UWB channel modeling for objects evolving in impulsive environnements

International audienceWe consider channel modeling issues in the context where communicating objects are evolving in impulsive environments. It was shown recently that α-stable random processes are attractive solution for representing the ultra wide band communication channel in relatively large spatial areas. In this paper, we consider the α-stable channel modeling in an evolutionary context where the model features depend on spatial locations. We introduce a methodological approach consisting of two parametric and non parametric components: the latter can be seen as black box model to describe the spatial evolution and it can be learned from historical observations of the transfer function. The other component concerns the frequency dependence and has an auto-regressive structure

Spectral representation of some non stationary alpha-stable processes

In this paper, we give a new covariation spectral representation of some non stationary symmetric $\alpha$-stable processes (S$\alpha$S). This representation is based on a weaker covariation pseudo additivity condition which is more general than the condition of independence. This work can be seen as a generalization of the covariation spectral representation of processes expressed as stochastic integrals with respect to independent increments S$\alpha$S processes (see Cambanis (1983)) or with respect to the general concept of independently scattered S$\alpha$S measures (Samorodnitsky and Taqqu 1994). Relying on this result we investigate the non stationarity structure of some harmonisable S$\alpha$S processes especially those having periodic or almost-periodic covariation functions

Analysis of COVID-19 evolution based on testing closeness of sequential data

A practical algorithm has been developed for closeness analysis of sequential data that combines closeness testing with algorithms based on the Markov chain tester. It was applied to reported sequential data for COVID-19 to analyze the evolution of COVID-19 during a certain time period (week, month, etc.)

On the detection of elderly equilibrium degradation using multivariate-EMD

International audienceThe aim of this paper is to provide a new methodology for the detection of an increased risk of falling in community-dwelling elderly. A new extended method of the empirical mode decomposition (EMD) called multivariate-EMD is employed in the proposed solution. This method will be mainly used to analyze the stabilogram center of pressure (COP) time series. In this paper, we describe also the remote non-invasive assessment method, which is suitable for static and dynamic balance. Balance was assessed using a miniature force plate, while gait was assessed using wireless sensors placed in a corridor of the home. The experimental results show the effectiveness of this indicator to identify the differences in standing posture between different groups of population

Machine Learning Mitigants for Speech Based Cyber Risk

Statistical analysis of speech is an emerging area of machine learning. In this paper, we tackle the biometric challenge of Automatic Speaker Verification (ASV) of differentiating between samples generated by two distinct populations of utterances, those of an authentic human voice and those generated by a synthetic one. Solving such an issue through a statistical perspective foresees the definition of a decision rule function and a learning procedure to identify the optimal classifier. Classical state-of-the-art countermeasures rely on strong assumptions such as stationarity or local-stationarity of speech that may be atypical to encounter in practice. We explore in this regard a robust non-linear and non-stationary signal decomposition method known as the Empirical Mode Decomposition combined with the Mel-Frequency Cepstral Coefficients in a novel fashion with a refined classifier technique known as multi-kernel Support Vector machine. We undertake significant real data case studies covering multiple ASV systems using different datasets, including the ASVSpoof 2019 challenge database. The obtained results overwhelmingly demonstrate the significance of our feature extraction and classifier approach versus existing conventional methods in reducing the threat of cyber-attack perpetrated by synthetic voice replication seeking unauthorised access

Bayesian spatio-temporal kriging with misspecified black-box

We propose a new algorithm for spatio-temporal prediction. At a given time t, we use a Bayesian kriging model for spatial prediction. The temporal evolution from t to t + 1 is given by a deterministic black-box which can be a complex numerical code or a partial differential equation. As often in practice, the black-box is misspecified, in the sense that its parameters are imprecisely known or may be varying randomly over time. At time t, we use the black-box to obtain a rough prediction at time t + 1. When new data are available, the black-box is used to estimate the hyperparameters of the Bayesian kriging at time t + 1 by using Monte Carlo methods. Through a numerical application, we show that our method improves the values predicted by the black-box only

