Decimated Signal Diagonalization Research Articles

Flow distribution imaging and sensing for leaks in pipelines using decimated signal diagonalization

Control and monitoring of pipelines, dedicated to liquids transportation and distribution, are generally performed by active and passive techniques. With active techniques, we intend the possibility of using external sources to target pipelines and/or liquid inside the infrastructure. The used radiation can be partly reflected by the liquid and pipeline. Passive techniques are related to spontaneous emissions from liquids within the pipelines. However, capturing water displacements and fluctuations within the pipeline, by means of sensors and transducers, is somehow a passive technique. This latter is here illustrated thanks to pressure sensors mounted on an experimental pipeline. The basic idea is to detect leaks but also to monitor water flow regime by means of imaging without using a camera. That is possible thanks to the algorithm here developed and based on 2D/3D Decimated Signal Diagonalization (DSD). It is a technique used in nuclear magnetic resonance, and susceptible to bring to excellent results if water flow within a pipeline is considered as blood flow inside an artery or a vein. The approach has been applied to an experimental hydraulic circuit.

A Comparison between the Decimated Padé Approximant and Decimated Signal Diagonalization Methods for Leak Detection in Pipelines Equipped with Pressure Sensors.

Pipelines conveying fluids are considered strategic infrastructures to be protected and maintained. They generally serve for transportation of important fluids such as drinkable water, waste water, oil, gas, chemicals, etc. Monitoring and continuous testing, especially on-line, are necessary to assess the condition of pipelines. The paper presents findings related to a comparison between two spectral response algorithms based on the decimated signal diagonalization (DSD) and decimated Padé approximant (DPA) techniques that allow to one to process signals delivered by pressure sensors mounted on an experimental pipeline.

Decimated Signal Diagonalization Method for Improved Spectral Leak Detection in Pipelines

Leak detection is an important issue in piping that deals with the management of water resources; nowadays large amounts of water in the network are dispersed as reported in current scientific literature. Among the methods for leak detection in water pipes, spectral analysis is very interesting. A classical spectral method is fast Fourier transform, but in this paper, we present an alternative method of spectral analysis, which has higher performance in terms of resolution and fast processing, namely decimated signal diagonalization (DSD). It is a nonlinear, parametric method for fitting time domain signals represented in terms of exponentially damped time signals. The aim is to reconstruct the unknown components as the harmonic variables, estimating the fundamental complex frequencies, and amplitudes. The DSD method partly uses the principles of the filter diagonalization method (FDM), which constructs matrices of a generalized eigenvalue problem directly from measured time signals of arbitrary length. However, the DSD because of its windowing technique produces a considerable reduction of size of the original data matrix, and consequently acquisition time can be shorter. We have tested the DSD method for leak detection problem in an experimental zigzag pipeline. We show as the DSD method produces good results in terms of resolution than FDM one.

Entropy Index in Quantitative EEG Measurement for Diagnosis Accuracy

Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. It is certainly a source of basic and interesting information to be extracted using specific and appropriate techniques. The most important aspect in processing EEG signals is to use less co-lateral assets and instrumentation in order to carried out a possible diagnosis; this is the approach of early diagnosis. Advanced estimate spectral analysis can reveal new information encompassed in EEG signals by means of specific parameters or indices. The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG. Important information, about the state of the patient under observation, must be extracted from calculated DSD bispectrum. For this aim, it is useful to define an assessment index about the dynamic process associated with the analyzed signal. This information is measured by means of entropy, since the degree of order/disorder of the recorded EEG signal will be reflected in the obtained DSD bispectrum. The general advantage of multidimensional approach is to reveal eventual stealth frequencies “in space and in time” giving a topological vision to be correlated to physical areas which these frequencies emerge from. Long term and sleeping EEG recorded are analyzed, and the results obtained are of interest for an accurate diagnosis of the patient's clinical condition.

