Zobrazeno 1 - 10 of 68 pro vyhledávání: '"I. Barrodale"'
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Autor: Cedric A. Zala, I. Barrodale
Publikováno v: Advances in Computational Mathematics. 11:211-227
Using an image rectification application arising in the field of forest management, we demonstrate in this paper the practical feasibility of applying thin plate spline (TPS) techniques to real image warping problems. The use of TPS‐based warping i
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9d927e64a2237fc8b0688de2e57a50d8
https://doi.org/10.1023/a:1018928026708
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2
Maximum entropy image processing using transform domain constraints
Autor: R.F. MacKinnon, I. Barrodale, C.A. Zala, C.E. Lucas
Publikováno v: Conference Proceeding IEEE Pacific Rim Conference on Communications, Computers and Signal Processing.
A formulation of the maximum entropy (ME) method is described, where the data constraints are expressed in the form of fixed bounds on the elements of an orthogonal transform of the model. The bounds are set on the basis of both the observed data and
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5a427988e8f9cd4c9b4f1d93497cce56
https://doi.org/10.1109/pacrim.1989.48312
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4
On the rate of growth of condition numbers for convolution matrices
Autor: F. Milinazzo, I. Barrodale, C. Zala
Publikováno v: IEEE Transactions on Acoustics, Speech, and Signal Processing. 35:471-475
When analyzing linear systems of equations, the most important indicator of potential instability is the condition number of the matrix. For a convolution matrix W formed from a series w (where W_{ij} = w_{i-j+1}, 1 \leq i-j + 1 \leq k, W_{ij} = 0 ot
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a11c468dff50486649577ede0b6004e3
https://doi.org/10.1109/tassp.1987.1165145
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5
Algorithms for least‐squares linear prediction and maximum entropy spectral analysis—Part II: Fortran program
Autor: R. E. Erickson, I. Barrodale
Publikováno v: GEOPHYSICS. 45:433-446
We present a set of Fortran subroutines which implement the least‐squares algorithm described in Part I (Barrodale and Erickson, 1980, this issue) for solving three variants of the linear prediction problem. When combined with a suitable driver pro
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::726046a0353d7ee3fbe83f5179f58233
https://doi.org/10.1190/1.1441092
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6
High-Resolution Signal and Noise Field Estimation Using theL1 (Least Absolute Values) Norm
Autor: J. Kennedy, C. A. Zala, I. Barrodale
Publikováno v: IEEE Journal of Oceanic Engineering. 12:253-264
In this paper a new method for obtaining a quantitative estimate of an acoustic field consisting of a set of discrete sources and background noise is described. The method is based on the L 1 (least absolute values) norm solution to an underdetermine
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bfd16d1da5bcd29d3c23501307d60787
https://doi.org/10.1109/joe.1987.1145232
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7
Computational experience with Marple’s algorithm for autoregressive spectrum analysis
Autor: L. M. Delves, C. A. Zala, I. Barrodale, R. E. Erickson
Publikováno v: GEOPHYSICS. 48:1274-1286
In 1980 two different recursive algorithms were published, complete with Fortran programs, for autoregressive (AR) spectral estimation, based on least‐squares solutions for the AR parameters using forward and backward linear prediction. The first o
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::be7157b189d0816e2d795788bae6932c
https://doi.org/10.1190/1.1441551
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