scholarly journals Dynamic Influence of Near-Source Seismic Ground Motion on Underground Mine Working Stability Using Time-Frequency Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Adam Lurka ◽  
Grzegorz Mutke ◽  
Piotr Małkowski
Keyword(s):  
Wavelet Transform ◽  
Underground Mining ◽  
Continuous Wavelet ◽  
Seismic Signals ◽  
Time Frequency ◽  
Frequency Group ◽  
Duration Time ◽  
Underground Mine Working ◽  
Frequency Properties ◽  
Strong Ground

Peak particle velocity parameter is very useful in assessing underground mine working stability. Its application is widespread and requires additional analysis of the dominant frequency of the seismic signal. In order to properly analyze the velocity amplitudes of strong ground motions generated from seismic sources, time-frequency properties of near-source seismic signals in underground mines should be quantified. Using numerical calculations, the continuous wavelet transform (CWT) of the recorded near-source seismic signals in three perpendicular directions was obtained to characterize its time-frequency properties. The properties of recorded strong ground motion velocity seismograms for two high energy seismic events and two blasts from two underground coal mines in Poland have been extracted with the use of continuous wavelet transform spectrograms showing the duration time of each frequency group. Assuming a constant peak particle velocity amplitude on the analyzed seismograms, the duration time of each frequency group starts to play a key role. The longer the duration time of the lower frequency group is on the CWT spectrogram, the more the damaging effect on underground mining excavations can be observed. Varying bandwidths of dominant frequencies in separate time intervals for the analyzed seismic signals have had significantly different influence on the potentially damaging effect on underground mining excavations.

scholarly journals Signature Matching For Seismic Signal Identification

2021 ◽  
Author(s):  
Pankaj Jadhav ◽  
Debabrata Datta ◽  
Siddhartha Mukhopadhyay
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
Wavelet Coherence ◽  
Continuous Wavelet ◽  
Seismic Signals ◽  
Time Frequency ◽  
Maximum Covariance Analysis ◽  
Cross Wavelet Transform ◽  
Matching Techniques

Seismic signals can be classified as natural or manmade by matching signature of similar events that have occurred in the past. Waveform matching techniques can be effectively used for discrimination since the events with similar location and focal mechanism have similar waveform irrespective of magnitude. The seismic signals are inherently non-stationary in nature. The analysis of such signals can be best achieved in multiresolution framework by resolving the signal using continuous wavelet transform (CWT) in time-frequency plane. In this paper similarity testing and classification of nuclear explosion and earthquake are exploited with correlation, continuous wavelet transform, cross-wavelet transform and wavelet coherence (WC) of P phase of seismogram. Clustering of seismic signals continuous wavelet spectra is performed using maximum covariance analysis. The proposed classifier has an average classification accuracy of 94%.

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.

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

2010 ◽  
Vol 49 (03) ◽  
pp. 230-237 ◽  
Author(s):  
K. Lweesy ◽  
N. Khasawneh ◽  
M. Fraiwan ◽  
H. Wenz ◽  
H. Dickhaus ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discriminant Analysis ◽  
Sleep Stage ◽  
Continuous Wavelet ◽  
Eeg Signal ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Mother Wavelets ◽  
Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

scholarly journals Ground Penetrating Radar for Belowground Phenotyping of High-biomass Grasses for Soil Carbon Sequestration

2021 ◽  
Author(s):  
Matthew Wolfe ◽  
Da Huo ◽  
Henry Ruiz-Guzman ◽  
Brody Teare ◽  
Tyler Adams ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Carbon Capture ◽  
Large Scale ◽  
Agricultural Practices ◽  
Signal Amplitude ◽  
Belowground Biomass ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Non Invasive

Abstract AimsMany governments and companies have committed to moving to net-zero emissions by 2030 or 2050 to tackle climate change, which require the development of new carbon capture and sequestration/storage (CCS) techniques. A proposed method of sequestration is to deposit carbon in soils as plant matter including root mass and root exudates. Adding perennial traits such as rhizomes to crops as part of a sequestration strategy would result in annual crop regrowth from rhizome meristems rather than requiring replanting from seeds which would in turn encourage no-till agricultural practices. Integrating these traits into productive agriculture requires a belowground phenotyping method compatible with high throughput breeding and selection methods (i.e., is rapid, inexpensive, reliable, and non-invasive), however none currently exist. MethodsGround penetrating radar (GPR) is a non-invasive subsurface sensing technology that shows potential as a phenotyping technique. In this study, a prototype GPR antenna array was used to scan roots of the perennial sorghum hybrid, PSH09TX15. A-scan level time-domain analyses and B-scan level time/frequency analyses using the continuous wavelet transform were utilized to extract features of interest from the acquired radargrams. ResultsOf six A-scan diagnostic indices examined, the standard deviation of signal amplitude correlated most significantly with belowground biomass. Time frequency analysis using the continuous wavelet transform yielded high correlations of B-scan features with belowground biomass. ConclusionThese results demonstrate that continued refinement of GPR data analysis workflows should yield a highly applicable phenotyping tool for breeding efforts in environments where selection is otherwise impractical on a large scale.

