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¥»¥Ã¥·¥ç¥ó A1-1 VLSI¤Î¥Î¥¤¥º¡¦¥¿¥¤¥ß¥ó¥°²òÀÏ
Æü»þ: 2006ǯ4·î24Æü(·î) 9:00-10:15
ºÂĹ: ±ü¼ δ¾» (ÉÙ»ÎÄÌVLSI³ô¼°²ñ¼Ò)

Âê̾Calculating the Effective Capacitance for Interconnect Loads Based on Thevenin Model
Ãø¼Ô ¡ûShuai Fang, Zhangcai Huang(Waseda University), Atsushi Kurokawa(Semiconductor Technology Academic Research Center), Yasuaki Inoue(Waseda University)
Pagepp. 1 - 4
Keyword Effective Capacitance, Interconnect Loads, CMOS Gates, Thevenin Model
AbstractInterconnect wires give large influences on circuit delay in very deep submicron designs. Thevenin model and effective capacitance Ceff concept are usually used to calculate the delay of gate with interconnect loads. In the researches before, the condition that the charges transferred to Ceff and RC-pi are not equal was not considered. With the progress of IC process technology, its influence on Static Timing Analysis becomes larger. In this paper, we consider this condition, and propose an new algorithm for calculating the effective capacitance based on Thevenin model. Experimental results show that it is in agreement with the Spice simulation.

Âê̾LSI ÇÛÀþ¤Ë¤ª¤±¤ëÍÆÎÌÀ­, ͶƳÀ­¥¯¥í¥¹¥È¡¼¥¯¥Î¥¤¥º¤ÎÄêÎÌŪ¾­Íèͽ¬
Ãø¼Ô ¡û¾®³Þ¸¶ ÂÙ¹°, ¶¶ËÜ ¾»µ¹, Èø¾å ¹§Íº(ÂçºåÂç³ØÂç³Ø±¡¾ðÊó²Ê³Ø¸¦µæ²Ê¾ðÊó¥·¥¹¥Æ¥à¹©³ØÀ칶)
Pagepp. 5 - 10
Keyword ÍÆÎÌÀ­¥¯¥í¥¹¥È¡¼¥¯¥Î¥¤¥º, ͶƳÀ­¥¯¥í¥¹¥È¡¼¥¯¥Î¥¤¥º, ÇÛÀþ¥¹¥±¡¼¥ê¥ó¥°
AbstractÍÆÎÌÀ­¥¯¥í¥¹¥È¡¼¥¯¥Î¥¤¥º¤Ï½¾Íè¤è¤êÇÛÀþÃÙ±ä¤ÎÊÑÆ°¤ÎÍ×°ø¤È¤·¤ÆÃΤé¤ì, ÇÛÀþ¤Î¥¹¥±¡¼¥ê¥ó¥°¤Ë¤è¤Ã¤Æº£¸å¤è¤ê¿¼¹ï¤Ë¤Ê¤ë¤È¹Í¤¨¤é¤ì¤Æ¤¤¤ë. °ìÊý, Àèü¤Î¥×¥í¥»¥¹¤Î¥°¥í¡¼¥Ð¥ëÇÛÀþ¤Ë¤ª¤¤¤Æ¤Ï¿®¹æ¼þÇÈ¿ô¤Î¾å¾º¤Ë¤è¤Ã¤ÆͶƳÀ­¥¯¥í¥¹¥È¡¼¥¯¥Î¥¤¥º¤¬½ÅÍפÊÌäÂê¤È¤Ê¤ê¤Ä¤Ä¤¢¤ë. ËÜÏÀʸ¤Ç¤Ï¾­Íè¤Î¥×¥í¥»¥¹¤Ë¤ª¤±¤ëÍÆÎÌÀ­, ͶƳÀ­¥¯¥í¥¹¥È¡¼¥¯¥Î¥¤¥º¤Î·¹¸þ¤Ë¤Ä¤¤¤ÆÄêÎÌŪ¤Êͽ¬¤ò¹Ô¤¦. ITRS ¤Îͽ¬¤Ë´ð¤Å¤­, ¥×¥í¥»¥¹¤Î¿Ê²½¤Ë´Ø¤¹¤ë 2 ¼ï¤Îͽ¬¥·¥Ê¥ê¥ª¤ò²¾Äꤷ¤Æ³Æ¼ï¥Ñ¥é¥á¡¼¥¿¤òÀßÄꤷ, ²óÏ©¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤ê¥Î¥¤¥º¿¶Éý, ¥¿¥¤¥ß¥ó¥°¤Ø¤Î±Æ¶Á¤òɾ²Á¤¹¤ë.

Âê̾ÅŸ»¥Î¥¤¥ºµ¯°ø¥¯¥í¥Ã¥¯¥¸¥Ã¥¿¤Î¸«ÀѤê¼êË¡¤ÎÄó°Æ
Ãø¼Ô ˪²° ¹§ÂÀϺ, ¡ûÂçÅè ¹§¹¬, ÃæÅç ±ÑÀÆ(NEC¥¨¥ì¥¯¥È¥í¥Ë¥¯¥¹¡Ê³ô¡Ë)
Pagepp. 11 - 16
Keyword ÅŸ»¥Î¥¤¥º, ¥¯¥í¥Ã¥¯¥¸¥Ã¥¿
AbstractLSIÆâÉô¤Î¥¯¥í¥Ã¥¯¿®¹æ¤Î¥¸¥Ã¥¿¤Î¤¦¤Á¡¤ÅŸ»¥Î¥¤¥º¤Ë¤è¤Ã¤Æ¥¯¥í¥Ã¥¯Ê¬Ç۷Ϥˤª¤¤¤ÆÀ¸¤¸¤ë¥¸¥Ã¥¿¤òÀß·×½é´üÃʳ¬¤ÈÀ߷׸¡¾ÚÃʳ¬¤Ç¸«ÀѤâ¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡¥Àß·×½é´üÃʳ¬¤Ç¤Ï¡¤ÅŸ»·Ï¤Î´Ê°×¥â¥Ç¥ë¤ÈÏÀÍý²óÏ©¤¬¾ÃÈñ¤¹¤ëÅŸ»ÅÅή¤È¤«¤éÅŸ»¥Î¥¤¥ºÇÈ·Á¤ò¸«ÀѤâ¤ê¡¤¤³¤ì¤ò¥¯¥í¥Ã¥¯¥Ñ¥¹ÃÙ±ä¤ÎÊÑÆ°¤ËÊÑ´¹¤¹¤ë¡¥À߷׸¡¾ÚÃʳ¬¤Ç¤Ï¡¢¥Á¥Ã¥×Æâ¤Î¥ì¥¤¥¢¥¦¥È¡¦¥Ç¡¼¥¿¤«¤é¾ÜºÙ¤ÊÅŸ»·Ï¤Î¥â¥Ç¥ë¤òÀ¸À®¤·¤ÆÅŸ»¥Î¥¤¥º²òÀϤò¹Ô¤¤¡¢³Æ¥¯¥í¥Ã¥¯¥É¥é¥¤¥Ð¤Ë¤ª¤±¤ë¼Â¸ústatic IR-drop¤òµá¤á¤Æ¥¸¥Ã¥¿¤¬ºÇÂç¤È¤Ê¤ë¥Ñ¥¹¤ò£±¤ÄÁª¤Ó½Ð¤·¡¢¤³¤Î¥Ñ¥¹¤Î¤ß¤Î¥¸¥Ã¥¿¤òSPICE¤Î²áÅϲòÀϤǵá¤á¤ë¡¥


¥»¥Ã¥·¥ç¥ó A1-2 ²óÏ©¤È¥·¥¹¥Æ¥à¤ÎÍýÏÀ
Æü»þ: 2006ǯ4·î24Æü(·î) 10:30-12:10
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Âê̾Àþ·Á¾õÂÖ¶õ´Ö¥·¥¹¥Æ¥à¤Î¥°¥é¥ß¥¢¥ó¤òÊݸ¤¹¤ë¼þÇÈ¿ôÊÑ´¹
Ãø¼Ô ¡û±ÛÅÄ ½Ó²ð , °¤Éô Àµ±Ñ, ÀîËô À¯À¬(ÅìËÌÂç³ØÂç³Ø±¡ ¹©³Ø¸¦µæ²Ê ÅŻҹ©³ØÀ칶)
Pagepp. 17 - 22
Keyword Àþ·ÁϢ³»þ´Ö¥·¥¹¥Æ¥à, ¾õÂÖ¶õ´Öɽ¸½, ²ÄÀ©¸æÀ­¥°¥é¥ß¥¢¥ó, ²Ä´Ñ¬À­¥°¥é¥ß¥¢¥ó, ¼þÇÈ¿ôÊÑ´¹
Abstract¼þÇÈ¿ôÊÑ´¹¤Ï¡¤Àþ·Á²óÏ©Ì֤ˤª¤±¤ë¸Ä¡¹¤Î¥¤¥ó¥À¥¯¥¿¤ª¤è¤Ó¥­¥ã¥Ñ¥·¥¿¤òÆÃÄê¤ÎLC²óÏ©ÌÖ¤ÇÃÖ¤­´¹¤¨¤ë¤³¤È¤Ë¤è¤ê¡¤Ê̤ÎÆÃÀ­¤òÍ­¤¹¤ë¥·¥¹¥Æ¥à¤òÀ¸À®¤¹¤ë¼êË¡¤Ç¤¢¤ë¡¥Ãø¼Ô¤é¤Ï¤³¤ì¤Þ¤Ç¡¤Àþ·ÁϢ³»þ´Ö¥·¥¹¥Æ¥à¤òÂоݤȤ·¤Æ¡¤¾õÂÖ¶õ´Öɽ¸½¤Ë´ð¤Å¤¯¼þÇÈ¿ôÊÑ´¹¤Îµ­½ÒË¡¤òƳ½Ð¤·¤Æ¤¤¤ë¡¥ËܹƤǤϡ¤¤³¤Îµ­½ÒË¡¤ËÂФ·¤Æ¡¤LC²óÏ©Ì֤ξõÂÖ¶õ´Öɽ¸½¤Ë´Ø¤¹¤ëÀ©Ìó¤ò¿·¤¿¤ËÀߤ±¤ë¤³¤È¤ò¹Í¤¨¤ë¡¥¤½¤·¤Æ¡¤¤³¤ÎÀ©Ìó¤Ë´ð¤Å¤¤¤Æ¼þÇÈ¿ôÊÑ´¹¤Îµ­½ÒË¡¤ò½¤Àµ¤·¡¤¤½¤Î·ë²Ì¤È¤·¤Æ¥·¥¹¥Æ¥à¤Î²ÄÀ©¸æÀ­¡¦²Ä´Ñ¬À­¥°¥é¥ß¥¢¥ó¤¬Êݸ¤µ¤ì¤ë¤è¤¦¤Ê¿·¤·¤¤¼þÇÈ¿ôÊÑ´¹¤Îµ­½ÒË¡¤òƳ½Ð¤¹¤ë¡¥¤Þ¤¿¡¤¤³¤Î¿·¤·¤¤µ­½ÒË¡¤Ë´ð¤Å¤¤¤ÆÀ¸À®¤µ¤ì¤ë¤µ¤Þ¤¶¤Þ¤Ê¥·¥¹¥Æ¥à¤¬¥°¥é¥ß¥¢¥ó¤Î´ÑÅÀ¤«¤éƱ°ì¤Î¹½Â¤¤òÍ­¤¹¤ë¡¤¤È¤¤¤¦¶½Ì£¿¼¤¤À­¼Á¤ò¼¨¤¹¡¥

Âê̾A Pseudo-Transient Method Using Compound Elements for Finding DC Operating Points
Ãø¼Ô ¡ûHong Yu, Yasuaki Inoue, Yuki Matsuya, Zhangcai Huang(Graduate School of Information, Production and Systems, Waseda University)
Pagepp. 23 - 28
Keyword pesudo-transient analysis, compound element, DC operating point, nonlinear circuit, oscillation
AbstractIn order to overcome the non-convergence of the Newton-Raphson method in circuit simulation, the pseudo-transient algorithm with the novel compound elements is proposed in this paper. Compared with the conventional pseudo-transient algorithms, the proposed algorithm is proved to be efficient and insensitive to oscillation problems.

Âê̾SPICE¤Ë´Êñ¤Ë¼ÂÁõ¤Ç¤­¤ë¸úΨŪ¤Ê¥Û¥â¥È¥Ô¡¼Ë¡
Ãø¼Ô ¡û¹õÌÚ ¾Ä, »³Â¼ À¶Î´(Ãæ±ûÂç³ØÍý¹©³ØÉôÅŵ¤ÅŻҾðÊóÄÌ¿®¹©³Ø²Ê)
Pagepp. 29 - 34
Keyword ²óÏ©¥·¥ß¥å¥ì¡¼¥·¥ç¥ó, ľή²òÀÏ, ¥Û¥â¥È¥Ô¡¼Ë¡, SPICE, ¥Ñ¥¹ÄÉÀײóÏ©
AbstractÂç°èŪ¼ý«À­¡ÊǤ°Õ¤Î½é´üÃͤ«¤éɬ¤º²ò¤Ë¼ý«¤¹¤ëÀ­¼Á¡Ë¤¬¤¢¤ê¡¤¤«¤ÄÈó¾ï¤Ë¸úΨ¤¬Îɤ¤ÈóÀþ·Á²óÏ©¤Îľή²òÀÏË¡¤È¤·¤Æ²ÄÊÑÍøÆÀ¥Ë¥å¡¼¥È¥ó¥Û¥â¥È¥Ô¡¼Ë¡¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¡¥¤·¤«¤·Â絬ÌϤÇÊ£»¨¤Ê²óÏ©¤Ë¤â¸úΨÎɤ¯Å¬ÍѤǤ­¤ë¡È¹âÅ٤ʡɥۥâ¥È¥Ô¡¼Ë¡¤ò¼Â¸½¤·¤è¤¦¤È¤¹¤ë¾ì¹ç¡¤¤«¤Ê¤ê¤ÎÀìÌçŪÃ챤ÈÊ£»¨¤Ê¥×¥í¥°¥é¥ß¥ó¥°¤¬É¬ÍפȤʤ뤿¤á¡¤ÈóÀìÌç²È¤ä½é¿´¼Ô¤Ë¤ÏÉßµï¤Î¹â¤¤ÊýË¡¤Ç¤¢¤Ã¤¿¡¥ËܹƤǤϡ¤¡Ö¹âÅ٤ʲÄÊÑÍøÆÀ¥Ë¥å¡¼¥È¥ó¥Û¥â¥È¥Ô¡¼Ë¡¤ò¡×¡Ö¥Û¥â¥È¥Ô¡¼Ë¡¤Î ¤³¤È¤ò¤è¤¯ÃΤé¤Ê¤¯¤Æ¤â¡×¡ÖÊ£»¨¤Ê¥×¥í¥°¥é¥ß¥ó¥°¤ò¹Ô¤¦¤³¤È¤Ê¤¯¡×¡ÖÍưפˡ×SPICE¤Ë¼ÂÁõ¤¹¤ëÊýË¡¤òÄó°Æ¤¹¤ë¡¥

Âê̾Behavioral Macromodeling of Analog LSI Implementation for Automobile Intake System
Ãø¼Ô ¡ûZhangcai Huang, Yasuaki Inoue, Quan Zhang, Shuai Fang, Yuehu Zhou(Waseda University)
Pagepp. 35 - 38
Keyword Behavioral Circuit Macromodeling, Analog LSI, Automobile Intake System
AbstractAccurate estimating or measuring intake manifold absolute pressure plays an important role for the effective automobile engine control. In order to achieve the real-time estimation of the absolute pressure, the high accuracy and high speed processing ability are required for the automobile engine control system. Therefore, in this paper, an analog LSI method is discussed for the automobile electronic control. Furthermore, a novel behavioral macromodel is proposed for further analog LSI design.


¥»¥Ã¥·¥ç¥ó A1-3 ¥Ë¥å¡¼¥é¥ë¥Í¥Ã¥È¥ï¡¼¥¯
Æü»þ: 2006ǯ4·î24Æü(·î) 13:30-14:45
ºÂĹ: ËÙÈø ´îɧ (ÅìµþÅŵ¡Âç³Ø)

Âê̾A Novel Autoassociation Model based on Entropy Minimization Approach
Ãø¼Ô ¡ûMasahiro Nakagawa(Ĺ²¬µ»½Ñ²Ê³ØÂç³Ø)
Pagepp. 39 - 44
Keyword Entropy, Association, Neural Network, Memory Model, Retrieval
AbstractIn this paper, an entropy based associative memory model will be proposed and applied to memory retrievals with an orthogonal learning model to compare with the conventional autoassociative model. In the present approach, the updating dynamics will be constructed on the basis of the entropy minimization strategy which may be reduced asymptotically to the above-mentioned autocorrelation dynamics as a special case. From numerical results, it will be found that the presently proposed novel approach realizes the larger memory capacity in comparison with the autocorrelation based dynamics such as associatron according to the higher-order correlation involved in the proposed dynamics.

Âê̾Analysis of the Number of Stimulation Units in Pulse-Driven Star-Coupled LC Oscillators
Ãø¼Ô ¡ûSeiichiro Moro, Keisuke Hamamoto(University of Fukui), Tadashi Matsumoto(Fukui University of Technology)
Pagepp. 45 - 50
Keyword star-coupled oscillators, synchronization, phase patterns, pulse train, stimulation units
AbstractThe LC oscillators star-coupled by one resistor exhibit N-phase oscillation and we can get (N-1)! stable phase states from the permutation of the phase of each oscillator. Using the relationship between the initial states and the phase patterns, we can use the system as some kinds of the neural networks and the associative memories. In this study, we analyze the scheme of the phase pattern switching and the number of stimulation units needed to derive all the phase patterns in pulse-driven star-coupled LC oscillators. In particular, we claim that we need N-2 stimulation units are needed to derive all the patterns.

Âê̾ÁÐÊý¸þSOM¤òÍѤ¤¤¿É÷¶·Í½Â¬¤Î°ì¼êË¡
Ãø¼Ô ¡ûÆ£¾¾ À¿°ìϺ(Ä»¼è´Ä¶­Âç³Ø Âç³Ø±¡ ´Ä¶­¾ðÊó³Ø¸¦µæ²Ê), Ïɸ« °éμ(Ä»¼è´Ä¶­Âç³Ø ´Ä¶­¾ðÊó³ØÉô), ¿¢ÅÄ ÂóÌé(Ä»¼è´Ä¶­Âç³Ø Âç³Ø±¡ ´Ä¶­¾ðÊó³Ø¸¦µæ²Ê), ¾®ÎÓ ÈôÄ»(Ä»¼è´Ä¶­Âç³Ø ´Ä¶­¾ðÊó³ØÉô), ÃÛë δͺ(¾¾¹¾¹©¶È¹âÅùÀìÌç³Ø¹»), Éû°æ ͵(Ä»¼èÂç³Ø ¹©³ØÉô)
Pagepp. 51 - 56
Keyword ¼«¸ÊÁÈ¿¥²½¥Þ¥Ã¥×, É÷¶·Í½Â¬, É÷ÎÏȯÅÅ
Abstract¸½ºß¡¢¿·¥¨¥Í¥ë¥®¡¼¤¬ÃíÌܤµ¤ì¤Æ¤¤¤ë¡£¿·¥¨¥Í¥ë¥®¡¼¤Î°ì¤Ä¤Ç¤¢¤ëÉ÷ÎϤϡ¢¸Ï³é¤Î¿´ÇÛ¤¬¤Ê¤¯Ìµ¿Ô¢¤Ç¤¢¤ê¡¢É÷ÎϤò»È¤Ã¤¿È¯ÅŤÏÆó»À²½ÃºÁǤòÇӽФ·¤Ê¤¤¥¯¥ê¡¼¥ó¤ÊȯÅÅÊýË¡¤È¤·¤Æ¡¢ÆüËܤǤâ°ÂÄꤷ¤¿É÷ÎϤ¬ÆÀ¤é¤ì¤ë±è´ßÉô¤òÃæ¿´¤ËÉ÷ÎÏȯÅŤ¬À¹¤ó¤ËƳÆþ¤µ¤ì¤Æ¤¤¤ë¡£Ïɸ«¸¦µæ¼¼¤Ç¤ÏÄãÉ÷ÎϤǤâȯÅŲÄǽ¤Ê²ÈÄíÍѤξ®·¿È¯Åŵ¡¤ËÃíÌܤ·¤¿¡£É÷ÎÏȯÅŤò´Þ¤à¥·¥¹¥Æ¥à¤ò°ÂÄê¤Ë±¿ÍѤ¹¤ë¤¿¤á¤ËÉ÷¶·¤Îͽ¬¤¬É¬ÍפǤ¢¤ë¤È¹Í¤¨¤Æ¤¤¤ë¡£²æ¡¹¤ÏÏɸ«¸¦µæ¼¼¤Ç¤Ï²ÈÄíÍѤξ®·¿É÷ÎÏȯÅŵ¡¤ò¥¿¡¼¥²¥Ã¥È¤Ë¤·¡¢¶¹°è¤Î¥Ç¡¼¥¿¤òÍѤ¤¤ÆÉ÷®¤Îͽ¬¤ò¹Ô¤Ã¤Æ¤¤¤ë¡£Ëܸ¦µæ¤Ç¤Ï¡¢º£¤Þ¤Ç»ÈÍѤ·¤Æ¤¤¤¿´ðËÜSOM¤ËÂå¤ï¤êÁÐÊý¸þSOM¤ò»ÈÍѤ·É÷®¤òͽ¬¤·¡¢Í½Â¬Î¨¤Î¸þ¾å¤ò¸¡Æ¤¤¹¤ë¡£


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Ãø¼Ô ¡û¹¾ºê ãÌé(¹­ÅçÂç³Ø), ¥Ê¥Ð¥í ¥É¥ó¥Ç¥£¡¼, Äç¶á ÎÑÉ×, ÎëÌÚ ³Ø, ÃÝÅÄ ÍÛ°ì, »°Âð Àµ¶Æ, ÏÏÌî ÃÒÇ·, ¿å·ó ÎÉͺ, Ä®ÅÄ ¸², °ð³À μ²ð, ¥Þ¥¿¥¦¥·¥å ¥Ï¥ó¥¹¥æ¥ë¥²¥ó, »°±º Æ»»Ò(¹­ÅçÂç³ØÂç³Ø±¡Àèüʪ¼Á²Ê³Ø¸¦µæ²Ê), Âç¹õ ãÌé, ÈÓÄÍ µ®¹°, Åĸý ¾»É§, ·§Âå À®¹§, µÜËÜ ½Ó²ð(ȾƳÂÎÍý¹©³Ø¥»¥ó¥¿¡¼)
Pagepp. 57 - 62

Âê̾(¾·ÂÔ)HiSIM¤ÈPSP¤Î¥Ù¥ó¥Á¥Þ¡¼¥¯·ë²Ì
Ãø¼Ô ¡ûÂç¹õ ãÌé(³ô¼°²ñ¼ÒÅì¼Ç ¥»¥ß¥³¥ó¥À¥¯¥¿¡¼¼Ò SoC¸¦µæ³«È¯¥»¥ó¥¿¡¼)
Pagepp. 63 - 67

Âê̾(¾·ÂÔ)°äÅÁŪ¥¢¥ë¥´¥ê¥º¥à¤òÍѤ¤¤¿SPICE¥â¥Ç¥ë¥Ñ¥é¥á¡¼¥¿¤ÎÃê½Ð¼êË¡¤Î¸¡Æ¤
Ãø¼Ô ¡ûÇÏ¾ì ½ÓÍ´(²­Åŵ¤¹©¶È³ô¼°²ñ¼Ò), ¹ÔÊý ½áÌé, ¼À¥ ÂçÊå, ÏÂÅÄ Å¯Åµ(ȾƳÂÎÀèü¥Æ¥¯¥Î¥í¥¸¡¼¥º), °ËÆ£ ·Ë°ì, ¼Àî Àµ¹¨(¿Ê²½¥·¥¹¥Æ¥àÁí¹ç¸¦µæ½ê)
Pagepp. 69 - 74
Keyword BSIM, GA, SPICE, ¥Ñ¥é¥á¡¼¥¿, Ãê½Ð
AbstractBsim¥â¥Ç¥ë¥Ñ¥é¥á¡¼¥¿¤ÎÃê½Ð¤Ë¤Ï¡¢½¾ÍèLMË¡¤¬ÍѤ¤¤é¤ì¤Æ¤¤¤ë¤¬¡¢¤³¤Î¼êË¡¤Ïû»þ´Ö¤Ë²ò¤òõºº¤¹¤ë¤³¤È¤¬¤Ç¤­¤ëÈ¿ÌÌ¡¢Ìµ¼ê½ç¡¦¼«Æ°¥Ñ¥é¥á¡¼¥¿Ãê½Ð¤¬º¤Æñ¤Ç¤¢¤Ã¤¿¡£°ìÊý¡¢¥Ñ¥é¥á¡¼¥¿¤ÎÃê½Ð¤ËGA¤ÎŬÍѤ¹¤ë¾ì¹ç¡¢Â¿¿ô¤Î¥Ñ¥é¥á¡¼¥¿¤òÀ踫Ãμ±Ìµ¤·¤ËÃê½Ð¤Ç¤­¤ëÈ¿ÌÌ¡¢Ãê½Ð¤Ë¿¤¯¤Î»þ´Ö¤¬É¬ÍפȤ¤¤¦·çÅÀ¤ò»ý¤Ä¡£ËÜÊó¹ð¤Ç¤Ï¡¢Bsim¥â¥Ç¥ë¥Ñ¥é¥á¡¼¥¿¤ÎÃê½Ð¤ËGA¤òŬÍѤ¹¤ë¾ì¹ç¤Ë¡¢¼ÂÍÑŪ¤Ê»þ´Ö¤ÇÃê½Ð¤¹¤ë¼êË¡¤Ë¤Ä¤¤¤Æ¸¡Æ¤¤·¤¿¡£


¥»¥Ã¥·¥ç¥ó Ba1-1 ADÊÑ´¹²óÏ©
Æü»þ: 2006ǯ4·î24Æü(·î) 9:25-10:15
ºÂĹ: °ÂÅÄ ¾´ (Ë¡À¯Âç³Ø)

