Assessment of compliance of dimensional tolerances in concrete slabs using TLS data and the 2D continuous wavelet transform

2018 ◽  
Vol 94 ◽  
pp. 62-72 ◽  
Author(s):  
Nisha Puri ◽  
Enrique Valero ◽  
Yelda Turkan ◽  
Frédéric Bosché
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Concrete Slabs ◽  
Continuous Wavelet ◽  
Assessment Of Compliance

Detecting Rebars and Tubes Inside Concrete Slabs Using Continuous Wavelet Transform of Elastic Waves

2004 ◽  
Vol 20 (4) ◽  
pp. 297-302 ◽  
Author(s):  
C. H. Chiang ◽  
C. C. Cheng
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Concrete Slab ◽  
Amplitude Spectrum ◽  
Concrete Slabs ◽  
Continuous Wavelet ◽  
Steel Tubes ◽  
Impact Echo ◽  
The Impact ◽  
Impact Echo Method

AbstractA typical problem of elastic wave methods, such as the impact echo method, is due to peak detection based solely on amplitude spectrum. Current study aims to improve the feature identification of impact-echo signals obtained from buried objects in concrete slabs. Steel rebar, steel tubes, and PVC tubes embedded in a concrete slab are tested. Numerical simulations are carried out based on models constructed using the finite element method. The received signals, both experimental and simulated, are analyzed using both fast Fourier transform and continuous wavelet transform (CWT). The amplitude spectra can only provide global information and lose some important local effects of frequency components. This can be resolved by continuous wavelet transform for preserving the transient effects in the frequency domain. Localized spectral contents are analyzed and thus better understanding is achieved for the impulse responses due to different objects below the surface of the concrete slab. Features related to steel rebar, PVC and steel tubes are readily identified in the coefficient plot of wavelet coefficients. Multiple reflections and vibration modes related to various characteristics of wave propagation in the concrete slab can now be decomposed into distinctive frequency bands with different time durations. The result of CWT provides more information and is easier to interpret than that of the spectral analysis. The same peak frequency found in the amplitude spectrum is now distinguishable between PVC and steel tubes at a resolution of 0.1kHz or better. Such findings provide a more effective way to pick up true rebar signals using the impact-echo method.

scholarly journals Continuous Wavelet Transform of Schwartz Distributions in DL2′(Rn), n ≥ 1

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1106
Author(s):  
Jagdish N. Pandey
Keyword(s):  
Wavelet Transform ◽  
Function Space ◽  
Continuous Wavelet Transform ◽  
Inversion Formula ◽  
Continuous Wavelet ◽  
The Real ◽  
Schwartz Distributions ◽  
Distributional Sense ◽  
Wavelet Inversion

We define a testing function space DL2(Rn) consisting of a class of C∞ functions defined on Rn, n≥1 whose every derivtive is L2(Rn) integrable and equip it with a topology generated by a separating collection of seminorms {γk}|k|=0∞ on DL2(Rn), where |k|=0,1,2,… and γk(ϕ)=∥ϕ(k)∥2,ϕ∈DL2(Rn). We then extend the continuous wavelet transform to distributions in DL2′(Rn), n≥1 and derive the corresponding wavelet inversion formula interpreting convergence in the weak distributional sense. The kernel of our wavelet transform is defined by an element ψ(x) of DL2(Rn)∩DL1(Rn), n≥1 which, when integrated along each of the real axes X1,X2,…Xn vanishes, but none of its moments ∫Rnxmψ(x)dx is zero; here xm=x1m1x2m2⋯xnmn, dx=dx1dx2⋯dxn and m=(m1,m2,…mn) and each of m1,m2,…mn is ≥1. The set of such wavelets will be denoted by DM(Rn).

scholarly journals Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 119
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Yining Sun ◽  
Mei Yang ◽  
Chun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Ecg Signals ◽  
Rr Interval ◽  
Frequency Components ◽  
Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

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