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Olivier J. J. Michel - IEEE Xplore Author Profile

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Extensive literature exists on how to test for normality, especially for identically and independently distributed (i.i.d) processes. The case of dependent samples has also been addressed, but only for scalar random processes. For this reason, we have proposed a joint normality test for multivariate time-series, extending Mardia’s Kurtosis test. In the continuity of this work, we provide here an o...Show More
Continuous traffic monitoring and analytics are fundamental to the operation of today’s networks. Network telemetry allows for performing fine-grained analytics on network flow or packet records for various use cases including intrusion detection and traffic engineering. While some analytics tasks can be offloaded to programmable switches, ultimately, telemetry data needs to be processed by analyt...Show More
As networks grow in speed, scale, and complexity, operating them reliably requires continuous monitoring and increasingly sophisticated analytics. Because of these requirements, the platforms that support analytics in cloud-scale networks face demands for both higher throughput (to keep up with high packet rates) and increased generality and programmability (to cover a wider range of applications)...Show More
Radars can be used as a non-invasive solution to monitor the vital signs of patients. The heart and respiratory rates are generally extracted by analyzing the phase variations of the radar signal, thus motivating the use of millimeter-waves. This, however, comes at the cost of a higher attenuation with the distance of travel, which in turn lowers the signal to noise ratio. While the state-of-the-a...Show More
Network virtualization is an extensively used approach to allow multiple tenants with different network architectures and services to coexist on a shared data center infrastructure. Core to its realization is the mapping (or embedding) of virtual networks onto the underlying substrate infrastructure. Existing approaches are not suitable for cloud environments as they lack its most fundamental requ...Show More
This paper deals with the sub-Nyquist sampling of analog multiband signals. The Modulated Wideband Converter (MWC) is a promising compressive sensing architecture, foreseen to be able to break the usual compromise between bandwidth, noise figure and energy consumption of Analog-to-Digital Converters. The pseudorandom code sequences yielding the sensing matrix are yet the bottleneck of it. Our cont...Show More
In this paper, a target detection procedure with global error control is proposed. The novelty of this approach consists in taking into account spatial structures of the target while ensuring proper error control over pixelwise errors. A generic framework is discussed and a method based on this framework is implemented. Results on simulated data show conclusive gains in detection power for a nomin...Show More
This paper establishes a tensor model for wideband coherent array processing including multiple physical diversities. A separable coherent focusing operation is proposed as a preprocessing step in order to ensure the multilinearity of the interpolated data. We propose an alternating least squares algorithm to process tensor data, taking into account the noise correlation structure introduced by th...Show More
Over the past several years, Software Defined Networking (SDN) has emerged as a new and promising paradigm for the management of computer networks. While we have seen many use-cases and deployments of SDN in data center networks, wide-area networks still heavily rely on legacy routing and traffic engineering technologies. Rapidly increasing traffic demands (mainly due to increasing usage of video ...Show More
In this study, a multiple-comparison approach is developed for detecting faint hyperspectral sources. The detection method relies on a sparse and nonnegative representation on a highly coherent dictionary to track a spatially varying source. A robust control of the detection errors is ensured by learning the test statistic distributions on the data. The resulting control is based on the false disc...Show More
In this letter, we investigated the connection between information and estimation measures for mismatched Gaussian models. In addition to the input prior mismatch, we take into account the noise mismatch and establish a new relation between relative entropy and excess mean square error. The derived formula shows that the input prior mismatch may be canceled by the noise mismatch. Finally, an examp...Show More
In passive monitoring using sensor networks, low energy supplies drastically constrain sensors in terms of calculation and communication abilities. Designing processing algorithms at the sensor level that take into account these constraints is an important problem in this context. Here we study the estimation of correlation functions between sensors using compressed acquisition and one-bit-quantiz...Show More
