Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2018-12-22 14:55
Linear processing type optimum future prediction of signals applying Kida's optimum signal-approximation to multi-dimensional signals that are made by arranging known region restriction data in a row
Takuro Kida (Tokyo Inst. Tech.), Yuichi Kida (OHU Univ.) DE2018-29
Abstract (in Japanese) (See Japanese page) 
(in English) With respect to a matrix-filterbank that the matrix analysis-filterbank ${bf H}$ and the matrix sampling-filterbank ${bf S}$ are given, it is accomplished to present the optimum matrix synthesis-filterbank ${bf Z}$ that minimizes all the worst-case measures of matrix-error-signals ${bf E}(omega)={bf F}(omega)-{bf Y}(omega)$ between the input matrix-signals ${bf F}(omega)$ and the output matrix-signals ${bf Y(omega)}$ of the matrix-filterbank, at the same time. In this analysis, we assume that a set of the one-dimensional scanned input matrix-signals ${bf f}(t)={bf f}({bf x}(t))$, $({bf x}(t)=(x_0(t), x_1(t), ldots, x_{N-1}(t))$ of the multi-dimensional input matrix-images ${bf f}({bf x})$, $({bf x}=(x_0, x_1, ldots, x_{N-1}))$, is given. We assume that ${bf f}(t)$ is band-limited with an arbitrary given band-width and is allowed to include that ${bf f}(t)$ has uniformly or non-uniformly arranged sample-values. %\
%hspace*{3mm}
Based on the concept of pseudo-inverse-matrix, we prove that the optimum synthesis-filterbank ${bf Z}$ is equal to the synthesis-filterbank that minimizes the upper-limit of a given matrix-norm of the error ${bf E}(omega)={bf F}(omega)-{bf Y}(omega)$ among all the input matrix-signals ${bf F}(omega)$ contained in the set of ${bf F}(omega)$. As the consequence of this fact, it is shown that there exists a linear calculation method which gives the optimum synthesis-matrix ${bf Z}$ by solving a set of linear equations. This result shows that, among all AI approximate estimation systems including well-known deep learning systems, there exists an optimum linear approximation system based on the set of the one-dimensional scanned data of the given multi-dimensional knowledge-data that is considered as the scanned input matrix-images ${bf f}({bf x}(t))$. In the final part of this paper, we show that there exists the explicit relation between the presented optimum approximation and the artificial intelligent system based on the given past knowledge data.
Keyword (in Japanese) (See Japanese page) 
(in English) signal approximation / pseudo inverse matrix / artificial intelligence / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 377, DE2018-29, pp. 65-70, Dec. 2018.
Paper # DE2018-29 
Date of Issue 2018-12-14 (DE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF DE2018-29

Conference Information
Committee DE IPSJ-DBS  
Conference Date 2018-12-21 - 2018-12-22 
Place (in Japanese) (See Japanese page) 
Place (in English) National Institute of Informatics 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To DE 
Conference Code 2018-12-DE-DBS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Linear processing type optimum future prediction of signals applying Kida's optimum signal-approximation to multi-dimensional signals that are made by arranging known region restriction data in a row 
Sub Title (in English)  
Keyword(1) signal approximation  
Keyword(2) pseudo inverse matrix  
Keyword(3) artificial intelligence  
Keyword(4)  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Takuro Kida  
1st Author's Affiliation Professor Emeritus, Tokyo Institute of Technology (Tokyo Inst. Tech.)
2nd Author's Name Yuichi Kida  
2nd Author's Affiliation The School of Pharmaceutical Sciences, Ohu University (OHU Univ.)
3rd Author's Name  
3rd Author's Affiliation ()
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2018-12-22 14:55:00 
Presentation Time 30 minutes 
Registration for DE 
Paper # DE2018-29 
Volume (vol) vol.118 
Number (no) no.377 
Page pp.65-70 
#Pages 6 
Date of Issue 2018-12-14 (DE) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan