Publications

2018

• Human Body Communication Channel Characterization for Leadless Cardiac Pacemakers
  
  • Maldari Mirko
  • Amara Karima
  • Rattalino Ismael
  • Jabbour Chadi
  • Desgreys Patricia
  , 2018, pp.185-188. (10.1109/ICECS.2018.8617987)
  DOI : 10.1109/ICECS.2018.8617987
• Full Coverage Hole Optimization in Cloud Radio Access Networks
  
  • Mharsi Niezi
  • Hadji Makhlouf
  • Martins Philippe
  , 2018.
• Performance Evaluation of Millimeter Wave Full-Duplex Cellular Networks
  
  • Arrano-Scharager Hernan-Felipe
  • Kelif Jean-Marc
  • Coupechoux Marceau
  , 2018, pp.1-7. (10.1109/GLOCOMW.2018.8644246)
  DOI : 10.1109/GLOCOMW.2018.8644246
• Distributed Hypothesis Testing Over Multi-Access Channels
  
  • Salehkalaibar Sadaf
  • Wigger Michèle
  , 2018, pp.1-6. Consider distributed hypothesis testing over multiple-access channels (MACs), where the receiver wishes to maximize the type-II error exponent under a constrained type-I error probability. For this setup, we propose a scheme that combines hybrid coding with a MAC-version of Borades unequal error protection. It achieves the optimal type-II error exponent for a generalization of testing against independence over an orthogonal MAC when the transmitters' sources are independent. In this case, hybrid coding can be replaced by the simpler separate source-channel coding. The paper also presents upper and lower bounds on the optimal type-II error exponent for generalized testing against independence of Gaussian sources over a Gaussian MAC. The bounds are close and significantly larger than a type-II error exponent that is achievable using separate source-channel coding. (10.1109/glocom.2018.8647744)
  DOI : 10.1109/glocom.2018.8647744
• Wideband Predistortion for Efficient Power Amplifiers
  
  • Pham Dang-Kièn Germain
  • Desgreys Patricia
  , 2018. For three decades, the exchange rates in telecommunication standards have been exponentially increasing thanks to major innovations integrated from one generation to the following. The 5th generation of mobile standards 5G will, as its predecessors, provide a large increase of the data rates that will be made possible thanks to larger transmission bandwidths (up to several hundred of MHz) and to modulation schemes with higher spectral efficiency such as OFDMA and M-QAM. However, the novelty for 5G is the will to drastically minimize the power consumption with an objective of improving by 90% the energy efficiency compared to LTE standard. To achieve this objective, optimizations and innovations will be integrated at the different layers and levels of the communication systems. For the physical layer and more specifically both for the mixed signal and RF part, the power consumption should be also minimized. In the transceiver, the most power consuming component is the power amplifier (PA) and classically there is a trade-off between PA’s efficiency and its linear behavior. In order to relax the design constraints due to this trade-off and to improve linearity and power efficiency, predistortion has emerged as a go-to solution. A digital predistortion (DPD) system is conventionally composed of a digital predistorter in the transmission path in charge of applying the inverse non-linear (NL) transfer characteristic so that combined with the PA, the overall transfer characteristic is linear. The adjustment of the predistorter coefficients should be done continuously or regularly to compensate for process, voltage and temperature (PVT) variations of the PA and therefore requires an observation path. In 5G, the main challenges for the predistortion technique are the higher peak to average power ratio (PAPR) of the new modulation schemes and the large signal bandwidth which has grown to several hundred MHz. The combination of these two effects leads to higher constraints on : The compensation blocks (estimator + predistorter) because of the large growth of the number of coefficients required to properly linearize PAs that are expected to exhibit more nonlinearities and memory effects. The data converters (Digital to Analog Converter (DAC) + Analog to Digital Converter (ADC)) because they have to process a very wideband signal which is composed of the main signal plus the intermodulation products components. In this tutorial, a review is conducted of the main recent and promising solutions to implement efficient predistortion techniques for PA while maintaining affordable power consumption.
• Decentralized Coded Caching for Wiretap Broadcast Channels
  
  • Kamel Sarah
  • Wigger Michèle
  • Sarkiss Mireille
  , 2018, pp.1-6. We consider a K-receiver wiretap broadcast channel where Kw receivers are weak and have cache memories and Ks receivers are strong and have no cache memories. We derive an upper bound on the secrecy rate-memory tradeoff under a joint secrecy constraint and under decentralized caching. In contrast to previous works, prefetching in our scheme is purely decentralized and receivers randomly sample from a random key stream available at the transmitter and from the files in a library. For small cache sizes, the performance of our scheme improves with increasing length of the random key stream. For moderate and large cache sizes, a small key stream suffices to perform close to the information-theoretic limit of the system. (10.1109/glocom.2018.8647549)
  DOI : 10.1109/glocom.2018.8647549
• Resource allocation for HARQ in mobile ad hoc networks
  
