Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances

Pérez-Cruz, Fernando; Vaerenbergh, Steven Van; Murillo-Fuentes, Juan José; Lázaro-Gredilla, Miguel; Santamarı́a, Ignacio

doi:10.1109/msp.2013.2250352

Cited by 125 publications

(86 citation statements)

References 21 publications

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“…Thus, they are suitable for problems where data are limited due to high experimental cost, as in the present case. Previous work using GP models for signal processing can be found in [9,10], though the focus and applications are mainly related to communications. GP models can naturally deal with Gaussian noise in the output.…”

Section: Experimental Data Analysismentioning

confidence: 99%

Prediction of impurities in hydrogen fuel supplies using a thermally-modulated CMOS gas sensor: Experiments and modelling

et al. 2017

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Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. AbstractWe report here on the results of a study on the response of copper oxide based low-power MEMS thermally modulated gas sensor to low ppm levels of hydrogen sulphide (H2S) in a hydrogen environment. It was found that by using this material with a method of transient frequency analysis, this resistive gas sensor can operate reliably in a harsh environment including hydrogen atmosphere and high humidity levels. We implemented a Bayesian method for data analysis to predict the concentration of H2S in hydrogen supplies used in fuel cells.

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Section: Experimental Data Analysismentioning

confidence: 99%

Prediction of impurities in hydrogen fuel supplies using a thermally-modulated CMOS gas sensor: Experiments and modelling

et al. 2017

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“…The Gaussian process allows for the representation of distribution over functions and provides a method for modeling the probability distribution under multiple corruptions in complicated or uncertain situations [31,32]. When the dynamic parameter process is difficult to accurately obtained in advance, Gaussian process regression (GPR) can be exploited to supply the approximation distribution of the degradation process through learning from the training data available [33].…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter

Fan

2015

Microelectronics Reliability

124

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“…We use a model for nonlinear time-series known as a recursive or dynamic Gaussian process [24,25]. Such models have been used extensively over the past decade in diverse tasks including human motion modeling/tracking [26,27] and nonlinear signal processing [28]. For notational clarity, we assume that the time t counts units of 30 minutes; that is, t − 1 means 30 minutes before t, t − 2 means 60 minutes before t, and so forth.…”

Section: Learning the Reduced Dynamicsmentioning

confidence: 99%

Data-driven model for solar irradiation based on satellite observations

2014

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We construct a data-driven model for solar irradiation based on satellite observations. The model yields probabilistic estimates of the irradiation field every thirty minutes starting from two consecutive satellite measurements. The probabilistic nature of the model captures prediction uncertainties and can therefore be used by solar energy producers to quantify the operation risks. The model is simple to implement and can make predictions in realtime with minimal computational resources. To deal with the high-dimensionality of the satellite data, we construct a reduced representation using factor analysis. Then, we model the dynamics of the reduced representation as a discrete (30-minute interval) dynamical system. In order to convey information about the movement of the irradiation field, the dynamical system has a two-step delay. The dynamics are represented in a nonlinear, nonparameteric way by a recursive Gaussian process. The predictions of the model are compared with observed satellite data as well as with a similar model that uses only ground observations at the prediction site. We conclude that using satellite data in an area including the prediction site signficantly improves the prediction compared with models using only ground observation site data.

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Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances

Cited by 125 publications

References 21 publications

Prediction of impurities in hydrogen fuel supplies using a thermally-modulated CMOS gas sensor: Experiments and modelling

Prediction of impurities in hydrogen fuel supplies using a thermally-modulated CMOS gas sensor: Experiments and modelling

A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter

Data-driven model for solar irradiation based on satellite observations

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