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      Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

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          Abstract

          The time-difference method is a common one for measuring wind speed ultrasonically, and its core is the precise arrival-time determination of the ultrasonic echo signal. However, because of background noise and different types of ultrasonic sensors, it is difficult to measure the arrival time of the echo signal accurately in practice. In this paper, a method based on the wavelet transform (WT) and Bayesian information criteria (BIC) is proposed for determining the arrival time of the echo signal. First, the time-frequency distribution of the echo signal is obtained by using the determined WT and rough arrival time. After setting up a time window around the rough arrival time point, the BIC function is calculated in the time window, and the arrival time is determined by using the BIC function. The proposed method is tested in a wind tunnel with an ultrasonic anemometer. The experimental results show that, even in the low-signal-to-noise-ratio area, the deviation between mostly measured values and preset standard values is mostly within 5 μs, and the standard deviation of measured wind speed is within 0.2 m/s.

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          Multisensor Image Fusion Using the Wavelet Transform

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            ANALYSIS OF SOUND PATTERNS THROUGH WAVELET TRANSFORMS

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              Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete.

              Determining the onset of transient signals like seismograms, acoustic emissions or ultrasound signals is very time consuming if the onset is picked manually. Therefore, different approaches exist, especially in seismology. The concepts of the most popular approaches are summarized. An own approach adapted to ultrasound signals and acoustic emissions, based on the Akaike Information Criterion (AIC), is presented. The AIC-picker is compared to an automatic onset detection algorithm based on the Hinkley criterion and also adapted to acoustic emissions. Manual picks performed by an analyst are used as reference values. Both automatic onset detection algorithms are applied to ultrasound signals which are used to monitor the setting and hardening of concrete. They are also applied to acoustic emissions recorded during a pull-out test. The AIC-picker produces sufficient reliable results for ultrasound signals where the deviation from the manual picks varies between 2% and 4%. Concerning acoustic emissions, only 10% of the events result in a mislocation vector greater than 5mm. It can be shown that our AIC-picker is a reliable tool for automatic onset detection for ultrasound signals and acoustic emissions of varying signal to noise ratio.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                02 January 2020
                January 2020
                : 20
                : 1
                : 269
                Affiliations
                [1 ]School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; weizhang@ 123456uestc.edu.cn (W.Z.); lizhipeng1202@ 123456std.uestc.edu.cn (Z.L.); xuyanggao@ 123456std.uestc.edu.cn (X.G.); ybshi@ 123456uestc.edu.cn (Y.S.)
                [2 ]Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
                Author notes
                [* ]Correspondence: yjli@ 123456uestc.edu.cn
                Author information
                https://orcid.org/0000-0002-1218-9475
                https://orcid.org/0000-0002-0415-087X
                https://orcid.org/0000-0001-5171-7149
                Article
                sensors-20-00269
                10.3390/s20010269
                6982878
                31906590
                1df2deb7-6568-4a42-95b5-232b9f59ac5c
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 November 2019
                : 01 January 2020
                Categories
                Article

                Biomedical engineering
                wind speed,arrival time,wavelet transform,bayesian information criteria
                Biomedical engineering
                wind speed, arrival time, wavelet transform, bayesian information criteria

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