluate the scene, the proposed approach considers it to generate new tracking hypotheses. In this way, our method is capable of generating relevant object candidates that are used to improve or recover the track of lost objects. The proposed method is evaluated on several videos of table tennis matches and on the ACASVA dataset. The results show that our approach is very robust, flexible and able to outperform other state-of-the-art methods in sports videos that present structural patterns.
• Topological relations between bipolar fuzzy sets based on mathematical morphology
  
  • Bloch Isabelle
  , 2017, LNCS 10225, pp.40-51. In many domains of information processing, both vagueness, or imprecision, and bipolarity, encompassing positive and negative parts of information, are core features of the information to be modeled and processed. This led to the development of the concept of bipolar fuzzy sets, and of associated models and tools. Here we propose to extend these tools by defining set theoretical and topological relations between bipolar fuzzy sets, including intersection, inclusion, adjacency and RCC relations widely used in mereotopology, based on bipolar connectives and on mathematical morphology operators.
• A new method based on template registration and deformable models for pelvic bones semi-automatic segmentation in pediatric MRI
  
  • Virzi Alessio
  • Marret Jean-Baptiste
  • Muller Cécile
  • Berteloot Laureline
  • Boddaert Nathalie
  • Sarnacki Sabine
  • Bloch Isabelle
  , 2017, pp.323-326. In this paper we address the problem of bone segmentation in MRI images of children, in the region of the pelvis. To cope with the complex structure of the bones in this region and their changing topology during growth, we propose a method relying on 3D bone templates. These models are built from 3D CT images. For a given MRI volume, the closest template is chosen and registered on the MRI data. This leads to an initial segmentation which is then refined using a deformable model approach, where the regularization parameters depend on the local curvature, and the landmarks used during the registration are fixed anchors during the deformation. This approach was successfully applied to 15 MRI volumes of children between 1 and 18 years old, with an average accuracy in terms of medium distance of M D = 1.17 ± 0.29 mm and Dice Index of DC = 0.81 ± 0.04.
• Brain MRI Segmentation using Fully Convolutional Network and Transfer Learning
  
  • Xu Yongchao
  • Géraud Thierry
  • Puybareau Elodie
  • Bloch Isabelle
  , 2017.
• A model of perceived dynamic range for HDR images
  
  • Hulusic Vedad
  • Debattista Kurt
  • Valenzise Giuseppe
  • Dufaux Frédéric
  Signal Processing: Image Communication, Elsevier, 2017, 51, pp.26 - 39. For High Dynamic Range (HDR) content, the dynamic range of an image is an important characteristic in algorithm design and validation, analysis of aesthetic attributes and content selection. Traditionally, it has been computed as the ratio between the maximum and minimum pixel luminance, a purely objective measure; however, the human visual system's perception of dynamic range is more complex and has been largely neglected in the literature. In this paper, a new methodology for measuring perceived dynamic range (PDR) of chromatic and achromatic HDR images is proposed. PDR can benefit HDR in a number of ways: for evaluating inverse tone mapping operators and HDR compression methods; aesthetically; or as a parameter for content selection in perceptual studies. A subjective study was conducted on a data set of 36 chromatic and achromatic HDR images. Results showed a strong agreement across participants' allocated scores. In addition, a high correlation between ratings of the chromatic and achromatic stimuli was found. Based on the results from a pilot study, five objective measures (pixel-based dynamic range, image key, area of bright regions, contrast and colorfulness) were selected as candidates for a PDR predictor model; two of which have been found to be significant contributors to the model. Our analyses show that this model performs better than individual metrics for both achromatic and chromatic stimuli. (10.1016/j.image.2016.11.005)
  DOI : 10.1016/j.image.2016.11.005
• Application cases of secret key generation in communication nodes and terminals
  
  • Sibille Alain
  • Delaveau François
  • Kameni Ngassa Christiane L.
  • Molière Renaud
  • Mazloum Taghrid
  • Kotelba Adrian
  • Suomalainen Jani
  • Boumard Sandrine
  • Shapira Nir
  , 2017. The main objective of this chapter is to study explicit key extraction techniques and algorithms for the security of radio communication. After some recalls on the main processing steps (Figure 19.1(a)) and on theoretical results relevant to the radio wiretap model (Figure 19.1(b)), we detail recent experimental results on randomness properties of real field radio channels. Furthermore, we detail a practical implantation of secret key generation (SKG) schemes, based on the Channel Quantization Alternate (CQA) algorithm helped with channel decorrelation techniques, into modern public networks such as WiFi and radio-cells of fourth generation (LTE, long-term evolution). Finally, through realistic simulations and real field experiments of radio links, we analyze the security performance of the implemented SKG schemes, and highlight their significant practical results and perspectives for future implantations into existing and next-generation radio standards.
• Optimal scaling of the Random Walk Metropolis algorithm under Lp mean differentiability
  