Âê̾¥¿¥¤¥à¥Ç¥¸¥¿¥¤¥¶¤òÍѤ¤¤¿ÈóƱ´ü¥µ¥ó¥×¥ê¥ó¥°ADÊÑ´¹´ï¤È¿®¹æ½èÍý
Ãø¼Ô ¡ûÀ¶¿å °ìÌé, ¸µß· ÆÆ»Ë(·²ÇÏÂç³Ø ¹©³ØÉô Åŵ¤ÅŻҹ©³Ø²Ê ¾®ÎÓ¸¦µæ¼¼), ¾®¼¼ µ®µª(¥¢¥¸¥ì¥ó¥È¡¦¥Æ¥¯¥Î¥í¥¸¡¼¡¦¥¤¥ó¥¿¡¼¥Ê¥·¥ç¥Ê¥ë¡Ê³ô¡ËSOC¥Æ¥¹¥È»ö¶ÈÀ½Éʳ«È¯Éô ), ÎÓ ³¤·³(·²ÇÏÂç³Ø ¹©³ØÉô Åŵ¤ÅŻҹ©³Ø²Ê ¾®ÎÓ¸¦µæ¼¼), ¾®ÎÓ ½ÕÉ× (·²ÇÏÂç³Ø ¹©³ØÉô Åŵ¤ÅŻҹ©³Ø²Ê ¾®ÎÓ¸¦µæ¼¼ )
Pagepp. 75 - 80
Keyword ADÊÑ´¹´ï, ¥¿¥¤¥à¥Ç¥¸¥¿¥¤¥¶, ÈóƱ´ü¥µ¥ó¥×¥ê¥ó¥°, Ʊ´ü¥µ¥ó¥×¥ê¥ó¥°, ÈóƱ´üÎ¥»¶¥Õ¡¼¥ê¥¨ÊÑ´¹
Abstract¤³¤ÎÏÀʸ¤Ç¤Ï¡¢ÈùºÙCMOS¥×¥í¥»¥¹¤Ç¤Î¼Â¸½¤ËŬ¤·¤¿LSI¥Æ¥¹¥¿ÍѤΥ¢¥×¥ê¥±¡¼¥·¥ç¥ó¤È¤·¤Æ¡¢ÈóƱ´ü¥µ¥ó¥×¥ê¥ó¥°ADÊÑ´¹´ï¥¢¡¼¥­¥Æ¥¯¥Á¥ã¤òÄó°Æ¤¹¤ë¡£´ð½àÀµ¸¹ÇȤÈÆþÎÏ¿®¹æ¤ò¥³¥ó¥Ñ¥ì¡¼¥¿²óÏ©¤ÇÈæ³Ó¤·¡¢¤½¤Î½ÐÎÏÇÈ·Á¤«¤é¥¿¥¤¥à¥Ç¥¸¥¿¥¤¥¶k²óÏ©¤òÍѤ¤ÈóƱ´ü¥µ¥ó¥×¥ê¥ó¥°¤ò¹Ô¤¦¡£¥¢¥Ê¥í¥°ºÇ¾®¡¢¥Ç¥¸¥¿¥ë¥ê¥Ã¥Á¤Ê²óÏ©¹½À®¤Ë¤¹¤ë¤³¤È¤Ë¤è¤ê¹â®¡¢¹âÀºÅÙ¤ò¼Â¸½¤¹¤ëÊýË¡¤Ë¤Ä¤¤¤Æ¸¡Æ¤¤·¤¿¡£

Âê̾A 15-bit 10-Msample/s Pipelined A/D Converter Based on Incomplete Settling Principle
Ãø¼Ô ¡ûShuaiqi Wang(Waseda University), Fule Li(Tsinghua University), Yasuaki Inoue(Waseda University)
Pagepp. 81 - 86
Keyword A/D conversion, pipelined, incomplete settling, low power dissipation
AbstractThis paper proposes a 15-bit 10-MS/s pipelined ADC based on the incomplete settling principle. The traditional complete settling stage is improved to the incomplete settling structure through dividing the sampling clock of the traditional stage into two parts for discharging the sampling and feedback capacitors and completing the sampling, respectively. It verifies the correction and validity of optimizing ADCs¡¦conversion speed without increasing power consumption through the incomplete settling. This ADC employs scaling-down scheme to achieve low power dissipation and utilizes fullËÅifferential structure, bottom-plate-sampling, and capacitor-sharing techniques as well as bit-by-bit digital self-calibration to increase the ADCÃÔ linearity and accuracy. It is processed in 0.18¡¦ 1P6M CMOS mixed-mode technology. Simulation results show that 82dB SNDR and 87dB SFDR are obtained at the sampling rate of 10MHz with the input sine frequency of 100KHz and the whole static power dissipation is 21.94mW.


¥»¥Ã¥·¥ç¥ó Ba1-2 RF¥·¥¹¥Æ¥à
Æü»þ: 2006ǯ4·î24Æü(·î) 10:45-12:00
ºÂĹ: ¹â°æ ¿­Ï (·²ÇÏÂç³Ø)

Âê̾A Study for the Frequency Analysis of CMOS Analog Multiplier
Ãø¼Ô ¡ûQuan Zhang, Zhangcai Huang, Yasuaki Inoue(Waseda University)
Pagepp. 87 - 91
Keyword CMOS Multiplier, Bandwidth, pole-zero pair
AbstractThe CMOS implementation of four quadrant multipliers is still a challenging topic for achieving a wide frequency bandwidth. In this paper, a design principle is proposed to construct high frequency CMOS four quadrant multipliers based on the AC properties analysis for typical architectures. The experimental results prove that the proposed principle can be a good refer for frequency design of CMOS analog multipliers.

Âê̾¥ê¥³¥ó¥Õ¥£¥®¥å¥é¥Ö¥ëRF CMOS̵Àþ½¸ÀѲóÏ©µ»½Ñ
Ãø¼Ô ¡û²¬ÅÄ ·ò°ì, Àîź ÂçÊå, ¿û¸¶ ¹°Íº, ±× °ìºÈ(Åìµþ¹©¶ÈÂç³Ø Åý¹ç¸¦µæ±¡)
Pagepp. 93 - 98
Keyword RF CMOS, ¥ê¥³¥ó¥Õ¥£¥®¥å¥é¥Ö¥ëRF, LNA, multi-standard
AbstractËܸ¦µæ¤Ç¤Ï¡¤Si CMOSµ»½Ñ¤Ë¤è¤ë¥Þ¥ë¥Á¥¹¥¿¥ó¥À¡¼¥É̵Àþ²óÏ©¤Î¼Â¸½¤Ë¸þ¤±¤Æ¡¤¥ê¥³¥ó¥Õ¥£¥®¥å¥é¥Ö¥ëRF²óÏ©µ»½Ñ¤òÄó°Æ¤¹¤ë¡¥Äó°Æ¤¹¤ë²óÏ©¥¢¡¼¥­¥Æ¥¯¥Á¥ã¤Ï¡¤RF²óÏ©Éô¤È¥Ç¥£¥¸¥¿¥ë¤ÎÀ©¸æ²óÏ©¤«¤é¹½À®¤µ¤ì¤Æ¤ª¤ê¡¤¥È¥é¥ó¥¸¥¹¥¿¤ä²ÄÊѼõÆ°ÁǻҤΥХ¤¥¢¥¹ÅÅ°µ¤òÀ©¸æ¤¹¤ë¤³¤È¤Ë¤è¤ê¡¤¤Þ¤¿¡¤²óÏ©¤ò¥Ö¥í¥Ã¥¯¤´¤ÈÀÚ¤êÂؤ¨¤ë¤³¤È¤Ë¤è¤ê¡¤²óÏ©µ¡Ç½¤òưŪ¤ËºÆ¹½À®¤¹¤ë¡¥ºÆ¹½À®µ¡Ç½¤òÍѤ¤¤ë¤³¤È¤Ç¡¤¥Þ¥ë¥Á¥Ð¥ó¥É²½¤Î¤ß¤Ê¤é¤º¡¤Êâα¤Þ¤ê¤Î¸þ¾å¤äÄã¾ÃÈñÅÅÎϲ½¤ò²Äǽ¤È¤¹¤ë¡¥ËܹƤǤϡ¤LNA¤ÎưŪÀ­Ç½Êä½þ¤Ë¤è¤ê¡¤ºÇÂç¤Ç80%¤Î¾ÃÈñÅÅÎϺ︺¤òãÀ®¤·¤¿¡¥

Âê̾ʣ¿ô¥¢¥ó¥Æ¥Ê¤òÍѤ¤¤¿¥Ñ¥Ã¥·¥ÖRFID¥¿¥°¤ÎÄÌ¿®µ÷Î¥ÁýÂç¸ú²Ì¤Ë´Ø¤¹¤ë¸¡Æ¤
Ãø¼Ô ¡ûÈøÊݼê Ìмù, ¼¯»ÒÅè ·û°ì(°ñ¾ëÂç³Ø¹©³ØÉô¥á¥Ç¥£¥¢ÄÌ¿®¹©³Ø²Ê), ¾¾ËÜ Åµ¹ä, ¹ÓÌÚ ·û»Ê(¡Ê³ô¡ËÆüΩÀ½ºî½ê ÆüΩ¸¦µæ½ê)
Pagepp. 99 - 104
Keyword RFID, ¥¢¥ó¥Æ¥Ê, ÄÌ¿®µ÷Î¥
AbstractËܹƤǤϡ¤¥Ñ¥Ã¥·¥ÖRFID¥¿¥°¤Î¥¢¥ó¥Æ¥Ê¤òÅŸ»ÍѤÈÊÑÄ´ÍѤËʬ¤±¤ëÊý¼°¤òÄó°Æ¤¹¤ë¡¥ÅŸ»ÍÑ¥¢¥ó¥Æ¥ÊÁǻҤϥѥå·¥ÖRFID¥¿¥°ÅŸ»Ã¼»Ò¤ËÀܳ¤µ¤ì¡¤¤³¤Îü»Ò¤ÎÆþÎÏ¥¤¥ó¥Ô¡¼¥À¥ó¥¹¤Ï¥¢¥ó¥Æ¥Ê¤ÎÆþÎÏ¥¤¥ó¥Ô¡¼¥À¥ó¥¹¤ÎÊ£ÁǶ¦Ìò¤È¤Ê¤Ã¤Æ¤ª¤ê¡¤¸ÇÄê¤Ç¤¢¤ë¡¥°ìÊý¡¤ÊÑÄ´ÍÑ¥¢¥ó¥Æ¥ÊÁǻҤϡ¤¥Ñ¥Ã¥·¥ÖRFID¥¿¥°¤ÎÊÑÄ´ÍÑü»Ò¤ËÀܳ¤µ¤ì¤ª¤ê¡¤¤³¤Îü»Ò¤ÎÆþÎÏ¥¤¥ó¥Ô¡¼¥À¥ó¥¹¤ò¥Ç¥£¥¸¥¿¥ë¾ðÊó¤Î1µÚ¤Ó0¤ËÂбþ¤·¤Æ¡¤¥·¥ç¡¼¥ÈµÚ¤Ó¥ª¡¼¥×¥ó¤ÇÀÚ¤êÂؤ¨¤ë¡¥


¥»¥Ã¥·¥ç¥ó Ba1-3 [ÆÃÊÌ¥»¥Ã¥·¥ç¥ó]̤Íè¼Ò²ñ¤ò¼Â¸½¤¹¤ë¥»¥ó¥µ²óÏ©µ»½Ñ1
Æü»þ: 2006ǯ4·î24Æü(·î) 13:30-15:00
ºÂĹ: ËÙÅÄ ÀµÀ¸ (É𢹩¶ÈÂç³Ø)

Âê̾(¾·ÂÔ)¿·¤·¤¤£Ó£ï£Ãµ»½Ñ¤òÍѤ¤¤¿»ØÌæǧ¾Ú¥Á¥Ã¥×
Ãø¼Ô ¡û½Å¾¾ ÃÒ»Ö, ¿¹Â¼ ¹Àµ¨, ±©ÅÄÌî ¹§Íµ, ÃæÀ¾ ±Ò, Æ£°æ ¹§¼£, ÃÓÅÄ ÆàÈþ»Ò, Åç¼ ½Ó½Å, Ä®ÅÄ ¹îÇ·, ²¬ºê ¹¬É×(£Î£Ô£Ô¥Þ¥¤¥¯¥í¥·¥¹¥Æ¥à¥¤¥ó¥Æ¥°¥ì¡¼¥·¥ç¥ó¸¦µæ½ê)
Pagepp. 105 - 110
Keyword »ØÌæǧ¾Ú, SoC, »ØÌ楻¥ó¥µ, ¥Ô¥¯¥»¥ë¥¢¥ì¥¤, ²èÁü½èÍý
Abstract»ØÌæ¤ÎÆɤ߼è¤ê¤«¤éǧ¾Ú¤Þ¤Ç¤ò¹Ô¤¦»ØÌæǧ¾Ú¥·¥¹¥Æ¥à¤Î¥ï¥ó¥Á¥Ã¥×²½¤Î¤¿¤á¡¢¥Ç¥Ð¥¤¥¹µ»½Ñ¤ä²óÏ©µ»½Ñ¤«¤é¥½¥Õ¥È¥¦¥¨¥¢µ»½Ñ¤Þ¤Ç¤ÎÍÍ¡¹¤ÊÍ×Áǵ»½Ñ¤òÄó°Æ¤·¡¢¤³¤ì¤éµ»½Ñ¤Î¶¨Ä´¤Ë¤è¤ê¥·¥¹¥Æ¥à¤Î¥ï¥ó¥Á¥Ã¥×²½¤ò¼Â¸½¤¹¤ë¿·¤·¤¤SoCµ»½Ñ¤È¡¢¼Â¸½¤·¤¿¥Á¥Ã¥×¤ÎÆÃħ¤ò³è¤«¤¹»ØÌæǧ¾Ú¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤ò¾Ò²ð¤¹¤ë¡£

Âê̾(¾·ÂÔ)¥Ð¥¤¥ªÊ¬Ìî¤Ë¤ª¤±¤ë²½³Øȯ¸÷·×¬¤Ø¤Î¥ï¥¤¥ä¥ì¥¹¸÷¥»¥ó¥µ¤Î±þÍÑ
Ãø¼Ô ¡ûÌðß· µÁ¾¼, ÅÏî´ °ì´õ, ÂçÀ¾ Ãé»Ö, ³øËÙ À¯ÃË(³ô¼°²ñ¼ÒÆüΩÀ½ºî½êÃæ±û¸¦µæ½ê), ĹëÉô ·òɧ(³ô¼°²ñ¼ÒÆüΩÀ½ºî½ê´ðÁø¦µæ½ê), ðÇÈ ±Éºö(³ô¼°²ñ¼ÒÆüΩĶ£Ì£Ó£É¥·¥¹¥Æ¥à¥º)
Pagepp. 111 - 116
Keyword ¥Ð¥¤¥ª¥»¥ó¥µ, ¥Ñ¥Ã¥·¥Ö£Ò£ÆÄÌ¿®, DNA, SNP
Abstract°Â²Á¤Ç´ÊÊؤʥХ¤¥ª·×¬¥·¥¹¥Æ¥à¤ò¼Â¸½¤¹¤ë¥­¡¼¥Ç¥Ð¥¤¥¹¤È¤·¤Æ¥Ñ¥Ã¥·¥ÖRFÄÌ¿®¤È¸÷¥»¥ó¥µµ¡Ç½¤ò2.5mm³Ñ¤Î¥·¥ê¥³¥ó´ðÈľå¤Ë¥â¥Î¥ê¥·¥Ã¥¯½¸ÀѤ·¤¿¥»¥ó¥µ¥Á¥Ã¥×¤ò³«È¯¤·¤¿¡£ËÜ¥Á¥Ã¥×¤òÍѤ¤¤¿·×¬¥·¥¹¥Æ¥à¤Ë¤è¤êDNA¤ò·×¬¤·¡¢°ì±ö´ð¿·¿(SNP: Single Nucleotide Polymorphism)¤ÎƱÄê¤ËÀ®¸ù¤·¤¿¡£


¥»¥Ã¥·¥ç¥ó Ba1-4 [ÆÃÊÌ¥»¥Ã¥·¥ç¥ó]̤Íè¼Ò²ñ¤ò¼Â¸½¤¹¤ë¥»¥ó¥µ²óÏ©µ»½Ñ2
Æü»þ: 2006ǯ4·î24Æü(·î) 15:30-17:00
ºÂĹ: ËÙÅÄ ÀµÀ¸ (É𢹩¶ÈÂç³Ø)

Âê̾(¾·ÂÔ)¼ê¤Ö¤ì¸¡½ÐÍÑ£²¼´¥¸¥ã¥¤¥í¥â¥¸¥å¡¼¥ë
Ãø¼Ô ¡û·ª¸¶ °ìÉ×(¥½¥Ë¡¼³ô¼°²ñ¼Ò)
Pagepp. 117 - 122
Keyword ¥¸¥ã¥¤¥í, ¿¶Æ°»Ò, ³Ñ®ÅÙ, °µÅÅËì, MEMS
Abstract¥»¥ó¥µÉô¤Ï¡¢MEMS¤ÈÇöËìµ»½Ñ¤Ë¤è¤ê¾®·¿²½¤ò¿Þ¤ë¤È¤È¤â¤Ë¡¢µ¡³£Åª¿¶Æ°¤ò¤·¤ä¤¹¤¤·Á¾õ¤ËºÇŬ²½¤·¤¿¡£¤³¤ÎºÇŬ²½¤È¿·³«È¯IC¤Ç½¾Íè¤Î1¼´¥¸¥ã¥¤¥í¤ÈƱÅù¤Î´¶ÅÙ¤òÊݤÁ¡¢¤µ¤é¤Ë¼þÊÕ²óÏ©¤ò¤âÅëºÜ¤·Ëܥ⥸¥å¡¼¥ë¤ò¼Â¸½¤·¤¿¡£

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Ãø¼Ô ¡û»³¸ý ÀµÍÎ(ÅìËÌÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê )
Pagepp. 123 - 128


¥»¥Ã¥·¥ç¥ó Bd1-1 ÈóÀþ·Á¡¦Å¬±þ¿®¹æ½èÍý
Æü»þ: 2006ǯ4·î24Æü(·î) 9:00-10:15
ºÂĹ: ¿ùÌî Īɧ (Åìµþ¹©¶ÈÂç³Ø)

Âê̾¿Ê²½ÏÀŪ¥Ç¥£¥¸¥¿¥ë¥Õ¥£¥ë¥¿¤ÎÊÂÎó¼Â¸½¤Î¤¿¤á¤Î¹½À®¤È¤½¤Î¥Ï¡¼¥É¥¦¥§¥¢¼Â¸½
Ãø¼Ô °¤Éô Àµ±Ñ, ¡ûÃæß· ÂÀͤ, ÀîËô À¯À¬(ÅìËÌÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²ÊÅŻҹ©³ØÀ칶)
Pagepp. 129 - 134
Keyword Ŭ±þ¥Ç¥£¥¸¥¿¥ë¥Õ¥£¥ë¥¿, ¿Ê²½ÏÀŪ¥Ç¥£¥¸¥¿¥ë¥Õ¥£¥ë¥¿, ¿Ê²½ÏÀŪ·×»»¼êË¡, ÊÂÎó½èÍý, FPGA
AbstractËܹƤǤϡ¤¿Ê²½ÏÀŪ¥Ç¥£¥¸¥¿¥ë¥Õ¥£¥ë¥¿¡Êevolutionary digital filter: EDF¡Ë¤Î¥¢¥ë¥´¥ê¥º¥à¤¬»ý¤Ä¸ÄÂδ֤Υǡ¼¥¿°Í¸¤¬¾¯¤Ê¤¯¡¤³Æ¸ÄÂΤνèÍý¤¬Æ±°ì¤Ç¤¢¤ë¤È¤¤¤¦ÊÂÎóÀ­¤ò¹Íθ¤·¤¿¥Ï¡¼¥É¥¦¥§¥¢¤ò¼Â¸½¤¹¤ë¡¥¤³¤ì¤ò¹Íθ¤·¤¿¥Ï¡¼¥É¥¦¥§¥¢¼Â¸½¤Î¸¡Æ¤¤ò¹Ô¤¤¡¤¤³¤ì¤òFPGA¾å¤Ë¼ÂÁõ¤¹¤ë¡¥¤Þ¤¿¡¤¥Õ¥£¥ë¥¿¥ê¥ó¥°±é»»¤Ë¤ª¤¤¤Æ¡¤Ê£¿ô¤ÎÀÑϱ黻´ï¤òÍѤ¤¤ë¤³¤È¤Ç¡¤±é»»¥ì¥Ù¥ë¤Ç¤ÎÊÂÎó½èÍý¤Î¸ú²Ì¤Ë¤Ä¤¤¤Æ¤Î¸¡Æ¤¤â¹Ô¤¤¡¤¤³¤ì¤òFPGA ¾å¤Ë¼ÂÁõ¤¹¤ë¡¥¤³¤ì¤é¤Î·ë²Ì¡¤Á´¸ÄÂΤ¬ÆþÎÏ¿®¹æ1 ¥µ¥ó¥×¥ë¤ò½èÍý¤¹¤ë¤Î¤ËɬÍפʥ¯¥í¥Ã¥¯¿ô¤Ï¡¤72.6¥¯¥í¥Ã¥¯¤È¤Ê¤ê¡¤¸½ºß¤Þ¤ÇÄó°Æ¤µ¤ì¤Æ¤¤¤ë¼êË¡¤Î1/176.4¤Ëºï¸º¤µ¤ì¤¿.

Âê̾ÀäÂÐÃÍ¸íº¹¤Ë¤è¤ë¥Æ¥ó¥½¥ëÀÑŸ³«¤òÍѤ¤¤¿ÈóÄê¾ï¿®¹æ¤ÎʬΥ
Ãø¼Ô ¡ûÈÄ°æ ÍÛ½Ó, °ÂÀî Çî(°¦Ãθ©Î©Âç³Ø), Æâ¾¢ °ï(̾¸Å²°¹©¶ÈÂç³Ø), Ȫ ²í¶³(ÃæÉôÂç³Ø¹©³ØÉô)
Pagepp. 135 - 140
Keyword ÈóÄê¾ï¿®¹æ, ¿ÊÑÎ̲òÀÏ, ¿®¹æÃê½Ð, ¥Æ¥ó¥½¥ëÀÑŸ³«
Abstract¼çÀ®Ê¬Ê¬ÀϤÏÆþÎÏ¥Ù¥¯¥È¥ë¤ÎÄ㼡¸µ²½¤Ë¤è¤ê¥Ç¡¼¥¿¤¬»ý¤Ä·¹¸þ¤òª¤¨¤ë¤³¤È¤¬¤Ç¤­¡¢ÍÍ¡¹¤ÊʬÌî¤Î¥Ç¡¼¥¿¤ËŬÍѤµ¤ì¤Æ¤¤¤ë¤¬¡¢¼çÀ®Ê¬Ê¬ÀϤò»þ·ÏÎó²òÀϤËŬÍѤ¹¤ë¾ì¹ç¤ÏÃåÌܤ·¤Æ¤¤¤ëÀ®Ê¬¤¬Äê¾ï¤Ç¤¢¤ë¤³¤È¤¬¾ò·ï¤È¤Ê¤ë¡£ËܹƤǤÏ2¼¡ÊÑ¿ô´Ø¿ô¤Ë¤ª¤¤¤Æ¼çÀ®Ê¬Ê¬ÀϤÈÅù²Á¤Ç¤¢¤ë¥Æ¥ó¥½¥ëÀÑŸ³«¤ò³ÈÄ¥¤·¡¢Â¿ÊÑÎÌ¿®¹æ¤«¤éÈóÄê¾ï¿®¹æ¤òÃê½Ð¤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡£2ÊÑ¿ô´Ø¿ô¤òÁÛÄꤷ¤¿¾ì¹ç¡¢½¾Íè¤Î¥Æ¥ó¥½¥ëÀÑŸ³«¤ÏÆþÎÏ¥Ù¥¯¥È¥ë¤È¤Îº¹Ê¬¤Î¼«¾è¸íº¹¤¬ºÇ¾®¤È¤Ê¤ë2¤Ä¤Î1ÊÑ¿ô´Ø¿ô¤ÎÀѤòµá¤á¤ë¤¬¡¢ËܹƤǤÏ1ÊÑ¿ô´Ø¿ô¤ÎÀѤËľήÀ®Ê¬¤ò¿äÄꤹ¤ë¹à¤ò²Ã¤¨¡¢¼«¾è¸íº¹¤òÀäÂÐÃÍ¸íº¹¤Ë¤¹¤ë¤³¤È¤Ë¤è¤êÈóÄê¾ï¿®¹æ¤ÎʬΥ¤¬²Äǽ¤Ç¤¢¤ë¤³¤È¤ò¼¨¤¹¡£

Âê̾A Refined Filtering Approach to Adaptive Line Enhancement
Ãø¼Ô ¡ûYusuke Tsuda(ºë¶ÌÂç³ØÂç³Ø±¡Íý¹©³Ø¸¦µæ²Ê), Tetsuya Shimamura(ºë¶ÌÂç³Ø¹©³ØÉô¾ðÊó¥·¥¹¥Æ¥à¹©³Ø²Ê)
Pagepp. 141 - 146
Keyword adaptive line enhancer, NLMS algorithm
AbstractWe propose a novel filtering technique for an adaptive line enhancer (ALE). The ALE is based on a linear transversal filter and updated by the normalized least mean square (NLMS) algorithm. The proposed filtering technique is to obtain a refined output signal by linear convolution of the updated coefficient vector and the current input vector. Theoretical analysis and computer simulation demonstrate the effectiveness of the RF technique.


¥»¥Ã¥·¥ç¥ó Bd1-2 ±ÇÁü¿®¹æ½èÍý
Æü»þ: 2006ǯ4·î24Æü(·î) 10:35-12:15
ºÂĹ: µ®²È ¿Î»Ö (¼óÅÔÂç³ØÅìµþ)

Âê̾Performance Evaluation of Motion-Compensated Spatio-Temporal Filtering with Spatial DWT
Ãø¼Ô ¡ûMinoru Hiki, Takuma Ishida, Shogo Muramatsu, Hisakazu Kikuchi(Dept. of Electrical and Electronic Engineering, Niigata University)
Pagepp. 147 - 152
Keyword Motion-compensated temporal filtering, 3-D filter banks, scalability, video coding, suppress PSNR fluctuation
AbstractIn this work, a novel hierarchical motion-compensated spatio-temporal filtering (MCSTF) is proposed for scalable video coding. MCSTF is an alternative technique of existing motion-compensated temporal filtering (MCTF), a fundamental component for scalable video coding in next generation. MCSTF is a non-separable sub-sampling version of MCTF and provides interlaced pictures as intermediate video sequence by using a spatio-temporal split process. Furthermore, the 1/3-transform structure, which excludes the lifting update-step, significantly suppresses the PSNR fluctuation which occurs in the existing MCTF technique. Our proposed system has an advantage of suppressing PSNR fluctuation with almost the same average PSNR as that of existing MCTF. Some experimental results with entropy coded scalar quantization show the significance of our proposed technique.

