Adaptive False Discovery Rate for Wavelet Denoising of Pavement Continuous Deflection Measurements

Katicha, Samer W.; Loulizi, Amara; Khoury, John El; Flintsch, Gerardo W.

doi:10.1061/(asce)cp.1943-5487.0000603

Cited by 19 publications

(12 citation statements)

References 23 publications

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“…Having modelled and extracted the conditional volatility, skewness, and kurtosis of Bitcoin and S&P500 returns via the GARCHSK, we study the pairwise co-movement for each moment within a wavelet coherence approach allowing for capturing it in the time-frequency domain (Grinsted et al, 2004). The wavelet coherence method has been applied in empirical finance PLOS ONE [27,33] and other research fields [34,35]. Importantly, the suitability and power of the wavelet coherence to the context of equity and Bitcoin markets cannot be overstated for several reasons.…”

Section: Wavelet Coherencementioning

confidence: 99%

Bitcoin and S&P500: Co-movements of high-order moments in the time-frequency domain

2022

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Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency. There is very weak or even non-existent connection between the two markets before 2018. Starting 2018, but mostly 2019 onwards, the interconnections emerge. The co-movements between the volatility of Bitcoin and S&P500 intensified around the COVID-19 outbreak, especially at mid-term scales. For skewness and kurtosis, the co-movement is stronger and more significant at mid- and long-term scales. A partial-wavelet coherence analysis underlines the intermediating role of economic policy uncertainty (EPU) in provoking the Bitcoin-S&P500 nexus. These results reflect the co-movement between US stock and Bitcoin markets beyond the second moment of return distribution and across time scales, suggesting the relevance and importance of considering fat tails and return asymmetry when jointly considering US equity-Bitcoin trading or investments and the policy formulation for the sake of US market stability.

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Section: Wavelet Coherencementioning

confidence: 99%

Bitcoin and S&P500: Co-movements of high-order moments in the time-frequency domain

2022

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“…Data processing—the data processing methods investigated are data averaging and calculation of deflections from the deflection slope data. Data denoising also falls under data processing but is not discussed in this paper (see Katicha et al) ( 9 – 11 ).…”

Section: Data Analysis Methodsmentioning

confidence: 99%

“…For measurements averaged over 3.3 ft (1 m), the standard error was about three times larger, while for measurements averaged over 330 ft (100 m) it was about three times smaller (following the statistical behavior that when measurements are averaged, the standard deviation decreases in proportion to the square root of the number of measurements being averaged). (11,12). The testing reported in Katicha et al was done as part of the Transportation Pooled Fund TPF-5(282) ''Demonstration of Network Level Pavement Structural Evaluation with Traffic Speed Deflectometer'' reflecting how the device performs in routine testing conditions rather than in an experimental setting (11).…”

Section: Device Precision/repeatability/standard Errormentioning

confidence: 99%

“…The testing reported in Katicha et al was done as part of the Transportation Pooled Fund TPF-5(282) ''Demonstration of Network Level Pavement Structural Evaluation with Traffic Speed Deflectometer'' reflecting how the device performs in routine testing conditions rather than in an experimental setting (11). Figures 4 and 5 show two examples of short-term SCI 300 repeatability of measurements at a 33ft (10 m) resolution (corrected to Although not reported in Katicha et al, the standard error of the difference between the two sets of measurements was observed to be of the same order as the one reported in Flintsch et al (11,12). Figures 6 and 7 show two examples of long-term repeatability (1 year interval) of measurements at a 10 m resolution (corrected to a reference temperature of 70°F [21.1°C]).…”

Section: Device Precision/repeatability/standard Errormentioning

confidence: 99%

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Ten Years of Traffic Speed Deflectometer Research in the United States: A Review

Katicha

Flintsch

Diefenderfer

2022

Transportation Research Record: Journal of the Transportation R

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This paper summarizes the research efforts into and applications of the traffic speed deflectometer (TSD) that have occurred over the last 10 years in the United States (U.S.). These are divided into three main topics which are: (1) data analysis methods including data processing, calculation of indices and structural parameters, and temperature correction; (2) evaluation efforts of TSD including repeatability and comparison with the falling weight deflectometer (FWD); and (3) applications of TSD with a focus on flexible pavement management applications, although some efforts on rigid and composite pavements are mentioned. The data analysis methods reviewed are data averaging length and reporting interval, and how deflections are calculated from the measured deflection slopes. For the calculation of indices and structural parameters the following are reviewed: deflection indices that have been proposed, the effective structural number ( SNeff), and backcalculation of layer moduli. Efforts to evaluate TSD precision (or measurement standard error) are discussed. Then, some comparisons of short-term (time frame of 1 day) and long-term (time frame of 1 year) repeatability of TSD are presented. Compared with FWD, TSD and FWD generally have good correlation, although the measurement principles of the two devices are slightly different. The focus is on network-level applications of TSD, and, more specifically, efforts to include TSD measurements in the pavement management system, especially for flexible pavements applications.

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“…To some extent, it can realize the reconstruction of time-domain and frequency-domain characteristics of speech signal and realize the conversion of speech signal between time-domain and frequencydomain; however, the processing effect of this traditional sig-nal processing method for time-varying nonstationary signals is very poor, and it cannot accurately track the timevarying characteristic structure corresponding to the signal. At the same time, it is precisely because of this defect that Fourier transform cannot be widely used [4][5][6]. In the conventional underlying algorithms of English speech recognition, the main basic recognition principles are mainly focused on the recognition algorithm based on phonetics, the recognition algorithm based on speech template matching, and the speech recognition algorithm based on neural network.…”

Section: Introductionmentioning

confidence: 99%

Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

Zhuo

2021

Advances in Mathematical Physics

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The signal corresponding to English speech contains a lot of redundant information and environmental interference information, which will produce a lot of distortion in the process of English speech translation signal recognition. Based on this, a large number of studies focus on encoding and processing English speech, so as to achieve high-precision speech recognition. The traditional wavelet denoising algorithm plays an obvious role in the recognition of English speech translation signals, which mainly depends on the excellent local time-frequency domain characteristics of the wavelet signal algorithm, but the traditional wavelet signal algorithm is still difficult to select the recognition threshold, and the recognition accuracy is easy to be affected. Based on this, this paper will improve the traditional wavelet denoising algorithm, abandon the single-threshold judgment of the original traditional algorithm, innovatively adopt the combination of soft threshold and hard threshold, further solve the distortion problem of the denoising algorithm in the process of English speech translation signal recognition, improve the signal-to-noise ratio of English speech recognition, and further reduce the root mean square error of the signal. Good noise reduction effect is realized, and the accuracy of speech recognition is improved. In the experiment, the algorithm is compared with the traditional algorithm based on MATLAB simulation software. The simulation results are consistent with the actual theoretical results. At the same time, the algorithm proposed in this paper has obvious advantages in the recognition accuracy of English speech translation signals, which reflects the superiority and practical value of the algorithm.

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Adaptive False Discovery Rate for Wavelet Denoising of Pavement Continuous Deflection Measurements

Cited by 19 publications

References 23 publications

Bitcoin and S&P500: Co-movements of high-order moments in the time-frequency domain

Bitcoin and S&P500: Co-movements of high-order moments in the time-frequency domain

Ten Years of Traffic Speed Deflectometer Research in the United States: A Review

Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

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