Blind Deconvolution to Improve Classification of Transient Source Signals in Multipath

2000 ◽  
Author(s):  
Lisa A. Pflug ◽  
George B. Smith ◽  
Michael K. Broadhead
Keyword(s):  
Blind Deconvolution ◽  
Transient Source ◽  
Source Signals

Blind deconvolution of remote‐source signals from ocean acoustic array recordings.

2010 ◽  
Vol 127 (3) ◽  
pp. 1963-1963
Author(s):  
Shima H. Abadi ◽  
David R. Dowling ◽  
Daniel Rouseff
Keyword(s):  
Blind Deconvolution ◽  
Acoustic Array ◽  
Source Signals ◽  
Array Recordings

Blind Source Separation for Under-Determined Mixtures Based on Time-Frequency Analysis

2016 ◽  
Vol 693 ◽  
pp. 1350-1356 ◽  
Author(s):  
Hong Kun Li ◽  
Hong Yi Liu ◽  
Chang Bo He
Keyword(s):  
Blind Source Separation ◽  
Frequency Analysis ◽  
Source Separation ◽  
Vibration Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Diagnosis And Classification ◽  
Source Signals

Blind source separation (BSS) is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration sources. The traditional BSS algorithm is applicable to the number of observed signals is no less to the source signals. But BSS performance is limit for the under-determined condition that the number of observed signals is less than source signals. In this research, we provide an under-determined BSS method based on the advantage of time-frequency analysis and empirical mode decomposition (EMD). It is suitable for weak feature extraction and pattern recognition. Firstly, vibration signal is decomposed by using EMD. The number of source signals are estimated and the optimal observed signals are selected according to the EMD. Then, the vibration signal and the optimal observed signals are used to construct the multi-channel observed signals. In the end, BSS based on time-frequency analysis are used to the constructed signals. Gearbox signals are used to verify the effectiveness of this method.

Shared and Specific Independent Components Analysis for Between-Group Comparison

2012 ◽  
Vol 24 (11) ◽  
pp. 3052-3090 ◽  
Author(s):  
Shahabeddin Vahdat ◽  
Mona Maneshi ◽  
Christophe Grova ◽  
Jean Gotman ◽  
Theodore E. Milner
Keyword(s):  
Data Sets ◽  
Independent Components ◽  
Group Data ◽  
Fastica Algorithm ◽  
Components Analysis ◽  
Source Signals ◽  
Proof Of Convergence ◽  
Membership Information ◽  
Better Than

Independent component analysis (ICA) has been extensively used in individual and within-group data sets in real-world applications, but how can it be employed in a between-groups or conditions design? Here, we propose a new method to embed group membership information into the FastICA algorithm so as to extract components that are either shared between groups or specific to one or a subset of groups. The proposed algorithm is designed to automatically extract the pattern of differences between different experimental groups or conditions. A new constraint is added to the FastICA algorithm to simultaneously deal with the data of multiple groups in a single ICA run. This cost function restricts the specific components of one group to be orthogonal to the subspace spanned by the data of the other groups. As a result of performing a single ICA on the aggregate data of several experimental groups, the entire variability of data sets is used to extract the shared components. The results of simulations show that the proposed algorithm performs better than the regular method in both the reconstruction of the source signals and classification of shared and specific components. Also, the sensitivity to detect variations in the amplitude of shared components across groups is enhanced. A rigorous proof of convergence is provided for the proposed iterative algorithm. Thus, this algorithm is guaranteed to extract and classify shared and specific independent components across different experimental groups and conditions in a systematic way.

BEARING COMPOUND FAULT ACOUSTIC DIAGNOSIS BASED ON IMPROVED BLIND DECONVOLUTION ALGORITHM

2015 ◽  
Vol 39 (3) ◽  
pp. 657-667 ◽  
Author(s):  
Nan Pan ◽  
Xing Wu ◽  
Yu Guo
Keyword(s):  
Blind Deconvolution ◽  
Working Environment ◽  
Blind Separation ◽  
Deconvolution Algorithm ◽  
Morphological Filtering ◽  
Fuzzy C Means Clustering ◽  
The Impact ◽  
Periodic Components ◽  
Source Signals ◽  
Acoustic Diagnosis

In the progress of bearing fault acoustic testing, signals picked up by acoustic sensors are usually mixed with fault source signals and other noise signals due to the complexity of mechanical signals and various interference sources. In order to solve the above problems, an improved blind deconvolution algorithm is put forward. The proposed algorithm applies adaptive generalized morphological filtering to the observed signals to retain their characteristic details, and then utilizes an OMP algorithm based on the minimum kurtosis to restore the periodical signals in the mixed signals in order to reduce the impact of the periodic components on blind separation. Finally, the improved Kullback–Leibler (KL) distance algorithm is employed to calculate the distances between independent components, which is used as the clustering index, and then to perform fuzzy C-means clustering. The experiment results of bearing compound fault extraction in real working-environment demonstrate the accuracy and reliability of the proposed algorithm.

scholarly journals Redshift of earthquakes via focused blind deconvolution of teleseisms

2020 ◽  
Vol 223 (3) ◽  
pp. 1864-1878
Author(s):  
Pawan Bharadwaj ◽  
Chunfang Meng ◽  
Aimé Fournier ◽  
Laurent Demanet ◽  
Mike Fehler
Keyword(s):  
Blind Deconvolution ◽  
Source Spectrum ◽  
Rupture Propagation ◽  
Source Characteristics ◽  
Eastern California Shear Zone ◽  
Source Signals ◽  
Source Extraction ◽  
Direction Opposite ◽  
Fraunhofer Approximation ◽  
High Frequency Content

SUMMARY We present a robust factorization of the teleseismic waveforms resulting from an earthquake source into signals that originate from the source and signals that characterize the path effects. The extracted source signals represent the earthquake spectrum, and its variation with azimuth. Unlike most prior work on source extraction, our method is data-driven, and it does not depend on any path-related assumptions, for example, the empirical Green’s function. Instead, our formulation involves focused blind deconvolution (FBD), which associates the source characteristics with the similarity among a multitude of recorded signals. We also introduce a new spectral attribute, to be called redshift, which is based on the Fraunhofer approximation. Redshift describes source-spectrum variation, where a decrease in high-frequency content occurs at the receiver in the direction opposite to unilateral rupture propagation. Using the redshift, we identified unilateral ruptures during two recent strike-slip earthquakes. The FBD analysis of an earthquake, which originated in the eastern California shear zone, is consistent with observations from local seismological or geodetic instrumentation.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

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