  • Leturc Xavier
  , 2018. This thesis addresses the Resource Allocation (RA) problem in multiuser mobile ad hoc networks. We assume that there is a node in the network, called the resource manager (RM), whose task is to allocate the resource and thus the other nodes send him there channel state information (CSI). This network model induces a delay between the time the nodes send the RM their CSI and the time the RM sends them their RA, which renders impossible the use of instantaneous CSI. Thus, we assume that only statistical CSI is available to perform the RA. Moreover, we assume that an Hybrid ARQ (HARQ) mechanism is used on all the links. In this context, the objective of the thesis is twofold: i) Propose procedures to estimate the statistical CSI, and more precisely to estimate the Rician K factor with and without shadowing. ii) Propose and analyse new RA algorithms using statistical CSI and taking into account the use of HARQ and practical modulation and coding schemes. We aim to maximize energy efficiency related metrics. The resource to allocate are per-link transmit energy and bandwidth proportion.
• Misbehavior Reporting Protocol for C-ITS
  
  • Kamel Joseph
  • Jemaa Ines Ben
  • Kaiser Arnaud
  • Urien Pascal
  , 2018. Misbehavior detection is a set of mechanisms that rely on monitoring C-ITS communications to detect potentially misbehaving entities. In this paper we focus on the reporting process of Misbehavior Detection. More precisely, we propose a misbehavior report message format that enables an entity to report a detected misbehaving entity. We explain first the functional requirements of a misbehavior reporting mechanism. Then, we detail the data information that are integrated in the reports in order to provide reliable evidences to the misbehavior authority.
• Multidimensionality of the models and the data in the side-channel domain
  
  • Marion Damien
  , 2018. Since the publication in 1999 of the seminal paper of Paul C. Kocher, Joshua Jaffe and Benjamin Jun, entitled "Differential Power Analysis", the side-channel attacks have been proved to be efficient ways to attack cryptographic algorithms. Indeed, it has been revealed that the usage of information extracted from the side-channels such as the execution time, the power consumption or the electromagnetic emanations could be used to recover secret keys. In this context, we propose first, to treat the problem of dimensionality reduction. Indeed, since twenty years, the complexity and the size of the data extracted from the side-channels do not stop to grow. That is why the reduction of these data decreases the time and increases the efficiency of these attacks. The dimension reduction is proposed for complex leakage models and any dimension. Second, a software leakage assessment methodology is proposed ; it is based on the analysis of all the manipulated data during the execution of the software. The proposed methodology provides features that speed-up and increase the efficiency of the analysis, especially in the case of white box cryptography.
• Removing objects from videos with a few strokes
  
  • Le Thuc Trinh
  • Almansa Andrés
  • Gousseau Yann
  • Masnou Simon
  , 2018, pp.1-4. We present a system for the removal of objects from videos. As an input, the system only needs a user to draw a few strokes in at least one frame, roughly delimiting the objects to be removed. ese rough masks are then automatically re ned and propagated through the video. e corresponding regions are resynthesized using video inpainting techniques. Our system is able to deal with multiple, possibly crossing objects, with complex motions and with dynamic textures. is results in a computational tool that can alleviate tedious manual operations for editing high-quality videos. (10.1145/3283254.3283276)
  DOI : 10.1145/3283254.3283276
• A Structured Prediction Approach for Label Ranking
  
  • Korba Anna
  • Garcia Alexandre
  • d'Alché-Buc Florence
  , 2018. We propose to solve a label ranking problem as a structured output regression task. In this view, we adopt a least square surrogate loss approach that solves a supervised learning problem in two steps: a regression step in a well-chosen feature space and a pre-image (or decoding) step. We use specific feature maps/embeddings for ranking data, which convert any ranking/permutation into a vector representation. These embeddings are all well-tailored for our approach, either by resulting in consistent estimators, or by solving trivially the pre-image problem which is often the bottleneck in structured prediction. Their extension to the case of incomplete or partial rankings is also discussed. Finally, we provide empirical results on synthetic and real-world datasets showing the relevance of our method.
• Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
  
