CHARACTERIZATION OF STOCHASTIC PROPAGATION AND SCATTERING VIA GABOR AND WAVELET TRANSFORMS

1994 ◽  
Vol 02 (03) ◽  
pp. 345-369 ◽  
Author(s):  
L. H. SIBUL ◽  
L. G. WEISS ◽  
T. L. DIXON
Keyword(s):  
Wavelet Transforms ◽  
Acoustic Scattering ◽  
Time Intervals ◽  
First Order ◽  
Spreading Function ◽  
Time Variations ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Methods Numerical

An application to remote acoustic sensing that remains unexploited is measuring acoustic scattering and spreading effects with wideband, coherent signal processing techniques. Such techniques allow distributed objects, such as a layer of scatterers due to bubbles or biological particles, and first order time variations in an ocean channel to be estimated. This paper presents narrowband and wideband methods for characterizing stochastic propagation and acoustic scattering in a time-varying ocean in terms of spreading functions. It is shown that the Gabor transform is the natural transform for estimating the narrowband spreading function, and the wavelet transform is the natural transform for estimating the wideband spreading function. Both techniques of characterization use a correlator processing structure in a monostatic transmitter/receiver configuration to estimate the spreading function. The narrowband and wideband spreading functions characterize the distribution of scatterers in range and velocity (time and frequency) in a propagation channel. It is shown that the wideband formulation follows directly from a physical derivation. Moreover, wideband processing removes many of the narrowband restrictions and allows first order time variations, caused by inhomogeneities and relative motion in the ocean channel, to be processed. In addition, wideband techniques allow for increased time intervals and, therefore, increased energy transmission when the transmitter is peak-power-limited. Thus, weak scatterers that may have been unidentified with narrowband techniques may be identified with the wideband methods. Numerical examples for wideband characterization of a distributed scatterer are presented.

scholarly journals Measurements of acoustic scattering from zooplankton and oceanic microstructure using a broadband echosounder

2009 ◽  
Vol 67 (2) ◽  
pp. 379-394 ◽  
Author(s):  
Andone C. Lavery ◽  
Dezhang Chu ◽  
James N. Moum
Keyword(s):  
High Frequency ◽  
System Performance ◽  
Pulse Compression ◽  
Acoustic Scattering ◽  
Single Frequency ◽  
Frequency Range ◽  
Range Resolution ◽  
Continuous Frequency ◽  
Processing Techniques ◽  
Signal Processing Techniques

Abstract Lavery, A. C., Chu, D., and Moum, J. N. 2010. Measurements of acoustic scattering from zooplankton and oceanic microstructure using a broadband echosounder. – ICES Journal of Marine Science, 67: 379–394. In principle, measurements of high-frequency acoustic scattering from oceanic microstructure and zooplankton across a broad range of frequencies can reduce the ambiguities typically associated with the interpretation of acoustic scattering at a single frequency or a limited number of discrete narrowband frequencies. With this motivation, a high-frequency broadband scattering system has been developed for investigating zooplankton and microstructure, involving custom modifications of a commercially available system, with almost complete acoustic coverage spanning the frequency range 150–600 kHz. This frequency range spans the Rayleigh-to-geometric scattering transition for some zooplankton, as well as the diffusive roll-off in the spectrum for scattering from turbulent temperature microstructure. The system has been used to measure scattering from zooplankton and microstructure in regions of non-linear internal waves. The broadband capabilities of the system provide a continuous frequency response of the scattering over a wide frequency band, and improved range resolution and signal-to-noise ratios through pulse-compression signal-processing techniques. System specifications and calibration procedures are outlined and the system performance is assessed. The results point to the utility of high-frequency broadband scattering techniques in the detection, classification, and under certain circumstances, quantification of zooplankton and microstructure.

scholarly journals Characterization of Nonstationary Phase Noise Using the Wigner–Ville Distribution

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Chagai Levy ◽  
Monika Pinchas ◽  
Yosef Pinhasi
Keyword(s):  
Phase Noise ◽  
Nonstationary Processes ◽  
Time Frequency ◽  
Physical Mechanisms ◽  
Amplitude Fluctuations ◽  
Time Invariance ◽  
Processing Techniques ◽  
Oscillator Stability ◽  
Signal Processing Techniques

