Rapid parameter estimation of discrete decaying signals using autoencoder networks
- Publication type:
- Journal article
- Metadata:
-
- Autoren
- Jim C Visschers
- Dmitry Budker
- Lykourgos Bougas
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000697954900001&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1088/2632-2153/ac1eea
- eISSN
- 2632-2153
- Externe Identifier
- Clarivate Analytics Document Solution ID: UT2MJ
- Ausgabe der Veröffentlichung
- 4
- Zeitschrift
- MACHINE LEARNING-SCIENCE AND TECHNOLOGY
- Schlüsselwörter
- signal processing
- data analysis
- statistics and probability
- machine learning
- parameter estimation
- Artikelnummer
- ARTN 045024
- Datum der Veröffentlichung
- 2021
- Status
- Published
- Titel
- Rapid parameter estimation of discrete decaying signals using autoencoder networks
- Sub types
- Article
- Ausgabe der Zeitschrift
- 2
Data source: Web of Science (Lite)
- Other metadata sources:
-
- Abstract
- <jats:title>Abstract</jats:title> <jats:p>In this work we demonstrate the use of neural networks for rapid extraction of signal parameters of discretely sampled signals. In particular, we use dense autoencoder networks to extract the parameters of interest from exponentially decaying signals and decaying oscillations. By using a three-stage training method and careful choice of the neural network size, we are able to retrieve the relevant signal parameters directly from the latent space of the autoencoder network at significantly improved rates compared to traditional algorithmic signal-analysis approaches. We show that the achievable precision and accuracy of this method of analysis is similar to conventional algorithm-based signal analysis methods, by demonstrating that the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cramér–Rao bound. Furthermore, we demonstrate that autoencoder networks are able to achieve signal analysis, and, hence, parameter extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional techniques with similar precision. Finally, our exploration of the limitations of our approach in different computational systems suggests that analysis rates of <jats:inline-formula> <jats:tex-math><?CDATA $\gt$?></jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>></mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="mlstac1eeaieqn1.gif" xlink:type="simple" /> </jats:inline-formula>200 kHz are feasible using neural networks in systems where the transfer time between the data-acquisition system and data-analysis modules can be kept below ∼3 <jats:italic>µ</jats:italic>s.</jats:p>
- Autoren
- Jim C Visschers
- Dmitry Budker
- Lykourgos Bougas
- DOI
- 10.1088/2632-2153/ac1eea
- eISSN
- 2632-2153
- Ausgabe der Veröffentlichung
- 4
- Zeitschrift
- Machine Learning: Science and Technology
- Online publication date
- 2021
- Paginierung
- 045024 - 045024
- Datum der Veröffentlichung
- 2021
- Status
- Published
- Herausgeber
- IOP Publishing
- Herausgeber URL
- http://dx.doi.org/10.1088/2632-2153/ac1eea
- Datum der Datenerfassung
- 2021
- Titel
- Rapid parameter estimation of discrete decaying signals using autoencoder networks
- Ausgabe der Zeitschrift
- 2
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