In this paper, we consider the problem of estimating an unknown random scalar observed by two modalities. We study two scenarios using mutual information and mean square error. In the first scenario, we consider that the noise correlation is known and examine its impact on the information content of two modalities. In the second scenario we quantify the information loss when the considered value o...Show More
Enforcing and routing based on network-wide policies remains a crucial challenge in the operation of large-scale enterprise and datacenter networks. As current dataplane devices solely rely on layer 2 - layer 4 identifiers to make forwarding decisions, there is no notion of the exact origin of a packet in terms of the sending user or process. In this paper we ask the question: Can we go beyond the...Show More
Our goal is to devise a wideband High-Resolution technique that does not require a priori knowledge of DoA rough estimates, and that is able to exploit multiple spatial invariances. Existing tensor array processing techniques are limited to the narrowband case. On the other hand, wideband Esprit has only been proposed with focusing matrices, requiring a priori DoA knowledge. We resort to the decom...Show More
A difficult aspect of multimodal estimation is the possible discrepancy between the sampling rates and/or the noise levels of the considered data. Many algorithms cope with these dissimilarities empirically. In this paper, we propose a conceptual analysis of multimodality where we try to find the "optimal" way of combining modalities. More specifically, we consider a simple Kalman filtering framew...Show More
In this paper, a new robust regression method based on the Least Trimmed Squares (LTS) is proposed. The novelty of this approach consists in a simple adaptive estimation of the number of outliers. This method can be applied to baseline estimation, for example to improve the detection of gas spectral signature in astronomical hyperspectral data such as those produced by the new Multi Unit Spectrosc...Show More
In this paper, we address the general issue of detecting rare and weak signatures in very noisy data. Multiple hypothe ses testing approaches can be used to extract a list of com ponents of the data that are likely to be contaminated by a source while controlling a global error criterion. However most of efficients methods available in the literature are de rived for independent tests. Based on th...Show More
This study presents an unsupervised method for detection of configurations of objects based on a point process in a nonparametric Bayesian framework. This is of interest as the model presented here has a number of parameters that increases with the number of objects detected. The marked point process yields a natural sparse representation of the object configuration, even in massive data fields. H...Show More
This paper focuses on estimated Gaussian Graphical Models (GGM) from sets of experimental data. Some extension of known Bayesian methods are proposed, allowing to introduce score functions to measure the relevance of the obtained GGM structure to describe the data. These score functions form the basic measurement to derive a new dissimilarity matrix based on the GGM structure. This latter is then ...Show More
To assess influence or information exchange between functional valued signals, we propose to extend the notion of Granger causality when stochastic processes are series of random variables in functional Hilbert spaces. We give strong definitions for Granger causality and instantaneous coupling using conditional independence. We then discuss a weaker form of the definitions which are valid for seco...Show More
In this paper a lower-bound for estimation of inter-sensor propagation delay using sources of opportunity is presented. This approach is referred to as passive identification. It relies on Ward identity, which is extended to the case of non white sources. Performances are studied in the case of an homogeneous non dispersive linear and time invariant wave propagation medium, under the assumption th...Show More
In this study, a method that aims at detecting small and faint objects in noisy hyperspectral astrophysical images is presented. The particularity of the hyperspectral images that we are interested in is the high dynamics between object intensities. Detection of the smallest and faintest objects is challenging, because their signal-to-noise ratio is low, and if the brightest objects are not well r...Show More
In this paper, we consider the use of seismic sensors for footstep localization in indoor environment. A popular strategy of localization is to use the measured time differences of arrival (TDOA) of the source signal at multiple pairs of sensors. In the literature, most of these algorithms assume that the perceived propagation velocity is constant. However, in dessipative and dispersive media, suc...Show More
In this paper we extend Geweke's approach of Granger causality by deriving a nonlinear framework based on functional regression in reproducing kernel Hilbert spaces (RKHS). After giving the definitions of dynamical and instantaneous causality in the Granger sense, we review Geweke's measures. These measures quantify improvement in predicting a time series when the past of another one is taken into...Show More