Oscillators and atomic clocks, as well as lasers and masers, are affected by physical mechanisms causing amplitude fluctuations, phase noise, and frequency instabilities. The physical properties of the elements composing the oscillator as well as external environmental conditions play a role in the coherence of the oscillatory signal produced by the device. Such instabilities demonstrate frequency drifts, modulation, and spectrum broadening and are observed to be nonstationary processes in nature. Most of the tools which are being used to measure and characterize oscillator stability are based on signal processing techniques, assuming time invariance within a temporal window, during which the signal is assumed to be stationary. This letter proposes a new time-frequency approach for the characterization of frequency sources. Our technique is based on the Wigner–Ville time-frequency distribution, which extends the spectral measures to include the temporal nonstationary behavior of the processes affecting the coherence of the oscillator and the accuracy of the clock. We demonstrate the use of the technique in the characterization of nonstationary phase noise in oscillators.

Cyclostationary Approach for Instabilities Detection and Condition Monitoring of Centrifugal Compressor

2020 ◽  
Author(s):  
Mateusz Stajuda ◽  
David Garcia Cava ◽  
Grzegorz Liśkiewicz
Keyword(s):  
Condition Monitoring ◽  
Centrifugal Compressor ◽  
Operating Conditions ◽  
Periodic Signal ◽  
Centrifugal Compressors ◽  
First Order ◽  
Standard Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Insight Into

Abstract This study intends to explore the capabilities of the cyclostationary approach for instabilities detection and operating conditions monitoring of centrifugal compressors. Cyclostationary approach offers powerful signal analysis methods, applicable to different processes. It was proven useful for analysis of vibration, acoustic and pressure data for systems exhibiting periodicity. Cyclostationarity has been used for extracting subtle changes between cycles of the periodic signal which could be used for condition monitoring. Recent research focuses on employing this method for fault indication. Cyclostationary approach has not been extensively used in the field of turbomachinery, except for a few cases when it was proven to give a better insight into flow structure than standard signal processing techniques and allow for the detection of instabilities in flow systems. Thus, the cyclostationary approach may be suitable for instabilities detection and condition monitoring in centrifugal compressors. This paper exploits various techniques employing a cyclostationary framework for instabilities detection and operating conditions monitoring with the use of pressure signals from the low-speed centrifugal compressor. The most prospective cyclostationarity-based indicators are applied for the detection of instabilities. Due to a lack of second-order cyclostationarity, the study confines to the analysis of first-order cyclostationarity strongly exhibited in the compressor pressure signal. First-order cyclostationarity analysis provides an indication of instabilities and working conditions differentiation, but due to time-domain sampling, it is not fully robust and reliable. The highest potential is perceived in the cyclostationary approach use to extract changes between cycles. Different measures of change in variability could serve as a valuable indicator of instabilities.

scholarly journals A New Approach for the Characterization of Nonstationary Oscillators Using the Wigner-Ville Distribution

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Chagai Levy ◽  
Monika Pinchas ◽  
Yosef Pinhasi
Keyword(s):  
New Approach ◽  
Time Frequency ◽  
Physical Mechanisms ◽  
Phase Errors ◽  
Amplitude Fluctuations ◽  
Time Invariance ◽  
Processing Techniques ◽  
Oscillator Stability ◽  
Signal Processing Techniques

Oscillators and clocks are affected by physical mechanisms causing amplitude fluctuations, phase noise, and frequency instabilities. The physical properties of the elements composing the oscillator as well as external environmental conditions play a role in the characteristics of the oscillatory signal produced by the device. Such instabilities demonstrate frequency drifts and modulation and spectrum broadening and are observed to be nonstationary processes in nature. Most of tools which are being used to measure and characterize oscillator stability are based on signal processing techniques, assuming time invariance during a temporal window, during which the signal is assumed to be stationary. This paper proposes a new time-frequency metric for the characterization of frequency sources. Our technique is based on the Wigner-Ville distribution, which extends the spectral measures to consist of the temporal nonstationary behavior of the processes affecting the accuracy of the clock. We demonstrate the use of the technique in the characterization of phase errors, frequency offsets, and frequency drift of oscillators.

scholarly journals Power Quality Detection, Classification and Monitoring Using LABVIEW

