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Noise measurement

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


Papers
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Journal ArticleDOI
TL;DR: A mutual incoherence condition which was previously used for exact recovery in the noiseless case is shown to be sufficient for stable recovery inThe noisy case and an oracle inequality is derived under the mutual incoherent condition in the case of Gaussian noise.
Abstract: This article considers sparse signal recovery in the presence of noise. A mutual incoherence condition which was previously used for exact recovery in the noiseless case is shown to be sufficient for stable recovery in the noisy case. Furthermore, the condition is proved to be sharp. A specific counterexample is given. In addition, an oracle inequality is derived under the mutual incoherence condition in the case of Gaussian noise.

163 citations

Proceedings ArticleDOI
01 Dec 2016
TL;DR: Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.
Abstract: Large datasets often have unreliable labels—such as those obtained from Amazon's Mechanical Turk or social media platforms—and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is underdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.

163 citations

Journal ArticleDOI
01 May 1959
TL;DR: The resulting system is similar to existing altimeters but is free of the ambiguities inherent in periodically modulated systems, avoids the "fixed error," and is capable of measuring distances down to a few feet, which makes it particularly suited for use as an altimeter in blind landing systems.
Abstract: Distance measuring systems using random noise as the modulating function are described. The distance measurement is accomplished by correlating the modulation on the transmitted and received signals. The spectrum of the modulating function determines the way in which this correlation, and hence system output, depends on distance to a reflecting target. Physical realizability of filters limits the output-to-distance behavior of linear, noise-modulated systems. Theoretically, either amplitude or frequency modulation can be used, but the latter has distinct advantages in overcoming incidental spurious signals generated within the system. Actual multiplication of signals is avoided through use of a conventional mixer. The resulting system is similar to existing altimeters but is free of the ambiguities inherent in periodically modulated systems, avoids the "fixed error," and is capable of measuring distances down to a few feet. This makes it particularly suited for use as an altimeter in blind landing systems.

163 citations

Patent
22 Jul 2005
TL;DR: In this article, a separation process using a set of a least two spaced-apart transducers is proposed to separate a good quality information signal from a noisy acoustic environment, where the noise signal is used to set process characteristics that are applied to the combination signal to efficiently reduce or eliminate the noise component.
Abstract: The present invention provides a process (26) for separating a good quality information signal from a noisy acoustic environment (12). The separation process uses a set of a least two spaced-apart transducers (19, 20) to capture noise (13, 15) and information components (14). The transducer signals, which have both a noise and information component, are received into a separation process. The separation process generates one channel that is substantially only noise, and another channel that is a combination of noise and information. An identification process (30) is used to identify which channel has the information component. The noise signal is then used to set process characteristics that are applied to the combination signal to efficiently reduce or eliminate the noise component. In this way, the noise is effectively removed from the combination signal to generate a good qualify information signal. The information signal may be, for example, a speech signal, a seismic signal, a sonar signal, or other acoustic signal.

161 citations

Proceedings ArticleDOI
Y.H. Peng1
04 Dec 2000
TL;DR: Based on soft thresholding, a new noise smoother is introduced in this paper, since a new statistics is used to make the estimation, the proposed algorithm can smooth both white and impulsive noise efficiently.
Abstract: Based on the soft-thresholding, a new noise smoother is introduced in this letter. Since a new statistics is used to make the estimation, the proposed algorithm can smooth both white and impulsive noise efficiently.

161 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202377
2022162
2021495
2020525
2019489
2018755