  • La Tour Tom Dupré
  • Moreau Thomas
  • Jas Mainak
  • Gramfort Alexandre
  , 2018. Frequency-specific patterns of neural activity are traditionally interpreted as sustained rhythmic oscillations, and related to cognitive mechanisms such as attention, high level visual processing or motor control. While alpha waves (8-12 Hz) are known to closely resemble short sinusoids, and thus are revealed by Fourier analysis or wavelet transforms, there is an evolving debate that electromagnetic neural signals are composed of more complex waveforms that cannot be analyzed by linear filters and traditional signal representations. In this paper, we propose to learn dedicated representations of such recordings using a multivariate convolutional sparse coding (CSC) algorithm. Applied to electroencephalography (EEG) or magnetoencephalography (MEG) data, this method is able to learn not only prototypical temporal waveforms, but also associated spatial patterns so their origin can be localized in the brain. Our algorithm is based on alternated minimization and a greedy coordinate descent solver that leads to state-of-the-art running time on long time series. To demonstrate the implications of this method, we apply it to MEG data and show that it is able to recover biological artifacts. More remarkably, our approach also reveals the presence of non-sinusoidal mu-shaped patterns, along with their topographic maps related to the somatosensory cortex.
• Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
  
  • Birdal Tolga
  • Şimşekli Umut
  • Eken M. Onur
  • Ilic Slobodan
  , 2018. We introduce Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm for initializing pose graph optimization problems, arising in various scenarios such as SFM (structure from motion) or SLAM (simultaneous localization and mapping). TG-MCMC is first of its kind as it unites asymptotically global non-convex optimization on the spherical manifold of quaternions with posterior sampling, in order to provide both reliable initial poses and uncertainty estimates that are informative about the quality of individual solutions. We devise rigorous theoretical convergence guarantees for our method and extensively evaluate it on synthetic and real benchmark datasets. Besides its elegance in formulation and theory, we show that our method is robust to missing data, noise and the estimated uncertainties capture intuitive properties of the data.
• Learning Sparse Neural Networks via Sensitivity-Driven Regularization
  
  • Tartaglione Enzo
  • Lepsoy Skjalg
  • Fiandrotti Attilio
  • Francini Gianluca
  , 2018. The ever-increasing number of parameters in deep neural networks poses challenges for memory-limited applications. Regularize-and-prune methods aim at meeting these challenges by sparsifying the network weights. In this context we quantify the output sensitivity to the parameters (i.e. their relevance to the network output) and introduce a regularization term that gradually lowers the absolute value of parameters with low sensitivity. Thus, a very large fraction of the parameters approach zero and are eventually set to zero by simple thresholding. Our method surpasses most of the recent techniques both in terms of sparsity and error rates. In some cases, the method reaches twice the sparsity obtained by other techniques at equal error rates.
• Asymptotic optimality of adaptive importance sampling
  
  • Delyon Bernard
  • Portier François
  , 2018, 31. Adaptive importance sampling (AIS) uses past samples to update the sampling policy qt. Each stage t is formed with two steps : (i) to explore the space with nt points according to qt and (ii) to exploit the current amount of information to update the sampling policy. The very fundamental question raised in this paper concerns the behavior of empirical sums based on AIS. Without making any assumption on the allocation policy nt, the theory developed involves no restriction on the split of computational resources between the explore (i) and the exploit (ii) step. It is shown that AIS is asymptotically optimal : the asymptotic behavior of AIS is the same as some “oracle” strategy that knows the targeted sampling policy from the beginning. From a practical perspective, weighted AIS is introduced, a new method that allows to forget poor samples from early stages.
• Proceedings of the ACM on Measurement and Analysis of Computing Systems
  
  • Bonald Thomas
  • Duffield N.
  acm, 2018, 2 (3).
• On the Cost of Geographic Forwarding for Information-Centric Things
  
  • Enguehard Marcel
  • E. Droms Ralph
  • Rossi Dario
  IEEE Transactions on Green Communications and Networking, IEEE, 2018, 2 (4), pp.1150 - 1163.
• Carrier-Noise-Enhanced Relative Intensity Noise of Quantum Dot Lasers
  
