scholarly journals Ultrawideband Impulse Radar Through-the-Wall Imaging with Compressive Sensing

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Wenji Zhang ◽  
Moeness G. Amin ◽  
Fauzia Ahmad ◽  
Ahmad Hoorfar ◽  
Graeme E. Smith
Keyword(s):  
Compressive Sensing ◽  
Time Domain ◽  
Measurement Data ◽  
Acquisition Time ◽  
Time Domain Signal ◽  
Spatially Extended ◽  
Data Volume ◽  
Order Of Magnitude ◽  
New Perspective ◽  
The Time Domain

Compressive Sensing (CS) provides a new perspective for addressing radar applications requiring large amount of measurements and long data acquisition time; both issues are inherent in through-the-wall radar imaging (TWRI). Most CS techniques applied to TWRI consider stepped-frequency radar platforms. In this paper, the impulse radar two-dimensional (2D) TWRI problem is cast within the framework of CS and solved by the sparse constraint optimization performed on time-domain samples. Instead of the direct sampling of the time domain signal at the Nyquist rate, the Random Modulation Preintegration architecture is employed for the CS projection measurement, which significantly reduces the amount of measurement data for TWRI. Numerical results for point-like and spatially extended targets show that high-quality reliable TWRI based on the CS imaging approach can be achieved with a number of data points with an order of magnitude less than that required by conventional beamforming using the entire data volume.

scholarly journals Is the time domain signal-averaged electrocardiogram helpful in patients with ventricular tachycardia without apparent structural heart disease?

1995 ◽  
Vol 18 (10) ◽  
pp. 568-572 ◽  
Author(s):  
Yelena S. K. Orlov ◽  
Michael A. Brodsky ◽  
Michael V. Orlov ◽  
Byron J. Allen ◽  
Rex J. Winters
Keyword(s):  
Heart Disease ◽  
Ventricular Tachycardia ◽  
Time Domain ◽  
Structural Heart Disease ◽  
Time Domain Signal ◽  
The Time Domain

Fault Diagnosis of Motor Rotor Based on Fuzzy C-Means Clustering Analysis

2013 ◽  
Vol 273 ◽  
pp. 409-413 ◽  
Author(s):  
Yu Xiang Cao ◽  
Xue Jun Li ◽  
Ling Li Jiang
Keyword(s):  
Time Domain ◽  
Clustering Analysis ◽  
Statistical Features ◽  
Fuzzy C Means ◽  
Time Domain Signal ◽  
Fuzzy C Means Clustering ◽  
Important Means ◽  
Fault Diagnostic ◽  
Characteristic Features ◽  
The Time Domain

For the fuzziness of the fault symptoms in motor rotor, this paper proposes a fault diagnostic method which based on the time-domain statistical features and the fuzzy c-means clustering analysis (FCM). This method is to extract the characteristic features of time-domain signal via time-domain statistics and to import the extracted characteristic vector to classifier. And then the fuzzy c-means realizes the classification by confirming the distance among samples, which is based on the degree of membership between the sample and the clustering center. The fault diagnostic cases of motor rotor show that the method which bases on the time-domain statistical features-FCM can detect the rotor fault effectively and distinguish the different types of fault correctly. Therefore, it can be used as an important means of rotor fault identification.

Exact Interpolation of Fourier Transform Spectra

1983 ◽  
Vol 37 (2) ◽  
pp. 153-166 ◽  
Author(s):  
Carlo Giancaspro ◽  
Melvin B. Comisarow
Keyword(s):  
Fourier Transform ◽  
Graphical Form ◽  
Acquisition Time ◽  
Time Domain Signal ◽  
Parabolic Interpolation ◽  
Interpolation Procedure ◽  
True Frequency ◽  
Fourier Transform Spectra ◽  
The Time Domain ◽  
Absorption Mode

A systematic study of interpolation of Fourier transform (FT) spectra is reported. Interpolation errors are examined for both frequency determination and intensity determination for different interpolation procedures for both absorption mode and magnitude mode FT spectra. The errors are presented in both analytical and graphical form as functions of the number of zero-fillings and ( T/τ), the ratio of the acquisition time to the relaxation time of the time domain signal. For interpolation of absorption mode spectra, parabolic interpolation is superior to Lorentzian interpolation if T/τ < 2. For T/τ > 2, Lorentzian interpolation is superior. For small values of T/τ, both parabolic interpolation and Lorentzian interpolation of the absorption line shape give greater errors than no interpolation. For interpolation of the magnitude lineshape, interpolation with the “magnitude-Lorentzian” function gives the exact frequency of the continuous spectrum. This interpolation procedure permits exact determination of the true frequency and true intensity for both absorption mode and magnitude mode FT spectra.

scholarly journals Investigation of Light Parameters on Image Quality and Optical Coherence Tomography

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Boka Fikadu ◽  
Bulcha Bekele ◽  
Leta Tesfaye Jule ◽  
Anatol Degefa ◽  
N. Nagaprasad ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Quality ◽  
Time Domain ◽  
Glass Sample ◽  
Michelson Interferometer ◽  
Signal Strength ◽  
Optical Coherence ◽  
Time Domain Signal ◽  
Significant Difference ◽  
The Time Domain

In this work, image quality and optical coherence tomography were studied. The results of the study show that there is a very significant difference between ultrasound and optical coherence tomography to produce an image with a different wave. To understand this, we studied the basic principle of optical coherence tomography in the Michelson interferometer using monochromatic and broadband sources. Time-domain and spectral-domain measurements, which exist at the detector level, are briefly described using a glass sample. The time-domain signal strength of the Michelson interferometer using a broadband source is a Gaussian envelope.