Âê̾A Strict Successive Elimination Algorithm for Fast Motion Estimation
Ãø¼Ô ¡ûYang Song(Graduate School of IPS, Waseda University), Zhenyu Liu(RISE, Waseda University), Takeshi Ikenaga, Satoshi Goto(Graduate School of IPS, Waseda University)
Pagepp. 153 - 157
Keyword Motion Estimation, SEA, MSEA, Strict SEA
AbstractThis paper present a simple and effective method to further reduce the search positions in successive elimination algorithm (SEA). Because the SEA values for most of the best matching search positions are much smaller than the current minimum SAD, we can simply increase the calculated SEA value to increase the elimination ratio without much affecting the coding efficiency. Experiments show that the proposed strict SEA algorithm (SSEA) can eliminate more than 70% of the search positions in SEA algorithm, and the maximum average PSNR and bitrate losses are 0.13dB and 3.33%, respectively. The proposed method also can be used in multilevel SEA (MSEA) and fine granularity SEA (FGSE) algorithms, and the corresponding factors for MSEA and associated experiments data are also given in this paper.

Âê̾Loss Free VLSI Oriented Full Computation Reusing Algorithm for H.264 Fractional Motion Estimation
Ãø¼Ô ¡ûMing Shao(The Graduate School of Information, Production and systems, Waseda University), Zhenyu Liu(Kitakyushu Foundation for the Advancement of Industry, Science and Technology), Satoshi Goto, Takeshi Ikenaga(The Graduate School of Information, Production and systems, Waseda University)
Pagepp. 159 - 164
Keyword H.264, FME, reusing, VLSI, loss free
AbstractFractional motion estimation (FME) is an advanced feature adopted in H.264/AVC video compression standard with quarter-pixel accuracy. Although FME could gain up to 5db encoding efficiency, sub-pixel interpolation and SATD computation, as main parts of FME, require a large mount of computation complexity. To reduce the complexity of FME, this paper proposes a full computation reusable VLSI oriented algorithm. Through exploiting the similarity among motion vectors (MVs), temporary computation results can be fully reused by other sub-blocks. Furthermore, since a simple, neat and effective searching method is adopted, the proposed method is very suitable for VLSI implementation. Experiment results shows that up to 50% add operations and 80% external memory access operations are saved without any deterioration in the coding quality.

Âê̾²ÄÊÑ¥¦¥£¥ó¥É¥¦¥¹¥Æ¥ì¥ª¥Þ¥Ã¥Á¥ó¥°¥×¥í¥»¥Ã¥µ¤Î¥¢¡¼¥­¥Æ¥¯¥Á¥ã
Ãø¼Ô µÜËÜ Î¶²ð, ¡ûέ ºÜ·®, Åû°æ ¹°, Ãæ¼ ¹Ô¹¨(µþÅÔÂç³ØÂç³Ø±¡¾ðÊó³Ø¸¦µæ²ÊÄÌ¿®¾ðÊó¥·¥¹¥Æ¥àÀ칶)
Pagepp. 165 - 170
Keyword stereo, processor architecture
Abstract¥¹¥Æ¥ì¥ª»ë¤Ë¤ª¤± ¤ëÂбþÅÀõº÷¤Ç¤¢¤ë¥¹¥Æ¥ì¥ª¥Þ¥Ã¥Á¥ó¥°¤Ï¿¤¯¤Î·×»»Î̤òÍפ·¡¤Æà ¤Ë¡¤¹âÀºÅ٤ʥޥåÁ¥ó¥°¼êË¡¤Ï¤è¤ê¿¤¯¤Î·×»»Î̤¬É¬ÍפȤʤ롥¤½ ¤Î¤¿¤á¡¤Áȹþ¤ßʬÌî¤Ë¤ª¤±¤ëÍøÍѤΤ¿¤á¤Ë¡¤¼Â»þ´Ö½èÍý¥·¥¹¥Æ¥à¤ä ÀìÍÑ¥×¥í¥»¥Ã¥µ¤Î³«È¯¤¬¹Ô¤ï¤ì¤Æ¤¤¤ë¤¬¡¤¹âÀºÅ٥ޥåÁ¥ó¥°¤È¼Â»þ ´Ö½èÍý¤òξΩ¤¹¤ë¥·¥¹¥Æ¥à¤Ï¸ºß¤·¤Ê¤¤¡¥ËܹƤǤϡ¤¶áǯ¤Î¥¹¥Æ¥ì ¥ª¥Þ¥Ã¥Á¥ó¥°¥¢¥ë¥´¥ê¥º¥à¤Ë¤ª¤±¤ë¸¦µæ¤ÎÀ®²Ì¤Ë´ð¤Å¤­¡¤¹âÀºÅÙ¤« ¤Ä¹â®¤Ê¼êË¡¤Ç¤¢¤ë²ÄÊÑ¥¦¥£¥ó¥É¥¦¥¹¥Æ¥ì¥ª¥Þ¥Ã¥Á¥ó¥°¼êË¡¤Î¥ê¥¢ ¥ë¥¿¥¤¥à¼Â¹Ô¤òÌܻؤ·¤¿¥×¥í¥»¥Ã¥µ¥¢¡¼¥­¥Æ¥¯¥Á¥ã¤ÎÄó°Æ¤ò¹Ô¤¤¡¤ ½èÍýÀ­Ç½¤ÈÊÂÎóÅ٤δط¸¤ò¼¨¤·¤¿¡¥


¥»¥Ã¥·¥ç¥ó Bd1-3 ²èÁü½èÍý¡ÊÉü¸µ¡¦¶¯Ä´¡Ë
Æü»þ: 2006ǯ4·î24Æü(·î) 13:30-15:10
ºÂĹ: Ĺ뻳 Èþµª (Ë̳¤Æ»Âç³Ø)

Âê̾Image Enhancement by Committee Machine with Symbiotic Evolution
Ãø¼Ô ¡ûNoriko Otani(Musashi Institute of Technology), Tomoaki Kimura(IBM Japan), Katsumi Nitta(Tokyo Institute of Technology)
Pagepp. 171 - 176
Keyword Symbiotic Evolution, Committee Machine, Image Enhancement
AbstractIn this paper, we present a novel approach to learning parameters of a committee machine which is used to enhance the blurred Gaussian noisy images. It is based on symbiotic evolution, which is a kind of Genetic Algorithm, to avoid overlearning and find an optimal solution in various candidates. We experimented in enhancement of various noisy images with various variance. The experimental results show that the proposed method doesn't depend on noise variance or the type of image, and the committee decision machine learned by the proposed method realized the noisy image enhancement superior than former methods.

Âê̾Improvement of Iterative Methods for Image Deconvolution Based on Subregion Decomposition and Optimal Local Iterations
Ãø¼Ô ¡ûKarn Patanukhom, Akinori Nishihara(Tokyo Institute of Technology)
Pagepp. 177 - 182
Keyword Image Deconvolution, Image Restoration, Iterative methods, Subregion
AbstractA technique based on subregion decomposition and optimal local iteration is proposed for iterative image deconvolution methods. The proposed technique improves restoration results of the conventional deconvolution methods by separately restoring each subregion of an image with different numbers of optimal local iteration. In order to decompose the image into subregion, an algorithm based on analysis of blur component is introduced. In addition, to estimate of the optimal local iteration, an error-analysis- based approach is also introduced. Simulation results are presented to demonstrate the promising performance of the proposed techniques.

Âê̾¿¿ô·èµ¡³£¤òÍѤ¤¤¿¥¤¥ó¥Ñ¥ë¥¹À­»¨²»¤Î¸¡½ÐË¡
Ãø¼Ô ¡ûÂçë µª»Ò(É𢹩¶ÈÂç³Ø´Ä¶­¾ðÊó³ØÉô), ÌÚ¼ À¿Áï(ÆüËÜIBM³ô¼°²ñ¼Ò), ¿·ÅÄ ¹î¸Ê(Åìµþ¹©¶ÈÂç³Ø)
Pagepp. 183 - 188
Keyword ¿¿ô·èµ¡³£, »¨²»¸¡½Ð
AbstractËܹƤǤϡ¤¥¤¥ó¥Ñ¥ë¥¹À­»¨²»¤Î¿·¤·¤¤¸¡½ÐÊýË¡¤È¤·¤Æ¡¤½èÍýÅÀ¤È¶á˵Îΰè¤È¤Îº¹Ê¬¤ò¿¿ô·èµ¡³£¤ËÆþÎϤ¹¤ë¤³¤È¤Ç¡¤½èÍýÅÀ¤Ë¥¤¥ó¥Ñ¥ë¥¹À­»¨²»¤¬½Å¾ö¤·¤Æ¤¤¤ë¤«Èݤ«¤òȽÃǤ¹¤ë¸¡½ÐÊýË¡¤òÄó°Æ¤¹¤ë¡¥½¾ÍèÊýË¡¤Ç¤Ï¡¤¥é¥ó¥À¥àÃÍ¥¤¥ó¥Ñ¥ë¥¹À­»¨²»¤ËÂФ·¤Æ¸ú²Ì¤¬¤Ê¤«¤Ã¤¿¤ê¡¤¥é¥ó¥À¥àÃÍ¥¤¥ó¥Ñ¥ë¥¹À­»¨²»¤ËÂФ·¤Æ»¨²»È¯À¸³ÎΨ¤Ë°Í¸¤·¤¿¥Ñ¥é¥á¡¼¥¿¤òÀßÄꤹ¤ëɬÍפ¬¤¢¤ë¤¬¡¤Äó°ÆË¡¤Ç¤ÏÍ£°ì¤Î¥Ñ¥é¥á¡¼¥¿¤ÇÍÍ¡¹¤Ê¼ïÎà¤Î²èÁü¤ò½èÍý¤¹¤ë¤³¤È¤¬¤Ç¤­¤ë¡¥¥Ù¥ó¥Á¥Þ¡¼¥¯¥Ç¡¼¥¿¤ËŬÍѤ·¤¿·ë²Ì¡¤²èÁü¤Î¼ïÎà¤ä»¨²»È¯À¸³ÎΨ¤Ë°Í¸¤¹¤ë¤³¤È¤Ê¤¯¡¤¸ÇÄêÃͤª¤è¤Ó¥é¥ó¥À¥àÃÍ¥¤¥ó¥Ñ¥ë¥¹À­»¨²»¤ËÂФ·¤Æ¡¤½¾ÍèÊýË¡¤ÈƱÅù¤Þ¤¿¤Ï¤½¤ì°Ê¾å¤ÎÀ­Ç½¤Ç¤¢¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥

Âê̾¿½Å²èÁü¤ÎÅý¹ç¤Ë¤è¤ëÆ°¤­¥Ö¥ì¤òȼ¤ï¤Ê¤¤¥«¥é¡¼¥À¥¤¥Ê¥ß¥Ã¥¯¥ì¥ó¥¸¤Î³ÈÄ¥
Ãø¼Ô µÜËÜ Î¶²ð(µþÅÔÂç³ØÂç³Ø±¡¾ðÊó³Ø¸¦µæ²ÊÄÌ¿®¾ðÊó¥·¥¹¥Æ¥àÀ칶), ¡û¸¶ ͪµ­(µþÅÔÂç³Ø¹©³ØÉôÅŵ¤ÅŻҹ©³Ø²Ê), Åû°æ ¹°, Ãæ¼ ¹Ô¹¨(µþÅÔÂç³ØÂç³Ø±¡¾ðÊó³Ø¸¦µæ²ÊÄÌ¿®¾ðÊó¥·¥¹¥Æ¥àÀ칶)
Pagepp. 189 - 192
Keyword multiple exposure, fusion, motion blur
Abstract¼ÖºÜ¤ä´Æ»ëÍÑÅӤˤª¤¤¤Æ¤Ï¡¤¥«¥á¥é¤Ë¤è¤ë»£Áü»þ¤Ë¹­¤¤¥À¥¤¥Ê¥ß¥Ã ¥¯¥ì¥ó¥¸¤¬Í׵ᤵ¤ì¤ë¡¥»£Áü²áÄø¤Ë¤ª¤±¤ë¥À¥¤¥Ê¥ß¥Ã¥¯¥ì¥ó¥¸¤Î³È Ä¥¼êË¡¤Î1¤Ä¤Ë¿½ÅϪ¸÷²èÁü¤ÎÅý¹ç¤¬¤¢¤ë¡¥Â¿½ÅϪ¸÷²èÁü¤ÎÅý¹ç¤Ë ¤è¤Ã¤Æ¥«¥é¡¼¥À¥¤¥Ê¥ß¥Ã¥¯¥ì¥ó¥¸¤ò³ÈÄ¥¤¹¤ë¼êË¡¤ÏÄó°Æ¤µ¤ì¤Æ¤¤¤ë ¤¬¡¤Èï¼ÌÂΤÎÆ°¤­¤ò¹Íθ¤·Æ°¤­¥Ö¥ì¤ò½üµî¤¹¤ë¼êË¡¤ÏÄó°Æ¤µ¤ì¤Æ¤¤ ¤Ê¤¤¡¥¤½¤³¤Ç¡¤ËܹƤǤϡ¤Ïª¸÷»þ´ÖÀ©¸æ¤Ë¤è¤Ã¤ÆÆÀ¤é¤ì¤¿Â¿½Å²èÁü ¤òÂоÝʪ¤ÎÆ°¤­¤ò¿äÄꤷ¤Ä¤ÄÅý¹ç¤¹¤ë¤³¤È¤Ë¤è¤Ã¤Æ¡¤Æ°¤­¥Ö¥ì¤Î¤Ê ¤¤¹­¥À¥¤¥Ê¥ß¥Ã¥¯¥ì¥ó¥¸¥«¥é¡¼²èÁü¤ò¹çÀ®¤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡¥


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Æü»þ: 2006ǯ4·î24Æü(·î) 15:30-17:10
ºÂĹ: ´ä¶¶ À¯¹¨ (Ĺ²¬µ»½Ñ²Ê³ØÂç³Ø)

Âê̾(¾·ÂÔ)MPEG ¥ª¡¼¥Ç¥£¥ª¤ÎºÇ¿·Æ°¸þ¤È±þÍÑ
Ãø¼Ô ¡ûÌî¼ ½ÓÇ·(NEC ¥á¥Ç¥£¥¢¾ðÊ󸦵æ½ê)
Pagepp. 193 - 198
Keyword MPEG, ¥ª¡¼¥Ç¥£¥ªÉä¹æ²½
AbstractISO/IEC ¤Ë¤ª¤±¤ë¥ª¡¼¥Ç¥£¥ªÉä¹æ²½¤Îɸ½à²½Æ°¸þ¤òƧ¤Þ¤¨¡¤µ»½ÑŪ¤ÊŸ˾¤È²ÝÂꡤ¸¦µæ»öÎã¤ò¾Ò²ð¤¹¤ë¡¥

Âê̾(¾·ÂÔ)ÃÎŪ¾ðÊó¥¢¥¯¥»¥¹¤ò¼Â¸½¤¹¤ë¤¿¤á¤Î±ÇÁü¸¡º÷¤Ë´Ø¤¹¤ë¸¦µæÆ°¸þ
Ãø¼Ô ¡ûĹ뻳 Èþµª(Ë̳¤Æ»Âç³ØÂç³Ø±¡ ¾ðÊó²Ê³Ø¸¦µæ²Ê)
Pagepp. 199 - 203
Keyword ²èÁü¸¡º÷, ±ÇÁü¥¤¥ó¥Ç¥­¥·¥ó¥°, ¾ðÊó¥¢¥¯¥»¥¹
Abstract¾ðÊóÂç¹Ò³¤»þÂå¤ò·Þ¤¨ÍøÍѼԤÎÃÎŪÍßµá¤òËþ¤¿¤¹¾ðÊó¥¢¥¯¥»¥¹µ»½Ñ¤Î³«È¯¤¬µÞ̳¤È¤µ¤ì¤Æ¤¤¤ë¡¥Ëֱܹé¤Ç¤Ï¡¤¤½¤Î¼çÍ×µ»½Ñ¤È¤µ¤ì¤ë¡¤±ÇÁü¥Ç¡¼¥¿¤Î¸¡º÷¤ÈʬÎà¤Ë¤Ä¤¤¤Æ¸½¾õ¤Î¸¦µæÆ°¸þ¤ò¾Ò²ð¤¹¤ë¡¥


¥»¥Ã¥·¥ç¥ó C1-1 Àß·×»öÎã
Æü»þ: 2006ǯ4·î24Æü(·î) 9:00-10:15
ºÂĹ: °Â°æ ÂîÌé (¾¾²¼ÅŴﻺ¶È³ô¼°²ñ¼Ò¡¡È¾Æ³Âμҡ¡¥·¥¹¥Æ¥àLSI³«È¯ËÜÉô¡¡Âè°ì¾¦ÉÊʬÌȯ¥»¥ó¥¿¡¼)

Âê̾²»À¼Ç§¼±¥·¥¹¥Æ¥à¤Î¥Ï¡¼¥É¥¦¥§¥¢²½
Ãø¼Ô ¡ûºÍÄÔ À¿(¶áµ¦Âç³ØÍý¹©³ØÉôÅŵ¤ÅŻҹ©³Ø²Ê), ¾¾Ìî ͵Ƿ(¶áµ¦Âç³ØÂç³Ø±¡Áí¹çÍý¹©³Ø¸¦µæ²Ê¥¨¥ì¥¯¥È¥í¥Ë¥¯¥¹·Ï¹©³ØÀ칶), ±üÅÄ ¿¿Ç¡(¶áµ¦Âç³ØÍý¹©³ØÉôÅŵ¤ÅŻҹ©³Ø²Ê), »³ÅÄ ¹¸µ×(¥·¥ã¡¼¥×(³ô) ÅŻҥǥХ¤¥¹³«È¯ËÜÉô Àèüµ»½Ñ³«È¯¸¦µæ½ê), ¿À¸Í ¾°»Ö(¶áµ¦Âç³Ø Íý¹©³ØÉô Åŵ¤ÅŻҹ©³Ø²Ê)
Pagepp. 205 - 210
Keyword ²»À¼Ç§¼±¤Î¥Ï¡¼¥É¥¦¥§¥¢²½

Âê̾FIFO¥Ð¥Ã¥Õ¥¡¤Ë¤è¤ë¹â¸úΨMessage-Passing¥¹¥±¥¸¥å¡¼¥ë¤òÍѤ¤¤¿LDPCÉü¹æ´ï
Ãø¼Ô ¡ûÀ¶¿å °ìÈÏ, ÀÐÀî ãǷ(Áá°ðÅÄÂç³ØÂç³Ø±¡¾ðÊóÀ¸»º¥·¥¹¥Æ¥à¸¦µæ²Ê), ¸ÍÀî ˾(Áá°ðÅÄÂç³ØÍý¹©³ØÉô¥³¥ó¥Ô¥å¡¼¥¿¡¦¥Í¥Ã¥È¥ï¡¼¥¯¹©³Ø²Ê), ÃÓ±Ê ¹ä, ¸åÆ£ ÉÒ(Áá°ðÅÄÂç³ØÂç³Ø±¡¾ðÊóÀ¸»º¥·¥¹¥Æ¥à¸¦µæ²Ê)
Pagepp. 211 - 216
Keyword LDPCÉä¹æ, Message-Passing Schedule, FPGA, FIFO¥Ð¥Ã¥Õ¥¡
AbstractËܹƤǤϡ¤¹â¸úΨMessage-Passing¥¹¥±¥¸¥å¡¼¥ë¤òŬÍѤ¹¤ë¤³¤È¤¬²Äǽ¤ÊLDPCÉü¹æ´ï¤Ë¤ª¤±¤ë¥á¥â¥ê¤ÎÀêÍ­ÌÌÀѤòºï¸º¤¹¤ë¤¿¤á¤Ë¡¤(1) ¹â¸úΨMessage-Passing¥¹¥±¥¸¥å¡¼¥ë¤òSingle Port¥á¥â¥ê¤Ç¼Â¸½¤¹¤ë¼êË¡¡¤(2) Á´¤Æ¤Î¥á¥â¥ê¥Ð¥ó¥¯¤òFIFO¥Ð¥Ã¥Õ¥¡¤òÍѤ¤¤Æ¼Â¸½¤¹¤ë¼êË¡¤òÄó°Æ¤·¤¿¡¥Äó°Æ¼êË¡¤òŬÍѤ·¤Æ¡¤FPGA (Virtex xc2v6000)¤Ë¼ÂÁõ¤·¤¿·ë²Ì¡¤½¾Íè¼êË¡¤ÈÈæ³Ó¤·¤Æ¡¤Ìó30%¤Î¥á¥â¥êÍÆÎ̤¬ºï¸º¤Ç¤­¡¤LDPCÉü¹æ´ïÁ´ÂΤˤª¤±¤ëÌó15%¤ÎFPGA SLICE¿ô¤òºï¸º¤Ç¤­¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥

Âê̾¿¹à¼°¶á»÷¤Ë¤è¤ëÇÜÀºÅÙ½éÅù´Ø¿ô±é»»²óÏ©¤ÎÌÌÀÑ-ÃÙ±äºÇŬ²½¼êË¡
Ãø¼Ô ¡û¶¶ËÜ ¹ÀÆó, ¥â¥·¥Ë¥ã¥¬¥ï¥·¥ê¡¼(Ê¡²¬Âç³Ø¹©³ØÉôÅŻҾðÊ󹩳زÊ), ¼¾å Ͼ´(¶å½£Âç³Ø¥·¥¹¥Æ¥à¾ðÊó²Ê³Ø¸¦µæ±¡¾ðÊóÍý³ØÉôÌç)
Pagepp. 217 - 222
Keyword ½éÅù´Ø¿ô, ÇÜÀºÅÙ·×»», ºÇŬ²½
AbstractHardware implementation of IEEE-754 double-precision elementary functions, such as square root, reciprocal, etc, requires search for a tradeoff between the logic gate/wire area and the look-up table area. In this paper, we propose a new technique to reduce both the look-up table size and access latency of IEEE-754 double-precision elementary functions. Simulations show that the optimization reduces the total memory requirements for five elementary functions by 1/50 while almost the same computation delay. The technique is simple yet suitable for the SoC design of arithmetic modules.


¥»¥Ã¥·¥ç¥ó C1-2 ÄÌ¿®¤È¹çÀ®¼êË¡
Æü»þ: 2006ǯ4·î24Æü(·î) 10:45-12:00
ºÂĹ: °ËÆ£ ÏÂ¿Í (ºë¶ÌÂç³Ø)

Âê̾ÄÌ¿®ÉʼÁ¤ò¹Íθ¤·¤¿¥Ç¥£¥¸¥¿¥ë̵ÀþÄÌ¿®¥·¥¹¥Æ¥à¤Î¾ÃÈñ¥¨¥Í¥ë¥®¡¼²òÀÏ
Ãø¼Ô  ¹¬Í´(¶å½£Âç³ØÂç³Ø±¡¥·¥¹¥Æ¥à¾ðÊó²Ê³ØÉÜ), ¡û¼¼»³ ¿¿ÆÁ(¶å½£Âç³Ø¥·¥¹¥Æ¥àLSI¸¦µæ¥»¥ó¥¿¡¼), °Â±º ´²¿Í(¶å½£Âç³ØÂç³Ø±¡¥·¥¹¥Æ¥à¾ðÊó²Ê³Ø¸¦µæ±¡)
Pagepp. 223 - 228
Keyword Äã¾ÃÈñ¥¨¥Í¥ë¥®¡¼, ¥Ç¥£¥¸¥¿¥ë̵ÀþÄÌ¿®, ÄÌ¿®ÉʼÁ
Abstract¥Ç¥£¥¸¥¿¥ë̵ÀþÄÌ¿®µ»½Ñ¤òÍøÍѤ·¤¿¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤ËÂФ¹¤ë¾ÃÈñ¥¨¥Í¥ë¥®¡¼¤Îºï¸ºÍ׵᤬Èó¾ï¤Ë¶¯¤¤¡£ËܹƤϡ¢ÄÌ¿®ÉʼÁ¤È¾ÃÈñ¥¨¥Í¥ë¥®¡¼¤Î´Ø·¸¤ËÃåÌܤ·¡¢¤¢¤ë°ìÄê°Ê¾å¤ÎÄÌ¿®ÉʼÁ¤ò³ÎÊݤ·¤Ä¤Ä¾ÃÈñ¥¨¥Í¥ë¥®¡¼¤òºï¸º¤¹¤ë¤³¤È¤òÌÜŪ¤È¤¹¤ë¡£ÄÌ¿®¤ò¹Ô¤¦¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤´¤È¤ËÍ¿¤¨¤é¤ì¤ëÄÌ¿®ÉʼÁ¤ò³ÎÊݤ·¤Ä¤Ä¡¢¥Ñ¥±¥Ã¥ÈĹ¡¦ÊÑÄ´Êý¼°¤È¤¤¤Ã¤¿Ê£¿ô¤Î¥Ñ¥é¥á¡¼¥¿¤òƱ»þ¤ËºÇŬ²½¤·¡¢¤Þ¤¿¡¢Á÷¿®¿®¹æÅÅÎÏ¡¦¿®¹æ½èÍý±é»»ÀºÅ٤Ȥ¤¤Ã¤¿²óÏ©¥Ñ¥é¥á¡¼¥¿À©¸æ¤òŬÀڤ˹Ԥ¦¤³¤È¤Ë¤è¤êÄã¾ÃÈñ¥¨¥Í¥ë¥®¡¼²½¤òÌܻؤ¹¡£¤½¤Î½àÈ÷¤È¤·¤Æ¡¢ÄÌ¿®ÉʼÁ¤ò¹Íθ¤·¤¿¥Ç¥£¥¸¥¿¥ë̵ÀþÄÌ¿®¥·¥¹¥Æ¥à¤Î¾ÃÈñ¥¨¥Í¥ë¥®¡¼¤ò²òÀϤ¹¤ë¡£

Âê̾À߷׺ÆÍøÍѤΰ٤Υץí¥È¥³¥ëÊÑ´¹´ï¹çÀ®¼êË¡
Ãø¼Ô ¡ûÅÏî´ æÆÂÀ(ÅìµþÂç³ØÂç³Ø±¡ ¹©³Ø·Ï¸¦µæ²Ê), À¥¸Í ¸¬½¤(ÅìµþÂç³ØÂ絬ÌϽ¸ÀÑ¥·¥¹¥Æ¥àÀ߷׶µ°é¸¦µæ¥»¥ó¥¿¡¼), ÀÐÀî ͪ»Ê(ÅìµþÂç³Ø ¹©³ØÉô), ¾®¾¾ Áï, Æ£ÅÄ ¾»¹¨(ÅìµþÂç³ØÂ絬ÌϽ¸ÀÑ¥·¥¹¥Æ¥àÀ߷׶µ°é¸¦µæ¥»¥ó¥¿¡¼)
Pagepp. 229 - 234
Keyword SoC, ¥×¥í¥È¥³¥ëÊÑ´¹, ¥¤¥ó¥¿¡¼¥Õ¥§¥¤¥¹¹çÀ®, IPºÆÍøÍÑ
Abstract¼ÂºÝ¤ËSoC¤ÎÀ߷פÇÍøÍѤµ¤ì¤Æ¤¤¤ëÊ£»¨¤Ê¥×¥í¥È¥³¥ë¤ËÂФ·¤ÆŬÍѲÄǽ¤Ê¥×¥í¥È¥³¥ëÊÑ´¹´ï¤Î¼«Æ°¹çÀ®µ»½Ñ¤òÄó°Æ¤¹¤ë¡£Äó°Æ¼êË¡¤Ï£³¤Ä¤ÎÍ×Áǵ»½Ñ¡§¥×¥í¥È¥³¥ë¥â¥Ç¥ê¥ó¥°¼êË¡¡¢¥ë¡¼¥×¥¨¥Ã¥¸¤Î½èÍý¡¢¥¹¡¼¥Ñ¡¼¥¹¥Æ¡¼¥È¤ÎƳÆþ¤Ë¤è¤Ã¤Æ¹½À®¤µ¤ì¡¢Ìµ¸Â¤Ë³¤¯¥×¥í¥È¥³¥ë¤Î¥È¥é¥ó¥¶¥¯¥·¥ç¥ó¤ò°·¤¦¤³¤È¤¬¤Ç¤­¤ë¡£¤Þ¤¿¡¢³Æ¥·¡¼¥±¥ó¥¹¤òÆÈΩ¤Ë°·¤¤¡¢·×»»Î̤òºï¸º¤¹¤ë¤³¤È¤¬¤Ç¤­¤ë¡£¤³¤ì¤é¤òÍѤ¤¤ë¤³¤È¤Ë¤è¤ê¡¢´û¸µ»½Ñ¤ÎŬÍѲÄǽÈϰϤò³ÈÂ礹¤ë¤³¤È¤¬¤Ç¤­¤ë¡£ËÜÏÀʸ¤Ç¤Ï¡¢AMBA¡¦OCP´Ö¤Î¥×¥í¥È¥³¥ëÊÑ´¹´ï¤ò¹çÀ®¤·¡¢Äó°Æ¼êË¡¤ÎÍ­¸úÀ­¤ò¼¨¤¹¡£

Âê̾Ãٱ䡦ÌÌÀѤΥȥ졼¥É¥ª¥Õ¤ò¹Íθ¤·¤¿²Ã»»´ïÍÑ prefix graph¤Î¹½À®¼êË¡¤Ë¤Ä¤¤¤Æ
Ãø¼Ô ¡û¾¾±Ê ¿ÉÄ»Ò(Ê¡²¬ÃÎŪ¥¯¥é¥¹¥¿¡¼¸¦µæ½ê), ¾¾±Ê ͵²ð(¶å½£Âç³Ø)
Pagepp. 235 - 240
Keyword ±é»»´ï¹çÀ®, parallel prefix adder
Abstract²Ã»»´ï¤Î¹çÀ®¤Ë¤ª¤¤¤Æ¤Ï¡¢¤Þ¤º¸ÄÊ̤Υƥ¯¥Î¥í¥¸¾ðÊó¤È¤ÏÆÈΩ¤Ë³µÎ¬¹½Â¤¤ò°ì °Õ¤Ë·è¤á,¤½¤ì¤ËÂФ·¤ÆÂоݤȤ·¤Æ¤¤¤ë¥é¥¤¥Ö¥é¥ê¤Î¥»¥ë¤Ë¥Æ¥¯¥Î¥í¥¸¥Þ¥Ã¥Ô ¥ó¥°¤ò¹Ô¤¦¤È¤¤¤¦2Ãʳ¬¤Î²áÄø¤Ç¼Â¸½¤µ¤ì¤ë¤³¤È¤¬Â¿¤¤¡£¤·¤«¤·¡¢¤è¤êÉʼÁ¤ò¤¢¤²¤ë¤¿¤á¤Ë¤Ï¡¢³µÎ¬¹½Â¤¤Î·èÄê¤È¥Þ¥Ã¥Ô¥ó¥°¤È¤òÍ»¹ç¤·¡¢¾ÜºÙ¤Ê¾ðÊó¤ò¹Íθ¤·¤Ä¤Ä³µÎ¬¹½Â¤¤ò·èÄꤹ¤ë¤³¤È¤¬Ë¾¤Þ¤·¤¤¡£ËܹƤǤϡ¢¤è¤ê¹âÉʼÁ¤Ê²Ã»»´ï¹çÀ®¤Î¤¿¤á¤Î°ì¤Ä¤ÎÏÈÁȤߤȤ·¤Æ¡¢ÃÙ±ä/ÌÌÀѤËÉý¤ò»ý¤¿¤»¤¿Ê£¿ô¤Î³µÎ¬¹½Â¤¤ò¸õÊä¤È¤·¤ÆÀ¸À®¤·¡¢¤½¤ì¤é¤ËÂФ·¤Æ¥Þ¥Ã¥Ô¥ó¥°¤ò¹Ô¤¦¤³¤È¤òÁ°Äó¤È¤·¤Æ¡¢ÌÌÀÑ¡¿ÃÙ±ä¤Î¥È¥ì¡¼¥É¥ª¥Õ¤ò¹Íθ²Äǽ¤Ê³µÎ¬¹½Â¤½¸¹ç¤òÀ¸À®¤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡£Parallel prefix adder ¤òÂоݤȤ·¤Æ¼êË¡¤ò¼Â¸½¤·¡¢¼Â¸³¤òÄ̤·¤Æɾ²Á¡¿¹Í»¡¤ò¹Ô¤¦¡£¡¡


¥»¥Ã¥·¥ç¥ó C1-3 ¥Õ¥í¥¢¥×¥é¥ó
Æü»þ: 2006ǯ4·î24Æü(·î) 13:30-14:45
ºÂĹ: ¹â¶¶ ÆÆ»Ê (Åìµþ¹©¶ÈÂç³Ø)

Âê̾¥¢¥Ê¥í¥°ICÀ߷פˤª¤±¤ëÌÚɽ¸½¤òÍѤ¤¤¿ÇÛÃÖ¼êË¡
Ãø¼Ô ¡ûÊ¿Àî ºÚÄÅÈþ(ÅìµþÇÀ¹©Âç³ØÂç³Ø±¡ Åŵ¤ÅŻҹ©³ØÀ칶), Æ£µÈ Ë®ÍÎ(ÅìµþÇÀ¹©Âç³Ø)
Pagepp. 241 - 246
Keyword O-tree, ÂоÎÇÛÃÖÀ©Ìó, ¥¢¥Ê¥í¥°IC¥ì¥¤¥¢¥¦¥È
Abstract¹âÀ­Ç½¥¢¥Ê¥í¥°IC¥ì¥¤¥¢¥¦¥ÈÀ߷פǤϡ¢¤·¤Ð¤·¤ÐÊ£¿ô¤Î¥»¥ëÂФò¼´¤ËÂФ·¤ÆÀþÂоΤËÇÛÃÖ¤¹¤ë¤³¤È¤¬Í׵ᤵ¤ì¤ë¡£Balasa¤é¤Ï¤É¤ó¤Êº¸²¼µÍ¤áÇÛÃÖ¤âɽ¤¹¤³¤È¤¬¤Ç¤­¤ëO-tree¤òÍѤ¤¤ÆÂоÎÇÛÃÖÀ©Ì󲼤ǤÎÇÛÃÖ¼êË¡¤òÄó°Æ¤·¤¿¡£ ¤·¤«¤·¤³¤Î¼êË¡¤Ç¤ÏÆþÎϤµ¤ì¤¿O-tree¤ÎÀ©Ìó¤òËþ¤¿¤µ¤Ê¤¤ÇÛÃÖ¤¬ÆÀ¤é¤ì¤¿¤ê¡¢À©Ìó¤òËþ¤¿¤·¤Æ¤¤¤Æ¤âºÇÌ©¤Ç¤Ê¤¤ÇÛÃÖ¤¬ÆÀ¤é¤ì¤ëÅù¤Î·ç´Ù¤¬¤¢¤ë¡£ ¤½¤³¤ÇËܹƤǤÏO-tree¤ÎÀ©Ìó¤ÈÂоÎÇÛÃÖÀ©Ìó¤Ï¶¦¤ËÀþ·Á¤Ê¼°¤ËÊÑ´¹¤Ç¤­¤ë¤³¤È¤òÍøÍѤ·¡¢¹ÃÅĤé¤Ë¤è¤ësequence-pair¤òÍѤ¤¤¿¼êË¡¤ÈƱÍͤËÀ©Ì󲼤ǺÇÌ©¤ÊÇÛÃÖ¤òÀþ·Á·×²èË¡¤òÍѤ¤¤ÆÆÀ¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡£

Âê̾Éô²°¤ÎÎÙÀÜ´Ø·¸¤ò´Þ¤ó¤À½çÎó¤È¥Õ¥í¥¢¥×¥é¥ó¤ÎÂбþ
Ãø¼Ô ¡ûÆ£´¬ μ, ¹â¶¶ ½Óɧ(¿·³ãÂç³ØÂç³Ø±¡¼«Á³²Ê³Ø¸¦µæ²Ê)
Pagepp. 247 - 252
Keyword ¥Õ¥í¥¢¥×¥é¥ó, ɽ¸½Ë¡, ½çÎó, ÎÙÀÜ´Ø·¸
Abstract¥Õ¥í¥¢¥×¥é¥ó¤È¤Ï¶ë·Á¤ò¿åÊ¿Àþʬ¤ª¤è¤Ó¿âľÀþʬ¤Ë¤è¤ê¡¢´ö¤Ä¤«¤Î¶ë·Á¤ØºÙʬ¤·¤¿¤â¤Î¤Ç¤¢¤ë¡£¤³¤ì¤Þ¤Ç¤ËVLSI¥ì¥¤¥¢¥¦¥ÈÀ߷פʤɤؤαþÍѤ«¤é¡¢ÍÍ¡¹¤Ê¥Õ¥í¥¢¥×¥é¥ó¤Îɽ¸½Ë¡¤¬Äó°Æ¤µ¤ì¤¿¡£·×»»µ¡¤Ë¤è¤Ã¤Æ¥Õ¥í¥¢¥×¥é¥ó¤ò¼è¤ê°·¤¦¾ì¹ç¡¢¤½¤Îɽ¸½Ë¡¤Ï¤Ç¤­¤ë¤À¤±´Êñ¤Ç¤¢¤ê¡¢°·¤¤¤ä¤¹¤¤¤³¤È¤¬Ë¾¤Þ¤ì¤ë¡£¶áǯ¤Î¸¦µæ¤Ë¤è¤ê¡¢Éô²°Æ±»Î¤ÎÎÙÀÜ´Ø·¸¤ò¹Íθ¤·¤Ê¤¤¾ì¹ç¤Ë¤Ï¡¢nÉô²°¤Î¥Õ¥í¥¢¥×¥é¥ó¤òn-½çÎó¤È¤¤¤¦´Êñ¤Ê¹½Â¤¤Çɽ¸½¤Ç¤­¤ë¤³¤È¤¬¼¨¤µ¤ì¤¿¡£ËÜÊó¹ð¤Ï¡¢Éô²°Æ±»Î¤ÎÎÙÀÜ´Ø·¸¤ò¹Íθ¤·¤¿¥Õ¥í¥¢¥×¥é¥ó¤Ç¤â¡¢Æ±¤¸¤¯n-½çÎó¤Ë¤è¤Ã¤Æɽ¸½¤Ç¤­¤ë¤³¤È¤ò¼¨¤¹¡£¤µ¤é¤Ë¡¢Äó°Æ¤¹¤ëɽ¸½Ë¡¤ÏÈóµöÍÆɽ¸½¤ò»ý¤¿¤Ê¤¤¡¢¤¹¤Ê¤ï¤ÁǤ°Õ¤În-½çÎó¤¬nÉô²°¤Î¥Õ¥í¥¢¥×¥é¥ó¤ËÂбþ¤¹¤ë¤È¤¤¤¦Ãµº÷¥¢¥ë¥´¥ê¥º¥à¤ËŬ¤·¤¿À­¼Á¤òÈ÷¤¨¤Æ¤¤¤ë¡£

Âê̾ÀÞ¤ì¶Ê¤¬¤ê¤Èʬ´ô¤òµöÍƤ·¤¿¥Ð¥¹¥É¥ê¥Ö¥ó¥Õ¥í¥¢¥×¥é¥óÀß·×¼êË¡
Ãø¼Ô ¡ûÊ¿ÎÉ ÍÎÍ´, Æ£µÈ Ë®ÍÎ(ÅìµþÇÀ¹©Âç³Ø )
Pagepp. 253 - 258
Keyword ¥Õ¥í¥¢¥×¥é¥ó, ¥Ð¥¹, ¥Ó¥¢, sequence-pair


¥»¥Ã¥·¥ç¥ó C1-4 ¥ì¥¤¥¢¥¦¥È¥¢¥ë¥´¥ê¥º¥à
Æü»þ: 2006ǯ4·î24Æü(·î) 15:10-16:00
ºÂĹ: ¹â¶¶ ½Óɧ (¿·³ãÂç³Ø)

Âê̾¥ì¥¸¥¹¥¿¤ÎºÆÇÛÃ֤ˤè¤ë½àƱ´ü¼°²óÏ©¤Î¥¯¥í¥Ã¥¯¼þ´üºÇ¾®²½¼êË¡
Ãø¼Ô ¡û¾®Ê¿ ¹Ô½¨, ¹â¶¶ ÆÆ»Ê(Åìµþ¹©¶ÈÂç³ØÂç³Ø±¡ Íý¹©³Ø¸¦µæ²Ê ½¸ÀÑ¥·¥¹¥Æ¥àÀ칶)
Pagepp. 259 - 264
Keyword ½àƱ´üÊý¼°, ¥ì¥¸¥¹¥¿¤ÎºÆÇÛÃÖ, ¥ê¥¿¥¤¥ß¥ó¥°
Abstract½àƱ´üÊý¼°¤Ç¤Ï¥¯¥í¥Ã¥¯¥¿¥¤¥ß¥ó¥°¤òÊѹ¹¤¹¤ë¤³¤È¤Ë¤è¤êºÇ¾®¥¯¥í¥Ã¥¯¼þ´ü¤ò²¼¤²¤é¤ì¤ë²ÄǽÀ­¤¬¤¢¤ë¤¬¡¤ºÇ¾®¥¯¥í¥Ã¥¯¼þ´ü¤Î²¼³¦¤òãÀ®¤Ç¤­¤ë¤È¤Ï¸Â¤é¤Ê¤¤¡¥°ìÊý¡¤´°Á´Æ±´üÊý¼°¤Ë¤ª¤±¤ë¥ì¥¸¥¹¥¿¤ÎºÆÇÛÃ֤ǤÏÃÙ±äÃͤòǤ°Õ¤ÎÃͤËʬ³ä¤Ç¤­¤ë¤È¤­¡¤ºÇ¾®¥¯¥í¥Ã¥¯¼þ´ü¤Î²¼³¦¤òãÀ®¤Ç¤­¤ë¤¬¡¤ÃÙ±äÃͤòǤ°Õ¤ÎÃͤËʬ³ä¤Ç¤­¤ë¤È¤¤¤¦¾ò·ï¤Ï¸½¼ÂŪ¤Ç¤Ï¤Ê¤¤¡¥ÃÙ±äÃͤòʬ³ä¤Ç¤­¤Ê¤¤¤È¤­¤Ï¡¤¸Â³¦ºÇ¾®¥¯¥í¥Ã¥¯¼þ´ü¤òãÀ®¤Ç¤­¤ë¤È¤Ï¸Â¤é¤Ê¤¤¡¥¤½¤³¤ÇËܹƤǤϡ¤³ÆÃÙ±äÁǻҤ¬°ì°Õ¤ÎÃÙ±äÃͤòÍ­¤·¤Æ¤¤¤ë¤È¤¤¤¦¾ò·ï¤Î²¼¤Ç¡¤ÃÙ±äÁǻҤòʬ³ä¤¹¤ë¤³¤È¤Ê¤¯¡¤½àƱ´üÊý¼°¤Ë¤ª¤±¤ë¥ì¥¸¥¹¥¿¤ÎºÆÇÛÃ֤ˤè¤êºÇ¾®¥¯¥í¥Ã¥¯¼þ´ü¤Î²¼³¦¤ò¼Â¸½¤¹¤ë¹â®¤Ê¼êË¡¤òÄó°Æ¤¹¤ë¡¥

Âê̾3¼¡¸µ¥¹¥é¥¤¥¹¹½Â¤¤Ë¤ª¤±¤ëľÊýÂβóž¤Ë¤è¤ëÂÎÀѺǾ®²½¼êË¡
Ãø¼Ô ¡ûÀи¶ ·¼Í´, Æ£µÈ Ë®ÍÎ(ÅìµþÇÀ¹©Âç³ØÂç³Ø±¡ Åŵ¤ÅŻҹ©³ØÀ칶)
Pagepp. 265 - 270
Keyword ľÊýÂΥѥå­¥ó¥°, ÂÎÀѺǾ®²½, Ê»¹çÁàºî


¥»¥Ã¥·¥ç¥ó C1-5 [ÆÃÊÌ¥»¥Ã¥·¥ç¥ó]VDEC
Æü»þ: 2006ǯ4·î24Æü(·î) 16:10-17:00
ºÂĹ: Ê¿ÅÄ ²íµ¬ (ȾƳÂÎÍý¹©³Ø¸¦µæ¥»¥ó¥¿¡¼)

Âê̾(¾·ÂÔ)VDEC¤Î¤³¤ì¤Þ¤Ç¤Î10ǯ¤Èº£¸å¤ÎŸ³«
Ãø¼Ô ¡ûÀõÅÄ Ë®Çî(ÅìµþÂç³ØÂ絬ÌϽ¸ÀÑ¥·¥¹¥Æ¥àÀ߷׶µ°é¸¦µæ¥»¥ó¥¿¡¼)
Pagepp. 271 - 276
Keyword LSI¶µ°é, ¥Á¥Ã¥×»îºî, CAD¥Ä¡¼¥ë, Àß·×¥»¥ß¥Ê, VDEC
AbstractVDEC¤¬1996ǯ5·î¤Ëȯ­¤·¤Æ10ǯ¤¬·Ð²á¤·¤¿¡£¤³¤ì¤Þ¤ÇLSIÀß·×´ðÈ׳ÎΩ¤Î¤¿¤á­¡ËܳÊŪÀ߷ץġ¼¥ëÀ°È÷­¢Âç³Ø¸¦µæ¶µ°é¥³¥¹¥È¤Ë¸«¹ç¤¦LSI¥Á¥Ã¥×»îºî¥·¥¹¥Æ¥à¹½ÃÛ­£¥»¥ß¥Ê¡¼¡¦¥È¥ì¡¼¥Ë¥ó¥°³«ºÅ¡¢Åù¤ò¹Ô¤Ã¤Æ¤­¤¿¡£¤Þ¤¿ºÇ¶È³¦¤Î¶¨ÎϤβ¼¡¢VDEC¥æ¡¼¥¶¤¬¶¦Ä̤ËÍøÍѤǤ­¤ëÀ߷ץ饤¥Ö¥é¥ê¡¿IP¥³¥¢¤ÎÀ°È÷¤ËÅؤᡢ¼ã¼ê¸¦µæ¼Ô¤ÎÍ¥¤ì¤¿LSIÀß·×¥×¥í¥¸¥§¥¯¥È¤ò»Ù±ç¤¹¤ë¤¿¤á¤Î¤µ¤Þ¤¶¤Þ¤Ê»ÅÁȤߤò¹½ÃÛ¤·¤Æ¤­¤¿¡£²áµî10ǯ´Ö¤ÎÃæ¤ÇÂç³Ø¤ÏË¡¿Í²½¤·¡¢¼è¤ê´¬¤¯´Ä¶­¤ÏÂ礭¤¯ÊѤï¤Ã¤¿¤¬¡¢ËܹƤǤÏVDEC10ǯ¤Î³èÆ°¤òÁí³ç¤·¡¢º£¸å¤Î³èÆ°Êý¿Ë¤Ë¤Ä¤¤¤Æ¹Í¤¨¤ò½Ò¤Ù¤¿¤¤¡£


¥»¥Ã¥·¥ç¥ó D1-2 ·Á¼°Åª¼êË¡
Æü»þ: 2006ǯ4·î24Æü(·î) 10:30-12:00
ºÂĹ: ²ÏÊÕ µÁ¿® (NTT¥³¥ß¥å¥Ë¥±¡¼¥·¥ç¥ó²Ê³Ø´ðÁø¦µæ½ê ¿Í´Ö¾ðÊ󸦵æÉô ¾ðÊó´ðÁÃÍýÏÀ¸¦µæ¥°¥ë¡¼¥×)

Âê̾´Ö·ç¼Â¹Ô¥í¥°¤«¤é¤Î¥×¥í¥°¥é¥à¤Î¼Â¹Ô·ÐÏ©¿äÄê¼êË¡¤ÎÄó°Æ
Ãø¼Ô ¡ûÂçÄÐ ÃλË((³ô)Åì¼Ç ¸¦µæ³«È¯¥»¥ó¥¿¡¼), Ì³À ľ¹À((³ô)Åì¼Ç¥»¥ß¥³¥ó¥À¥¯¥¿¡¼¼Ò SoC¸¦µæ³«È¯¥»¥ó¥¿¡¼), °¦¿Ü ±ÑÇ·((³ô)Åì¼Ç ¸¦µæ³«È¯¥»¥ó¥¿¡¼)
Pagepp. 277 - 282
Keyword ¥×¥í¥Õ¥¡¥¤¥ë, ¼Â¹Ô¥í¥°, ¥¢¥ë¥´¥ê¥º¥à
Abstract¥×¥í¥°¥é¥à¤ÎÀ­Ç½¥Á¥å¡¼¥Ë¥ó¥°¤Î¤¿¤á¤Î¥×¥í¥Õ¥¡¥¤¥ë¥Ä¡¼¥ë¤È¤·¤Æ¡¢¤¿¤È¤¨¤Ðunix¤Ê¤É¤Ç»ÈÍѤµ¤ì¤ëgprofÅù¤¬¤è¤¯ÃΤé¤ì¤Æ¤¤¤ë¡£¤·¤«¤·¡¢½¾Íè¤Î¥×¥í¥Õ¥¡¥¤¥ë¥Ä¡¼¥ë¤Ï¡¢¥³¥ó¥Ñ¥¤¥ë»þ¤Ë¡¢¥½¡¼¥¹¥³¡¼¥É¤Ë¥×¥í¡¼¥Ö¤òÁÞÆþ¤¹¤ë¿¯½±Êý¼°¤Ç¤¢¤ê¡¢¥½¡¼¥¹¥³¡¼¥É¤¬É¬¿Ü¤Ç¤¢¤ë¡£¤Þ¤¿¡¢¥×¥í¡¼¥ÖÁÞÆþ¤Ë¤è¤ë¾ñÍð¤¬ÌäÂê¤È¤Ê¤ë¡£¤½¤³¤ÇËܹƤǤϡ¢½¾Íè¤Î¿¯½±Êý¼°¤È¤Ï°Û¤Ê¤ê¡¢¼Â¹Ô»þ¤ÎÆ°ºî¤ò³°Éô¤«¤é´Æ»ë¤¹¤ë¤³¤È¤Ë¤è¤ê¼Â¹Ô¾ðÊó¤òÆÀ¤ëÈ󿯽±Êý¼°¤Î¥×¥í¥Õ¥¡¥¤¥ë¤òÄó°Æ¤¹¤ë¡£È󿯽±Êý¼°¤Î¥×¥í¥Õ¥¡¥¤¥ë¤Ç¤Ï¡¢¿¯½±Êý¼°¤Î¥×¥í¥Õ¥¡¥¤¥ë¤È¤Ï°Û¤Ê¤ê¡¢¥½¡¼¥¹¥³¡¼¥É¾å¤Ë¥í¥°¼èÆÀ°ÌÃÖ¤ò»ØÄê¤Ç¤­¤Ê¤¤¤¿¤á¡¢¼ý½¸¤·¤¿¼Â¹Ô¥í¥°¤Î¥Ç¡¼¥¿¤«¤é¡¢¥×¥í¥°¥é¥à¤Î¼Â¹Ô·ÐÏ©¤ò¿äÄꤹ¤ëɬÍפ¬¤¢¤ë¡£¤½¤³¤Ç¡¢ËܹƤǤϡ¢¥×¥í¥°¥é¥à¤Î¼Â¹Ô»þ¤Î´Ö·ç¥í¥°¤«¤é¥×¥í¥°¥é¥à¤Î¼Â¹Ô·ÐÏ©¤ÎÈϰϤò¿äÄꤹ¤ë¥¢¥ë¥´¥ê¥º¥à¤òÄó°Æ¤·¡¢¤½¤Î¸¡¾Ú·ë²Ì¤Ë´Ø¤·¤Æ½Ò¤Ù¤ë¡£

Âê̾A Tableau Construction for Control Synthesis of FSMs
Ãø¼Ô ¡ûYoshisato Sakai(Toshiba Corporation)
Pagepp. 283 - 288
Keyword temporal logic, tableau, control synthesis, formal method
AbstractWe propose a tableau construction in the form of FSM instead of Kripke structure, and show a method to synthesize controllers for FSMs using the tableaus. This method accepts a formula of ASTL (a kind of temporal logic) as a specification of desired behaviors of the system. The model of a plant (component to be controlled) is given as an FSM, which can be a synchronous circuit.

