Transmission of array data by multivariate convolution and cross-correlation using white-noise reference signal

An information-transmission scheme with holographic-like properties is described for encoding and decoding multidimensional imagery data. An m-dimensional message pattern is mapped into an mth order Wiener kernel, creating a non-linear system. The sender stimulates this system with a gaussian white-...

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Vydané v:International Journal of Systems Science Ročník 15; číslo 5; s. 525 - 541
Hlavní autori: YASUI, SYOZO, KOBLASZ, ARTHUR
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Taylor & Francis Group 01.05.1984
Informa UK Limited
Taylor & Francis
Predmet:
Adaptative systems
Applied sciences
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
White noise
Multidimensional analysis
Decoding
Image transmission
Information transmission
ISSN:0020-7721, 1464-5319
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Popis
Shrnutí:An information-transmission scheme with holographic-like properties is described for encoding and decoding multidimensional imagery data. An m-dimensional message pattern is mapped into an mth order Wiener kernel, creating a non-linear system. The sender stimulates this system with a gaussian white-noise input and the response is transmitted to the receiving end. Thus, the encoding algorithm is a convolutional operation. The white-noise input serves as a reference signal which may be also transmitted, or alternatively may be conveyed a priori to the intended receiver. The message pattern is decoded by performing an m -dimensional cross-correlation between the stimulus and response signals. The method is particularly suitable when the transmission is subject to severe extraneous noise disturbances including periods of complete signal loss. This is demonstrated by making comparisons with the photographic telecommunication operating on a point-by-point basis, as well as through a computer-simulated example.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0020-7721
1464-5319
DOI:10.1080/00207729408926581