Application of Wavelets in Detection of Cavities Under Pavements by Surface Waves

2003 ◽  
Vol 1860 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Parisa Shokouhi ◽  
Nenad Gucunski ◽  
Ali Maher
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Element Model ◽  
Energy Concentration ◽  
Response Analysis ◽  
Analysis Tool ◽  
Cavity Detection ◽  
Time Frequency ◽  
Homogeneous Half Space ◽  
Power Spectral

Application of wavelet transforms in the detection of underground shallow cavities is investigated. Wave propagation is simulated through a transient response analysis on an axisymmetric finite element model. Cavities in a homogeneous half-space and a pavement system of a variety of shapes and embedment depths are considered. The continuous wavelet transform is introduced as a new tool for cavity detection. Effects of different types of cavities on power spectral surfaces (power spectral amplitudes versus frequency and receiver location) and Gaussian wavelet time-frequency maps (wavelet transform coefficients versus time and frequency) are studied. Results show strong energy concentration in power spectral surfaces right in front of a cavity in certain frequency bands. Time and frequency signatures of waves reflected from near and far faces of the cavity can be clearly observed in the wavelet time-frequency maps. These observations are used to locate and estimate the size of the cavity. It is demonstrated that the wavelet transform is a promising analysis tool for cavity detection and characterization.

scholarly journals Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Timur Düzenli ◽  
Nalan Özkurt
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Vanishing Moments ◽  
Zero Crossings ◽  
Time Frequency ◽  
Common Time ◽  
Classification Tool

The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.

CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

2020 ◽  
Vol 18 (06) ◽  
pp. 2030001 ◽  
Author(s):  
Rodrigo Capobianco Guido ◽  
Fernando Pedroso ◽  
André Furlan ◽  
Rodrigo Colnago Contreras ◽  
Luiz Gustavo Caobianco ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Time ◽  
Wavelet Transforms ◽  
Signal Analysis ◽  
Applied Mathematics ◽  
Discrete Wavelet ◽  
Frequency Signal ◽  
Time Frequency ◽  
Subsequent Investigation ◽  
Different Types

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

scholarly journals Detection of Patterns in Pressure Signal of Compressed Air System Using Wavelet Transform

2021 ◽  
pp. 61-67
Author(s):  
Mohamad Thabet ◽  
David Sanders ◽  
Nils Bausch
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Compressed Air ◽  
Learning Tools ◽  
Time Frequency ◽  
Pressure Signal ◽  
Operating Modes ◽  
Air System ◽  
The Time Domain ◽  
Future Work

AbstractThis paper investigates detecting patterns in the pressure signal of a compressed air system (CAS) with a load/unload control using a wavelet transform. The pressure signal of a CAS carries useful information about operational events. These events form patterns that can be used as ‘signatures’ for event detection. Such patterns are not always apparent in the time domain and hence the signal was transformed to the time-frequency domain. Three different CAS operating modes were considered: idle, tool activation and faulty. The wavelet transforms of the CAS pressure signal reveal unique features to identify events within each mode. Future work will investigate creating machine learning tools for that utilize these features for fault detection in CAS.

scholarly journals Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170250 ◽  
Author(s):  
Mark P. Wachowiak ◽  
Renata Wachowiak-Smolíková ◽  
Michel J. Johnson ◽  
Dean C. Hay ◽  
Kevin E. Power ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Wavelet Transforms ◽  
Continuous Wavelet ◽  
Theme Issue ◽  
Practical Applications ◽  
Time Frequency ◽  
New Family ◽  
Physiological Processes ◽  
Analytic Wavelet

Theoretical and practical advances in time–frequency analysis, in general, and the continuous wavelet transform (CWT), in particular, have increased over the last two decades. Although the Morlet wavelet has been the default choice for wavelet analysis, a new family of analytic wavelets, known as generalized Morse wavelets, which subsume several other analytic wavelet families, have been increasingly employed due to their time and frequency localization benefits and their utility in isolating and extracting quantifiable features in the time–frequency domain. The current paper describes two practical applications of analysing the features obtained from the generalized Morse CWT: (i) electromyography, for isolating important features in muscle bursts during skating, and (ii) electrocardiography, for assessing heart rate variability, which is represented as the ridge of the main transform frequency band. These features are subsequently quantified to facilitate exploration of the underlying physiological processes from which the signals were generated. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