Âê̾¥¤¥ó¥¿¡¼¥ï¡¼¥¯¥Õ¥í¡¼¤Î¥ï¡¼¥¯¥Õ¥í¡¼¥Í¥Ã¥È¥â¥Ç¥ë¤È¤½¤Î·òÁ´À­¤Ë¤Ä¤¤¤Æ
Ãø¼Ô ¡û»³¸ý ¿¿¸ç(»³¸ýÂç³ØÂç³Ø±¡/Íý¹©³Ø¸¦µæ²Ê), ¾¾Èø Âç(ÂçÆüËÜ°õºþ(³ô)), ³ë ºê°Î(»³¸ýÂç³Ø/¶µ°é³ØÉô), ÅÄÃæ Ì­(»³¸ýÂç³ØÂç³Ø±¡/Íý¹©³Ø¸¦µæ²Ê)
Pagepp. 289 - 294
Keyword ¥ï¡¼¥¯¥Õ¥í¡¼, ¥¤¥ó¥¿¡¼¥ï¡¼¥¯¥Õ¥í¡¼, ¥Ú¥È¥ê¥Í¥Ã¥È, ·òÁ´À­, WfMC
AbstractËܹƤǤϥ¤¥ó¥¿¡¼¥ï¡¼¥¯¥Õ¥í¡¼¤Î¥ï¡¼¥¯¥Õ¥í¡¼(WF)¥Í¥Ã¥È¥â¥Ç¥ë¤È¤½¤Î·òÁ´À­¤Ë¤Ä¤¤¤ÆµÄÏÀ¤¹¤ë¡£¤Þ¤º¥¤¥ó¥¿¡¼¥ï¡¼¥¯¥Õ¥í¡¼¤Îɸ½à¥×¥í¥È¥³¥ë¤ÇÄê¤á¤é¤ì¤¿3¼ïÎà¤ÎÏ¢·È·¿¤Î¤½¤ì¤¾¤ì¤ËÂФ·¤ÆWF¥Í¥Ã¥È¤Ë¤è¤ë¥â¥Ç¥ë²½¤ò¼¨¤¹¡£Ç¤°Õ¤Î¥¤¥ó¥¿¡¼¥ï¡¼¥¯¥Õ¥í¡¼¤Ï¤½¤ì¤é¤òÁȤ߹ç¤ï¤»¤ë¤³¤È¤Ë¤è¤Ã¤Æ¥â¥Ç¥ë²½¤Ç¤­¤ë¡£¼¡¤Ë¡¢Ç¤°Õ¤Î¥¤¥ó¥¿¡¼¥ï¡¼¥¯¥Õ¥í¡¼¤ò¥â¥Ç¥ë²½¤·¤¿WF¥Í¥Ã¥È¤¬¤É¤Î¤è¤¦¤Ê¥¯¥é¥¹¤Ë¤Ê¤ë¤«¤òÌÀ¤é¤«¤Ë¤¹¤ë¡£¤½¤·¤Æ¡¢¤½¤Î¥µ¥Ö¥¯¥é¥¹¤´¤È¤Ë¡¢¤½¤Î·òÁ´À­¤Ë¤Ä¤¤¤ÆµÄÏÀ¤·¡¢ºÇ¸å¤Ë¥¤¥ó¥¿¡¼¥ï¡¼¥¯¥Õ¥í¡¼¤Î»öÎ㤬Àµ¾ï¤Ë½ªÎ»¤¹¤ë¤«¤É¤¦¤«¤ò¸¡ºº¤¹¤ëÎã¤ò¼¨¤¹¡£


¥»¥Ã¥·¥ç¥ó D1-3 [ÆÃÊÌ¥»¥Ã¥·¥ç¥ó]¿·À¤Âå¤Î·×»»¸Â³¦I
Æü»þ: 2006ǯ4·î24Æü(·î) 13:30-14:30
ºÂĹ: ̦ÇÈ ¹°²Â (´ØÀ¾³Ø±¡Âç³Ø Íý¹©³ØÉô)

Âê̾(¾·ÂÔ)Extended Skip Graph for P2P File Exploration
Ãø¼Ô ¡ûÆ£ÅÄ Áï(¹­ÅçÂç³ØÂç³Ø±¡ ¹©³Ø¸¦µæ²Ê ¾ðÊ󹩳ØÀ칶)
Pagepp. 295 - 300
Keyword P2P, ¥ª¡¼¥Ð¡¼¥ì¥¤¥Í¥Ã¥È¥ï¡¼¥¯, ¥¹¥­¥Ã¥×¥°¥é¥Õ
AbstractËܹƤǤϡ¤³Æ¥Î¡¼¥É¤ÎÊ¿¶Ñ¼¡¿ô¤¬Äê¿ô¤Ç¡¤¤·¤«¤â¿¹àÂпô»þ´Ö¤Ç¤Î¥¯¥¨¥ê¤Î¥ë¡¼¥Æ¥£¥ó¥°¤ò²Äǽ¤È¤¹¤ë¤è¤¦¤Ê¡¤P2P¥·¥¹¥Æ¥à¤Î¤¿¤á¤Î¿·¤·¤¤¹½Â¤·¿¥ª¡¼¥Ð¡¼¥ì¥¤¥Í¥Ã¥È¥ï¡¼¥¯¤ÎÄó°Æ¤ò¹Ô¤¦¡¥Äó°Æ¤¹¤ë¥ª¡¼¥Ð¡¼¥ì¥¤¥Í¥Ã¥È¥ï¡¼¥¯¤Ï¡¤¥¹¥­¥Ã¥×¥°¥é¥Õ¤Î³ÈÄ¥¤Ç¤¢¤ê¡¢¥¹¥­¥Ã¥×¥°¥é¥Õ¤ÇÍѤ¤¤é¤ì¤Æ¤¤¤¿¥Î¡¼¥É¤¢¤¿¤êO(log N)ËܤΥ·¥ç¡¼¥È¥«¥Ã¥È¥ê¥ó¥¯¤Î¤¦¤Á¡¤Äê¿ôËܤΤߤò¥é¥ó¥À¥à¤ËÁªÂò¤¹¤ë¤³¤È¤Ç¡¤O(log^2 N)¥¹¥Æ¥Ã¥×¤ÎÊ¿¶Ñ¥ë¡¼¥Æ¥£¥ó¥°»þ´Ö¤ò¼Â¸½¤¹¤ë¡¥


¥»¥Ã¥·¥ç¥ó D1-4 [ÆÃÊÌ¥»¥Ã¥·¥ç¥ó]¿·À¤Âå¤Î·×»»¸Â³¦II
Æü»þ: 2006ǯ4·î24Æü(·î) 15:00-17:00
ºÂĹ: ̦ÇÈ ¹°²Â (´ØÀ¾³Ø±¡Âç³Ø Íý¹©³ØÉô)

Âê̾(¾·ÂÔ)An Experimental Study of the Webgraph
Ãø¼Ô ¡û±§Ìî ͵Ƿ, ÂçÅÄ µÁ¿®, ¾åÆ» ÌÀÀ¸, ÇÏÌî ¸µ½¨(ÂçºåÉÜΩÂç³Ø Íý³Ø·Ï¸¦µæ²Ê ¾ðÊó¿ôÍý²Ê³ØÀ칶)
Pagepp. 301 - 306
Keyword link analysis, scale-free network, webgraph, web community, web structure mining
AbstractThe link structure of the Web is generally viewed as the webgraph. Web structure mining is a research area that aims to find hidden communities in the Web based on the webgraph, and communities or their cores are supposed to constitute dense subgraphs. Then, structure mining is realized by enumerating those substructures. In this paper, we focus on bicliques, cliques and isolated cliques as such candidate substructures, and attempt to investigate properties of the webgraph and to find useful information from the real web data. As a result, we observed several interesting structural properties of the Web. Furthermore, we discovered that isolated cliques can be quite useful for detecting harmful link farms, while isolated cliques that lie over multiple domains sometimes stand for useful communities, which implies the validity of isolated cliques in web structure mining.

Âê̾(¾·ÂÔ)The Weakest Failure Detectors for Solving the Stable Leader Election
Ãø¼Ô ¡ûHirotaka Ono(Department of Computer Science and Communication Science, Kyushu University), Sung Hoon Park(School of Electrical and Computer Engineering, Chungbuk National University), Masafumi Yamashita(Department of Computer Science and Communication Science, Kyushu University)
Pagepp. 307 - 312
Keyword Weakest Failure Detectors, Stable Leader Election, Consensus, Fault-


ÆÃÊ̾·ÂÔ¹Ö±é
Æü»þ: 2006ǯ4·î24Æü(·î) 17:40-18:40
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Pagepp. 313 - 318


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Æü»þ: 2006ǯ4·î25Æü(²Ð) 9:30-10:45
ºÂĹ: ÄÚº¬ Àµ (Ĺ²¬µ»²ÊÂç³Ø)

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Ãø¼Ô º¬´ß ¹â¹°, ¡û´Ø²° Âçͺ, Ϥ ·úÌÀ, ëÇë δ»Ì(ÀéÍÕÂç³Ø)
Pagepp. 319 - 324
Keyword ²óÏ©²òÀÏ, ÁýÉý´ï
AbstractËÜÏÀʸ¤Ç¤Ï, ºÇŬ¾õÂÖ³°¤Ë¤ª¤±¤ëDEµéÁýÉý´ï¤ÎÆ°ºî²òÀϤò¹Ô¤¦. ÍÍ¡¹¤ÊÀß·×»ÅÍͤËÂбþ¤·¤¿ DE µéÁýÉý´ï¤ÎÆ°ºî²òÀϤòãÀ®¤¹¤ë¤¿¤á¤Ë, ¶èʬÀþ·Á¤Çɽ¸½¤µ¤ì¤¿¾ïÈùʬÊýÄø¼°¤ò²ò¤¯¤³¤È¤Ë¤è¤ê²òÀϤò¹Ô¤¦. ¤³¤³¤Ç, ½¾Íè¤Î¸¦µæ¤Ç¤ÏEµéÆ°ºî¾ò·ï¤òËþ¤¿¤¹²óÏ©¤ÎÀ߷פ¬ÌÜŪ¤Ç¤¢¤ë¤¿¤á, ³Æ¶èʬ¤ÎÅÅή¤ª¤è¤ÓÅÅ°µ¤Î½é´üÃͤòÀܳ¾ò·ï¤«¤é¿ôÃÍŪ¤ËƳ½Ð¤·¤Æ¤¤¤ë¤¬, ËÜÏÀʸ¤Ç¤Ï¤½¤ì¤é¤ò²òÀÏŪ¤ËƳ½Ð¤¹¤ë. ½é´üÃͤò²òÀÏŪ¤ËƳ½Ð¤¹¤ë¤³¤È¤Ë¤è¤ê, DE µéÁýÉý´ï¤Ë¤ª¤¤¤Æ½ÅÍפʰÕÌ£¤ò»ý¤Ä¥¹¥¤¥Ã¥ÁÅÅ°µ, ¥¹¥¤¥Ã¥ÁÅÅ°µ¤Î·¹¤­, ½ÐÎÏÅÅ°µ¤ª¤è¤ÓÅÅÎÏÊÑ´¹¸úΨ¤òÁ´¤Æ²òÀÏŪ¤Ëɽ¸½¤¹¤ë¤³¤È¤¬²Äǽ¤È¤Ê¤ë. Ƴ½Ð¤·¤¿²òÀϼ°¤òÍѤ¤¤ÆºÇŬ¾õÂÖ¤«¤éÆ°ºî¼þÇÈ¿ô, »þÈæΨ, ÆþÎÏÅÅ°µ, Éé²ÙÄñ¹³¤¬ÊѲ½¤·¤¿¤È¤­¤Î DE µéÁýÉý´ï¤ÎÆ°ºî¤Ë¤Ä¤¤¤Æ¸¡Æ¤¤¹¤ë. ¤Þ¤¿, ²óÏ©¼Â¸³¤È¤ÎÈæ³Ó¤Ë¤è¤ê, ²òÀÏ·ë²Ì¤ÎÂÅÅöÀ­¤ò¼¨¤¹.

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Ãø¼Ô ¡ûÀÐÀî ͵¸Ê, ¾¾²¬ Í´²ð, ºØÆ£ ÍøÄÌ(Ë¡À¯Âç³Ø)
Pagepp. 325 - 329
Keyword ¥Ñ¥ï¡¼¥³¥ó¥Ð¡¼¥¿, Ʊ´ü, ʬ´ô
Abstract¡¡ÊÂÎó²½DC/DC¥³¥ó¥Ð¡¼¥¿¤Ë¤Ä¤¤¤Æ¡¢¥ê¥¿¡¼¥ó¥Þ¥Ã¥×¤Ë¤è¤ë²òÀÏË¡¤òÄó°Æ¤¹¤ë¡£ËÜÏÀʸ¤Ç¤Ï¡¢DC/DC¥³¥ó¥Ð¡¼¥¿¤Î°ì¼ï¤Ç¤¢¤ëBuck¥³¥ó¥Ð¡¼¥¿¤ò¥â¥Ç¥ë¤È¤·¡¢WTA¤Ë´ð¤Å¤¯ÊÂÎó²½DC/DC¥³¥ó¥Ð¡¼¥¿¤ÎƱ´ü¸½¾Ý¤äʬ´ô¸½¾Ý¤ò¡¢¥ê¥¿¡¼¥ó¥Þ¥Ã¥×¤òÍѤ¤¤Æ²òÀϤ¹¤ë¡£ ¡¡¤Þ¤¿¡¢´ÊÁǤʲóÏ©¤ò»îºî¤·¡¢´ðËܸ½¾Ý¤ò¼Â¸³Åª¤Ë¸¡¾Ú¤¹¤ë¡£

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Ãø¼Ô ¡ûÊÒ»³ ´´, Ĺë ¹¨Ç·, ´Ø²° Âçͺ, Ϥ ·úÌÀ, ëÇë δ»Ì(ÀéÍÕÂç³ØÂç³Ø±¡¼«Á³²Ê³Ø¸¦µæ²Ê)
Pagepp. 331 - 336
Keyword DEµéÁýÉý´ï, ¥Ð¥ó¥É¥Ñ¥¹¥Õ¥£¥ë¥¿
AbstractDEµéÁýÉý´ï ¤Ï¹âÆ°ºî¼þÇÈ¿ô²¼¤Ë¤ª¤¤¤Æ¹âÅÅÎÏÊÑ´¹¸úΨ¤òãÀ®¤¹¤ëÁýÉý´ï¤Ç¤¢¤ë¡£¤·¤«¤·, DEµéÁýÉý´ï¤Ï¶¦¿¶¥Õ¥£¥ë¥¿¤òÍѤ¤¤Æ¤¤¤ë¤¿¤á, ½ÐÎÏÅÅ°µ¤¬Æ°ºî¼þÇÈ¿ô¤ÎÊÑÆ°¤ä¥Õ¥£¥ë¥¿¤ÎÁÇ»ÒÃͤΤФé¤Ä¤­¤Ë±ÔÉҤǤ¢¤ë¤È¤¤¤¦ÌäÂêÅÀ¤ò¤â¤Ä. ¤Þ¤¿, Æ°ºî¼þÇÈ¿ôÊÑÆ°»þ¤Ë¤ÏEµéÆ°ºî¾ò·ï¤òËþ¤¿¤¹¤³¤È¤¬ÉÔ²Äǽ¤Ë¤Ê¤ë. Ëܸ¦µæ¤Ç¤Ï, DEµéÁýÉý´ï¤Î½ÐÎÏ¥Õ¥£¥ë¥¿¤ò¥Ð¥ó¥É¥Ñ¥¹¥Õ¥£¥ë¥¿¤È¤·¤¿DEµéÁýÉý´ï¤òÄó°Æ¤¹¤ë. ¥Ð¥ó¥É¥Ñ¥¹¥Õ¥£¥ë¥¿¤òÍѤ¤¤ë¤³¤È¤ÇÆ°ºî¼þÇÈ¿ôÊÑÆ°»þ¤Î½ÐÎÏÅÅ°µ¤ÎÊÑÆ°¤òÍÞÀ©¤Ç¤­¤ë. ¤µ¤é¤Ë, Æ°ºî¼þÇÈ¿ô¤¬Ä㤯¤Ê¤Ã¤¿¾ì¹ç¤ÏµÕÊÂÎó¥À¥¤¥ª¡¼¥É¤Ë¤è¤êÎíÅÅ°µ¥¹¥¤¥Ã¥Á¥ó¥°¤òËþ¤¿¤¹¤³¤È¤¬²Äǽ¤Ç¤¢¤ë. ¤Þ¤¿, ²óÏ©¼Â¸³¤ò¹Ô¤¦¤³¤È¤Ë¤è¤êÀ߷פÎÂÅÅöÀ­¤ò¼¨¤¹.


¥»¥Ã¥·¥ç¥ó A2-2 [ÆÃÊÌ¥»¥Ã¥·¥ç¥ó]¥¹¥¤¥Ã¥Á¥ó¥°·Ï¤Î¥À¥¤¥Ê¥ß¥¯¥¹¤È¥Ï¥¤¥Ö¥ê¥Ã¥ÉÀ­
Æü»þ: 2006ǯ4·î25Æü(²Ð) 11:00-12:00
ºÂĹ: ºØÆ£ÍøÄÌ (Ë¡À¯Âç³Ø)

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Ãø¼Ô ¡û°ú¸¶ δ»Î(µþÅÔÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê)
Pagepp. 337 - 342
Keyword ¥¹¥¤¥Ã¥Á, ÀÚÂؤ¨, ÈóÀþ·Á¥À¥¤¥Ê¥ß¥¯¥¹, ¥Ï¥¤¥Ö¥ê¥Ã¥ÉÀ­
AbstractϢ³¤ÊÎϳؤ˻ÙÇÛ¤µ¤ì¤¿·Ï¤¬¡¤¤¢¤ë¾ò·ï¤ÎÀ®Î©¤È¤È¤â¤Ë¾õÂÖ¥¹¥¤¥Ã¥Á¤Ë¤è¤Ã¤ÆÉÔϢ³¤Ë°Û¤Ê¤ëÎϳطϤËÁ«°Ü¤¹¤ë¥·¥¹¥Æ¥à¡¤¤¹¤Ê¤ï¤ÁϢ³¤ÊÎϳطϤÈÉÔϢ³¤Ê¾õÂÖÁ«°Ü¤ò´Þ¤à·Ï¤Ï¡¤¹©³Ø¥·¥¹¥Æ¥à¤À¤±¤Ç¤Ê¤¯¼«Á³³¦¤Ë¤â¹­¤¯¸«¤é¤ì¤ë¡¥¥¹¥¤¥Ã¥Á¤Ï¡¤¾ðÊóʬÌ¤è¤Ó¥Ñ¥ï¡¼Ê¬Ìî¤Ç²óÏ©Æ°ºî¤òÀ©¸æ¤¹¤ë½ÅÍפÊÍ×ÁǤǤ¢¤ë¤¬¡¤¤½¤ÎÆ°ºîɽ¸½¤ÏÍýÁÛ¥¹¥¤¥Ã¥Á¤Ëα¤Þ¤Ã¤Æ¤¤¤ë¡¥ËÜÏÀʸ¤Ï¡¤¥¹¥¤¥Ã¥Á¤ò´Þ¤à·Ï¤Î¥À¥¤¥Ê¥ß¥¯¥¹¤ò¥¹¥¤¥Ã¥Á¤Îɽ¸½¤Èµ¡Ç½¤«¤éºÆÅÙ¸¡Æ¤¤·¡¤¥¹¥¤¥Ã¥Á¤Ëȼ¤¦·Ï¤ÎÈóÀþ·Á¥À¥¤¥Ê¥ß¥¯¥¹¤Î¥Ï¥¤¥Ö¥ê¥Ã¥ÉÀ­¤òµÄÏÀ¤¹¤ë¤â¤Î¤Ç¤¢¤ë¡¥


¥»¥Ã¥·¥ç¥ó A2-3 [An/Dʬ²Ê²ñ¹çƱÆÃÊÌ¥»¥Ã¥·¥ç¥ó]¥Ï¥¤¥Ö¥ê¥Ã¥É¥À¥¤¥Ê¥ß¥«¥ë¥·¥¹¥Æ¥à
Æü»þ: 2006ǯ4·î25Æü(²Ð) 13:30-14:45
ºÂĹ: ´Ø²° Âçͺ (ÀéÍÕÂç³ØÂç³Ø±¡¼«Á³²Ê³Ø¸¦µæ²Ê)

Âê̾Durability of Affordable Neural Networks during Back Propagation Learning
Ãø¼Ô ¡û¾å¼ê ÍλÒ, À¾Èø ˧ʸ(ÆÁÅçÂç³Ø), Ruedi Stoop(University / ETH Zurich)
Pagepp. 343 - 348
Keyword durability, feedforward neural network, BP learning
AbstractIn this study, we investigate the durability of the affordable neural network when some of the neurons in the hidden layer are damaged, after the learning process. By computer simulations, we confirm that the affordable neural network keeps its efficiency. We conclude that the affordable neurons exert an important influence on durability of the network.

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Pagepp. 349 - 353
Keyword ¥¹¥Ñ¥¤¥­¥ó¥°¥Ë¥å¡¼¥í¥ó, ¥«¥ª¥¹, ʬ´ô
AbstractËÜÏÀʸ¤Ç¤Ï¼«Î§·ÏÀÑʬȯ²Ð¥«¥ª¥¹²óÏ©¤Ë¤Ä¤¤¤Æ¹Í»¡¤¹¤ë¡£¤³¤Î²óÏ©¤Ï2¥Ý¡¼¥ÈÅÅ°µÀ©¸æÅÅή¸»¡¢2¤Ä¤Î¥­¥ã¥Ñ¥·¥¿¡¢¾õÂ֤˰͸¤·¤¿¥¹¥¤¥Ã¥Á¤Ç¹½À®¤µ¤ì¤ë¡£ÀÑʬȯ²ÐÆ°ºî¤Ë¤è¤ê¡¢Â¿ºÌ¤Ê¥Ñ¥ë¥¹Îó¤ò½ÐÎϤ·¡¢¥«¥ª¥¹¤äʬ´ô¸½¾Ý¤òÄ褹¤ë¡£ ¤³¤ì¤é¤Î¸½¾Ý¤ò²òÀϤ¹¤ë¤¿¤á¤Ë¡¢É¸½à·Á¤ÎÊýÄø¼°¤òƳÆþ¤·°ì¼¡¸µ¥ê¥¿¡¼¥ó¥Þ¥Ã¥×¤òÄêµÁ¤¹¤ë¡£ ¤³¤ì¤òÍѤ¤¤Æ¡¢Åµ·¿Åª¤Ê¸½¾ÝÎã¤òÄ´¤Ù¡¢¥Ñ¥ë¥¹Îó½ÐÎϤβòÀϤò¹Ô¤¦¡£

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Pagepp. 355 - 360
Keyword ¥Ï¥¤¥Ö¥ê¥Ã¥É¥ª¡¼¥È¥Þ¥È¥ó, À©¸æÉÔÊÑ, ¾õÂÖ¥Õ¥£¡¼¥É¥Ð¥Ã¥¯
AbstractSilva¤ÈKrogh¤Ï, »þ´Ö¤ËƱ´ü¤¹¤ë»ö¾Ý¤ò°·¤¦¥â¥Ç¥ë¤È¤·¤Æ¥µ¥ó¥×¥ëÃÍÀ©¸æ¥Ï¥¤¥Ö¥ê¥Ã¥É¥ª¡¼¥È¥Þ¥È¥ó(SDHA)¤òÄó°Æ¤·, ¤½¤Î¸¡¾ÚË¡ ¤Ë¤Ä¤¤¤ÆµÄÏÀ¤·¤Æ¤¤¤ë. ËÜÏÀʸ¤Ç¤Ï, SDHA¤Î¾õÂÖ¥Õ¥£¡¼¥É¥Ð¥Ã¥¯¤Ë¤Ä¤¤¤ÆµÄÏÀ¤¹¤ë. ¤Þ¤º, SDHA¥â¥Ç¥ë¤Î¥»¥Þ¥ó¥Æ¥£¥¯¥¹¤È¤·¤Æ, 2¤Ä¤Î¥È¥é¥¸¥·¥ç¥ó¥·¥¹¥Æ¥à¤òÄêµÁ¤¹¤ë. ¼¡¤Ë, ¤½¤ì¤Ë´ð¤Å¤¤¤Æ ½Ò¸ì¤ÎÀ©¸æÉÔÊÑÀ­¤Ë´Ø¤¹¤ëɬÍ×½½Ê¬¾ò·ï¤ò¼¨¤¹. ºÇ¸å¤Ë, Ǥ°Õ¤Î½Ò¸ì¤ËÂФ·É¬¤ººÇÂçÀ©¸æÉÔÊÑÉôʬ½Ò¸ì¤¬Â¸ºß¤¹¤ë¤³¤È¤ò¼¨¤¹.


