scholarly journals On the Feasibilities of Using the Wavelet Analysis of Mueller Matrix Images of Biological Crystals

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
O. V. Dubolazov ◽  
A. G. Ushenko ◽  
V. T. Bachynsky ◽  
A. P. Peresunko ◽  
O. Ya. Vanchulyak
Keyword(s):  
Wavelet Analysis ◽  
Power Spectra ◽  
Mueller Matrix ◽  
Phase Changes ◽  
Statistical Moments ◽  
Human Tissues ◽  
Wavelet Coefficients ◽  
Pathological Changes ◽  
Crystal Network ◽  
Fractal Analyses

The efficiency of using the statistical and fractal analyses for distributions of wavelet coefficients for Mueller matrix images of biological crystal networks inherent to human tissues is theoretically grounded in this work. The authors found interrelations between statistical moments and power spectra for distributions of wavelet coefficients as well as orientation-phase changes in networks of biological crystals. Also determined are the criteria for statistical and fractal diagnostics of changes in the birefringent structure of biological crystal network, which corresponds to pathological changes in tissues.

scholarly journals The Interconnection between the Coordinate Distribution of Mueller-Matrixes Images Characteristic Values of Biological Liquid Crystals Net and the Pathological Changes of Human Tissues

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Oleg V. Angelsky ◽  
Yuriy A. Ushenko ◽  
Alexander V. Dubolazov ◽  
Olha Yu. Telenha
Keyword(s):  
Liquid Crystals ◽  
Human Tissue ◽  
Mueller Matrix ◽  
Human Tissues ◽  
Matrix Elements ◽  
Pathological Changes ◽  
Crystal Network ◽  
Characteristic Points ◽  
Biological Liquid ◽  
Characteristic Values

We have theoretically grounded conceptions of characteristic points observed in coordinate distributions of Mueller matrix elements for a network of human tissue biological crystals. The interrelation between polarization singularities of laser images inherent to these biological crystals and characteristic values of above matrix elements is found. We have determined the criteria for statistical diagnostics of pathological changes in the birefringent structure of biological crystal network by using myometrium tissue as an example.

scholarly journals Bayesian modeling and significant features exploration in wavelet power spectra

2007 ◽  
Vol 14 (1) ◽  
pp. 79-88 ◽  
Author(s):  
D. V. Divine ◽  
F. Godtliebsen
Keyword(s):  
Data Analysis ◽  
Traditional Approach ◽  
Statistical Significance ◽  
Power Spectra ◽  
Simulated Data ◽  
Scale Space ◽  
Independent Random Variables ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Testing Procedures

Abstract. This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes' rule for constructing the posterior distribution of the smoothed wavelet coefficients. Samples drawn from the posterior are subsequently used for finding the estimate of the true wavelet spectrum at each scale. The method offers two different significance testing procedures for wavelet spectra. A traditional approach assesses the statistical significance against a red noise background. The second procedure tests for homoscedasticity of the wavelet power assessing whether the spectrum derivative significantly differs from zero at each particular point of the spectrum. Case studies with simulated data and climatic time-series prove the method to be a potentially useful tool in data analysis.

scholarly journals Wavelet analysis applied on temporal data sets in order to reveal possible pre-seismic radio anomalies and comparison with the trend of the raw data 

2021 ◽  
Author(s):  
Giovanni Nico ◽  
Pier Francesco Biagi ◽  
Anita Ermini ◽  
Mohammed Yahia Boudjada ◽  
Hans Ulrich Eichelberger ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Power Spectrum ◽  
Power Spectra ◽  
Wavelet Spectrum ◽  
Time Signal ◽  
Time Behavior ◽  
Sudden Increase ◽  
Night Time ◽  
Raw Data

