Search results for "Signal processing"
showing 10 items of 2451 documents
Signal Denoising with Harten’s Multiresolution Using Interpolation and Least Squares Fitting
2014
Harten’s multiresolution has been successfully applied to the signal compression using interpolatory reconstructions with nonlinear techniques. Here we study the applicability of these techniques to remove noise to piecewise smooth signals. We use two reconstruction types: interpolatory and least squares, and we introduce ENO and SR nonlinear techniques. The standard methods adaptation to noisy signals and the comparative of the different schemes are the subject of this paper.
Robust finite-time fuzzy H∞ control for uncertain time-delay systems with stochastic jumps
2014
Abstract This paper investigates the problem of robust finite-time H ∞ control for a class of uncertain discrete-time Markovian jump nonlinear systems with time-delays represented by Takagi–Sugeno (T–S) model. Initially, the concepts of stochastic finite-time boundedness and stochastic finite-time H ∞ stabilization are presented. Then, by using stochastic Lyapunov–Krasovskii functional approach, sufficient conditions are derived such that the resulting close-loop system is stochastically finite-time bounded and satisfies a prescribed H ∞ disturbance attenuation level in a given finite-time interval. Furthermore, sufficient criteria on stochastic finite-time H ∞ stabilization using a fuzzy s…
<title>Reaction-diffusion electrical network for image processing</title>
2006
We consider an experimental setup, modelling the FitzHugh-Nagumo equation without recovery term and composed of a 1D nonlinear electrical network made up of discrete bistable cells, resistively coupled. In the first place, we study the propagation of topological fronts in the continuum limit, then in more discrete case. We propose to apply these results to the domain of signal processing. We show that erosion and dilation of a binary signal, can be obtained. Finally, we extend the study to 2D lattices and show that it can be of great interest in image processing techniques.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted fo…
Edge detection insensitive to changes of illumination in the image
2010
In this paper we present new edge detection algorithms which are motivated by recent developments on edge-adapted reconstruction techniques [F. Arandiga, A. Cohen, R. Donat, N. Dyn, B. Matei, Approximation of piecewise smooth functions and images by edge-adapted (ENO-EA) nonlinear multiresolution techniques, Appl. Comput. Harmon. Anal. 24 (2) (2008) 225-250]. They are based on comparing local quantities rather than on filtering and thresholding. This comparison process is invariant under certain transformations that model light changes in the image, hence we obtain edge detection algorithms which are insensitive to changes in illumination.
Über eine methode zur bestimmung der intensität komplexer photopeaks
1966
Abstract Analysing photopeaks really occuring in γ-ray spectra their asymmetrical structure can easily be demonstrated. To describe the shape of photopeaks this study therefore recommends the use of empirical functions instead of the normal distribution function. It can be shown, that in a wide energy and intensity range photopeaks are exactly described by two empirical functions which are normalized with respect to the fractional peak height. Taking account of this fact, a new procedure is derived which allows the decomposition of overlapping photopeaks even in the case of small energy distance and unfavorable intensity ratio. The method applied to numerous examples under practical conditi…
Propagation pattern analysis during atrial fibrillation based on sparse modeling.
2012
In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using si…
Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO.
2012
The present study introduces sparse modeling for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence (PDC) function, derived from fitting a multivariate autoregressive model to the observed signals. A sparse optimization method is proposed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO). In simulations aLASSO was found superior to the commonly used least-squares (LS) estimation with respect to estimation performance. The normalized error between the true and estimated model parameters dropped from 0.200.04 for LS estimatio…
Data-independent acquisition strategies for quantitative proteomics
2013
In shotgun proteomics, data-dependent precursor acquisition (DDA) is widely used to profile protein components in complex samples. Although very popular, there are some inherent limitations to the DDA approach, such as irreproducible precursor ion selection, under-sampling and long instrument cycle times. Unbiased ‘data-independent acquisition’ (DIA) strategies try to overcome those limitations. In MSE, which is supported by Waters Q-TOF instrument platforms, such as the Synapt G2-S, a wide band pass filter is used for precursor selection. During acquisition, alternating MS scans are collected at low and high collision energy (CE), providing precursor and fragment ion information, respectiv…
Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals
2019
Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. T…
A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification
2020
Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or shortcut connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers making up a residual block. While residual networks for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, their a…