scholarly journals Aligning Molecular Sequences by Wavelet Transform using Cross Correlation Similarity Metric

2017 ◽  
Vol 9 (11) ◽  
pp. 62-70
Author(s):  
J. Jayapriya ◽  
◽  
Michael Arock
Keyword(s):  
Wavelet Transform ◽  
Cross Correlation ◽  
Similarity Metric ◽  
Molecular Sequences

Wavelet and cross correlation analysis on some climatology parameters of Nepal

BIBECHANA ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 105-116
Author(s):  
Babu Ram Tiwari ◽  
Jiyao Xu ◽  
Binod Adhikari ◽  
Narayan Prasad Chapagain
Keyword(s):  
Wavelet Transform ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Spectral Characteristics ◽  
Time Lag ◽  
Rate Of Change ◽  
Policy Implications ◽  
Sunspot Numbers ◽  
Cross Correlation Analysis ◽  
Convective Motions

This study has been performed to understand the relationship between sunspot numbers (SSN) with climatology related parameters like temperature and rainfall from 1901 to 2016. The spectral characteristics of sunspot numbers, temperature and rainfall have been observed using continuous wavelet transform. Cross-correlation analyses were also performed to find any relation among temperature, rainfall, and sunspot numbers. The 9–11 year periodicity of sunspot numbers confirmed by wavelet transform in annual scale. The periodicity of high-frequency signals is identified between 4 to 11 years whereas the low frequencies signal is found throughout the periods of observation for temperature. Similarly, it is clear that there is more concentration of power between 8–16 years for rainfall. Cross-correlation analysis shows that the sunspot numbers is highly correlated with rainfall and temperature (correlation coefficient ~ 0.8054). The time lag relationship resulted in the almost simultaneous linear relationship between the temperature, rainfall, and the SSN tendency. The development of convective motions over the subtropics might be affected by the time rate of change of SSN combined with the surface temperature changes of diverse time scales. The convective motions were mostly controlled by the available amount of water vapor and the stability of the atmosphere that had a strong connection with the heat capacity of the concerned region. To produce more authentic findings for policy implications, further comprehensive and appropriate research can be undertaken and implemented in this very important field. BIBECHANA 18 (2) (2021) 105-115

Techniques for Decomposition of EMG Signals

2011 ◽  
pp. 467-477
Author(s):  
Arun Kumar Wadhwani ◽  
Sulochana Wadhwani
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Muscle Fatigue ◽  
Cross Correlation ◽  
Neuromuscular Disorders ◽  
Clinical Importance ◽  
Neuromuscular Control ◽  
Diagnostic Information ◽  
Self Organizing Map ◽  
Statistical Pattern

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.

Wavelet transform and cross-correlation as tools for seizure prediction

2010 ◽  
Author(s):  
Claudia C. Botero Suarez ◽  
Erich Talamoni Fonoff ◽  
Mario Alonso Munoz G ◽  
Antonio Carlos Godoi ◽  
Gerson Ballester ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Cross Correlation ◽  
Seizure Prediction

scholarly journals Application of an Automatic Noise or Signal Removal Algorithm Based on Synchrosqueezed Continuous Wavelet Transform of Passive Surface Wave Imaging: A Case Study in Sichuan, China

2021 ◽  
Vol 11 (24) ◽  
pp. 11718
Author(s):  
Jie Fang ◽  
Guofeng Liu ◽  
Yu Liu
Keyword(s):  
Wavelet Transform ◽  
Surface Wave ◽  
Surface Waves ◽  
Continuous Wavelet Transform ◽  
Cross Correlation ◽  
Underground Structure ◽  
Inversion Method ◽  
Continuous Wavelet ◽  
Dispersion Measurement ◽  
Wave Imaging

Passive surface wave imaging based on noise cross-correlation has been a research hotspot in recent years. However, because randomness of noise is difficult to achieve in reality, prominent noise sources will inevitably affect the dispersion measurement. Additionally, in order to recover high-fidelity surface waves, the time series input during cross-correlation calculation is usually very long, which greatly limits the efficiency of passive surface wave imaging. With an automatic noise or signal removal algorithm based on synchrosqueezed continuous wavelet transform (SS-CWT), these problems can be alleviated. We applied this method to 1-h passive datasets acquired in Sichuan province, China; separated the prominent noise events in the raw field data, and enhanced the cross-correlation reconstructed surface waves, effectively improving the accuracy of the dispersion measurement. Then, using the conventional surface wave inversion method, the shear wave velocity profile of the underground structure in this area was obtained.

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