scispace - formally typeset
Search or ask a question
Topic

Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.


Papers
More filters
Journal ArticleDOI
01 Dec 1951
TL;DR: The principle of inverse probability is applied to the problem of determining the time delay of a periodically modulated rf waveform in the presence of white Gaussian noise when the undelayed waveform without noise is exactly known.
Abstract: The paper deals with the problem, frequently encountered in radar, of extracting simple numerical information from a noisy waveform. It is suggested that the only ideal way of doing this is to use the principle of inverse probability and convert the wave-form into a probability distribution for the quantity sought. The method is applied to the problem of determining the time delay of a periodically modulated rf waveform in the presence of white Gaussian noise when the undelayed waveform without noise is exactly known. As a result, the matched predetection filter of Van Vleck and Middleton is automatically specified, and the theory of ideal detection is briefly indicated.

84 citations

Journal ArticleDOI
TL;DR: Wavelet-based noise removal techniques are very effective in removing noise from differentiated signals with sharp transients while leaving these transients intact, as indicated by quantitative merit measures.
Abstract: The purpose of this paper is to present wavelet-based noise removal (WBNR) techniques to remove noise from biomechanical acceleration signals obtained from numerical differentiation of displacement data. Manual and semiautomatic methods were used to determine thresholds for both orthogonal and biorthogonal filters. This study also compares the performance of WBNR approaches with four automatic conventional noise removal techniques used in biomechanics. The conclusion of this work is that WBNR techniques are very effective in removing noise from differentiated signals with sharp transients while leaving these transients intact. For biomechanical signals with certain characteristics, WBNR techniques perform better than conventional methods, as indicated by quantitative merit measures.

84 citations

Journal ArticleDOI
TL;DR: Three novel and alternative methods for estimating the noise standard deviation are proposed in this work and compared with the MAD method, which assumes specific characteristics of the noise-contaminated image component.
Abstract: The estimation of the standard deviation of noise contaminating an image is a fundamental step in wavelet-based noise reduction techniques. The method widely used is based on the mean absolute deviation (MAD). This model-based method assumes specific characteristics of the noise-contaminated image component. Three novel and alternative methods for estimating the noise standard deviation are proposed in this work and compared with the MAD method. Two of these methods rely on a preliminary training stage in order to extract parameters which are then used in the application stage. The sets used for training and testing, 13 and 5 images, respectively, are fully disjoint. The third method assumes specific statistical distributions for image and noise components. Results showed the prevalence of the training-based methods for the images and the range of noise levels considered.

83 citations

Journal ArticleDOI
TL;DR: It is shown that if enough noise is present to push the orbits into the basin boundary, behavior similar to intrinsic chaos results.
Abstract: By digital simulations and experiment, we study a Josephson system in a highly nonlinear regime. High experimental noise values appear to correspond in simulations to intrinsic chaotic motion in some regions and to noise-induced hopping between periodic solutions in others. Focusing on the latter, we find correlation between high noise sensitivity and the fractal dimension of the boundary between the basins of the periodic attractors. We show that if enough noise is present to push the orbits into the basin boundary, behavior similar to intrinsic chaos results.

83 citations

Proceedings ArticleDOI
W Williem1, In Kyu Park1
01 Jun 2016
TL;DR: The proposed method is more robust to occlusion and less sensitive to noise, and outperforms the state-of-the-art light field depth estimation methods in qualitative and quantitative evaluation.
Abstract: Light field depth estimation is an essential part of many light field applications. Numerous algorithms have been developed using various light field characteristics. However, conventional methods fail when handling noisy scene with occlusion. To remedy this problem, we present a light field depth estimation method which is more robust to occlusion and less sensitive to noise. Novel data costs using angular entropy metric and adaptive defocus response are introduced. Integration of both data costs improves the occlusion and noise invariant capability significantly. Cost volume filtering and graph cut optimization are utilized to improve the accuracy of the depth map. Experimental results confirm that the proposed method is robust and achieves high quality depth maps in various scenes. The proposed method outperforms the state-of-the-art light field depth estimation methods in qualitative and quantitative evaluation.

83 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
88% related
Convolutional neural network
74.7K papers, 2M citations
84% related
Deep learning
79.8K papers, 2.1M citations
84% related
Artificial neural network
207K papers, 4.5M citations
83% related
Wireless
133.4K papers, 1.9M citations
83% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202377
2022162
2021495
2020525
2019489
2018755