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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.


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Journal ArticleDOI
TL;DR: An ensemble-based noise ranking methodology for explicit noise and outlier identification, named Noise-Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation and a methodology for visual performance evaluation of noise detection algorithms in the precision-recall space, named Viper are presented.
Abstract: Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based noise ranking methodology for explicit noise and outlier identification, named Noise- Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of noise detection algorithms on data with randomly injected noise. A methodology for visual performance evaluation of noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new noise detection algorithms into the proposed noise detection and performance evaluation workflows.

69 citations

Journal ArticleDOI
TL;DR: This work focuses on estimating the information conveyed to a user by hyperspectral image data, establishing the extent to which an increase in spectral resolution enhances the amount of usable information.
Abstract: This work focuses on estimating the information conveyed to a user by hyperspectral image data. The goal is establishing the extent to which an increase in spectral resolution enhances the amount of usable information. Indeed, a tradeoff exists between spatial and spectral resolution due to physical constraints of multi-band sensors imaging with a prefixed SNR. After describing an original method developed for the automatic estimation of variance and correlation of the noise introduced by hyperspectral imagers, lossless interband data compression is exploited to measure the useful information content of hyperspectral data. In fact, the bit rate achieved by the reversible compression process takes into account both the contribution of the "observation" noise (i.e., information regarded as statistical uncertainty, but whose relevance to a user is null) and the intrinsic information of radiance sampled and digitized through an ideally noise-free process. An entropic model of the decorrelated image source is defined and, once the parameters of the noise, assumed to be Gaussian and stationary, have been measured, such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate. Results are reported and discussed on both simulated and AVIRIS data.

69 citations

Proceedings ArticleDOI
13 May 2012
TL;DR: In this article, a nonlinear parameter estimator based on the Unscented Transformation (UWT) is proposed to estimate the offset and sensitivity parameters more precisely than the uncertainty introduced through the measurement noise.
Abstract: In this paper a new approach of an auto calibration method for micromechanical sensors is proposed. In particular, recalibration of acceleration sensors without any additional laboratory equipment is considered. If the device is stationary, the proposed procedure exploits the fact that the output vector of the acceleration sensor should match the gravity acceleration. The calibration method computes the scale factors and the bias components of the unbalanced acceleration sensor. These parameters are computed through nonlinear optimization. The applied optimization method is a nonlinear parameter estimator based on the Unscented Transformation. This methodology uses the robust statistical linearization instead of the common analytical linearization. In addition, the applied methodology minimizes the amount of temporarily stored measurement data which are mandatory to launch the recalibration algorithm. Reducing the amount of temporarily stored data is equivalent to reducing the memory space and the power required for the algorithm. An effective method for rejecting disturbance acceleration is also included in order to apply user generated data for the recalibration. First the calibration method is evaluated through simulations and second with real data generated by an acceleration sensor. The simulation results show that the algorithm estimates the offset and sensitivity parameters more precisely than the uncertainty introduced through the measurement noise.

69 citations

Journal ArticleDOI
TL;DR: Results show that two statistical clusters differentiated by rush hour traffic flow are sufficient and better for categorization than the road types provided by Italian road regulation.

69 citations

Journal ArticleDOI
Junbo Zhao1, Lamine Mili1
TL;DR: A robust state estimation framework to address the unknown non-Gaussian noise and the measurement time skewness issue is proposed and it is shown that the state estimates provided by the SHGM estimator follow an asymptotical Gaussian distribution.
Abstract: In practical applications like power systems, the distribution of the measurement noise is usually unknown and frequently deviates from the assumed Gaussian model, yielding outliers. Under these conditions, the performances of the existing state estimators that rely on Gaussian assumption can deteriorate significantly. In addition, the sampling rates of measurements from supervisory control and data acquisition (SCADA) system and phasor measurement unit (PMU) are quite different, causing time skewness problem. In this paper, we propose a robust state estimation framework to address the unknown non-Gaussian noise and the measurement time skewness issue. In the framework, robust Mahalanbis distances are proposed to detect system abnormalities and assign appropriate weights to each chosen buffered PMU measurements. Those weights are further utilized by the Schweppe-type Huber generalized maximum-likelihood (SHGM) estimator to filter out non-Gaussian PMU measurement noise and help suppress outliers. In the meantime, the SHGM estimator is used to handle unknown noise of the received SCADA measurements, yielding another set of state estimates. We show that the state estimates provided by the SHGM estimator follow an asymptotical Gaussian distribution. This nice property allows us to obtain the optimal state estimates by resorting to the data fusion theory for the fusion of the estimation results from two independent SHGM estimators. Extensive simulation results carried out on the IEEE 14, 30 and 118-bus test systems demonstrate the effectiveness and robustness of the proposed method.

69 citations


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