COMPLEMENTARY ANALYSIS TO HEART SOUNDS WHILE USING THE SHORT TIME FOURIER AND THE CONTINUOUS WAVELET TRANSFORMS

2007 ◽  
Vol 19 (05) ◽  
pp. 331-339
Author(s):  
S. M. Debbal ◽  
F. Bereksi-Reguig
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Wavelet Transforms ◽  
Continuous Wavelet ◽  
Short Time Fourier Transform ◽  
Continuous Wavelet Transforms ◽  
Time Frequency ◽  
Quantitative Measurements ◽  
Short Time

This paper presents the analysis and comparisons of the short time Fourier transform (STFT) and the continuous wavelet transform techniques (CWT) to the four sounds analysis (S1, S2, S3 and S4). It is found that the spectrogram short-time Fourier transform (STFT), cannot perfectly detect the internals components of these sounds that the continuous wavelet transform. However, the short time Fourier transform can provide correctly the extent of time and frequency of these four sounds. Thus, the STFT and the CWT techniques provide more features and characteristics of the sounds that will hemp physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.

scholarly journals A general theory on frequency and time–frequency analysis of irregularly sampled time series based on projection methods – Part 2: Extension to time–frequency analysis

2018 ◽  
Vol 25 (1) ◽  
pp. 175-200 ◽  
Author(s):  
Guillaume Lenoir ◽  
Michel Crucifix
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Frequency Analysis ◽  
Continuous Wavelet Transform ◽  
Background Noise ◽  
Noise Model ◽  
Analysis Tool ◽  
Continuous Wavelet ◽  
Time Frequency Analysis ◽  
Time Frequency

Abstract. Geophysical time series are sometimes sampled irregularly along the time axis. The situation is particularly frequent in palaeoclimatology. Yet, there is so far no general framework for handling the continuous wavelet transform when the time sampling is irregular. Here we provide such a framework. To this end, we define the scalogram as the continuous-wavelet-transform equivalent of the extended Lomb–Scargle periodogram defined in Part 1 of this study (Lenoir and Crucifix, 2018). The signal being analysed is modelled as the sum of a locally periodic component in the time–frequency plane, a polynomial trend, and a background noise. The mother wavelet adopted here is the Morlet wavelet classically used in geophysical applications. The background noise model is a stationary Gaussian continuous autoregressive-moving-average (CARMA) process, which is more general than the traditional Gaussian white and red noise processes. The scalogram is smoothed by averaging over neighbouring times in order to reduce its variance. The Shannon–Nyquist exclusion zone is however defined as the area corrupted by local aliasing issues. The local amplitude in the time–frequency plane is then estimated with least-squares methods. We also derive an approximate formula linking the squared amplitude and the scalogram. Based on this property, we define a new analysis tool: the weighted smoothed scalogram, which we recommend for most analyses. The estimated signal amplitude also gives access to band and ridge filtering. Finally, we design a test of significance for the weighted smoothed scalogram against the stationary Gaussian CARMA background noise, and provide algorithms for computing confidence levels, either analytically or with Monte Carlo Markov chain methods. All the analysis tools presented in this article are available to the reader in the Python package WAVEPAL.

ON THE USE OF THE REASSIGNED WAVELET TRANSFORM FOR MODE IDENTIFICATION

2004 ◽  
Vol 12 (02) ◽  
pp. 175-196 ◽  
Author(s):  
MICHAEL I. TAROUDAKIS ◽  
GEORGE TZAGKARAKIS
Keyword(s):  
Wavelet Transform ◽  
Shallow Water ◽  
Underwater Acoustics ◽  
Low Frequency ◽  
Energy Concentration ◽  
Propagation Mode ◽  
Mode Identification ◽  
Time Frequency ◽  
Frequency Representation ◽  
Calculation Point

This paper is concerned with the use of the reassigned wavelet transform for mode identification in shallow water acoustic propagation. Mode identification is important for inverse procedures in underwater acoustics. An efficient way to recognize the modal structure of the acoustic field when a single hydrophone is available is to refer to the time frequency analysis of the recorded signal using wavelet transform. However, the standard wavelet transform in some cases may result in an obscure representation of the dispersion curves. Thus, a reassigned process is proposed which brings important improvements in the time frequency representation of the signal. This is achieved by moving the calculation point of the scalogram in the center of gravity of the energy concentration, associated with each one of the propagating modes. This argument is supported by two illustrative examples corresponding to propagation of low frequency tomographic signals, in shallow water.

