This work introduces the concept of time-frequency map of the phase difference between the cantilever response signal and the driving signal, calculated with a wavelet cross-correlation technique. The wavelet cross-correlation quantifies the common power and the relative phase between the response of the cantilever and the exciting driver, yielding "instantaneous" information on the driver-response phase delay as a function of frequency. These concepts are introduced through the calculation of the response of a free cantilever subjected to continuous and impulsive excitation over a frequency band.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323919 | PMC |
http://dx.doi.org/10.3762/bjnano.3.33 | DOI Listing |
Comput Biol Med
January 2025
Laboratory of Electrical Engineering and Smart Systems, ENSET, Hassan II University of Casablanca, BP 159 Bd Hassan II, Mohammedia, 28800, Morocco. Electronic address:
This paper introduces a new advanced model for denoising and classification of ECG signals, focusing on the use of a hybrid filter and Bayesian optimization. The hybrid filter synergistically combines enhanced empirical mode decomposition (EEMD), Chebyshev Type II filters, Butterworth, Daubechies Wavelet, and Savitzky-Golay filters, leveraging their respective advantages for effective noise reduction while preserving the essential features of the ECG signal. We employ a multi-criteria Bayesian optimization process, using cross-correlation and mean squared error (MSE) as key metrics, to refine the filter parameters to further improve the signal quality.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Changchun Special Equipment Inspection & Research Institute (Changchun Special Equipment Safety Monitoring Center), Changchun 130013, China.
This study presents a location method for buried polyethylene (PE) pipelines based on the double-tree complex wavelet cross-correlation delay. Initially, the dual-tree complex wavelet transform (DTCWT) is applied to denoise the acquired signal, followed by extracting the delay time through the cross-correlation function to locate the buried pipeline. A simulation model is established to analyze the peak values of the time-domain signals in both asymmetric and symmetric sensor layouts using COMSOL, determining the relationship between the signal time differences and pipeline positions.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Changchun Special Equipment Inspection & Research Institute (Changchun Special Equipment Safety Monitoring Center), Changchun 130013, China.
This study proposes a buried PE gas pipeline positioning method based on the elliptical method of an acoustic signal analysis. The cross-correlation time delay positioning technology is combined with the elliptical equation, forming an effective mechanism for pipeline depth positioning. First, a dual-tree complex wavelet transform is employed to denoise the collected signals, enhancing the quality and accuracy of the data.
View Article and Find Full Text PDFWe propose and experimentally demonstrate a parallel ultra-fast random bit generation (RBG) scheme based on wideband chaotic microcomb, which utilizes a phase modulation and dispersive component broadening spectrum. The effective bandwidth of each comb tooth is increased by over 10-fold. Wavelet high-pass filtering (WHPF) is employed to make the probability density functions (PDFs) of the chaotic signal's amplitude unbiased, achieving high symmetry with a skewness coefficient |S| of 0.
View Article and Find Full Text PDFSensors (Basel)
September 2024
College of Energy and Power Engineering, Xihua University, Chengdu 610039, China.
In order to enhance the accuracy and adaptability of urban water supply pipeline leak localization, based on the Northern Goshawk Optimization, a novel joint denoising method is proposed in this paper to reduce noise in negative pressure wave signals caused by leaks. Firstly, the Northern Goshawk Optimization optimizes the decomposition levels and penalty factors of Variational Mode Decomposition, and obtains their optimal combination. Subsequently, the optimized parameters are used to decompose the pressure signals into modal components, and the effective components and noise components are distinguished according to the correlation coefficients.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!