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Æü»þ: 2006ǯ4·î25Æü(²Ð) 15:15-16:55
ºÂĹ: ÅÄÃæ ´²¿Í (ÂçºåÂç³ØÂç³Ø±¡´ðÁù©³Ø¸¦µæ²Ê)

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Pagepp. 361 - 364
Keyword ¥«¥ª¥¹, ʬ´ô, °ÂÄêÀ­
Abstract´ÊÁǤʶèʬÄê¿ô¥¹¥Ñ¥¤¥­¥ó¥°²óÏ©¤Ë¤Ä¤¤¤Æ¹Í»¡¤¹¤ë¡£ Ʊ²óÏ©¤Ï¶èʬÄê¿ô¥Ù¥¯¥È¥ë¾ì¤ò¤â¤Á¶èʬÀþ·Á¤Ê¼ÌÁü¤Ë¤è¤Ã¤Æ¸·Ì©¤Ë ¸½¾Ý¤ò²òÀϤ¹¤ë¤³¤È¤¬¤Ç¤­¤ë¡£²óÏ©¤Ï»þ´ÖÀ©¸æ¤µ¤ì¤ë¥¹¥¤¥Ã¥Á¤ò´Þ¤ß¡¢ Ķ°ÂÄê¼þ´ü²ò¤Ê¤É¤Î¿ºÌ¤ÊÈóÀþ·Á¸½¾Ý¤äʬ´ô¸½¾Ý¤¬Â¸ºß¤¹¤ë¡£ ¤³¤Î¸½¾Ý¤ËÂФ·¤Æ²æ¡¹¤ÏÀºÌ©¤Ê¿ôÃͼ¸³¤ò¹Ô¤Ã¤¿¡£¤½¤Î·ë²Ì¡¢Ê£»¨¤Ê Ķ°ÂÄê¼þ´ü²ò¤Ï¥Ñ¥é¥á¡¼¥¿¤ËÈó¾ï¤Ë±ÔÉÒ¤ËÊѲ½¤¹¤ë¤³¤È¤Ê¤É¤¬Ê¬¤«¤Ã¤¿¡£

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Pagepp. 365 - 370
Keyword ¥Ð¡¼¥¹¥Èȯ²Ð¡¤¥Ë¥å¡¼¥é¥ë¥Í¥Ã¥È¥ï¡¼¥¯, ¥Ë¥å¡¼¥é¥ë¥Í¥Ã¥È¥ï¡¼¥¯, ºÇŬ²½ÌäÂê
AbstractInverse function Delayed(ID)¥â¥Ç¥ë¤Ï½ÐÎÏÃÙ±äÆÃÀ­¤¬Æ³Æþ¤µ¤ì¤¿ ¥â¥Ç¥ë¤Ç¤¢¤ê¡¤¸ÇÍ­¤Î¥À¥¤¥Ê¥ß¥¯¥¹¤Ë¤è¤ê¥Ë¥å¡¼¥é¥ë¥Í¥Ã¥È¥ï¡¼¥¯¤ÎÀ­Ç½¤Î¸þ¾å¤µ¤»¤ë¤³¤È¤¬¤Ç¤­¤ë¥â¥Ç¥ë¤Ç¤¢¤ë¡¥Hodgkin-Huxley¥â¥Ç¥ë¤ÈID¥â¥Ç¥ë¤È¤Î´Ø·¸¤ËÃåÌܤ·¡¤¥Ð¡¼¥¹¥Èȯ²Ð¸½¾Ý¤ÎºÆ¸½¤òÌÜŪ¤ËID¥â¥Ç¥ë¤Î³ÈÄ¥¤ò»î¤ß¤¿¡¥¤Þ¤¿¡¢ÆÀ¤¿¥â¥Ç¥ë¤Î¥Ð¡¼¥¹¥Èȯ²ÐÇÈ·Á¤ò¼¨¤·¤¿¡¥N-QueenÌäÂê¤ËŬÍѤ·¡¤ID¥â¥Ç¥ë¤Ë¤ª¤¤¤ÆÀ­Ç½¤¬Äã²¼¤¹¤ë¤è¤¦¤Ê¾ì¹ç¤Ç¤â¡¤Í¥¤ì¤¿²òõº÷À­Ç½¤ò»ý¤Ä¤³¤È¤¬¤ï¤«¤Ã¤¿¡¥¤³¤Î¥â¥Ç¥ë¤Î½¸ÀѲóÏ©²½¤ò¹Ô¤Ã¤¿¡¥

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Pagepp. 371 - 375
Keyword ¥¹¥Ñ¥¤¥­¥ó¥°¥Ë¥å¡¼¥í¥ó, ¥«¥ª¥¹, ʬ´ô
Abstract´ÊÁǤʥ˥塼¥í¥ó¥â¥Ç¥ë¤È¤·¤ÆÃΤé¤ì¤Æ¤¤¤ë¥¹¥Ñ¥¤¥­¥ó¥°¥Ë¥å¡¼¥í¥ó¤Ë»°³ÑÇÈ¥Ù¡¼¥¹¿®¹æ¤òÆþÎϤ·¤¿¥â¥Ç¥ë¤Ë¤Ä¤¤¤Æ¹Í»¡¤¹¤ë¡£¤³¤Î¥â¥Ç¥ë¤Ï¥Ù¡¼¥¹¿®¹æ¤¬Àµ¸¹ÇȤΤâ¤Î¤ÈÈæ¤Ù¤Æ²òÀϤ¬Èó¾ï¤Ë´ÊÁǤǤ¢¤ë¤È¤¤¤¦ÆÃħ¤¬¤¢¤ë¡£ ËÜÏÀʸ¤Ï¼ç¤Ë¡¢¥Ù¡¼¥¹¿®¹æ¤Ë»°³ÑÇȤòÍѤ¤¤ë¤³¤È¤ÇȯÀ¸¤¹¤ëĶ°ÂÄê¤Ê¥Ñ¥ë¥¹·ÏÎó¤Ë¤Ä¤¤¤Æ²òÀϤò¹Ô¤Ã¤¿¡£¤³¤ì¤Ï¡¢¥Ù¡¼¥¹¿®¹æ¤¬Àµ¸¹ÇȤΤȤ­¤Ë¤ÏȯÀ¸¤·¤Ê¤¤¸½¾Ý¤Ç¤¢¤ë¡£

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Ãø¼Ô ¡ûÎÓ Í´¸ã(ÆüËÜÂç³ØÍý¹©³Ø¸¦µæ²ÊÅŻҹ©³ØÀ칶), º´Çì ¾¡ÉÒ, ´Øº¬ ¹¥Ê¸(ÆüËÜÂç³ØÍý¹©³ØÉôÅŻҾðÊ󹩳زÊ)
Pagepp. 377 - 382
Keyword STDP, ¥Ñ¥ë¥¹·Á¥Ï¡¼¥É¥¦¥§¥¢¥Ë¥å¡¼¥í¥ó¥â¥Ç¥ë, °ÌÁêÍɤ餮, °ÌÁ꺹
Abstract¶áǯ¡¤È¯²Ð¥¿¥¤¥ß¥ó¥°¤Ë°Í¸¤·¤¿¥·¥Ê¥×¥¹²ÄÁºÀ­¡¤Spike Timing Dependent synaptic Plasticity(STDP)¤¬È¯¸«¤µ¤ì¤Æ¤¤¤ë¡£STDP¤Ïȯ²Ð¥¿¥¤¥ß¥ó¥°¤ÎÁêÂдط¸¤Ë¤è¤ë³Ø½¬Â§¤Ç¤¢¤ë¤¿¤á¡¤»þ´ÖŪ¤ÊÁê´Ø´Ø·¸¤ò»ý¤Äȯ²Ð¥Ñ¥¿¡¼¥ó¤ÎÃê½ÐǽÎϤ¬´üÂԤǤ­¤ë¡£ËܹƤϡ¤STDP¤òƳÆþ¤·¤¿¥Ñ¥ë¥¹·Á¥Ï¡¼¥É¥¦¥§¥¢¥Ë¥å¡¼¥í¥ó¥â¥Ç¥ë¤òÍѤ¤¥Í¥Ã¥È¥ï¡¼¥¯¤ò¹½À®¤·¡¤È¯²Ð¥¿¥¤¥ß¥ó¥°¤Î°ÌÁ꤬Íɤ餤¤À¾õÂÖ¤òÀ¸À®¤µ¤»¡¤STDP¤¬»ý¤ÄÍɤ餮¤ËËä¤â¤ì¤¿°ÌÁê¾ðÊó¤ÎÃê½ÐǽÎϤÎÍ­¸úÀ­¤Ë¤Ä¤¤¤Æ¸¡Æ¤¤ò¹Ô¤Ã¤¿¡£¤½¤Î·ë²Ì¡¤STDP¤¬»þ´ÖŪ¤ÊÍɤ餮¤ËËä¤â¤ì¤¿°ÌÁê¾ðÊó¤ò¥·¥Ê¥×¥¹¤Î·ë¹ç²Ù½Å¤Ø¤È¥Ç¥³¡¼¥É²Äǽ¤Ç¤¢¤ë¤³¤È¤ò¼¨º¶¤·¤Æ¤¤¤ë¡£


¥»¥Ã¥·¥ç¥ó Ba2-1 ¿®¹æÅÁÁ÷²óÏ©
Æü»þ: 2006ǯ4·î25Æü(²Ð) 9:00-10:15
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Âê̾An Effective Large Current and High Speed Laser Diode Driver Circuit Design
Ãø¼Ô ¡ûYun Yang, Jia Guo, Yasuaki Inoue(Waseda University)
Pagepp. 383 - 386
Keyword laser diode driver (LDD), large current, high speed, switch position modification (SPM), combination switch mode (CSM)
AbstractThis paper describes how to realize the high performance laser diode driver (LDD) circuit design. The new effective driver sends out large current to drive the laser diode device with high speed output signal. The signal integrity (overshoot/undershoot/slew-rate) problems have also been improved by the idea of ¡ÈSwitch Position Modification (SPM)¡É. In addition, appropriate transistor size selection and circuit combination can further amend the signal waveform. Experimental results show that the proposed LDD circuit of ¡ÈCombination Switch Mode (CSM)¡É can send out large current to drive the laser diode device with high performance output signal.

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Ãø¼Ô ¡ûµ×ÊÝÌÚ ÌÔ, ÅÚë μ, ¾®Ìî»û ½¨½Ó(µþÅÔÂç³Ø¾ðÊó³Ø¸¦µæ²ÊÄÌ¿®¾ðÊó¥·¥¹¥Æ¥àÀ칶)
Pagepp. 387 - 392
Keyword CML, ÄãÅÅÎÏ
AbstractËܹƤǤϡ¤¥ª¥ó¥Á¥Ã¥×¹â®¿®¹æÅÁÁ÷ÍÑCML¥É¥é¥¤¥Ð¤ÎÄã¾ÃÈñÅÅÎϲ½¤Ë¤Ä¤¤¤ÆµÄÏÀ¤¹¤ë¡¥½¾Í衤ÅÁÁ÷ÀþÏ©¤Î¶îÆ°²óÏ©¤Ï¥¤¥ó¥Ô¡¼¥À¥ó¥¹À°¹ç¤òÁ°Äó¤È¤·¤ÆÀ߷פ¹¤ë¤Î¤¬°ìÈÌŪ¤Ç¤¢¤ë¡¥¤·¤«¤·¡¤¥ª¥ó¥Á¥Ã¥×¤Ç¤ÏÀþÏ©¤Î¸º¿ê¤¬Â礭¤¤¤¿¤á¡¤ÉÔÀ°¹ç¤Ë¤è¤ëÈ¿¼Í¤¬È¯À¸¤·¤Æ¤âÀ­Ç½¤Ë¿¼¹ï¤Ê±Æ¶Á¤òÍ¿¤¨¤Ê¤¤¡¥Äó°Æ¼êË¡¤Ç¤Ï¤³¤ÎÀ­¼Á¤òÍøÍѤ·¡¤½ÐÎÏÄñ¹³¤òÆÃÀ­¥¤¥ó¥Ô¡¼¥À¥ó¥¹¤è¤ê¤â¹â¤¯À߷פ¹¤ë¤³¤È¤Ç¡¤¥É¥é¥¤¥Ð¤ÎÄã¾ÃÈñÅÅÎϲ½¤ò¼Â¸½¤¹¤ë¡¥¼Â¸³¤Ë¤è¤ê¡¤ÂÓ°è¤ÎÄã²¼ 2.9%¤Ç¡¤Ìó13%¤ÎÅÅÎϺ︺¤¬²Äǽ¤Ç¤¢¤ë¤³¤È¤ò³Îǧ¤·¤¿¡¥

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Ãø¼Ô ¡ûÅÚë μ, ¾®Ìî»û ½¨½Ó(µþÅÔÂç³Ø)
Pagepp. 393 - 398
Keyword CML, ¹â®¿®¹æÅÁÁ÷, À­Ç½Í½Â¬
AbstractËܹƤǤϡ¤¥ª¥ó¥Á¥Ã¥×ŵ÷Î¥¹â®¿®¹æÅÁÁ÷¤ÎÀ­Ç½Í½Â¬¤Ë¤Ä¤¤¤ÆµÄÏÀ¤¹¤ë¡¥ ¥Á¥Ã¥×Æâ¤Î¿®¹æÅÁÁ÷¤Ï½ÅÍ×ÅÙ¤òÁý¤·¤Æ¤ª¤ê¡¤ ¤½¤ÎÀ­Ç½¸«ÀѤ굻½Ñ¤Ï¥Á¥Ã¥×¤ÎÀ߷פˤª¤¤¤Æ½ÅÍפǤ¢¤ë¡¥ ¤·¤«¤·¡¤¿®¹æÅÁÁ÷·Ï¤ÎÀ­Ç½¤Ï¥È¥é¥ó¥¸¥¹¥¿¤ÎÆÃÀ­¡¤ÇÛÀþ¤ÎÆÃÀ­¤ÎξÊý¤ò ¹Íθ¤¹¤ëɬÍפ¬¤¢¤ê¡¤¤¢¤ë¾ò·ï¤¬Í¿¤¨¤é¤ì¤¿¾ì¹ç¤Ë ¤É¤ÎÄøÅÙ¤ÎÅÁÁ÷®ÅÙ¤ò¼Â¸½¤Ç¤­¤ë¤«¤ÏÌÀ¤é¤«¤Ç¤Ï¤Ê¤¤¡¥ ËܹƤǤϡ¤¶Ë¤Î°ÌÃÖ¤òÍѤ¤¤¿ CML¥É¥é¥¤¥Ð¤ÎÂӰ踫ÀѤê¤òÄó°Æ¤·¡¤ ´û¤ËÄó°Æ¤µ¤ì¤Æ¤¤¤ëÇÛÀþ¤ÎÀ­Ç½Í½Â¬¤È¹ç¤ï¤»¤Æ ¿®¹æÅÁÁ÷·Ï¤ÎÀ­Ç½Í½Â¬¤ò¼Â¸½¤·¤¿¡¥ ºÇÂçÅÁÁ÷®ÅÙ¤ÈÇÛÀþĹ¤Î´Ø·¸¤Ë¤Ä¤¤¤Æɾ²Á¤ò¹Ô¤Ê¤¤¡¤ ²òÀÏŪ¤Ê¸«ÀѤ꤬ÂÅÅö¤Ç¤¢¤ë¤³¤È¤ò¼Â¸³Åª¤Ë³Îǧ¤·¤¿¡¥


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Æü»þ: 2006ǯ4·î25Æü(²Ð) 10:45-12:00
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Pagepp. 399 - 404
Keyword £Ä£Ã¡Ý£Ä£Ã¥³¥ó¥Ð¡¼¥¿, ¥¹¥¤¥Ã¥Á¥È¥­¥ã¥Ñ¥·¥¿²óÏ©, Î¥»¶»þ´Ö²óÏ©
AbstractËÜÏÀʸ¤Ë¤ª¤¤¤Æ¤Ï¡¢ÄãÅÅ°µ¡¦ÂçÅÅή¤ò¶¡µë¤Ç¤­¤ë¹ß°µ·Á£Ä£Ã¡Ý£Ä£ÃÅÅ°µÊÑ´¹²óÏ©¤ò¥¹¥¤¥Ã¥Á¥È¥­¥ã¥Ñ¥·¥¿µ»½Ñ¤òÍѤ¤¤ÆÀ߷פ·¤Æ¤¤¤ë¡£Äó°Æ²óÏ©¤Ï¡¢£²¡¿£³Çܤι߰µÊÑ´¹¤ò¹Ô¤¦¥³¥ó¥Ð¡Ý¥¿¥Ö¥í¥Ã¥¯¤ò£Î¡Ê¡á£²¡¤£³¡¤Ž¥Ž¥Ž¥¡Ë¸ÄÊÂÎó¤ËÀܳ¤¹¤ë¤³¤È¤Ç¼Â¸½¤µ¤ì¤Æ¤ª¤ê¡¢ÄãÅÅ°µ¡¦ÂçÅÅή¤ò¶¡µë¤Ç¤­¤ë¡£Äó°Æ²óÏ©¤Ë¤Ä¤¤¤Æ¤Ï¡¢ÍýÏÀ²òÀϤˤè¤êÊÂÎóÀܳ¿ô¤ËÂФ¹¤ëÊÑ´¹¸úΨ¤ä½ÐÎÏ¥ê¥×¥ë¤òÌÀ¤é¤«¤Ë¤·¤Æ¤¤¤ë¡£¤Þ¤¿¡¢²óÏ©À߷פÈÍýÏÀ²òÀϤÎÂÅÅöÀ­¤Ë¤Ä¤¤¤Æ¡¢²óÏ©¥·¥ß¥å¥ì¡¼¥¿SPICE ¤Ë¤è¤ë¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤ò²ð¤·¤Æ³Îǧ¤·¤Æ¤¤¤ë¡£²óÏ©¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Î·ë²Ì¤Ï¡¢ÍýÏÀ²òÀϤηë²Ì¤ÈƱ¤¸¤¯¡¢ÊÂÎóÀܳ¿ô¤òÁý²Ã¤·¤¿¾ì¹ç¤Ë¤Ï¹âÉé²Ù»þ¤Ë¤ª¤¤¤Æ¸úΨ¤¬¸º¾¯¤¹¤ë¤¬¡¢¥ê¥×¥ë¤òÄ㸺¤Ç¤­¤ë¤³¤È¤ò¼¨¤·¤¿¡£

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Ãø¼Ô ¡û×¢À¥ ůÌé, Àõ°æ ůÌé, ±«µÜ ¹¥¿Î(Ë̳¤Æ»Âç³Ø/Âç³Ø±¡¾ðÊó²Ê³Ø¸¦µæ²Ê)
Pagepp. 405 - 410
Keyword ¥¹¥¤¥Ã¥Á¥È¥­¥ã¥Ñ¥·¥¿, DC-DC¥³¥ó¥Ð¡¼¥¿, ¥µ¥Ö¥¹¥ì¥Ã¥·¥ç¥ë¥É
Abstract¾ÃÈñÅÅÎϤ¬¿ô¦ÌW°Ê²¼¤Î¥¹¥Þ¡¼¥È¥»¥ó¥µLSI¤ò¼Â¸½¤¹¤ë¤¿¤á¤Ë¡¢MOSFET¤ò¥µ¥Ö¥¹¥ì¥Ã¥·¥ç¥ë¥ÉÎΰè¤ÇÆ°ºî¤µ¤»¤Æ¶ËÄã¾ÃÈñÅÅÎϲ½¤ò¼Â¸½¤¹¤ë¤³¤È¤ò¹Í¤¨¤ë¡£¤³¤Î¾ì¹ç¤Ë¤Ï¡¢MOS²óÏ©¤ò¤«¤Ê¤êÄ㤤ÅÅ°µ¤ÇÆ°ºî¤µ¤»¤ë¤³¤È¤Ë¤Ê¤ë¡£¤½¤Î¤¿¤á¡¢³°ÉôÅŸ»¤ÎÅÅ°µ¤ò¸úΨ¤è¤¯ÄãÅÅ°µ¤Ë²¼¤²¤ëɬÍפ¬¤¢¤ë¡£Ëܸ¦µæ¤Ç¤Ï¡¢¥µ¥Ö¥¹¥ì¥Ã¥·¥ç¥ë¥ÉMOS LSI¤ËÄãÅÅÎϤò¶¡µë¤¹¤ë¤¿¤á¤Î¥¹¥¤¥Ã¥Á¥È¥­¥ã¥Ñ¥·¥¿·¿DC-DC¹ß°µ¥³¥ó¥Ð¡¼¥¿¤Ë¤Ä¤¤¤Æ½Ò¤Ù¤ë¡£

Âê̾A Sub-1-V Low-Voltage Low-Power Reference With a Back-Gate Connection MOSFET
Ãø¼Ô ¡ûJun Pan, Yasuaki Inoue(Graduate School of Information, Production and Systems, Waseda University)
Pagepp. 411 - 416
Keyword reference, back-gate, low-voltage, low-power
AbstractA sub-1-V self-biased low-voltage low-power reference is presented for micropower electronic applications. The proposed circuit has very low temperature dependence by using a back-gate connection MOSFET. An Hspice simulation shows that the reference voltage and total power dissipation are 181 mV and 1.1 uW, respectively. The temperature coefficient of the reference voltage is 33 ppm/C within a temperature range from -40 to 100 C. The supply voltage dependence is -0.36 mV/V (Vdd=0.95~3.3 V), and the supply voltage can be as low as 0.95 V in a standard CMOS 0.35 um technology with threshold voltages of about 0.5 V and -0.65 V for n-channel and p-channel MOSFETs, respectively.


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Æü»þ: 2006ǯ4·î25Æü(²Ð) 13:30-14:45
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Pagepp. 417 - 422
Keyword DAC
AbstractËÜÏÀʸ¤Ç¤Ï¡¢¥ß¥¹¥Þ¥Ã¥Á¥·¥§¡¼¥Ô¥ó¥°µ¡Ç½¤ò´Þ¤á¤¿¥¢¥Ê¥í¥°FIR¥Õ¥£¥ë¥¿¤òÍѤ¤¤¿¥«¥¹¥±¡¼¥É·¿¦¤¦²DAC¤Î¹½À®Ë¡¤òÄó°Æ¤¹¤ë¡£¥ß¥¹¥Þ¥Ã¥Á¥·¥§¡¼¥Ô¥ó¥°µ¡Ç½¤Ï¾¯¿ô¤Î¥¹¥¤¥Ã¥Á¤À¤±¤Ë¤è¤Ã¤Æ¼Â¸½¤¹¤ë¤³¤È¤¬¤Ç¤­¡¢¥ß¥¹¥Þ¥Ã¥Á¥·¥§¡¼¥Ô¥ó¥°µ¡Ç½¤Î¥Ï¡¼¥É¥¦¥§¥¢¥µ¥¤¥º¤òÂçÉý¤Ë¸º¾¯¤¹¤ë¤³¤È¤¬¤Ç¤­¤ë¡£¤Þ¤¿¡¢¥¢¥Ê¥í¥°FIR¥Õ¥£¥ë¥¿¤òÍѤ¤¤ë¤³¤È¤Ç¡¢DAC¤Ë¸åÃÖ¤µ¤ì¤ë¥í¡¼¥Ñ¥¹¥Õ¥£¥ë¥¿¤Ø¤ÎÍ×µá¤ò´ËϤ¹¤ë¤³¤È¤¬²Äǽ¤È¤Ê¤ë¡£

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Ãø¼Ô ¡û¸µß· ÆÆ»Ë , Ç븶 ¹­Ç·, »³ÅÄ ²Â±û, ¾®ÎÓ ½ÕÉ×, ¾®¼¼ µ®µª, »± Úß(·²ÇÏÂç³Ø¹©³ØÉôÅŵ¤ÅŻҹ©³Ø²Ê/¾®ÎÓ¸¦µæ¼¼)
Pagepp. 423 - 428
Keyword ¦¤¦²ÊÑÄ´´ï, ¥Þ¥ë¥Á¥Ð¥ó¥É, ¥Þ¥ë¥Á¥Ó¥Ã¥È, DWA¥¢¥ë¥´¥ê¥º¥à, ¥Î¥¤¥º¥·¥§¡¼¥Ô¥ó¥°
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Ãø¼Ô ¡û»³ËÜ ¾Ïʸ(ÀŲ¬Âç³ØÂç³Ø±¡Íý¹©³Ø¸¦µæ²Ê), ÎëÌÚ ÊÙ(ÀŲ¬Âç³ØÅŻҲʳظ¦µæ²Ê), Àõ°æ ½¨¼ù(ÀŲ¬Âç³Ø¹©³ØÉô)
Pagepp. 429 - 433
Keyword Verilog-A, ¥Ó¥Ø¥¤¥Ó¥¢¥â¥Ç¥ë, ¦¤¦²ÊÑÄ´
AbstractËܹƤǤϡ¢¥¢¥Ê¥í¥°/¥Ç¥£¥¸¥¿¥ëº®ºÜ²óÏ©¤Î°ì¤Ä¤Ç¤¢¤ë¦¤¦²ÊÑÄ´´ï¤Î¥³¥ó¥«¥ì¥ó¥ÈÀß·×¼êË¡¤ò¼¨¤¹¡£Äó°Æ¼êË¡¤Ç¤Ï¡¢¥·¥¹¥Æ¥à¤ò¥Ó¥Ø¥¤¥Ó¥¢¥â¥Ç¥ë¤È¾ÜºÙ¤Ê¥â¥Ç¥ë¤Ë¤è¤ëº®ºÜ¥â¥Ç¥ë¤Çµ­½Ò¤¹¤ë¡£°ìÎã¤È¤·¤Æ¡¢ÀÑʬ´ï¤È¤·¤Æ¥¹¥¤¥Ã¥Á¥È¥­¥ã¥Ñ¥·¥¿¡ÊSC¡Ë¤òÍѤ¤¤ëÎ¥»¶»þ´Ö·¿¤Î°ì¼¡¦¤¦²ÊÑÄ´´ï¤ÎÀ߷פò¼¨¤¹¡£Àß·×¼ê½ç¤È¤·¤Æ¡¢µ¡Ç½¥Ö¥í¥Ã¥¯¤´¤È¤ËVerilog-A¤òÍѤ¤¤Æ¥Ó¥Ø¥¤¥Ó¥¢¥â¥Ç¥ë¤òºîÀ®¤¹¤ë¡£¼¡¤Ë¡¢ºîÀ®¤·¤¿¥Ó¥Ø¥¤¥Ó¥¢¥â¥Ç¥ë¤òÍѤ¤¤Æ¥ª¥Ú¥¢¥ó¥×¤Î»ÅÍͤò·èÄꤹ¤ë¡£ºÇ¸å¤Ë³Æµ¡Ç½¥Ö¥í¥Ã¥¯¤ò¥È¥é¥ó¥¸¥¹¥¿¥ì¥Ù¥ë¤ÇÀ߷פ·¡¢µ¡Ç½¥Ö¥í¥Ã¥¯¤´¤È¤Ë¥Ó¥Ø¥¤¥Ó¥¢¥â¥Ç¥ë¤ò²óÏ©¤ÈÃÖ¤­´¹¤¨¤Æ¤¤¤¯¤³¤È¤Ë¤è¤ê°ì¼¡¦¤¦²ÊÑÄ´´ï¤ÎÀ߷פò¹Ô¤¦¡£


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Æü»þ: 2006ǯ4·î25Æü(²Ð) 15:15-16:30
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Pagepp. 435 - 440
Keyword ÄãÅÅ°µ, ÀÑʬ²óÏ©, ¥¢¥Ê¥í¥°¥Õ¥£¥ë¥¿, ¥³¥ó¥Ñ¥ó¥Ç¥£¥ó¥°, ¥í¥°¥É¥á¥¤¥ó
AbstractÄãÅÅ°µ¤«¤Ä¹­¥À¥¤¥Ê¥ß¥Ã¥¯¥ì¥ó¥¸¤Ê¥¢¥Ê¥í¥°¿®¹æ½èÍý¤ò¼Â¸½¤¹¤ë°ì¼êË¡¤È¤·¤Æ¡¤¥³¥ó¥Ñ¥ó¥Ç¥£¥ó¥°²óÏ©µ»½Ñ¤¬¤¢¤ë¡¥ËÜÏÀʸ¤Ç¤Ï¡¤½¾Íè¸ÄÊ̤˹ͤ¨¤é¤ì¤Æ¤­¤¿ÄãÅÅ°µCMOS¥³¥ó¥Ñ¥ó¥Ç¥£¥ó¥°ÀÑʬ²óÏ©¤òÅý°ìŪ¤Ë¼è¤ê°·¤¦¤³¤È¤Ç¡¤ºÇŬÀ߷פμêË¡¤òÄó°Æ¤¹¤ë¡¥¥³¥ó¥Ñ¥ó¥Ç¥£¥ó¥°ÀÑʬ²óÏ©¤Î¸¶Íý¤ÇɬÍפȤʤ뤤¤¯¤Ä¤«¤ÎÉôʬ²óÏ©¤Î¼Â¸½¤Ë¼«Í³ÅÙ¤¬¤¢¤ë¤³¤È¤ò»ØŦ¤·¡¤¤½¤ì¤é¤ÎÁȹç¤ï¤»¤Ç¿¤¯¤ÎÀÑʬ²óÏ©¤ò¹½À®¤¹¤ë¡¥ËÜÀ߷פˤè¤ê¡¤Á´¤Æ¤Î¥³¥ó¥Ñ¥ó¥Ç¥£¥ó¥°ÀÑʬ²óÏ©¤Î¹½À®¤òÎóµó¤Ç¤­¡¤ºÇŬ¤Ê²óÏ©¹½À®¤¬Ãµº÷²Äǽ¤È¤Ê¤ë¡¥

Âê̾A Highly Linear and Wide Dynamic Range Four-quadrant CMOS Analog Multiplier Using Active Feedback
Ãø¼Ô ¡ûZhangcai Huang, Yasuaki Inoue, Quan Zhang, Hong Yu(Waseda University)
Pagepp. 441 - 445
Keyword CMOS Analog Multiplier, Active Feedback
AbstractA new circuit configuration for an CMOS fourquadrant analog multiplier is presented. In this circuit, the active feedback technique is used to obtain high linearity and wide input dynamic range. The experimental results show that the proposed multiplier can offer +-1.8V input dynamic range for a +-2.5V supply voltage, which are much larger than the previous CMOS analog multiplier.

Âê̾¥é¥Æ¥£¥é¥ëÅý¹çC-BiCMOS¥Ð¥Ã¥Õ¥¡¶îÆ°¤ÎCMOS¥¯¥í¥Ã¥¯À¸À®²óÏ©
Ãø¼Ô ¡ûßÀȪ ¹§(¶áµ¦Âç³ØÂç³Ø±¡À¸ÊªÍý¹©³Ø¸¦µæ²Ê), ½©Ç» ½ÓϺ(¶áµ¦Âç³Ø)
Pagepp. 447 - 452
Keyword SOI, Éôʬ¶õ˳ÁØ, C-BiCMOS, ¥é¥Æ¥£¥é¥ëBJT
AbstractSOI´ðÈľå¤ÎÉôʬ¶õ˳·¿CMOS¥¤¥ó¥Ð¡¼¥¿¤Ç¡¢MOSFET¤ÈBJT¤ÎÊÂÎóº®À® ¥â¡¼¥ÉÆ°ºî¤¬Äó°Æ¤µ¤ì¡¢¤½¤Î²óÏ©À߷פȲóÏ©¥·¥ß¥å¥ì¡¼¥¿¤Ë¤è¤ë ¸¡¾Ú¤¬¹Ô¤ï¤ì¤ÆÍ褿¡£¤³¤Î¥¤¥ó¥Ð¡¼¥¿¤ò¹½À®¤¹¤ë4ü»Ò¤În{p}¥Á¥ã ¥Í¥ëMOSFET¤Ï¡¢3ü»Ò¤Î¥é¥Æ¥£¥é¥ënpn{pnp}BJT¤òÆ⸤¹¤ë¡£Ä̾ï¤Î ¶¦Í­¤·¤¿´ðÈĤΥץ륢¥Ã¥×¡¿¥×¥ë¥À¥¦¥óMOSFET¤òÅÅή¸»¤È¤·¤Æ¡¢ ¤½¤Î¥É¥ì¥¤¥óü»Ò¤«¤é¥é¥Æ¥£¥é¥ë¡¦¥Ç¥Ð¥¤¥¹¤òÅý¹ç(Unified) C-BiCMOS¤È¸Æ¤Ö¡Ú°Ê¹ßUC-BiCMOS¤Èµ­¤¹¡Û¡£½¾Íè¤ÎUC-BiCMOS¤Î ¸¦µæ¤Ç¤Ï¡¢CMOS¥¤¥ó¥Ð¡¼¥¿¤Î¥²¡¼¥Èü»Ò¤È¡¢Æ⸤¹¤ëBJT¤ò ³èÀ­²½¤¹¤ë¥×¥ë¥¢¥Ã¥×¡¿¥×¥ë¥À¥¦¥óMOSFET¤Î¥²¡¼¥Èü»Ò¤ÎξÊý¤ò ÆþÎϤȤ·¤¿¤¬¡¢Ëܸ¦µæ¤Ç¤Ï¥×¥ë¥¢¥Ã¥×¡¿¥×¥ë¥À¥¦¥óMOSFET¤Î¥²¡¼¥È ü»Ò¤Î¤ß¤òÆþÎϤȤ¹¤ë²óÏ©Êý¼°¤òÄó°Æ¤¹¤ë¡£Â¨¤Á¡¢º®À®¥â¡¼¥É¤Î ξMOSFET¤Ï¡Ö¥ª¥Õ¡×¤Î¾õÂ֤˸ÇÄꤷ¤Æ¡¢Î¾¥é¥Æ¥£¥é¥ëBJT¤Î¤ß¤¬ ¶îÆ°¤¹¤ë¥Ð¥Ã¥Õ¥¡²óÏ©¤È¤Ê¤ë¡£¹¹¤Ë¡¢¥ê¥ó¥°¥ª¥·¥ì¡¼¥¿¤Îȯ¿¶ ÇÈ·Á¤ò¶îÆ°¤¹¤ë¥Ð¥Ã¥Õ¥¡²óÏ©¤È¤·¤Æ±þÍѤ¹¤ë¡£¤³¤³¤Ç¤Ï0.35¦Ìm¤Î CMOS¥×¥í¥»¥¹¤ËÂФ¹¤ë²óÏ©¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤ò¹Ô¤Ã¤Æ¡¢¤½¤ì¤é¤Î ²óÏ©ÆÃÀ­¤òÄ´¤Ù¤ë¡£Éé²ÙÍÆÎÌCout=1.417pF¤ÇÅŸ»ÅÅ°µVdd=1V¤Î ¾ì¹ç¡¢21ÃÊ¥¤¥ó¥Ð¡¼¥¿¤Î¥ê¥ó¥°¥ª¥·¥ì¡¼¥¿¤ÎÇÈ·Á¤òUC-BiCMOS ¥Ð¥Ã¥Õ¥¡¤ËÆþÎϤ¹¤ë¾ì¹ç¤Ï¡¢¥Õ¥¡¥ó¥¢¥¦¥È4¤Î4ÃÊCMOS¥¤¥ó¥Ð¡¼¥¿ ¤Î¾ì¹ç¤ËÈæ¤Ù¤Æ¡¢4Çܰʾå¹â®¤Ç¡¢Ìó36%¥¨¥Í¥ë¥®¡¼¤¬Ä㤤¡£


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Keyword ¾õÂÖ¶õ´Ö¥Ç¥£¥¸¥¿¥ë¥Õ¥£¥ë¥¿, L2´¶ÅÙ, L2¥¹¥±¡¼¥ê¥ó¥°À©Ìó, ÊĤ¸¤¿·Á¤Î²òË¡
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Âê̾The Optimum Approximation of a FIR Filter Bank Realizing Simultaneous Minimization of Various Worst-Case Measures of Error
Ãø¼Ô ¡ûYuichi Kida(School of Pharmaceutical Sciences, Ohu University), Takuro Kida(Department of EE Eng., Nihon University)
Pagepp. 465 - 470
Keyword digital signal processing, interpolation approximation, filter bank
AbstractWe present the optimum approximation of FIR filter bank that minimizes various measures of error of approximation, simultaneously. The presented approximation is quite flexible in choosing band-width of sub-bands, sample points and analysis filters.


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Abstract¥ì¥¾¥ë¥Ð¤ä¥¤¥ó¥¯¥ê¥á¥ó¥¿¥ë¥¨¥ó¥³¡¼¥ÀÅù¤Î¥»¥ó¥µ¤Ç¤Ï¤è¤ê¹âÀºÅ٤ʰÌÃÖ¸¡½Ð¤Î¤¿¤á¤ËÆâÁޤȤ¤¤¦¿®¹æ½èÍý¤¬É¬ÍפȤµ¤ì¤ë¡£ÆâÁÞ¤ò¼Â¸½¤¹¤ë¤¿¤á¤ÎÊýË¡¤Î¤¦¤Á¡¢2Áê(Ê£ÁÇ)°ÌÁêƱ´ü¥ë¡¼¥×¤Î¸¶Íý¤Ë´ð¤Å¤¯¥¢¥ë¥´¥ê¥º¥à¤Ë¤Ä¤¤¤Æ¡¢²óÏ©µ¬ÌϤòÍÞ¤¨¤ë¤³¤È¤òÌÜŪ¤È¤·¤¿¿·¤¿¤Ê¥·¥¹¥Æ¥à¹½À®¤ò¸¡Æ¤¤·¤¿¡£Äó°Æ¼êË¡¤Ï¦¤¦²ÊÑÄ´¤È¥ª¡¼¥Ð¡¼¥µ¥ó¥×¥ê¥ó¥°¤Ë¤è¤ë¿®¹æ½èÍý¥¢¡¼¥­¥Æ¥¯¥Á¥ã¤Ë´ð¤Å¤¤¤Æ¤¤¤ë¡£ËÜÏÀʸ¤Ç¤Ï´Êñ¤Ê¥·¥¹¥Æ¥à¹½À®¤òÎ㼨¤·¡¢¤½¤Î¥·¥¹¥Æ¥à¤ÎÀ­Ç½¤ò¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ç¸¡Æ¤¤·¤¿·ë²Ì¤ò¼¨¤¹¡£

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Keyword ²»À¼¿®¹æ½èÍý, ÆÍȯÀ­»¨²», Basis Pursuit, Basis Pursuit»¨²»½üµî, ¿®¹æʬΥ
AbstractËÜÊó¹ð¤Ç¤Ï¡¤Basis Pursuit¤ò²»À¼¿®¹æ¤ÎÆÍȯÀ­»¨²»½üµî¤ØŬÍѤ·¤Æ¤¤¤ë¡¥ÆÍȯÀ­»¨²»¤Ï¡¤¤½¤ÎȯÀ¸»þ¹ï¤¬ÉÔµ¬Â§¤«¤Ä»ý³»þ´Ö¤¬Ã»¤¤¤³¤È¤«¤é¡¤»¨²»¥¹¥Ú¥¯¥È¥ë¤ª¤è¤ÓȯÀ¸»þ¹ï¤Î¿äÄ꤬º¤Æñ¤Ç¤ê¡¤¥¹¥Ú¥¯¥È¥ë¸º»»Ë¡Åù¤òŬÍѤ¹¤ë¤³¤È¤¬º¤Æñ¤Ê»¨²»¤Ç¤¢¤ë¡¥ËÜÊó¹ð¤Ç¤Ï¡¤²»À¼¿®¹æ¤ÈÆÍȯÀ­»¨²»¤Î»ý³»þ´Ö¤Î°ã¤¤¤ËÃåÌܤ·¡¤Basis Puruit¤òÍѤ¤¤Æ°Û¤Ê¤ë2¤Ä¤Î¥Õ¥ì¡¼¥àŤÎDFT´ðÄ줽¤ì¤¾¤ì¤Ø»¨²»¤È²»À¼¿®¹æ¤òʬΥ¤¹¤ë¤³¤È¤òÄó°Æ¤·¤Æ¤¤¤ë¡¥ËÜÊó¹ð¤Ç¤ÏBlock Coordinate RelaxationË¡¤Ë¤è¤ëBasis Pursuit»¨²»½üµîË¡¤Ë¤è¤ê¶á»÷Ū¤ËÊ£ÁÇ´ðÄì·²¤Ë¤è¤ëBasis Pursuit¤ò¼Â¸½¤·¤Æ¤¤¤ë¡¥¼Â¸³¤Ç¤Ï¡¤·×»»µ¡¾å¤ÇºîÀ®¤·¤¿»¨²»¡¤¤ª¤è¤Ó»Ä¶Á¤Î¤¢¤ë¼Â´Ä¶­¤ÇÏ¿²»¤µ¤ì¤¿»¨²»¤òÍѤ¤¤Æ»¨²»½üµî¼Â¸³¤ò¹Ô¤¤¡¤Í­¸úÀ­¤ò³Îǧ¤·¤Æ¤¤¤ë¡¥


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Keyword ưŪ¥Í¥Ã¥È¥â¥Ç¥ë, ¥¢¥¯¥Æ¥£¥Ö¥Í¥Ã¥È, ²èÁü, Îΰ踡½Ð
AbstractËܹƤϡ¢Æ°Åª¥Í¥Ã¥È¥â¥Ç¥ë¤ÎÊáªÀ­Ç½¤ÎÌäÂê¤È¤·¤Æ¡¢ÆþÎϲèÁü¤Ë¸ºß¤¹¤ë»¨²»¤Î±Æ¶Á¤Ë¤Ä¤¤¤Æ¸¡Æ¤¤ò¹Ô¤Ê¤Ã¤¿·ë²Ì¤Ë¤Ä¤¤¤Æ½Ò¤Ù¤Æ¤¤¤ë¡£ ½¾Íè¼êË¡¤Ë¤ª¤¤¤Æ¤Ï¡¢ÆþÎϲèÁü¤Ë¡¢¥¿¡¼¥²¥Ã¥È¤Î¾¤Ë»¨²»¤¬Â¸ºß¤·¤Æ¤·¤Þ¤Ã¤¿¾ì¹ç¡¢¥¿¡¼¥²¥Ã¥È¤È»¨²»¤ÎξÊý¤òÊ᪤·¡¢¥¿¡¼¥²¥Ã¥È¤Î¤ß¤ò¤½¤Î¤Þ¤Þ¤Ç¤ÏÀµ¤·¤¯¸¡½Ð¤Ç¤­¤Ê¤¤¾ì¹ç¤¬¤¢¤ë¡£¤½¤³¤Ç¡¢²æ¡¹¤Ï¥Í¥Ã¥È¤Î³Ê»ÒÅÀ¤ËÂбþ¤¹¤ë²èÁü¤ÎÇ»ÅÙÃͤòÊÑÆ°¤µ¤»¤ë¼êË¡¤òÄó°Æ¤¹¤ë¡£¤³¤ì¤Ë¤è¤ê¡¢»¨²»¤Î±Æ¶Á¤ò·Ú¸º¤·¡¢»¨²»¤ò²óÈò¤¹¤ë¤³¤È¤¬²Äǽ¤Ç¤¢¤ë¤³¤È¤ò¡¢¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¼Â¸³¤Î·ë²Ì¤«¤é¼¨¤¹¤³¤È¤¬¤Ç¤­¤¿¡£¤µ¤é¤Ë¡¢ÆþÎϲèÁü¤ËÂФ·¤Æ·«¤êÊÖ¤·¥Ñ¥¿¡¼¥ó²èÁü¤ò²Ã»»¤·¡¢¤½¤Î²èÁü¤ËÂФ·¤Æ½¾Íè¼êË¡¤òŬÍѤ·¤¿¡£¤½¤Î·ë²Ì¡¢¥¿¡¼¥²¥Ã¥ÈÊ᪤ξõ¶·¤¬ÊѤï¤ë²ÄǽÀ­¤¬¤¢¤ë¤³¤È¤¬¼¨¤»¤¿¡£

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Ãø¼Ô ¡ûº£Àô ¾Í»Ò(¿·³ã¸©¹©¶Èµ»½ÑÁí¹ç¸¦µæ½ê), Æ£µÈ ÀµÌÀ(¼óÅÔÂç³ØÅìµþ¥·¥¹¥Æ¥à¥Ç¥¶¥¤¥ó³ØÉô), °¤Éô ½Ê¿Í(¿·³ã¸©¹©¶Èµ»½ÑÁí¹ç¸¦µæ½ê), µ®²È ¿Î»Ö(¼óÅÔÂç³ØÅìµþ¥·¥¹¥Æ¥à¥Ç¥¶¥¤¥ó³ØÉô)
Pagepp. 513 - 518
Keyword °Å¹æ²½, JPEG 2000, ¥¢¥¯¥»¥¹À©¸æ, ¥Ï¥Ã¥·¥å´Ø¿ô, ·ëÂ÷¹¶·â
AbstractJPEG 2000 ¤Î½ÅÍפÊÆÃŤΰì¤Ä¤Ç¤¢¤ë³¬ÁØÀ­¤òÊÝ»ý¤·¡¤¤«¤Ä¡¤½ÀÆð¤Ê¥¢¥¯¥»¥¹À©¸æ¤ò²Äǽ¤È¤·¤¿°Å¹æ²½Ë¡¤òÄó°Æ¤¹¤ë¡¥JPEG 2000 Éä¹æ²½²èÁü¤Ç¤Ï¡¤¥¹¥±¡¼¥é¥Ó¥ê¥Æ¥£¤òÍøÍѤ¹¤ë¤³¤È¤Ë¤è¤ê¡¤²èÁü¤ÎÍ­ÎÁÇÛ¿®¤Ë¤ª¤¤¤Æ¡¤²Ý¶â¤Ë±þ¤¸¤¿¼Á¤Ç²èÁü¤ò¥æ¡¼¥¶¤ËÇÛ¿®¤¹¤ë¥¢¥¯¥»¥¹À©¸æ¤¬²Äǽ¤È¤Ê¤ë¡¥Äó°ÆË¡¤Ç¤Ï¡¤´ÉÍý¤¹¤ë°Å¹æ¸°¡Ê¥Þ¥¹¥¿¡¼¥­¡¼¡Ë¤¬°ì¤Ä¤Ç¤¢¤ê¡¤¤¢¤ë»ØÄꤵ¤ì¤¿¼Á¤Î²èÁüºÆÀ¸¤òµöÂú¤µ¤ì¤¿¥æ¡¼¥¶¤ËÂФ·¤Æ¡¤¥Þ¥¹¥¿¡¼¥­¡¼¤«¤é½¾Â°Åª¤ËÀ¸À®¤µ¤ì¤¿°ì¤Ä¤Î¸°¤òÇÛÁ÷¤¹¤ë¡¥¤Þ¤¿¡¤°Û¤Ê¤ë¼Á¤òµöÂú¤µ¤ì¤¿Â¾¤Î¥æ¡¼¥¶¤ËÂФ·¤Æ¤Ï¥Þ¥¹¥¿¡¼¥­¡¼¤«¤é½¾Â°Åª¤Ë·èÄꤵ¤ì¤¿Â¾¤Î¸°¤òÇÛÁ÷¤¹¤ë¡¥²èÁü¤Î¼Á¤Î»ØÄê¤Ï¡¤JPEG2000 ¤¬¤â¤Ä¤¹¤Ù¤Æ¤Î¥¹¥±¡¼¥é¥Ó¥ê¥Æ¥£¤ËÂбþ¤¹¤ë¡¥¤µ¤é¤Ë¡¤Äó°ÆË¡¤Ï¡¤·ëÂ÷¹¶·â¤ËÂФ·¤ÆÂÑÀ­¤òÍ­¤·¡¤Ê£¿ô¤Î¥æ¡¼¥¶¤¬°Å¹æ¸°¤ò¶¦Í­¤·¤Æ¤â¡¤µöÂú¤µ¤ì¤¿²è¼Á¤è¤ê¹â¤¤²è¼Á¤Ç¤ÎºÆÀ¸¤òº¤Æñ¤Ë¤¹¤ë¡¥


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Pagepp. 519 - 524
Keyword È¿±þ³È»¶¥·¥¹¥Æ¥à, ·×»»´ö²¿³Ø, ¥Ü¥í¥Î¥¤¿Þ, ¥¹¥±¥ë¥È¥ó, ¶½Ê³À­¥À¥¤¥Ê¥ß¥¯¥¹
AbstractËÜÏÀʸ¤Ç¤Ï¡¤¶½Ê³À­¥Ç¥£¥¸¥¿¥ëÈ¿±þ³È»¶¥·¥¹¥Æ¥à¤òÍѤ¤¤¿¥Ü¥í¥Î¥¤¿ÞÀ¸À®¥¢¥ë¥´¥ê¥º¥à¤òÄó°Æ¤¹¤ë¡¥¶½Ê³À­È¿±þ³È»¶¥À¥¤¥Ê¥ß¥¯¥¹¤Ë¤è¤êȯÀ¸¤Ç¤­¤ë¶½Ê³ÇȤϡ¤¡ÖÅù®¤ÇÅÁȤ¹¤ë¡×¡¤¡Ö¾¤ÎÇȤȾ×Æͤ¹¤ë¤È¾ÃÌǤ¹¤ë¡×¤È¤¤¤¦À­¼Á¤ò»ý¤Ã¤Æ¤¤¤ë¡¥¤³¤ÎÀ­¼Á¤òÍøÍѤ¹¤ë¤³¤È¤Ç¡¤¶á˵¤Î2ÅÀ´Ö¤«¤éÅö¶¿Î¤¤Ë¤¢¤ëÀþ¤òÄ´¤Ù¤ë¤³¤È¤¬¤Ç¤­¤ë¡¥ËÜÏÀʸ¤Ç¤Ï¡¤¤³¤ÎÀ­¼Á¤òÍøÍѤ¹¤ë¤³¤È¤Ç¡¤·×»»´ö²¿³Ø¤ÎʬÌî¤Ç¤è¤¯°·¤ï¤ì¤ë¥Ü¥í¥Î¥¤¿Þ¤òÀ¸À®¤¹¤ë¤³¤È¤¬¤Ç¤­¤ë¤³¤È¤ò¼¨¤¹¡¥¤Þ¤¿¡¤2¼¡¸µÊ¿Ì̾å¤ËÇÛÃÖ¤·¤¿ÅÀ·²¡¤´ö²¿Åª¤Ê¥Þ¥Ã¥×¡¤¼ê½ñ¤­Ê¸»ú¤Ê¤É¤òÍѤ¤¤¿¼Â¸³¤Ë¤è¤ê¡¤¥Ü¥í¥Î¥¤¿Þ¤ä¥¹¥±¥ë¥È¥ó¤òÀ¸À®¤Ç¤­¤ë¤³¤È¤ò¼¨¤¹¡¥

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Pagepp. 525 - 530
Keyword ²èÁüǧ¼±, ¥Õ¥£¥ë¥¿, ²ÏÀî¿å°Ì, ËɺÒ
Abstract¶áǯ¡¤½¸Ãæ¹ë±«¤ÎÁý²Ã¤Ë¤è¤ê²ÏÀîºÒ³²¤¬Â¿È¯¤·¤Æ¤¤¤ë¡¥Âкö¤È¤·¤Æ¤ÏÎÌ¿åÈĤòÍѤ¤¤¿¿å°Ì¸¡½ÐÊýË¡¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¤¬¡¤²ÏÀîÆâ¤Ø¤Î¹½Â¤Êª¤ÎÀßÃ֤ϲÏÀîË¡¤Ë¤è¤ê¸·¤·¤¯´ÉÍý¤µ¤ì¤Æ¤ª¤ê¹¥¤Þ¤·¤¯¤Ê¤¤¡¥¤½¤³¤ÇËÜÊó¹ð¤Ç¤Ï¡¤ÎÌ¿åÈĤòÀßÃÖ¤·¤Æ¤¤¤Ê¤¤²ÏÀî¤Î±ÇÁü¤Î¤ß¤Ç¿å°Ì¤ò¸¡½Ð¤¹¤ë¥¢¥ë¥´¥ê¥º¥à¤òÄó°Æ¤¹¤ë¡¥¤Þ¤º²ÏÀî±ÇÁü¤ËÂФ·¥Õ¥ì¡¼¥àƱ´ü²Ã»»¡¤Èùʬ½èÍý¤ò¹Ô¤¤¡¤¥¨¥Ã¥¸²èÁǤò¸¡½Ð¤¹¤ë¡¥¤½¤·¤Æ¥¨¥Ã¥¸²èÁǤβ£Êý¸þ¥Ò¥¹¥È¥°¥é¥à¤òºîÀ®¡¤¤½¤Î½ÄÊý¸þÀÑ»»ÃͤòÍѤ¤¤Æ¿å°Ì¸¡½Ð¤ò¹Ô¤¦¡¥Èùʬ½èÍý¤Ë¤Ä¤¤¤Æ¤Ï¡¤»þ¡¹¹ï¡¹¤ÈÊѲ½¤¹¤ë±ÇÁü¤´¤È¤ËºÇŬ¤Ê¥Õ¥£¥ë¥¿¤ÎÀß·×Ë¡¤òÄó°Æ¤¹¤ë¡¥¼Â¸³¤Î·ë²Ì¡¤ÌÜ»ë¿å°Ì¤ËÂФ·¤Æ¸íº¹10%¤Ç¤Î¿å°Ì¸¡½Ð¤¬³Îǧ¤µ¤ì¤¿¡¥

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Pagepp. 531 - 534
Keyword ²èÁüǧ¼±, JPEG2000, ²ÏÀî, ËɺÒ
Abstract¶áǯ¡¢¿å³²¤¬Â¿È¯¤·¤Æ¤ª¤êËɺÒÂкö¤È¤·¤Æ¿å°Ì·×¤ä¥«¥á¥é±ÇÁü¤Ë¤è¤ë²ÏÀî´Æ»ë¤¬¹­¤¯¼Â»Ü¤µ¤ì¤Æ¤¤¤ë¡£½¾ÍèË¡¤è¤ê¿å°Ì·×¤äÎÌ¿åÈĤòÀßÃÖ¤·¤Æ¿å°Ì¤ò·×¤ëÊýË¡¤¬¤¢¤ë¤¬¡¢¥³¥¹¥È¤ä²ÏÀîË¡¤Ë¤è¤ë²ÏÀî´ÉÍý¾å¤ÎÌäÂ꤬¤¢¤ë¡£¤½¤³¤ÇËÜÊó¹ð¤Ç¤Ï¡¢¥«¥á¥é±ÇÁü¤Î¤ß¤«¤éÎÌ¿åÈĤòÍѤ¤¤º¤Ëή¿å°è¤ò¸¡½Ð¤¹¤ë¥¢¥ë¥´¥ê¥º¥à¤òÄó°Æ¤¹¤ë¡£Äó°ÆË¡¤Ç¤Ï¡¢²ÏÀî´Æ»ë¥Ó¥Ç¥ª±ÇÁü¤ÎÉä¹æ²½ÅÁÁ÷¤ò¹Ô¤¦¤³¤È¤â¹Íθ¤·¤Æ¡¢²èÁü°µ½Ìµ»½Ñ¤Ç¤¢¤ëJPEG2000¤Î¥¦¥§¡¼¥Ö¥ì¥Ã¥ÈÊÑ´¹¤ò³èÍѤ·Î®¿åÉô¤ÈΦÉô¤ÎÆÃħÎ̤«¤éÎΰèȽÊ̤ò¹Ô¤¦¡£¤Þ¤¿¡¢¥Õ¥ì¡¼¥àƱ´ü²Ã»»¤òƳÆþ¤·¡¢Î®¿åÉô¤ÈΦÉô¤Ë¤ª¤±¤ëÆÃħÎ̤κ¹¤òÂ礭¤¯¤¹¤ë¤³¤È¤ÇȽÊÌÀºÅÙ¤ò¸þ¾å¤µ¤»¤ë¡£