Application of Wavelet Transform to Modal Parameter Identification of the Concrete-Filled Steel Tube Arch Bridge

2011 ◽  
Vol 250-253 ◽  
pp. 2446-2450
Author(s):  
Wei Huang ◽  
Guo Jing He
Keyword(s):  
Finite Element ◽  
Wavelet Transform ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Dynamic Properties ◽  
Element Model ◽  
System Response ◽  
Response Analysis ◽  
Modal Parameter ◽  
Transform Method

It’s important to identify structural modal parameter in time and accurately for structural health monitoring and damage identification. Wavelet analysis is one of the various kinds of identification methods, which has been used in linear and nonlinear system response data since it can decompose signals simultaneously both in time-domain and frequency-domain with adaptive windows. In this paper, taking Bariba Bridge as an example, the modal analysis results obtained from the finite element model are compared with those estimated from the wavelet transform method. Good coincidence of results can be observed, which demonstrates that the built-up finite element model reflects the bridge’s real dynamic properties, and can serve as a baseline model for its dynamic response analysis under complicated excitations, long-term health monitoring and structural service state assessment.

scholarly journals Detection of cracks in Micro structured Cantilever Beam using Wavelet Transforms

2020 ◽  
Vol 8 (6) ◽  
pp. 501-505
Keyword(s):  
Wavelet Transform ◽  
Cantilever Beam ◽  
Wavelet Transforms ◽  
Micro Electro Mechanical System ◽  
Mems Devices ◽  
Device Structure ◽  
Time Frequency ◽  
Cantilever Structure ◽  
Small Change ◽  
Micro Cantilever

Wavelet Analysis, the improved version of Fourier transform is used to investigate and analyze the variant transient signals in time-frequency domain with higher accuracy and precision. Wavelet theory found its promising application in various fields not limited to Physics, Biology, Geophysics, Engineering and Medicine which becomes a common tool to analyze data. In this work we present new insight using wavelet transform to detect the cracks present in micro structured cantilever beam which found its application in various Micro Electro Mechanical System (MEMS) devices such as Transducers, Sensors, Switches, Actuators and Probes. Even a small change in microstructure will reflect in its dynamic output, so it is desired to locate the presence of cracks or damages over the device structure accurately. The modeling of such microstructure is designed and simulated using COMSOL Multiphysics. The displacement (Static Response) and stress of the beam for simulated damage were analyzed by wavelet transform using MATLAB. The obtained results highlights this method of analysis provides accurate location and effect of the crack over the Micro cantilever structure.

New Evaluation Method on Gear Dynamics Using Continuous and Discrete Wavelet Transforms

2003 ◽  
Vol 125 (3) ◽  
pp. 274-281 ◽  
Author(s):  
Yuji Ohue ◽  
Akira Yoshida
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Evaluation Method ◽  
Dynamic Condition ◽  
Testing Machine ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Gear Dynamics ◽  
Time Frequency ◽  
The Difference

The aim of this study is to propose a new evaluation method of gear dynamics using the continuous and discrete wavelet transforms. The wavelet transform (WT) is a method for the time-frequency analysis of signals. In order to evaluate the difference in the gear dynamics due to the gear materials, which are sintered and steel ones, the dynamic characteristics of gears were measured using a power circulating gear testing machine. The gear dynamics were analyzed in a time-frequency domain by the continuous and discrete WTs. The new evaluation method using the WTs proposed in this paper was more useful compared with the conventional one to investigate the damping characteristic and the dynamic condition of the gear equipment.

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