Wavelet transform for cross correlation processing in PW ultrasound

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

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Techniques for Decomposition of EMG Signals

Clinical Technologies ◽

10.4018/978-1-60960-561-2.ch216 ◽

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.

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Signal Processing for Parametric Acoustic Sources Applied to Underwater Communication

Sensors ◽

10.3390/s20205878 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5878

Author(s):

María Campo-Valera ◽

Ivan Felis-Enguix ◽

Isidro Villó-Pérez

Keyword(s):

Cross Correlation ◽

Sound Propagation ◽

Signal To Noise Ratio ◽

Processing Technique ◽

Underwater Communication ◽

Carrier Wave ◽

Acoustic Communications ◽

Correlation Processing ◽

Novel Processing ◽

Special Relevance

For years, in the field of underwater acoustics, a line of research with special relevance for applications of environmental monitoring and maritime security has been developed that explores the possibilities of non-linear phenomena of sound propagation, especially referring to the so-called parametric effect or self-modulation. This article shows the results of using a new modulation technique based on sine-sweep signals, compared to classical modulations (FSK and PSK). For each of these modulations, a series of 16-bit strings of information with different frequencies and durations have been performed, with the same 200 kHz carrier wave. All of them have been tested in the Hydroacoustic Laboratory of the CTN and, through the application of cross-correlation processing, the limitations and improvements of this novel processing technique have been evaluated. This allows reaching better limits in discrimination of bits and signal-to-noise ratio used in underwater parametric acoustic communications.

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Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

Applied Sciences ◽

10.3390/app9112357 ◽

2019 ◽

Vol 9 (11) ◽

pp. 2357 ◽

Cited By ~ 3

Author(s):

Niccolò Dematteis ◽

Daniele Giordan ◽

Paolo Allasia

Keyword(s):

Cross Correlation ◽

Earth Science ◽

Operative Procedure ◽

Support Vector ◽

Diffuse Illumination ◽

Monitoring Applications ◽

Correlation Processing ◽

Classification Of Images ◽

Active Processes

In Earth Science, image cross-correlation (ICC) can be used to identify the evolution of active processes. However, this technology can be ineffective, because it is sometimes difficult to visualize certain phenomena, and surface roughness can cause shadows. In such instances, manual image selection is required to select images that are suitably illuminated, and in which visibility is adequate. This impedes the development of an autonomous system applied to ICC in monitoring applications. In this paper, the uncertainty introduced by the presence of shadows is quantitatively analysed, and a method suitable for ICC applications is proposed: The method automatically selects images, and is based on a supervised classification of images using the support vector machine. According to visual and illumination conditions, the images are divided into three classes: (i) No visibility, (ii) direct illumination and (iii) diffuse illumination. Images belonging to the diffuse illumination class are used in cross-correlation processing. Finally, an operative procedure is presented for applying the automated ICC processing chain in geoscience monitoring applications.

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