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JaLCDOI 10.18926/14057
FullText URL Mem_Fac_Eng_OU_42_36.pdf
Author Kato, Hidehiro| Nekado, Kenta| Nogami, Yasuyuki| Morikawa, Yoshitaka|
Abstract This paper proposes an exponentiation method with Frobenius mappings. Our method is closely related to so-called interleaving exponentiation. Different from the interleaving exponentiation methods, our method can carry out several exponentiations using same base at the same time. The efficiency to use Frobenius mappings for an exponentiation in extension field is well introduced by Avanzi and Mihailescu. This exponentiation method is based on so-called simultaneous exponentiation and uses many Frobenius mappings. Their method more decreased the number of multiplications; however, the number of Frobenius mappings inversely increased. Compared to their method , the number of multiplications needed for the proposed method becomes about 20% larger; however, that of Frobenius mappings becomes small enough.
Keywords exponentiation Frobenius mapping extension field
Publication Title Memoirs of the Faculty of Engineering, Okayama University
Published Date 2008-01
Volume volume42
Issue issue1
Start Page 36
End Page 43
ISSN 0475-0071
language English
File Version publisher
NAID 120002308105
JaLCDOI 10.18926/14056
FullText URL Mem_Fac_Eng_OU_42_18.pdf
Author Kanatani, Kenichi| Yasuyuki Sugaya|
Abstract We classify and review existing algorithms for computing the fundamental matrix from point correspondences and propose new effective schemes: 7-parameter Levenberg-Marquardt (LM) search, EFNS, and EFNS-based bundle adjustment. Doing experimental comparison, we show that EFNS and the 7-parameter LM search exhibit the best performance and that additional bundle adjustment does not increase the accuracy to any noticeable degree.
Publication Title Memoirs of the Faculty of Engineering, Okayama University
Published Date 2008-01
Volume volume42
Issue issue1
Start Page 18
End Page 35
ISSN 0475-0071
language English
File Version publisher
NAID 120002308468
JaLCDOI 10.18926/14055
FullText URL Mem_Fac_Eng_OU_42_10.pdf
Author Kanatani, Kenichi|
Abstract The author introduced the "geometric AIC" and the "geometric MDL" as model selection criteria for geometric fitting problems. These correspond to Akaike’s "AIC" and Rissanen's "BIC", respectively, well known in the statistical estimation framework. Another criterion well known is Schwarz’ "BIC", but its counterpart for geometric fitting has been unknown. This paper introduces the corresponding criterion, which we call the "geometric BIC", and shows that it is of the same form as the geometric MDL. We present the underlying logical reasoning of Bayesian estimation.
Publication Title Memoirs of the Faculty of Engineering, Okayama University
Published Date 2008-01
Volume volume42
Issue issue1
Start Page 10
End Page 17
ISSN 0475-0071
language English
File Version publisher
NAID 120002308447
JaLCDOI 10.18926/14053
FullText URL Mem_Fac_Eng_OU_42_1.pdf
Author Asatani Jun| Koumoto, Takuya| Toru Fujiwara| Tadao Kasami|
Abstract Two typical examples, the (32, 21, 6) and (64, 45, 8) extended code of primitive permuted BCH codes, are considered. The sets of minimum weight codewords are analyzed in terms of Boolean polynomial representation. They are classied by using their split weight structure with respect to the left and right half trellis sections, and for each class, the standard form is presented. Based on the results, we can generate a proper list of the minimum weight codewords of the codes.
Keywords Boolean polynomial representation extended BCH codes minimum weight codewords binary shift invariance property
Publication Title Memoirs of the Faculty of Engineering, Okayama University
Published Date 2008-01
Volume volume42
Issue issue1
Start Page 1
End Page 9
ISSN 0475-0071
language English
File Version publisher
NAID 120002308333
Author 肥田 和之| 和田 淳| 江口 潤| Zhang Hong| 馬場 雅子| 清田 綾| 橋本 泉| 岡田 達夫| 安原 章浩| 中司 敦子| 赤木 滋| 四方 賢一| 宝来 真志| 二見 淳一郎| 渡辺 英二郎| 松木 泰| 平松 隆司| 槇野 博史| Yashpal S. Kanwar|
Published Date 2007-01-04
Publication Title 岡山医学会雑誌
Volume volume118
Issue issue3
Content Type Journal Article