Modelling of long wavelength detection of objects using elman network modified covariance combination
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 5, Issue 3 (31 Jul. 2008), pp.265-272, 8 p.
Publisher
Publication Date
2008-07-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
The problem of spatially detection and imaging of closely separated buried objects is investigated.
A high resolution modified covariance method is employed.
A recurrent neural network is used as a preprocessing technique to decrease the effect of concealing media on the results.
The in-line holography is applied to increase the signal to noise ratio.
Different concealing media and different values of signal to noise ratio are used to investigate the performance of such combination experimental results show that pre-processing the noisy data with recurrent neural network improves the performance.
American Psychological Association (APA)
Badri, Lubna& al-Azzo, Mujahid. 2008. Modelling of long wavelength detection of objects using elman network modified covariance combination. The International Arab Journal of Information Technology،Vol. 5, no. 3, pp.265-272.
https://search.emarefa.net/detail/BIM-11473
Modern Language Association (MLA)
Badri, Lubna& al-Azzo, Mujahid. Modelling of long wavelength detection of objects using elman network modified covariance combination. The International Arab Journal of Information Technology Vol. 5, no. 3 (Jul. 2008), pp.265-272.
https://search.emarefa.net/detail/BIM-11473
American Medical Association (AMA)
Badri, Lubna& al-Azzo, Mujahid. Modelling of long wavelength detection of objects using elman network modified covariance combination. The International Arab Journal of Information Technology. 2008. Vol. 5, no. 3, pp.265-272.
https://search.emarefa.net/detail/BIM-11473
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 271-272
Record ID
BIM-11473