scholarly journals Fall Detection Using Multiple Bioradars and Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5569 ◽  
Author(s):  
Lesya Anishchenko ◽  
Andrey Zhuravlev ◽  
Margarita Chizh
Keyword(s):  
Wavelet Transform ◽  
Input Data ◽  
Detection Method ◽  
Fall Detection ◽  
Modern Medicine ◽  
Radar Signal ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Representation ◽  
Life Threatening

A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%.

scholarly journals Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1199 ◽  
Author(s):  
Hyeon Kyu Lee ◽  
Young-Seok Choi
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Computer Interface ◽  
Classification Performance ◽  
Computer Interface ◽  
Continuous Wavelet ◽  
Time Frequency

The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.

scholarly journals Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170254 ◽  
Author(s):  
Jean Baptiste Tary ◽  
Roberto Henry Herrera ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
American Football ◽  
Frequency Resolution ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Information ◽  
Synchrosqueezing Transform

The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to reduce these effects. In this paper, we present the synchrosqueezing transform together with the CWT and illustrate their relative performances using four signals from different fields, namely the LIGO signal showing gravitational waves, a ‘FanQuake’ signal displaying observed vibrations during an American football game, a seismic recording of the M w 8.2 Chiapas earthquake, Mexico, of 8 September 2017, followed by the Irma hurricane, and a volcano-seismic signal recorded at the Popocatépetl volcano showing a tremor followed by harmonic resonances. These examples illustrate how high-localization techniques improve analysis of the time-frequency information of time-varying signals. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

scholarly journals Time-frequency analysis of the Surge Onset in the Centrifugal Blower

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Grzegorz Liskiewicz ◽  
Longin Horodko
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Continuous Wavelet Transform ◽  
Working Condition ◽  
Continuous Wavelet ◽  
Transient Phase ◽  
Centrifugal Blower ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Pressure Signal

Abstract Time frequency analysis of the surge onset was performed in the centrifugal blower. A pressure signal was registered at the blower inlet, outlet and three locations at the impeller shroud. The time-frequency scalograms were obtained by means of the Continuous Wavelet Transform (CWT). The blower was found to successively operate in four different conditions: stable working condition, inlet recirculation, transient phase and deep surge. Scalograms revealed different spectral structures of aforementioned phases and suggest possible ways of detecting the surge predecessors.

Spectral decomposition of seismic data with continuous-wavelet transform

Geophysics ◽  
2005 ◽  
Vol 70 (6) ◽  
pp. P19-P25 ◽  
Author(s):  
Satish Sinha ◽  
Partha S. Routh ◽  
Phil D. Anno ◽  
John P. Castagna
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Time Scale ◽  
Fixed Time ◽  
Frequency Resolution ◽  
Continuous Wavelet ◽  
Window Length ◽  
Time Frequency ◽  
Frequency Map

This paper presents a new methodology for computing a time-frequency map for nonstationary signals using the continuous-wavelet transform (CWT). The conventional method of producing a time-frequency map using the short time Fourier transform (STFT) limits time-frequency resolution by a predefined window length. In contrast, the CWT method does not require preselecting a window length and does not have a fixed time-frequency resolution over the time-frequency space. CWT uses dilation and translation of a wavelet to produce a time-scale map. A single scale encompasses a frequency band and is inversely proportional to the time support of the dilated wavelet. Previous workers have converted a time-scale map into a time-frequency map by taking the center frequencies of each scale. We transform the time-scale map by taking the Fourier transform of the inverse CWT to produce a time-frequency map. Thus, a time-scale map is converted into a time-frequency map in which the amplitudes of individual frequencies rather than frequency bands are represented. We refer to such a map as the time-frequency CWT (TFCWT). We validate our approach with a nonstationary synthetic example and compare the results with the STFT and a typical CWT spectrum. Two field examples illustrate that the TFCWT potentially can be used to detect frequency shadows caused by hydrocarbons and to identify subtle stratigraphic features for reservoir characterization.

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’.

Sign in / Sign up

Export Citation Format

Share Document