A high-resolution algorithm for wave number estimation using holographic array processing

2004 ◽  
Vol 115 (3) ◽  
pp. 1059-1067 ◽  
Author(s):  
Philippe Roux ◽  
Didier Cassereau ◽  
André Roux
Keyword(s):  
High Resolution ◽  
Array Processing ◽  
Wave Number ◽  
Resolution Algorithm

A high‐resolution algorithm for wave number inversion using a holographic technique

1999 ◽  
Vol 105 (2) ◽  
pp. 1106-1106
Author(s):  
Philippe Roux ◽  
Megan McArthur ◽  
Paul Hursky ◽  
William A. Kuperman
Keyword(s):  
High Resolution ◽  
Wave Number ◽  
Resolution Algorithm

High-resolution algorithm for fan beam-CT system

1986 ◽  
Vol 17 (7) ◽  
pp. 19-29
Author(s):  
Isao Horiba ◽  
Shigenobu Yanaka ◽  
Akira Iwata ◽  
Nobuo Suzumura
Keyword(s):  
High Resolution ◽  
Resolution Algorithm ◽  
Ct System

THREE‐DIMENSIONAL FILTERING WITH AN ARRAY OF SEISMOMETERS

Geophysics ◽  
1964 ◽  
Vol 29 (5) ◽  
pp. 693-713 ◽  
Author(s):  
John P. Burg
Keyword(s):  
Array Processing ◽  
Three Dimensional ◽  
Processing System ◽  
Seismic Signal ◽  
P Wave ◽  
Wave Number ◽  
Theoretical Problem ◽  
Least Mean Square ◽  
Optimum Frequency ◽  
Filtering Theory

The development of the Wiener linear least‐mean‐square‐error processing theory for seismic signal enhancement through use of a two‐dimensional array of seismometers leads to the theory of three‐dimensional filtering. The array processing system for this theory consists of applying individual frequency filters to the outputs of the seismometers in the array before summation. The basic design equations for the optimum frequency filters are derived from the Wiener multichannel theory. However, the development of the three‐dimensional frequency and vector‐wave‐number‐filtering theory results in a physical understanding of generalized linear array processing. The three‐dimensional filtering theory is illuminated by a theoretical problem of P‐wave enhancement in the presence of ambient seismic noise. An analysis of the results shows why optimum three‐dimensional filtering gives greater signal‐to‐noise ratio improvements than achieved by conventional array processing techniques.

Array design

1968 ◽  
Vol 58 (3) ◽  
pp. 977-991
Author(s):  
Richard A. Haubrich
Keyword(s):  
High Resolution ◽  
Data Processing ◽  
Least Square ◽  
Wave Number ◽  
Array Design ◽  
Least Square Fit ◽  
The Difference ◽  
Set Of Points ◽  
Array Output

abstract Arrays of detectors placed at discrete points are often used in problems requiring high resolution in wave number for a limited number of detectors. The resolution performance of an array depends on the positions of detectors as well as the data processing of the array output. The performance can be expressed in terms of the “spectrum window”. Spectrum windows may be designed by a general least-square fit procedure. An alternate approach is to design the array to obtain the largest uniformly spaced coarray, the set of points which includes all the difference spacings of the array. Some designs obtained from the two methods are given and compared.

Extension of least squares spectral resolution algorithm to high-resolution lipidomics data

2016 ◽  
Vol 914 ◽  
pp. 35-46 ◽  
Author(s):  
Ying-Xu Zeng ◽  
Svein Are Mjøs ◽  
Fabrice P.A. David ◽  
Adrien W. Schmid
Keyword(s):  
High Resolution ◽  
Least Squares ◽  
Spectral Resolution ◽  
Resolution Algorithm

A high-resolution algorithm for complex spectrum search

1998 ◽  
Vol 104 (1) ◽  
pp. 288-299 ◽  
Author(s):  
I-Tai Lu ◽  
Robert C. Qiu ◽  
Jaeyoung Kwak
Keyword(s):  
High Resolution ◽  
Complex Spectrum ◽  
Resolution Algorithm
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