<p>Since 2009, several radio receivers have been installed throughout Europe in order to realize the INFREP European radio network for studying the VLF (10-50 kHz) and LF (150-300 kHz) radio precursors of earthquakes. Precursors can be related to “anomalies” in the night-time behavior of  VLF signals. A suitable method of analysis is the use of the Wavelet spectra.  Using the “Morlet function”, the Wavelet transform of a time signal is a complex series that can be usefully represented by its square amplitude, i.e. considering the so-called Wavelet power spectrum.</p><p>The power spectrum is a 2D diagram that, once properly normalized with respect to the power of the white noise, gives information on the strength and precise time of occurrence of the various Fourier components, which are present in the original time series. The main difference between the Wavelet power spectra and the Fourier power spectra for the time series is that the former identifies the frequency content along the operational time, which cannot be done with the latter. Anomalies are identified as regions of the Wavelet spectrogram characterized by a sudden increase in the power strength.</p><p>On January 30, 2020 an earthquake with Mw= 6.0 occurred in Dodecanese Islands. The results of the Wavelet analysis carried out on data collected some INFREP receivers is compared with the trends of the raw data. The time series from January 24, 2020 till January 31, 2000 was analyzed. The Wavelet spectrogram shows a peak corresponding to a period of 1 day on the days before January 30. This anomaly was found for signals transmitted at the frequencies 19,58 kHz, 20, 27 kHz, 23,40 kHz with an energy in the peak increasing from 19,58 kHz to 23,40 kHz. In particular, the signal at the frequency 19,58 kHz, shows a peak on January 29, while the frequencies 20,27 kHz and 23,40 kHz are characterized by a peak starting on January 28 and continuing to January 29. The results presented in this work shows the perspective use of the Wavelet spectrum analysis as an operational tool for the detection of anomalies in VLF and LF signal potentially related to EQ precursors.</p>

Micro-polarimetry for pre-clinical diagnostics of pathological changes in human tissues

2008 ◽  
Author(s):  
Andrzej Golnik ◽  
Natalia Golnik ◽  
Tadeusz Pałko ◽  
Tomasz Sołtysiński
Keyword(s):  
Clinical Diagnostics ◽  
Human Tissues ◽  
Pathological Changes

scholarly journals Close Frequency Pairs in Delta Scuti Stars

2002 ◽  
Vol 185 ◽  
pp. 326-327
Author(s):  
K.M. Bischof ◽  
M. Breger
Keyword(s):  
Phase Shift ◽  
Power Spectra ◽  
Phase Changes ◽  
Single Frequency ◽  
Sufficient Data ◽  
Amplitude Variability ◽  
Pulsation Mode ◽  
Delta Scuti ◽  
Close Frequency ◽  
Scuti Stars

AbstractThe power spectra of several δ Scuti stars show close peaks with similar frequencies and amplitudes. Apart from possible observational problems, this can be interpreted in terms of two separate pulsation modes with similar, close frequencies or an artifact of amplitude variability of a single pulsation mode. If sufficient data are available, it is possible to distinguish between the two hypotheses on the basis of expected systematic phase changes associated with the amplitude variations of an assumed single frequency. This phase-shift test has been applied to modes found for BI CMi. In this paper we present the evidence for one of the close frequency pairs found in this star.

IMPROVEMENT OF IMAGE CLASSIFICATION USING WAVELET COEFFICIENTS WITH STRUCTURED-BASED NEURAL NETWORK

2008 ◽  
Vol 18 (03) ◽  
pp. 195-205 ◽  
Author(s):  
WEIBAO ZOU ◽  
ZHERU CHI ◽  
KING CHUEN LO
Keyword(s):  
Neural Network ◽  
Wavelet Analysis ◽  
Image Classification ◽  
Dimensional Space ◽  
Training Data ◽  
Wavelet Coefficients ◽  
Classification Rate ◽  
Data Set ◽  
Low Dimensional ◽  
High Level

Image classification is a challenging problem in organizing a large image database. However, an effective method for such an objective is still under investigation. A method based on wavelet analysis to extract features for image classification is presented in this paper. After an image is decomposed by wavelet, the statistics of its features can be obtained by the distribution of histograms of wavelet coefficients, which are respectively projected onto two orthogonal axes, i.e., x and y directions. Therefore, the nodes of tree representation of images can be represented by the distribution. The high level features are described in low dimensional space including 16 attributes so that the computational complexity is significantly decreased. 2800 images derived from seven categories are used in experiments. Half of the images were used for training neural network and the other images used for testing. The features extracted by wavelet analysis and the conventional features are used in the experiments to prove the efficacy of the proposed method. The classification rate on the training data set with wavelet analysis is up to 91%, and the classification rate on the testing data set reaches 89%. Experimental results show that our proposed approach for image classification is more effective.