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Ãø¼Ô ¡û¿¢ÅÄ ÂóÌé, Ïɸ« °éμ(Ä»¼è´Ä¶­Âç³ØÂç³Ø±¡´Ä¶­¾ðÊó³Ø¸¦µæ²Ê), ÌùÌÚ ÅÐ(ÄÅ»³¹©¶È¹âÅùÀìÌç³Ø¹»), ¾¾Á° ¿Ê, Ê¡ËÜ Á±ÍÎ(Ä»¼è´Ä¶­Âç³Ø´Ä¶­¾ðÊó³ØÉô), Éû°æ ͵(Ä»¼èÂç³Ø¹©³ØÉô)
Pagepp. 535 - 540
Keyword ƻϩɸ¼±, ¼«¸ÊÁÈ¿¥²½¥Þ¥Ã¥×, ²èÁüǧ¼±
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Pagepp. 541 - 546
Keyword ¥á¥â¥ê¥³¥ó¥Ñ¥¤¥é, ¥­¥ã¥é¥¯¥¿¥é¥¤¥º, LPE
Abstract¶áǯ¤ÎSoCÀ߷פˤª¤¤¤Æ¤Ï¡¢Â¿¼ï¿Íͤʥá¥â¥ê¥³¥ó¥Ñ¥¤¥é¤òû´ü´Ö¤Ë¹âÀºÅÙ¤ÇÀ¸À®¤¹¤ë¤³¤È¤¬Í׵ᤵ¤ì¤Æ¤¤¤ë¡£²æ¡¹¤ÎÄó°Æ¤¹¤ë¥á¥â¥êÀ߷״Ķ­¤Ç¤Ï¡¢ÀºÅÙ¤òÍî¤È¤µ¤º¤Ë¹â®¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤ò¹Ô¤¦¤Î¤ËŬ¤·¤¿³¬ÁØRC¥Í¥Ã¥È¥ê¥¹¥È¤òÀ¸À®¤·¡¢Ã»´ü´Ö¤Ç¥­¥ã¥é¥¯¥¿¥é¥¤¥º¤ò¹Ô¤¤¡¢¥á¥â¥ê¥é¥¤¥Ö¥é¥ê¤òÀ¸À®¤¹¤ë¤¿¤á¤Î¥á¥â¥ê¥³¥ó¥Ñ¥¤¥é¤ò¹½ÃÛ¤¹¤ë¡£ËܼêË¡¤Ï´û¤Ë90nmµé¤Î¥á¥â¥ê¥â¥¸¥å¡¼¥ë³«È¯¤ËŬÍѤµ¤ì¤Æ¤ª¤ê¡¢À߷פθúΨ²½¡¢¥é¥¤¥Ö¥é¥ê¤Î¹âÉʼÁ²½¤ËÂ礭¤¯´óÍ¿¤·¤Æ¤¤¤ë¡£

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Pagepp. 547 - 552
Keyword FPGA, ElmoreÃÙ±ä, ¤Ð¤é¤Ä¤­, ÇÛÀþ¥â¥Ç¥ë
AbstractËܹƤǤϡ¢ºÆ¹½À®¥Ç¥Ð¥¤¥¹¤Ç¤¢¤ëFPGA¤Ë¤ª¤¤¤Æ¡¢²æ¡¹¤ÎÄó°Æ¤¹¤ë ¥Á¥Ã¥×Æâ¤Ð¤é¤Ä¤­¤òÊä½þ¤¹¤ë¼êË¡¤òÍѤ¤¤ëºÝ¤ÎÇÛÀþ·ÐÏ©¤ÎÊѹ¹ ¤Ë¤è¤ë¥ª¡¼¥Ð¡¼¥Ø¥Ã¥É¤Ë¤Ä¤¤¤Æ¹Í»¡¤ò¹Ô¤¦¡£ËܹƤDz¾Äꤷ¤Æ¤¤¤ë FPGA¤Î¥¢¡¼¥­¥Æ¥¯¥Á¥ã¤Ç¤Ï½ÄÊý¸þ¤È²£Êý¸þ¤ÎÇÛÀþ¤Ç¤½¤ÎÍÆÎ̤¬ °Û¤Ê¤ê¡¢ÇÛÀþ·ÐÏ©¤Î¤È¤êÊý¤Ë¤è¤êÃÙ±äÃͤ¬ÊѲ½¤¹¤ë¡£¥È¥é¥ó¥¸¥¹¥¿ ¤ÎÀ­Ç½¤Ð¤é¤Ä¤­Éý¤ËÂФ··ÐÏ©¤ÎÊѹ¹¤Ë¤è¤ëÃÙ±äÃͤÎÊѲ½¤¬Â礭¤±¤ì ¤Ð¡¢¤Ð¤é¤Ä¤­¤òÍøÍѤ·¤¿FPGA¤ÎÀ­Ç½¤Î¸þ¾å¤¬¸«¹þ¤á¤Ê¤¯¤Ê¤ë¡£¤¤¤¯¤Ä¤«¤Î ÇÛÀþ·ÐÏ©¤Ë¤Ä¤¤¤Æ¤Ð¤é¤Ä¤­Êä½þ¤ÎÍ­¸úÀ­¤Ë¤Ä¤¤¤Æ¸¡Æ¤¤ò¹Ô¤Ã¤¿¡£

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Pagepp. 553 - 558
Keyword SSTA, ¥â¥ó¥Æ¥«¥ë¥í²òÀÏ
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Pagepp. 559 - 564
Keyword ¥²¡¼¥ÈÃÙ±ä¥â¥Ç¥ë, À½Â¤¤Ð¤é¤Ä¤­, ´Ä¶­¤Ð¤é¤Ä¤­
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Æü»þ: 2006ǯ4·î25Æü(²Ð) 11:10-12:00
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Pagepp. 565 - 570
Keyword À½Â¤Íưײ½Àß·×, ¤Ð¤é¤Ä¤­, Åý·×ŪÃÙ±ä²òÀÏ, DFM
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Æü»þ: 2006ǯ4·î25Æü(²Ð) 13:30-14:45
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Pagepp. 571 - 576
Keyword ¥Ç¥Ð¥¤¥¹Î³ÅÙ, ¥¢¡¼¥­¥Æ¥¯¥Á¥ã¸¡Æ¤, ¥¢¡¼¥­¥Æ¥¯¥Á¥ã¥â¥Ç¥ë, ưŪɾ²Á
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Pagepp. 577 - 582
Keyword ưŪºÆ¹½À®²Äǽ¥×¥í¥»¥Ã¥µ, ¥¿¥¹¥¯Ê¬³ä, Áȹ礻ºÇŬ²½ÌäÂê, Simulated AnnealingË¡
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Pagepp. 583 - 588
Keyword ¥×¥í¥»¥Ã¥µ¥³¥¢, ¥­¥ã¥Ã¥·¥å, ºÇŬ²½, ÁȤ߹þ¤ß¥·¥¹¥Æ¥à
AbstractÁȤ߹þ¤ß¥·¥¹¥Æ¥à¤Ë¤ª¤¤¤ÆÆÃÄê¤Î¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤ËÆò½¤·¤¿¥×¥í¥»¥Ã¥µ¥³¥¢¤¬É¬ÍפȤʤ롥ÆÃ¤Ë¥×¥í¥»¥Ã¥µ¥³¥¢¤Î¥­¥ã¥Ã¥·¥å¤ËÃíÌܤ·¤¿¤È¤­¡¤ÁȤ߹þ¤ß¥·¥¹¥Æ¥à¤Ç¤ÏÌÌÀÑÀ©Ì󤬸·¤·¤¤¤¿¤á¼Â¹Ô»þ´ÖÀ©Ìó¤òËþ¤¿¤¹Ãæ¤Ç¥á¥â¥ê¥µ¥¤¥ººÇ¾®¤«¤Ä¤è¤êñ½ã¤Ê¥­¥ã¥Ã¥·¥å¹½À®¤òÆÀ¤ë¤³¤È¤¬¶¯¤¯Ë¾¤Þ¤ì¤ë¡¥ËܹƤǤÏÊ£¿ô¤Î¥­¥ã¥Ã¥·¥å¹½À®¤ÎÃ椫¤é¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤ËŬ¤·¤¿¹½À®¤òÁªÂò¤¹¤ë¥­¥ã¥Ã¥·¥å¹½À®ºÇŬ²½¥·¥¹¥Æ¥à¤òÄó°Æ¤¹¤ë¡¥¥­¥ã¥Ã¥·¥å¹½À®ºÇŬ²½¥·¥¹¥Æ¥à¤Ï¥­¥ã¥Ã¥·¥å¹½À®ºÇŬ²½¥¢¥ë¥´¥ê¥º¥à¤È¥­¥ã¥Ã¥·¥åÀ­Ç½É¾²Á·Ï¤«¤é¹½À®¤µ¤ì¤ë¡¥¥­¥ã¥Ã¥·¥å¹½À®ºÇŬ²½¥¢¥ë¥´¥ê¥º¥à¤ÏÊ£¿ô¤Î¥­¥ã¥Ã¥·¥å·¿¡¤¥á¥â¥ê¥µ¥¤¥º¡¤¥Ö¥í¥Ã¥¯¥µ¥¤¥º¤òºÇŬ²½¤·¥¢¥×¥ê¥±¡¼¥·¥ç¥ó¤ËºÇŬ¤Ê¥­¥ã¥Ã¥·¥å¹½À®¤òÁªÂò¤¹¤ë¡¥¥­¥ã¥Ã¥·¥åÀ­Ç½É¾²Á·Ï¤Ï¥­¥ã¥Ã¥·¥å¹½À®ºÇŬ²½¥¢¥ë¥´¥ê¥º¥à¤Ë¤ª¤¤¤Æ¥­¥ã¥Ã¥·¥å¹½À®¤òÁªÂò¤¹¤ë¤¿¤á¤Î»Øɸ¤È¤·¤Æ¥á¥â¥ê¥¢¥¯¥»¥¹Í׵ᡤ¥á¥â¥êÀ­Ç½¡¤¥­¥ã¥Ã¥·¥å¹½À®¤«¤éÁí¥á¥â¥ê¥¢¥¯¥»¥¹»þ´Ö¤òµá¤á¤ë¡¥¥­¥ã¥Ã¥·¥å¹½À®ºÇŬ²½¥¢¥ë¥´¥ê¥º¥à¤È¥­¥ã¥Ã¥·¥åÀ­Ç½É¾²Á·Ï¤òϢư¤µ¤»¤ë¤³¤È¤ÇºÇŬ¤Ê¥­¥ã¥Ã¥·¥å¹½À®¤òÆÀ¤ë¤³¤È¤¬²Äǽ¤È¤Ê¤ë¡¥É¾²Á¼Â¸³¤Ë¤è¤êÄó°Æ¥·¥¹¥Æ¥à¤ÎÍ­¸úÀ­¤ò³Îǧ¤·¤¿¡¥


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Æü»þ: 2006ǯ4·î25Æü(²Ð) 15:15-16:55
ºÂĹ: »³ÅÄ ¹¸µ× (¥·¥ã¡¼¥×)

Âê̾Dual-Rail Two-Phase Asynchronous Datapath Synthesis Based on Aggressive Register Sharing Model
Ãø¼Ô ¡ûKoji Ohashi, Mineo Kaneko(ËÌΦÀèü²Ê³Øµ»½ÑÂç³Ø±¡Âç³Ø)
Pagepp. 589 - 594
Keyword Asynchronous systems, Datapath synthesis, Resource sharing, Dual-rail two-phase style
AbstractThis paper discusses a model for resource sharing, especially register sharing, in dual-rail two-phase asynchronous datapath synthesis. We introduce a new type of register which is driven by data to be latched and a control signal. The register latches input data by the state transition of the data itself and the transition of the control signal. By using these registers, multiple data can share a same register aggressively in a dual-rail two-phase asynchronous datapath. The main advantage of our register and register sharing model is to provide (1) datapaths with the reduced number of registers and demultiplexers, while keeping higher-speed, and (2) a larger solution space of resource sharing.

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Pagepp. 595 - 600
Keyword ¥ê¥¢¥ë¥¿¥¤¥à¼Âµ¡¸¡¾Ú, ¥×¥í¥È¥¿¥¤¥×¸¡¾Ú
Abstract¥·¥¹¥Æ¥à¥Æ¥¹¥È¤Î¸¡¾Ú»þ´Ö¤ò¹â®²½¤¹¤ë¤¿¤á¤Ë¡¢¥×¥í¥È¥¿¥¤¥Ô¥ó¥°¤ä¼Âµ¡¤òÍѤ¤¤¿¸¡¾Ú¤¬ÍѤ¤¤é¤ì¤Æ¤¤¤ë¤¬¡¢¿®¹æ¤Î²Ä´Ñ¬À­¤¬Ä㤤¤¿¤á¡¢¥Æ¥¹¥È»þ¤ËȯÀ¸¤·¤¿ÌäÂê¤Î¡¢È¯À¸¾ò·ï¤ÎÆÃÄ꤬º¤Æñ¤Ç¤¢¤ë¡£¥×¥í¥È¥¿¥¤¥Ô¥ó¥°¤ä¼Âµ¡¤Ç¤Î¹â®¡¦ÂçÎ̤ο®¹æÊѲ½¤ò¿®¹æÁ«°Ü¤Îñ°Ì¤ÇÀÚ¤êʬ¤±¡¢¥È¥é¥ó¥¶¥¯¥·¥ç¥ó¤È¤·¤Æª¤¨¡¢¤½¤ÎȯÀ¸ÉÑÅÙ¾ðÊ󤫤鵩¤ÊÆ°ºî¤äÀß·×¼Ô¤ÎÁÛÄꤷ¤Æ¤¤¤Ê¤¤µóÆ°¤òʬÀϤ·¡¢¥Ï¡¼¥É¥¦¥¨¥¢¸¡¾Ú¤Î¸úΨ¤ò¸þ¾å¤µ¤»¤ë¼êË¡¤¬Äó°Æ¤µ¤ì¤Æ¤¤¤ë¡£¤·¤«¤·¡¢¥È¥é¥ó¥¶¥¯¥·¥ç¥ó¤òÀÚ¤ê½Ð¤¹¾ò·ï¤¬¤¢¤é¤«¤¸¤á»ÈÍѼԤ˼«ÌÀ¤Ç¤Ê¤±¤ì¤ÐŬÍѤǤ­¤Ê¤¤¡£ËÜÏÀʸ¤Ç¤Ï¡¢Â¬ÄêÂоݤο®¹æÁ«°Ü¤«¤é¥È¥é¥ó¥¶¥¯¥·¥ç¥ó¤òÀڤ뤿¤á¤ÎʬΥ¾ò·ï¤ò¼«Æ°Åª¤ËȽÊ̤¹¤ë¼êË¡¤Ë¤Ä¤¤¤Æ½Ò¤Ù¤ë¡£

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Pagepp. 601 - 606
Keyword Æ°ºî¹çÀ®, ±é»»¤Î¥Á¥§¥¤¥Ë¥ó¥°, ¥Þ¥ë¥Á¥Õ¥¡¥ó¥¯¥·¥ç¥ó±é»»´ï
AbstractÆ°ºî¹çÀ®¤Ë¤ª¤¤¤Æ½¾Íè¤Î±é»»¤Î¥Á¥§¥¤¥Ë¥ó¥°¼êË¡¤Ç¤Ï¡¢ »ÈÍѤ¹¤ë±é»»´ï¤Ï²Ã»»´ï¤Ê¤É¤Îñ°ì¤Îµ¡Ç½¤òÍ­¤¹¤ë±é»»´ï ¤·¤«ÍѤ¤¤é¤ì¤Æ¤¤¤Ê¤«¤Ã¤¿¡£ ËÜÏÀʸ¤Ç¤Ï¡¢Ê£¿ô¤Îµ¡Ç½¤òÀÚ¤êÂؤ¨²Äǽ¤Ê ¥Þ¥ë¥Á¥Õ¥¡¥ó¥¯¥·¥ç¥ó±é»»´ï¤ò¥Á¥§¥¤¥Ë¥ó¥°¤ÎºÝ¤Ë¹Íθ¤¹¤ë¤³¤È¤Ç¡¢ ½¾Íè¤è¤ê¤â¥ì¥¤¥Æ¥ó¥·¤¬ºï¸º²Äǽ¤Ê¼êË¡¤òÄó°Æ¤¹¤ë¡£ Äó°Æ¼êË¡¤Ï¡¢ÌäÂê¤ò0/1À°¿ôÀþ·Á·×²èÌäÂê¤È¤·¤Æ Äê¼°²½¤¹¤ë¤³¤È¤Ç¡¢»ñ¸»À©ÌóµÚ¤ÓÆ°ºî¼þÇÈ¿ôÀ©Ìó²¼¤Ç ¥ì¥¤¥Æ¥ó¥·¤¬ºÇ¾®¤È¤Ê¤ë±é»»´ï¥»¥Ã¥ÈµÚ¤Ó¥¹¥±¥¸¥å¡¼¥ë¤ò µá¤á¤ë¤³¤È¤ò²Äǽ¤È¤¹¤ë¡£ ¼Â¸³¤Ç¤Ï¡¢Äó°Æ¼êË¡¤¬Í­¸ú¤Ç¤¢¤ë¤³¤È¤¬³Îǧ¤µ¤ì¤¿¡£

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Pagepp. 607 - 612
Keyword Æ°ºî¹çÀ®, ´Ø¿ô¸Æ½Ð¤·, À°¿ô·×²èÌäÂê
AbstractËÜÏÀʸ¤Ç¤Ï¡¢Æ°ºî¹çÀ®¤Ë¤ª¤±¤ë´Ø¿ô¸Æ½Ð¤·¤ÎºÇŬ²½¼êË¡¤òÄó°Æ¤¹¤ë¡£Äó°Æ¼êË¡¤Ï¡¢Ê£¿ô¤Î´Ø¿ô¤«¤é¹½À®¤µ¤ì¤ë¥×¥í¥°¥é¥à¤òÂоݤȤ·¤Æ¡¢¥¤¥ó¥é¥¤¥óŸ³«¤¹¤ë´Ø¿ô¤È¥¤¥ó¥é¥¤¥óŸ³«¤·¤Ê¤¤´Ø¿ô¤ÎºÇŬ¤ÊÁȹ礻¤ò·èÄꤹ¤ë¡£¤Þ¤¿¡¢Ê£¿ô¤Î´Ø¿ô¤ò£±¤Ä¤ËÊ»¹ç¤¹¤ë¤³¤È¤Ë¤è¤ê¡¢»ñ¸»¤Î¶¦Í­¤ò¼Â¸½¤¹¤ë¡£ËÜÏÀʸ¤Ç¤Ï¡¢´Ø¿ô¸Æ½Ð¤·¤ÎºÇŬ²½¼êË¡¤Î³µÎ¬¤òÀâÌÀ¤·¤¿¸å¡¢¤³¤ÎºÇŬ²½ÌäÂê¤òÀ°¿ô·×²èÌäÂê¤È¤·¤ÆÄê¼°²½¤¹¤ë¡£ºÇ¸å¤Ë¡¢¼Â¸³¤Ë¤è¤ê½¾Íè¼êË¡¤ËÂФ¹¤ëÍ¥°ÌÀ­¤ò¼¨¤¹¡£


¥»¥Ã¥·¥ç¥ó D2-1 ¥Í¥Ã¥È¥ï¡¼¥¯¤Î¿®ÍêÀ­
Æü»þ: 2006ǯ4·î25Æü(²Ð) 9:15-10:15
ºÂĹ: ÅÏÊÕ °ê (ÂçºåÅŵ¤ÄÌ¿®Âç³Ø Áí¹ç¾ðÊó³ØÉô¥á¥Ç¥£¥¢¥³¥ó¥Ô¥å¡¼¥¿¥·¥¹¥Æ¥à³Ø²Ê)

Âê̾Tri-Connectivity Augmentation Problems for Bi-connected Graphs with Upper Bounds on Vertex-Degree Increase
Ãø¼Ô Takanori Fukuoka, Satoshi Taoka(Graduate School of Engineering, Hiroshima University), ¡ûToshiya Mashima(Hiroshima International University), Toshimasa Watanabe(Graduate School of Engineering, Hiroshima University)
Pagepp. 613 - 618
Keyword graphs, connectivity augmenation, vertex-connectivity, degree constrains
AbstractThe 3-vertex-connectivity augmentation problem of a graph with degree constraints, 3VCA-DC, is defined as follows: ``Given an undirected graph G=(V,E) and an upper bound a(v;G) ¢º Z+ ¢À ¡ç on vertex-degree increase for each v\in V, find a smallest set E' of edges such that (V,E ¢À E') has at least three internally-disjoint paths between any pair of vertices in V and such that vertex-degree increase of each v ¢º V by the addition of E' to G is at most a(v;G), where Z+ is the set of nonnegative integers.'' In this paper we show that finding an optimum solution to 3VCA-DC for a bi-connected graph G can be done in O(|V||V|+|E|) time.

Âê̾System-Level Diagnosis in the Presence of Intermittent Faults
Ãø¼Ô Daisuke Kiri, ¡ûToshinori Yamada(ºë¶ÌÂç³ØÂç³Ø±¡ Íý¹©³Ø¸¦µæ²Ê ¿ôÍýÅŻҾðÊóÉôÌç)
Pagepp. 619 - 624
Keyword System-Level Diagnosis, Intermittent Faults
AbstractFu and Beigel introduced a graph-theoretical model for a system diagnosis in the presence of intermittent faults, which we call the FB model. This paper gives a necessary and sufficient condition for a digraph to be diagnosable in the FB model, and presents two algorithms for a system diagnosis in the presence of intermittent faults.


¥»¥Ã¥·¥ç¥ó D2-2 [ÆÃÊÌ¥»¥Ã¥·¥ç¥ó]¥â¥Ç¥ë¸¡ºº¤Î±þÍÑ
Æü»þ: 2006ǯ4·î25Æü(²Ð) 10:45-11:45
ºÂĹ: °ëÉô ¾Í¾° (»º¶Èµ»½ÑÁí¹ç¸¦µæ½ê ¾ðÊ󵻽Ѹ¦µæÉôÌç)

Âê̾(¾·ÂÔ)¥â¥Ç¥ë¸¡ºº¤òÍѤ¤¤¿¥¿¥°VLAN¤ÎÀßÄ긡ºº
Ãø¼Ô ¡ûݯÅÄ ±Ñ¼ù(NTT¥³¥ß¥å¥Ë¥±¡¼¥·¥ç¥ó²Ê³Ø´ðÁø¦µæ½ê ¿Í´Ö¾ðÊ󸦵æÉô ¾ðÊó´ðÁÃÍýÏÀ¸¦µæ¥°¥ë¡¼¥×)
Pagepp. 625 - 630
Keyword ¥Í¥Ã¥È¥ï¡¼¥¯, ¸¡ºº, LAN, ¥â¥Ç¥ë¸¡ºº, ÍÍÁêÏÀÍý
Abstract¥Í¥Ã¥È¥ï¡¼¥¯µ¡´ï¤ÎÀßÄê¤ÏÄ̾ï¿Í¼ê¤Ë¤è¤Ã¤Æ¹Ô¤ï¤ì¤ë. ¤³¤Î¤¿¤á, ÄÌ¿®¤ÎÃÇÀä¤ä¾ðÊó¤Îϳ±È¤Ê¤É¥Í¥Ã¥È¥ï¡¼¥¯¤Ë¿¼¹ï¤Ê¾ã³²¤ò¤â¤¿¤é¤¹ÀßÄê¥ß¥¹¤¬È¯À¸¤¹¤ë¤³¤È¤¬¤¢¤ë. ËܹƤǤϥͥåȥ¥¯µ¡´ï, Æä˥¿¥°VLAN¤òÍѤ¤¤ëEthernetµ¡´ï¤ÎÀßÄê¤ÎÀµ¤·¤µ¤ò¥â¥Ç¥ë¸¡ººË¡¤òÍѤ¤¤Æ¸¡ºº¤¹¤ëÊýË¡¤òÄó°Æ¤¹¤ë. ¤³¤ÎÊýË¡¤Ë¤è¤ê, ¥Í¥Ã¥È¥ï¡¼¥¯¤ËÍ׵ᤵ¤ì¤ëÍÍ¡¹¤ÊÀ­¼Á¤ò¸úΨŪ¤Ë¸¡ºº¤Ç¤­¤ë¤è¤¦¤Ê¤ë. ¤Þ¤¿, ÀßÄê¤Ë¸í¤ê¤¬¤¢¤ë¾ì¹ç¤Ë¤Ï, ¤³¤ÎÊýË¡¤Ë¤è¤êµ¯¤³¤êÆÀ¤ë²ÄǽÀ­¤Î¤¢¤ë¾ã³²¤Ë¤Ä¤¤¤ÆÄ´¤Ù¤ë¤³¤È¤¬¤Ç¤­¤ë. ¤³¤ì¤ÏÀßÄê¥ß¥¹¤ò½¤Àµ¤¹¤ëºÝ¤ËÊØÍø¤Ç¤¢¤ë.


¥»¥Ã¥·¥ç¥ó D2-4 ¥°¥é¥Õ¥¢¥ë¥´¥ê¥º¥à
Æü»þ: 2006ǯ4·î25Æü(²Ð) 15:15-16:15
ºÂĹ: Ä»µï ·òÂÀϺ (¡Ê³ô¡ËÅì¼Ç ¸¦µæ³«È¯¥»¥ó¥¿¡¼ ¥·¥¹¥Æ¥àµ»½Ñ¥é¥Ü¥é¥È¥ê¡¼)

Âê̾Polynomial-Time Algorithm for Finding a Solution in the Core of a Multicommodity Flow Game
Ãø¼Ô Kazuhiro Karasawa, ¡ûToshinori Yamada(ºë¶ÌÂç³ØÂç³Ø±¡ Íý¹©³Ø¸¦µæ²Ê ¿ôÍýÅŻҾðÊóÉôÌç)
Pagepp. 631 - 636
Keyword Multicommodity flow game, Coalitional game, Core, Spider, Complete Graph
AbstractMotivated by the development of an efficient and stable routing scheme for the Internet, Papadimitriou introduced a multicommodity flow game and raised the problem of whether the core of a multicommodity flow game is always nonempty. Markakis and Saberi settled the problem affirmatively. However, thier proof is not constructive, and it is not known how to find a solution in the core of the game, to the best of my knowledge. This paper presents a polynomial-time algorithm for finding a multicommodity flow game if G is a spider or complege graph.

Âê̾Performance Comparison of Algorithms for the Dynamic Shortest Path Problem
Ãø¼Ô Takashi Iguchi, ¡ûSatoshi Taoka, Daisuke Takafuji, Toshimasa Watanabe(Graduate School of Engineering, Hiroshima University)
Pagepp. 637 - 642
Keyword networks, shortest path problems, dynamic algorithms, static algorithms
AbstractA network in an edge-weighted directed graph and an edge operation is an operation that increases or decreases an edge weight. Decreasing an edge weight from an infinite value to a finite one or increasing an edge weight from a finite value to an infinite one, respectively, corresponds to addition or deletion of an edge. The dynamic shortest path problem(DSPP for short) is defined by ``Given any network, with a specified vertices, and any sequence of edge operations, construct a shortest path tree of each network obtained by executing those edge operations one by one in the order of the sequence.'' As an application, fast routing for an interior network using link state protocols, such as OSPF and IS-IS, requires efficiently solving DSPP. Based on experimental results on networks of large size and a lot of edge operations, the paper shows that NST(BF)+ proposed by Narvaez et.al in 2000 is the fastest among 25 existing algorithms that we have tried. Also shown is that dynamic algorithms are faster than repeating a static algorithm in solving DSPP.