Decimated Signal Diagonalization for Fourier Transform Spectroscopy

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionORIGINAL ARTICLEThis notice is a correctionDecimated Signal Diagonalization for Fourier Transform SpectroscopyDž. Belkić, P. A. Dando, J. Main, , and Cite this: J. Phys. Chem. A 2001, 105, 2, 514Publication Date (Web):December 21, 2000Publication History Published online21 December 2000Published inissue 1 January 2001https://doi.org/10.1021/jp0043569Copyright © 2001 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views194Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (7 KB) Get e-Alerts Get e-Alerts

Decimated Signal Diagonalization for Fourier Transform Spectroscopy

A nonlinear parameter estimator with frequency-windowing for signal processing, called Decimated Signal Diagonalization (DSD), is presented. This method is used to analyze exponentially damped time signals of arbitrary length corresponding to spectra that are sums of pure Lorentzians. Such time signals typically arise in many experimental measurements, e.g., ion cyclotron resonance (ICR), nuclear magnetic resonance or Fourier transform infrared spectroscopy, etc. The results are compared with the standard spectral estimator, the Fast Fourier Transform (FFT). It is shown that the needed absorption spectra can be constructed simply, without any supplementary experimental work or concern about the phase problems that are known to plague FFT. Using a synthesized signal with known parameters, as well as experimentally measured ICR time signals, excellent results are obtained by DSD with significantly shorter acquisition time than that which is needed with FFT. Moreover, for the same signal length, DSD is demonstrated to exhibit a better resolving power than FFT.

Three novel high-resolution nonlinear methods for fast signal processing

Three novel nonlinear parameter estimators are devised and implemented for accurate and fast processing of experimentally measured or theoretically generated time signals of arbitrary length. The new techniques can also be used as powerful tools for diagonalization of large matrices that are customarily encountered in quantum chemistry and elsewhere. The key to the success and the common denominator of the proposed methods is a considerably reduced dimensionality of the original data matrix. This is achieved in a preprocessing stage called beamspace windowing or band-limited decimation. The methods are decimated signal diagonalization (DSD), decimated linear predictor (DLP), and decimated Padé approximant (DPA). Their mutual equivalence is shown for the signals that are modeled by a linear combination of time-dependent damped exponentials with stationary amplitudes. The ability to obtain all the peak parameters first and construct the required spectra afterwards enables the present methods to phase correct the absorption mode. Additionally, a new noise reduction technique, based upon the stabilization method from resonance scattering theory, is proposed. The results obtained using both synthesized and experimental time signals show that DSD/DLP/DPA exhibit an enhanced resolution power relative to the standard fast Fourier transform. Of the three methods, DPA is found to be the most efficient computationally.

Nuclear magnetic resonance study of xenon-131 interacting with surfaces: Effective Liouvillian and spectral analysis

Coherent and dissipative phenomena induced by transient adsorption of xenon-131 on a glass surface are studied by nuclear magnetic resonance spectroscopy. We report the results of multiple-quantum filtered experiments over a range of temperatures, covering different relaxation regimes. An analysis in terms of an effective Liouvillian is proposed, the complex eigenvalues of which may be directly extracted from the experimental spectra. For this purpose, we apply recently developed techniques for spectral analysis, in particular decimated signal diagonalization. The results are compared with a conventional fit of the multiple-quantum build-up behavior.

Decimation and harmonic inversion of periodic orbit signals

We present and compare three generically applicable signal processing methods for periodic orbit quantization via harmonic inversion of semiclassical recurrence functions. In a first step of each method, a band-limited decimated periodic orbit signal is obtained by analytical frequency windowing of the periodic orbit sum. In a second step, the frequencies and amplitudes of the decimated signal are determined by either Decimated Linear Predictor, Decimated Pade Approximant, or Decimated Signal Diagonalization. These techniques, which would have been numerically unstable without the windowing, provide numerically more accurate semiclassical spectra than does the filter-diagonalization method.

Decimated signal diagonalization for obtaining the complete eigenspectra of large matrices

An alternative method for obtaining high and interior eigenvalues of a dense spectrum is presented. The method takes advantage of the accurate, well-tested and fully understood algorithms for the fast Fourier transform to create, in a natural manner, a `window' containing only a small number of eigenvalues of the spectrum. The method is easy to implement, stable, efficient and accurate.