scholarly journals Infrared Doppler Oscillations in a Solar Filament

1998 ◽  
Vol 167 ◽  
pp. 139-142 ◽  
Author(s):  
R. Molowny-Horas ◽  
R. Oliver ◽  
J.L. Ballester ◽  
F. Baudin
Keyword(s):  
Wavelet Analysis ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Power Spectra ◽  
Doppler Shifts ◽  
Solar Filament ◽  
Periodic Signals

AbstractWe present the results of a high spatial resolution investigation of Doppler oscillations in a solar filament, using the He I 10830Å infrared line. Fourier power spectra of Doppler shifts reveal the presence of periodic signals. Two features, showing oscillations at 2.7 min and 12.5 min, have been studied. The use of the so-called wavelet analysis enables us to estimate the size of both features at 2.7 arc sec and 4.75 arc sec, respectively. Their approximate lifetimes are 10 min and 20 min.

Relationship between Scale and Period and its Ecological Applications in Wavelet Analysis

2013 ◽  
Vol 726-731 ◽  
pp. 4252-4257
Author(s):  
Yu Yang Song ◽  
Rong Li ◽  
Ming Yan Li ◽  
Wen Hui Zhang
Keyword(s):  
Wavelet Analysis ◽  
Distribution Pattern ◽  
Power Spectra ◽  
Wavelet Function ◽  
Haloxylon Ammodendron ◽  
Mexican Hat ◽  
Ecological Applications

The relations between the scales and periods of Mexican Hat (Mexh) and Morlet (Morl) wavelets have been deduced. Based on these relations, variances, coefficients, and power spectra of these two wavelets’ original and eco-used wavelets are compared and analyzed theoretically and experimentally for the distribution pattern of Haloxylon ammodendron Bunge population in Gurban Tonggut desert, China. The research shows that: (1) Mexh and Morl eco-used wavelets can be simultaneously used to describe the distribution period of Haloxylon population and to study the same phenomenon by combining these two wavelet advantages. (2) The primary period value identified using Mexh eco-used wavelet than using its original wavelet is closer to the true one, while Morl eco-used wavelet helps find all changes in the period earlier. (3) For the same wavelet function, with its period enlarging, its primary period can be found in a smaller scale, inversely found later.

Structural Health Monitoring of Rotordynamic Systems by Wavelet Analysis

2006 ◽  
Author(s):  
Girindra Mani ◽  
D. Dane Quinn ◽  
Mary E. F. Kasarda
Keyword(s):  
Wavelet Analysis ◽  
Crack Depth ◽  
Dynamical Behavior ◽  
Vibration Response ◽  
External Loading ◽  
Time Varying ◽  
External Forcing ◽  
Wavelet Coefficients ◽  
Combination Resonance ◽  
Detection Techniques

This work describes a foundation of sophisticated diagnostic techniques for the detection of shaft cracks in rotordynamic systems, considering the dynamical behavior of a rotating cracked shaft under the application of external loads. The response is modeled as a modified Jeffcott rotor, while the crack is assumed to induce a time-varying stiffness in the model. The focus of this work is the development of external loading strategies to create damage sensitive measures of vibration response and then analyze that using advanced technologies such as wavelet analysis. This will enable the detection of the crack depth, as represented by the magnitude of the damage-induced time-varying stiffness, from vibration measurements. This entails developing external forcing functions for which features of the vibration response are sensitive to the presence of the damage. The development of such inputs is based on a multiple-scales analysis of the full equations of motion, including the time-varying stiffness. From this, a resonance (called combination resonance) is identified between the operating speed of the shaft, the fundamental frequency of the shaft, and the frequency of the external forcing. When the system is operated at this resonant condition, the translational vibrations of the shaft contain a spectral component near the fundamental shaft frequency that is proportional to the amplitude of the time-varying stiffness. The resonance bandwidth, obtained from this analysis, enables us to build a framework for the development of damage detection techniques for rotating machinery. Continuous Wavelet Transform (CWT) is applied to the vibration response of a rotordynamic system that utilizes harmonic forcing satisfying combination resonance. The variation of wavelet coefficients with respect to the variation of different system parameters is examined. Attention is focused on how the resonant bandwidth affects the variation of wavelet coefficients as crack grows.

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