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On the Optimal Domain of the Laplace Transform
Bulletin of the Malaysian Mathematical Sciences Society, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Fourier Transform in Bounded Domains
Meccanica, 1997The author maintains that the concept of Schwartz distributions and their extension to ultra distributions of Gelfand and Shilov, enable one to find, by means of the Fourier transform, a second `language' to characterize physical behaviour. The author also maintains that almost any expression with physical meaning, can be transformed even if it is ...
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Transform domain neural filters
ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349), 2003A neural filter is effective for the system identification of a nonlinear system and the noise reduction in a nonlinear signal. However, the neural filter requires large number of iterations for convergence. This paper presents new structures of the multi-layered neural filter (Transform Domain Neural Filter; TDNF) where the orthonormal transform is ...
Isao Nakanishi +2 more
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Ontology Transformation in Multiple Domains
2004We have proposed a new approach called ontology services-driven integration of business intelligence (BI) to designing an integrated BI platform In such a BI platform, multiple ontological domains may get involved, such as domains for business, reporting, data warehouse, and multiple underlying enterprise information systems In general, ontologies in ...
Longbing Cao +3 more
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Fast sliding transforms in transform-domain adaptive filtering
1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002Transform-domain adaptive signal processing proved to be very successful in very many applications especially where systems with long impulse responses are to be evaluated. The popularity of these methods is due to the efficiency of the fast signal transformation algorithms and that of the block oriented adaptation mechanisms.
Annamária R. Várkonyi-Kóczy +1 more
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Transform domain adaptive filtering with the chirp Z transform
International Conference on Acoustics, Speech, and Signal Processing, 2002The use of the chirp Z transform (CZT) is proposed to improve the rate of convergence of transform domain adaptive output error algorithms. The use of the CZT is novel and is shown to possess advantages over the DFT. Other attempts at incorporating prior knowledge of input spectra use frequency sampling structures and their generalizations.
Andrew W. Hull, W. Kenneth Jenkins
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2005 5th International Conference on Information Communications & Signal Processing, 2006
In this paper we consider the blind equalization in the transform domain. For blind equalization, multimodulus algorithm (MMA), which is known to be very efficient, is used. To improve the rate of convergence, we used the band-partitioning property of transform domain (TD).
M.R. Gholami, S. Nader-Esfahani
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In this paper we consider the blind equalization in the transform domain. For blind equalization, multimodulus algorithm (MMA), which is known to be very efficient, is used. To improve the rate of convergence, we used the band-partitioning property of transform domain (TD).
M.R. Gholami, S. Nader-Esfahani
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A transform domain approach to spatial domain image scaling
1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, 2002Straightforward techniques for spatial domain scaling of compressed video via decompression and re-compression are computationally expensive. We describe an alternative approach wherein the compressed stream is processed in the compressed, DCT domain without explicit decompression and spatial domain scaling, so that the output compressed stream ...
Neri Merhav, Vasudev Bhaskaran
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Transients in the Transform Domain
2018In this first example you’ll see the full power of the Laplace transform in doing a traditional transient analysis. (You’ll also experience its full grubbiness!) Figure 4.1 shows a circuit that is suddenly hit by a unit step voltage v(t) = u(t), and our problem is to determine the resulting voltage e(t).
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