Filtre passe-haut | EPFL Graph Search

thumb|right|Image sur laquelle a été appliqué un filtre passe-haut (résultat à droite) Un filtre passe-haut (en anglais, high-pass filter ou HPF) est un filtre qui laisse passer les hautes fréquences et qui atténue les basses fréquences, c'est-à-dire les fréquences inférieures à la fréquence de coupure. Il pourrait également être appelé filtre coupe-bas. Le filtre passe-haut est l'inverse du filtre passe-bas et ces deux filtres combinés forment un filtre passe-bande. Le concept de filtre passe-haut est une transformation mathématique appliquée à des données (un signal). L'implémentation d'un filtre passe-haut peut se faire numériquement ou avec des composantes électroniques. Cette transformation a pour fonction d'atténuer les fréquences inférieures à sa fréquence de coupure f_c et ce, dans le but de conserver uniquement les hautes fréquences. La fréquence de coupure du filtre est la fréquence séparant les deux modes de fonctionnement idéaux du filtre : bloquant ou passan

The impact of denoising on independent component analysis of functional magnetic resonance imaging data

Christoph Michel, Thierry Pun, Dimitri Nestor Alice Van De Ville

Independent component analysis (ICA) is a suitable method for decomposing functional magnetic resonance imaging (fMRI) activity into spatially independent patterns. Practice has revealed that low-pass filtering prior to ICA may improve ICA results by reducing noise and possibly by increasing source smoothness, which may enhance source independence; however, it eliminates useful information in high frequency features and it amplifies low signal fluctuations leading to independence loss. On the other hand, high-pass filtering may increase the independence by preserving spatial information, but its denoising properties are weak. Thus, such filtering strategies did not lead to simultaneous enhancements in independence and noise reduction; therefore, band-pass filtering or more sophisticated filtering methods are expected to be more appropriate. We used advanced wavelet filtering procedures, such as wavelet-based methods relying upon hard and soft coefficient thresholding and non-stationary Gaussian modelling based on geometrical prior information, to denoise artificial and real fMRI data. We compared the performance of these methods with the performance of traditional Gaussian smoothing techniques. First, we demonstrated both analytically and empirically the consistent performance increase of spatial filtering prior to ICA using spatial correlation and statistical sensitivity as quality measures. Second, all filtering methods were computationally efficient. Finally, denoising using low-pass filters was needed to improve ICA, suggesting that noise reduction may have a more significant effect on the component independence than the preservation of information contained within high frequencies. (C) 2012 Elsevier B.V. All rights reserved.

Elsevier2013

Cartographie et spectroscopie des propriétés mécaniques à l'échelle du nanomètre par spectrométrie acoustique locale à fréquence variable

This work aims at completing the development of local mechanical spectroscopy as initiated by F. Oulevey during his thesis. More precisely, the measurement of the amplitude and phase lag of the strain as a function of the applied stress frequency bas to be settled. Parallel to the development of the technical aspects making such an acquisition possible, the model used to analyze the measured quantities is modified in order to describe the studied system more realistically. The local mechanical spectrometer is based on an Atomic Force Microscope (AFM) using an optical method to detect the deflection of the lever. The stress is applied locally on the sample by a transducer fixed at the base of the cantilever. The bandwidth of the microscope' s electronics does not allow one to detect the cantilever movement at frequencies higher than the MHz. Two different approaches are followed to overcome this limit. The first approach exploits the nonlinearity of the contact to down-convert, via mechanical mixing, the high-frequency signals into low-frequency ones. This work demonstrates the feasibility of this technique, as well as its limits, mainly due to the difficulty to analyze confidently the obtained results. The second approach takes advantage of the stroboscopic detection of the movement of the cantilever. The intensity of the laser used to detect the deflection of the cantilever is modulated at a frequency close to the applied stress frequency. The amplitude of the signal recorded at the difference frequency corresponds then to the high-frequency amplitude of the cantilever movement. The frequency range attainable with this method is only limited by the bandwidth of the transducer providing the excitation. The model connecting the measured quantities (strain amplitude and phase lag) to the desired mechanical properties (elasticity and damping) consists in a beam clamped at one end, with a tip attached at some point along its length. The tip itself is connected to the sample via a spring and a dashpot in parallel, representing the normal interaction, and a spring and a dashpot in parallel representing the interaction in the plane of the sample. Combined with the stroboscopic detection, this model enables one to quantitatively determine the elastic modulus of stiff samples. Many results obtained on a wide range of samples exhibiting very diverse mechanical properties are presented. They demonstrate the validity of the stroboscopic approach, which combines simplicity of use and efficiency.

EPFL2000

Double Sparsity: Towards Blind Estimation of Multiple Channels

Rémi Gribonval

We propose a framework for blind multiple filter estimation from convolutive mixtures, exploiting the time-domain sparsity of the mixing filters and the disjointness of the sources in the time-frequency domain. The proposed framework includes two steps: (a) a clustering step, to determine the frequencies where each source is active alone; (b) a filter estimation step, to recover the filter associated to each source from the corresponding incomplete frequency information. We show how to solve the filter estimation step (b) using convex programming, and we explore numerically the factors that drive its performance. Step (a) remains challenging, and we discuss possible strategies that will be studied in future work.

Springer-Verlag's2010

Cartographie et spectroscopie des propriétés mécaniques à l'échelle du nanomètre par spectrométrie acoustique locale à fréquence variable

EPFL2000