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Committee Date Time Place Paper Title / Authors Abstract Paper #
MW 2016-12-16
10:00		 Kanagawa National Defense Academy Improved Extraction Method for Coupling-Matrix of Microstrip Bandpass Filters with Lossy Input/Output Lines
Ryo Tomita, Masataka Ohira, Zhewang Ma, Xiaolong Wang (Saitama Univ.) MW2016-157		 A coupling matrix extraction technique is very useful for designs and tunings of microwave bandpass filters. In recent y... [more] MW2016-157
pp.141-146
EMCJ, WPT
(Joint)		 2015-01-23
16:15		 Okinawa Okinawaken Ｊichikaikan Rational Function Approximation of Frequency Response and Equivalent Circuit Model for Three-Terminal Capacitor
Yuto Takagaki (Kyoto Univ.), Hiroshi Mifune, Seiji Hidaka (Murata Manufacturing Co., Ltd.), Masayuki Hiromoto, Takashi Sato (Kyoto Univ.) EMCJ2014-101		 This paper describes a method to model the equivalent circuit of a three-terminal capacitor for broadband frequency resp... [more] EMCJ2014-101
pp.89-94
VLD, DC, IPSJ-SLDM, CPSY, RECONF, ICD, CPM
(Joint) [detail]		 2012-11-28
10:55		 Fukuoka Centennial Hall Kyushu University School of Medicine Rational Function Approximation Using Vector Fitting and Equivalent Circuit Synthesis of Transmission Line Characteristics
Daisuke Honda, Tadatoshi Sekine, Hideki Asai (Shizuoka Univ.) VLD2012-92 DC2012-58		 This report describes the way how to approximate the frequency response of a transmission line by using a rational funct... [more] VLD2012-92 DC2012-58
pp.189-194
SP 2009-07-17
13:50		 Fukushima   Geometric Margin Control for Minimum Error Classification
Kouta Yamada, Shigeru Katagiri (Doshisha Univ.), Erik McDermott (NTT), Hideyuki Watanabe (NICT), Atsushi Nakamura, Shinji Watanabe (NTT), Miho Ohsaki (Doshisha Univ.) SP2009-43		 The recent dramatic growth of computation power and data availability has increased research interests in discriminative... [more] SP2009-43
pp.13-18
MBE, NC
(Joint)		 2007-12-22
10:15		 Aichi   Probabilistic Outputs for Multiclass Support Vector Machine
Liu Liu, Mauricio Kugler, Susumu Kuroyanagi, Akira Iwata (Nagoya Inst. of Tech.) NC2007-74		 Support Vector Machines (SVM) have been successfully applied in many classification tasks with great generalization perf... [more] NC2007-74
pp.19-24
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