  • Durmus Alain
  • Le Corff Sylvain
  • Moulines Éric
  • Roberts Gareth O. O.
  Journal of Applied Probability, Cambridge University press, 2017, 54 (4), pp.1233 -1260. This paper considers the optimal scaling problem for high-dimensional random walk Metropolis algorithms for densities which are differentiable in Lp mean but which may be irregular at some points (like the Laplace density for example) and/or are supported on an interval. Our main result is the weak convergence of the Markov chain (appropriately rescaled in time and space) to a Langevin diffusion process as the dimension d goes to infinity. Because the log-density might be non-differentiable, the limiting diffusion could be singular. The scaling limit is established under assumptions which are much weaker than the one used in the original derivation of [6]. This result has important practical implications for the use of random walk Metropolis algorithms in Bayesian frameworks based on sparsity inducing priors. (10.1017/jpr.2017.61)
  DOI : 10.1017/jpr.2017.61
• Optimize Wireless Networks for Energy Saving by Distributed Computation of Čech Complex
  
  • Le Ngoc-Khuyen
  • Vergne Anais
  • Martins Philippe
  • Decreusefond Laurent
  , 2017. In this paper, we introduce a distributed algorithm to compute the \v{C}ech complex. This algorithm is aimed at solving the coverage problems in self organized wireless networks. The complexity to compute the minimal \v{C}ech complex that gives information about coverage and connectivity of the network is $\mathcal{O}(n^2)$, where $n$ is the average number of neighbors of each cell. An application based on the distributed computation of the \v{C}ech complex, which is aimed at optimizing the wireless network for energy saving, is also proposed. This application also has polynomial complexity. The performance of the proposed algorithm and its application are evaluated. The simulation results show that the distributed computation of the \v{C}ech complex provides a consistent outcome with the one obtained by the centralized computation that is introduced in [6], while requires a much shorter calculation time. The optimized coverage saves 65\% of the total transmission power, while also keeps the maximal coverage for the network.
• Fair throughput allocation in Information-Centric Networks
  
  • Bonald Thomas
  • Mekinda Léonce
  • Muscariello Luca
  Computer Networks, Elsevier, 2017, 125, pp.122 - 131. Cache networks are the cornerstones of today's Internet, helping it to scale by an extensive use of Content Delivery Networks (CDN). Benefiting from CDN's successful insights, ubiquitous caching through Information-Centric Networks (ICN) is increasingly regarded as a premier future Internet architecture contestant. However, the use of in-network caches seems to cause an issue in the fairness of resource sharing among contents. Indeed, in legacy communication networks, link buffers were the principal resources to be shared. Under max-min flow-wise fair bandwidth sharing [14], content throughput was not tied to content popularity. Including caches in this ecosystem raises new issues since common cache management policies such as probabilistic Least Recently Used (p-LRU) or even more, Least Frequently Used (LFU), may seem detrimental to low popularity objects, even though they significantly decrease the overall link load [3]. In this paper, we demonstrate that globally achieving LFU is a first stage of content-wise fairness. Indeed, any investigated content-wise α-fair throughput allocation permanently stores the most popular contents in network caches by ensuring them a cache hit ratio of 1. As ICN caching traditionally pursues LFU objectives, content-wise fairness specifics remain only a matter of fair bandwidth sharing, keeping the cache management intact. (10.1016/j.comnet.2017.05.019)
  DOI : 10.1016/j.comnet.2017.05.019
• Cognitive Management of Self -Organized Radio Networks Based on Multi Armed Bandit
  
  • Daher Tony
  • Jemaa Sana Ben
  • Decreusefond Laurent
  , 2017. Many tasks in current mobile networks are automated through Self-Organizing Networks (SON) functions. The actual implementation consists in a network with several SON functions deployed and operating independently. A Policy Based SON Manager (PBSM) has been introduced to configure these functions in a manner that makes the overall network fulfill the operator objectives. Given the large number of possible configurations (for each SON function instance in the network), we propose to empower the PBSM with learning capability. This Cognitive PBSM (C-PBSM) learns the most appropriate mapping between SON configurations and operator objectives based on past experience and network feedback. The proposed learning algorithm is a stochastic multi-armed bandit, namely the UCB1. We evaluate the performances of the proposed C-PBSM on an LTE-A simulator. We show that it is able to learn the optimal SON configuration and quickly adapts to objective changes.
• Guest Editorial AWPL Special Cluster on “Impact of User-Related Randomness on Antennas and Channels”
  
  • Sibille Alain
  • Kildal Per-Simon
  IEEE Antennas and Wireless Propagation Letters, Institute of Electrical and Electronics Engineers, 2017, 16. The guest editorial explains the motivation for the AWPL special cluster and briefly introduce each of the nine selected papers. (10.1109/LAWP.2017.2696621)
  DOI : 10.1109/LAWP.2017.2696621
• LISP EID Block Management Guidelines
  
  • Iannone Luigi
  • Jorgensen Roger
  • Conrad David
  • Huston Geoff
  , 2017.
• Mesure de dissimilarité pour les patchs utilisant la corrélation
  
  • Riot Paul
  • Almansa Andrés
  • Gousseau Yann
  • Tupin Florence
  , 2017.
• Failure Analysis in Magnetic Tunnel Junction Nanopillar with Interfacial Perpendicular Magnetic Anisotropy
  
  • Zhao Weisheng
  • Wang You
  • Naviner Lirida
  Materials Science Journal, 2017, 9 (41), pp.1-17.