Search results for "Noise Reduction"
showing 10 items of 71 documents
Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics
2015
The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and dai…
Experimental investigation of holes interaction effect on the sound absorption coefficient of micro-perforated panels under high and medium sound lev…
2011
Abstract This paper experimentally investigates the holes interaction effect on the sound absorption coefficient of micro-perforated panels under high and medium sound levels. The theoretical formulations are based on a semi-empirical approach and the use of Fok’s function to model the acoustic surface impedance. For the high sound level regime, an empirical power law involving three coefficients is adapted. It is shown theoretically and experimentally that these coefficients can lead to optimized absorption performance and particularly, a formula relating the critical Reynolds number (Reynolds number value after which the absorption coefficient decreases with the increase of sound level) a…
Assisted baseline subtraction in complex chromatograms using the BEADS algorithm.
2017
The data processing step of complex signals in high-performance liquid chromatography may constitute a bottleneck to obtain significant information from chromatograms. Data pre-processing should be preferably done with little (or no) user supervision, for a maximal benefit and highest speed. In this work, a tool for the configuration of a state-of-the-art baseline subtraction algorithm, called BEADS (Baseline Estimation And Denoising using Sparsity) is developed and verified. A quality criterion based on the measurement of the autocorrelation level was designed to select the most suitable working parameters to obtain the best baseline. The use of a log transformation of the signal attenuate…
Dimension reduction: additional benefit of an optimal filter for independent component analysis to extract event-related potentials.
2011
The present study addresses benefits of a linear optimal filter (OF) for independent component analysis (ICA) in extracting brain event-related potentials (ERPs). A filter such as the digital filter is usually considered as a denoising tool. Actually, in filtering ERP recordings by an OF, the ERP' topography should not be changed by the filter, and the output should also be able to be modeled by the linear transformation. Moreover, an OF designed for a specific ERP source or component may remove noise, as well as reduce the overlap of sources and even reject some non-targeted sources in the ERP recordings. The OF can thus accomplish both the denoising and dimension reduction (reducing the n…
A Neural Architecture for 3D Segmentation
2003
An original neural scheme for segmentation of range data is presented, which is part of a more general 3D vision system for robotic applications. The entire process relies on a neural architecture aimed to perform first order image irradiance analysis, that is local estimation of magnitude and orientation of the image irradiance gradient.
2013
To identify factors limiting performance in multitone intensity discrimination, we presented sequences of five pure tones alternating in level between loud (85 dB SPL) and soft (30, 55, or 80 dB SPL). In the “overall-intensity task”, listeners detected a level increment on all of the five tones. In the “masking task”, the level increment was imposed only on the soft tones, rendering the soft tones targets and loud tones task-irrelevant maskers. Decision weights quantifying the importance of the five tone levels for the decision were estimated using methods of molecular psychophysics. Compatible with previous studies, listeners placed higher weights on the loud tones than on the soft tones i…
2D ECG Image Based Biometric Identification Using Stacked Autoencoders
2021
The handcrafted features extraction methods have achieved remarkable results in ECG based biometric identification. However, they are sensitive to many factors: (1) intra and inter-individual variability, (2) heart rate variability, (3) powerline interference, baseline wander and muscle artifacts. To deal with these issues, deep learning approaches have been proposed to extract automatically the important features almost from original data without any preprocessing step (i.e., The original ECG signal mostly contains noise). Unlike conventional ECG based biometric approaches, which based either on fiducial and non-fiducial methods, the proposed approach can be implemented on end to end syste…
A sensor-data-based denoising framework for hyperspectral images
2015
Many denoising approaches extend image processing to a hyperspectral cube structure, but do not take into account a sensor model nor the format of the recording. We propose a denoising framework for hyperspectral images that uses sensor data to convert an acquisition to a representation facilitating the noise-estimation, namely the photon-corrected image. This photon corrected image format accounts for the most common noise contributions and is spatially proportional to spectral radiance values. The subsequent denoising is based on an extended variational denoising model, which is suited for a Poisson distributed noise. A spatially and spectrally adaptive total variation regularisation term…
Noise reduction in magnetic resonance images by Wavelet transforms: an application to the study of capillary water absorption in sedimentary rocks
2007
Magnetic resonance imaging (MRI) is a powerful technique to study capillary water absorption kinetics in sedimentary rocks. However, the noise in the images can limit the correct identification and the quantitative measurement of the average height reached by the wetting front inside the porous material where imbibition occurs. Therefore, denoising methods can be applied to improve the image quality for a more accurate analysis, without the disadvantages of longer acquisition times. This study attempts to improve the signal-to-noise ratio of the images acquired by MRI on a sedimentary rock (Pietra di Lecce) using a waveletbased thresholding technique. The idea is to average some slightly di…
A Variational Approach for Denoising Hyperspectral Images Corrupted by Poisson Distributed Noise
2014
Poisson distributed noise, such as photon noise is an important noise source in multi- and hyperspectral images. We propose a variational based denoising approach, that accounts the vectorial structure of a spectral image cube, as well as the poisson distributed noise. For this aim, we extend an approach for monochromatic images, by a regularisation term, that is spectrally and spatially adaptive and preserves edges. In order to take the high computational complexity into account, we derive a Split Bregman optimisation for the proposed model. The results show the advantages of the proposed approach compared to a marginal approach on synthetic and real data.