CLASSIFYING HEARTRATE BY CHANGE DETECTION AND WAVELET METHODS FOR EMERGENCY PHYSICIANS

10 pages.Heart Rate Variability (HRV) carries a wealth of information about the physiological state and the behaviour of a living subject. Indeed, the heart rate variation is intrinsically linked to the autonomic nervous system: the Parasympathetic and Sympathetic systems. Thus, any imbalance in these two opposite systems results in a variation of the cardiac frequency modulation. It is also recognized that this alternation between equilibrium and disequilibrium (frequency variability) is an indicator of well being and good health. In other words, decreased heart rate variability is always linked to stress, fatigue and decreased physical performances. The aim of this work is to exploit the heart rate signals to detect situations of stress in different populations: emergency physicians, sportsmen, animal behaviours, etc...This paper introduces a methodological framework for the detection of stress and eventually well being. Our contribution is based on first extracting high and low frequencies energies which are linked to the Parasympathetic and Sympathetic systems. We then detect change points on these energies using the Filtered Derivative with p-value (FDpV) method. Finally, we develop a typology of cardiac activity by distinguishing homogeneous groups or state profiles having a characteristic similarity. We apply our methodology on a real dataset corresponding to an emergency doctor

Quelques contributions au traitement de données, de signaux et à ses applications

This document summarises my research work on statistical modelling and data processing issued from different application areas. The general themes of this research are part of the mainstream of statistical data analysis, signal processing and their applications. We insist, in particular, on the multi-disciplinary character of the results obtained. These results combine both theoretical work and application purposes. The main part of this work concerned the spectral analysis of $\alpha$-stable processes and their applications in communication, data processing and in impulsive signals modelling The other part, which is not too far from the spectral analysis, deals with the time-frequency analysis of specific non-stationary signals and their applications, in particular in the analysis of physiological signals and data. The third part is devoted to regression models in small high dimensional contextes. Many applications have been devloped for variable and model selection for volcanological data among others.Ce document de synthèse résume mes travaux de recherche sur la modélisation et le traitement statistique de données issues de différents domaines d'applications. Les thématiques générales de ces recherches s'inscrivent dans le cadre de l'analyse statistique des données et du traitement du signal ainsi que de leurs applications. Nous insistons notamment sur le caractère multi-disciplinaire des résultats obtenus qui allient à la fois un travail souvent théorique avec des finalités applicatives. La part importante de ces travaux a concerné l'analyse spectrale des processus $\alpha$-stables ainsi que leurs applications en communication, en traitement de données et des signaux impulsifs. L'autre volet, qui n'est pas très éloigné de l'analyse spectrale a concerné l'analyse temps-fréquences de certains signaux non stationnaires et leurs applications; en particulier en analyse de signaux physiologiques. La troisième partie est consacrée aux modèles de régressions, de sélection des variables ainsi que leurs applications en volcanologie entre autres

Analyse et Estimations Spectrales des Processus alpha-Stables non-Stationnaires

In this work a new spectral representation of a symmetric alpha-stable processes is introduced. It is based on a covariation pseudo-additivity and Morse-Transue's integral with respect to a bimesure built by using pseudo-additivity property. This representation, specific to S$\alpha$S processes, is analogous to the covariance of second order processes. On the other hand, it generalizes the representation established for stochastic integrals with respect to symmetric alpha-stable process of independent increments. We provide a classification of non-stationary harmonizable processes; this classification is based on the bimesure structure. In particular, we defined and investigated periodically covariated processes. To simulate and build this unusual class, a new decomposition in the Lepage's type series was derived. Finally, to apply this results in practical situations, a nonparametric estimation of spectral densities are discussed. In particular, in the case of periodically covariated processes, an almost sure convergent estimators was derived under the strong mixing condition.Dans cette thèse une nouvelle représentation spectrale des processus symétriques alpha-stables est introduite. Elle est basée sur une propriété de pseudo-additivité de la covariation et l'intégrale au sens de Morse-Transue par rapport à une bimesure que nous construisons en utilisant la pseudo-additivité. L'intérêt de cette représentation est qu'elle est semblable à celle de la covariance des processus du second ordre; elle généralise celle établie pour les intégrales stochastiques par rapport à un processus symétrique alpha-stable à accroissements indépendants. Une classification des processus harmonisables non stationnaires a été étudiée selon la structure de la bimesure qui les caractérise et les processus périodiquement covariés ont été définis. Pour pouvoir simuler cette inhabituelle classe de processus, une nouvelle décomposition en séries de type Lepage a été apportée. Finalement des techniques non paramétriques d'estimation spectrale sont discutées. En particulier un estimateur presque sûrement convergeant sous une condition de mélange fort, a été introduit pour les processus périodiquement covariés