2019 ◽  
Vol 4 (2) ◽  
pp. 101-111
Author(s):  
Fatma Zohra DEKHANDJI ◽  
Salim TALHAOUI ◽  
Youcef ARKAB
Keyword(s):  
Signal Processing ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Labview Software ◽  
Quality Detection ◽  
Power Quality Disturbances ◽  
Processing Techniques ◽  
Signal Processing Techniques

In recent years, Power Quality becomes increasingly a major concern for both electric utilities and end users. Accordingly, the electrical engineering community has to deal with the analysis, diagnosis and solution of PQ issues using system approach rather than handling these issues as individual problems. This paper describes the analysis of PQ using advanced signal processing tools represented in Hilbert & Wavelet Transforms (HT-WT) and artificial intelligence tools represented in Artificial Neural Network & Support Vector Machine (ANN-SVM) for detection and classification of power quality disturbances respectively. These techniques were successfully simulated using LABVIEW software capabilities. The results of simulation indicate that the signal processing techniques are effective mechanisms to detect and classify power quality disturbances. At the end, the combination of WT as a tool of detection and features extraction with SVM as a classifier tool resulted as the best combination for PQ monitoring system.

scholarly journals Characterization of Brain Stroke Using Image and Signal Processing Techniques

2021 ◽  
Author(s):  
Abdullah Alamoudi ◽  
Yousif Abdallah
Keyword(s):  
Signal Processing ◽  
Brain Injuries ◽  
Cross Sectional ◽  
Manual Contour ◽  
Cross Sectional Imaging ◽  
Brain Stroke ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques

Cross-sectional imaging approaches play a key role in assessing bleeding brain injuries. Doctors commonly determine bleeding size and severity in CT and MRI. Separating and identifying artifacts is extremely important in processing medical images. Image and signal processing are used to classify tissues within images closely linked to edges. In CT images, a subjective process takes a stroke ‘s manual contour with less precision. This chapter presents the application of both image and signal processing techniques in the characterization of Brain Stroke field. This chapter also summarizes how to characterize the brain stroke using different image processing algorithms such as ROI based segmentation and watershed methods.

Analysis of atrial fibrillation: from electrocardiogram signal processing to clinical management

2008 ◽  
Vol 367 (1887) ◽  
pp. 235-253 ◽  
Author(s):  
Leif Sörnmo ◽  
Martin Stridh ◽  
Daniela Husser ◽  
Andreas Bollmann ◽  
S. Bertil Olsson
Keyword(s):  
Atrial Fibrillation ◽  
Signal Processing ◽  
Clinical Management ◽  
Advanced Characterization ◽  
Non Invasive ◽  
Therapy Efficacy ◽  
Electrocardiogram Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

The analysis of atrial fibrillation in non-invasive ECG recordings has received considerable attention in recent years, spurring the development of signal processing techniques for more advanced characterization of the atrial waveforms than previously available. The present paper gives an overview of different approaches to the extraction of atrial activity in the ECG and to the characterization of the resulting atrial signal with respect to its spectral properties. So far, the repetition rate of the atrial waves is the most studied parameter and its significance in clinical management is briefly considered, including the identification of pathomechanisms and prediction of therapy efficacy.

A Comparative Study of Power Quality Monitoring Using Various Techniques

2021 ◽  
pp. 259-288
Author(s):  
Fatma Zohra Dekhandji ◽  
Mohamed Cherif Rais
Keyword(s):  
Signal Processing ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Labview Software ◽  
Power Quality Monitoring ◽  
Power Quality Disturbances ◽  
Processing Techniques ◽  
Signal Processing Techniques

In recent years, power quality (PQ) has become an increasingly major concern for both electric utilities and the end users. Accordingly, the electrical engineering community has to deal with the analysis, diagnosis, and solution of PQ issues using system approach rather than handling these issues as individual problems. This chapter describes the analysis of PQ using advanced signal processing tools represented in Hilbert and wavelet transforms (HT-WT) and artificial intelligence tools represented in artificial neural network and support vector machine (ANN-SVM) for detection and classification of power quality disturbances, respectively. These techniques were successfully simulated using LABVIEW software capabilities. The results of simulation indicate that the signal processing techniques are effective mechanisms to detect and classify power quality disturbances. At the end, the combination of WT as a tool of detection and features extraction with SVM as a classifier tool resulted as the best combination for PQ monitoring system.

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