Frequency Interpolation of Discrete, Apodized, Magnitude Lineshapes

1987 ◽  
Vol 41 (2) ◽  
pp. 288-295 ◽  
Author(s):  
Alessio Serreqi ◽  
Melvin B. Comisarow
Keyword(s):  
Systematic Error ◽  
Dynamic Range ◽  
Frequency Measurement ◽  
Residual Error ◽  
Interpolation Error ◽  
Acquisition Time ◽  
Absolute Maximum ◽  
Channel Spacing ◽  
Time Domain Signal ◽  
The Time Domain

All Fourier spectrometers have a residual error in frequency measurement arising from the discrete nature of the experimental Fourier spectrum. This residual error is a systematic error which has a maximum value of half the channel spacing in the discrete spectrum. This systematic error can be reduced by interpolation of values on the discrete lineshape. The residual error remaining after interpolation has not yet been determined for apodized Fourier spectra. In this work, a systematic study of frequency interpolation of discrete, apodized, magnitude-mode lineshapes is reported. Absolute maximum frequency errors as a percentage of the discrete channel spacing are reported in graphical and tabular form as a function of the type of apodization window, the type of function used for three-point frequency interpolation, the number of zero-fillings, and ( T/ r), the ratio of the acquisition time to the relaxation time of the time domain signal. The results allow independent choice of the window function most appropriate for the dynamic range of the spectrum and the interpolating function/zero-filling level which optimizes the accuracy of frequency measurement. General observations are (1) that the interpolation error is reduced by an order of magnitude for each additional level of zero-filling and (2) that the interpolation error is essentially independent of T/r. For the Hanning window, the Hamming window, the three-term Blackman-Harris window, and the Kaiser-Bessel window, the parabola is the interpolating function of choice.

Rolling Bearing Fault Diagnosis Research

2012 ◽  
Vol 155-156 ◽  
pp. 87-91
Author(s):  
Zhong Hu Yuan ◽  
Yang Su ◽  
Xiao Xuan Qi
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Recognition Rate ◽  
Principal Component ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Time Domain Signal ◽  
Feature Information ◽  
The Time Domain

According to the characteristics of the rolling bearing fault, we make the research on fault diagnosis. Time domain signal can not perform the fault feature information well. The power spectrum changes the time domain signals into the frequency signals. It sets up the new data model. It uses the principal component analysis on fault diagnosis. It uses T square statistics and Q statistics methods to make fault diagnosis. Simulation experiment results demonstrate that this method provides a high recognition rate.

Large-scale Gauss-Newton inversion of transient controlled-source electromagnetic measurement data using the model reduction framework

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. E161-E171 ◽  
Author(s):  
M. Zaslavsky ◽  
V. Druskin ◽  
A. Abubakar ◽  
T. Habashy ◽  
V. Simoncini
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Large Scale ◽  
Jacobian Matrix ◽  
Computational Cost ◽  
Measurement Data ◽  
Multiple Sources ◽  
Time Data ◽  
Controlled Source ◽  
The Time Domain

Transient data controlled-source electromagnetic measurements are usually interpreted via extracting few frequencies and solving the corresponding inverse frequency-domain problem. Coarse frequency sampling may result in loss of information and affect the quality of interpretation; however, refined sampling increases computational cost. Fitting data directly in the time domain has similar drawbacks, i.e., its large computational cost, in particular, when the Gauss-Newton (GN) algorithm is used for the misfit minimization. That cost is mainly comprised of the multiple solutions of the forward problem and linear algebraic operations using the Jacobian matrix for calculating the GN step. For large-scale 2.5D and 3D problems with multiple sources and receivers, the corresponding cost grows enormously for inversion algorithms using conventional finite-difference time-domain (FDTD) algorithms. A fast 3D forward solver based on the rational Krylov subspace (RKS) reduction algorithm using an optimal subspace selection was proposed earlier to partially mitigate this problem. We applied the same approach to reduce the size of the time-domain Jacobian matrix. The reduced-order model (ROM) is obtained by projecting a discretized large-scale Maxwell system onto an RKS with optimized poles. The RKS expansion replaces the time discretization for forward and inverse problems; however, for the same or better accuracy, its subspace dimension is much smaller than the number of time steps of the conventional FDTD. The crucial new development of this work is the space-time data compression of the ROM forward operator and decomposition of the ROM’s time-domain Jacobian matrix via chain rule, as a product of time- and space-dependent terms, thus effectively decoupling the discretizations in the time and parameter spaces. The developed technique can be equivalently applied to finely sampled frequency-domain data. We tested our approach using synthetic 2.5D examples of hydrocarbon reservoirs in the marine environment.

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