  • Duan Jianan
  • Wang Xing-Guang
  • Zhou Yue-Guang
  • Wang Cheng
  • Grillot Frederic
  IEEE Journal of Quantum Electronics, Institute of Electrical and Electronics Engineers, 2018, 54 (6), pp.1-7. This paper numerically investigates the relative intensity noise of quantum dot lasers through a rate equation model taking into account both the spontaneous emission and carrier contributions. In particular, results show that the carrier noise originating from the ground and excited states significantly enhances the relative intensity noise of the laser, while that from the carrier reservoir does not. Simulations also point out that a large energy interval between the quantum confined levels is more suitable for low-intensity noise operation due to the reduced contribution from the carrier noise in the excited state. Finally, the carrier noise is found to have little impact on the frequency noise, thus being negligible for the investigation of the spectral linewidth. Overall, this paper is useful for designing low-noise quantum dot oscillators for high-speed communications, optical frequency combs, and radar applications. Index Terms-Semiconductor lasers, quantum dots, relative intensity noise, frequency noise. (10.1109/JQE.2018.2880452)
  DOI : 10.1109/JQE.2018.2880452
• Topic management for an engaging conversational agent
  
  • Glas Nadine
  • Pelachaud Catherine
  International Journal of Human-Computer Studies, Elsevier, 2018, 120, pp.107-124. (10.1016/j.ijhcs.2018.07.007)
  DOI : 10.1016/j.ijhcs.2018.07.007
• Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
  
  • Commowick Olivier
  • Istace Audrey
  • Kain Michael
  • Laurent Baptiste
  • Leray Florent
  • Simon Mathieu
  • Pop Sorina Camarasu
  • Girard Pascal
  • Ameli Roxana
  • Ferré Jean-Christophe
  • Kerbrat Anne
  • Tourdias Thomas
  • Cervenansky Frédéric
  • Glatard Tristan
  • Beaumont Jeremy
  • Doyle Senan
  • Forbes Florence
  • Knight Jesse
  • Khademi April
  • Mahbod Amirreza
  • Wang Chunliang
  • Mckinley Richard
  • Wagner Franca
  • Muschelli John
  • Sweeney Elizabeth
  • Roura Eloy
  • Llado Xavier
  • Santos Michel
  • Santos Wellington P
  • Silva-Filho Abel G
  • Tomas-Fernandez Xavier
  • Urien Hélène
  • Bloch Isabelle
  • Valverde Sergi
  • Cabezas Mariano
  • Vera-Olmos Francisco Javier
  • Malpica Norberto
  • Guttmann Charles R G
  • Vukusic Sandra
  • Edan Gilles
  • Dojat Michel
  • Styner Martin
  • Warfield Simon K.
  • Cotton François
  • Barillot Christian
  Scientific Reports, Nature Publishing Group, 2018, 8 (1), pp.13650. We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores. (10.1038/s41598-018-31911-7)
  DOI : 10.1038/s41598-018-31911-7
• Method for managing a group of electrical energy consuming devices, and electrical energy management module
  
  • Al Zahr Sawsan
  • Rousselle Mélaine
  • Forestier Philippe
  , 2018.
• Self-sacrifice as social signal (Commentary on H. Whitehouse: Dying for the group: Towards a general theory of extreme self-sacrifice)
  
  • Dessalles Jean-Louis
  Behavioral and Brain Sciences, Cambridge University Press (CUP), 2018, 41, pp.e200. Self-sacrifice can be modeled as a costly social signal carried to the ultimate extreme. Such signaling may be evolutionarily stable if social status is, in part, inherited.
• DEVICES AND METHODS FOR PARALLELIZED RECURSIVE BLOCK DECODING
  
  • Rekaya-Ben Othman Ghaya
  , 2018, pp.31. A decoder (300) for determining an estimate of a vector of information symbols carried by a signal received through a transmission channel represented by a channel matrix. 5 The decoder comprises: - a block division unit (303) configured to divide the vector of information symbols into two or more sub-vectors, each sub-vector being associated with a block level; - two or more processors configured to determine, in parallel, candidate sub-vectors and to store the candidate sub-vectors in a first stack (310). Each processor is configured to 10 determine at least a candidate sub-vector by applying a symbol estimation algorithm and to store each candidate sub-vector with a decoding metric and the block level associated with the candidate sub-vector. The decoding metric is lower than or equal to a decoding metric threshold. A processor among the two or more processors is configured to determine at least a candidate vector from candidate sub-vectors stored in the first stack (310), the candidate 15 vector being associated with a cumulated decoding metric and to update the decoding metric threshold from the cumulated decoding metric.
• Energy-Latency Tradeoff in Ultra-Reliable, Low-Latency Communication with Short Packets
  
  • Avranas Apostolos
  • Kountouris Marios
  • Ciblat Philippe
  , 2018.
• Machine Learning for Survival Analysis: Empirical Risk Minimization for Censored Distribution-Free Regression with Applications.
  
  • Ausset Guillaume
  • Clémençon Stéphan
  • Portier François
  , 2018.