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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) ¿þÀ̺귿 ½ºÆåÆ®·³ ºÐ¼®À» ÀÌ¿ëÇÑ ½ÅÈ£ÀÇ Æ¯Â¡ °ËÃâ
¿µ¹®Á¦¸ñ(English Title) Feature Detection of Signals using Wavelet Spectrum Analysis
ÀúÀÚ(Author) ¹è»ó¹ü   ±è³²È£   Sang-Bum Bae   Nam-Ho Kim  
¿ø¹®¼ö·Ïó(Citation) VOL 10 NO. 04 PP. 0758 ~ 0763 (2006. 04)
Çѱ۳»¿ë
(Korean Abstract)
±âÃÊ°úÇаú °øÇÐÀÇ ´Ù¾çÇÑ ºÐ¾ß¿¡¼­, ½ÅÈ£¿Í ½Ã½ºÅÛÀ» Á¤È®ÇÏ°Ô Ç¥ÇöÇÏ°í, ½ÅÈ£ÀÇ °ø°£Àû, ½Ã°£Àû º¯È­·ÎºÎÅÍ À¯¿ëÇÑ Á¤º¸¸¦ ȹµæÇϱâ À§ÇÑ ¸¹Àº ¿¬±¸µéÀÌ ¼öÇàµÇ¾î ¿Ô´Ù. ÀÌ·¯ÇÑ ºÐ¼® ¹æ¹ýµé¿¡¼­, ½ÅÈ£¸¦ ÁÖÆļö ¼ººÐµéÀÇ Á¶ÇÕÀ¸·Î¼­ Ç¥ÇöÇÏ´Â Ç»¸®¿¡ º¯È¯Àº °¡Àå ¸¹Àº ºÐ¾ß¿¡¼­ ÀÀ¿ëµÇ°í ÀÖ´Ù. ±×·¯³ª Ç»¸®¿¡ º¯È¯Àº ½Ã°£Á¤º¸¸¦ °í·ÁÇÏÁö ¾Ê´Â º¯È¯À¸·Î¼­ ÀÀ¿ëÀÇ ÇѰ輺À» Áö´Ï°í ÀÖÀ¸¹Ç·Î À̸¦ ±Øº¹Çϱâ À§ÇØ, ¿þÀ̺귿 º¯È¯À» ºñ·ÔÇÑ ´Ù¾çÇÑ ¹æ¹ýµéÀÌ Á¦½ÃµÇ¾ú´Ù. ¿þÀ̺귿 º¯È¯Àº ½ºÄÉÀÏ º¯¼ö¿¡ µû¶ó º¯È­ÇÏ´Â À©µµ¿ì¸¦ »ç¿ëÇÏ¿© ½ÅÈ£¸¦ ½Ã°£-½ºÄÉÀÏ °ø°£»ó¿¡¼­ Ç¥ÇöÇÏ´Â º¯È¯À¸·Î¼­, ´ÙÁßÇØ»óµµ ºÐ¼®ÀÌ °¡´ÉÇϸç, ÀÀ¿ëȯ°æ¿¡ µû¶ó ´Ù¾çÇÑ ÇüÅÂÀÇ ÇÔ¼ö¸¦ Á¤ÀÇÇÒ ¼ö ÀÖ´Ù. µû¶ó¼­ º» ³í¹®¿¡¼­ ½ÅÈ£ÀÇ Æ¯Â¡À» °ËÃâÇϱâ À§ÇØ, Ç»¸®¿¡ º¯È¯ÀÇ ±âÀúÇÔ¼ö¸¦ »ç¿ëÇÏ¿© ¿þÀ̺귿 ½ºÆåÆ®·³À» ºÐ¼®ÇÏ¿´´Ù.
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(English Abstract)
In various fields of basic science and engineering, in order to present signals and systems exactly and acquire useful information from spatial and timely changes, many researches have been processed. In these methods, the Fourier transform which represents signal as the combination of the frequency component has been applied to the most fields. But as transform not to consider time information, the Fourier transform has its limitations of application. To overcome this problem, a variety of methods including the wavelet transform have been proposed. As transform to represent signal by using the changing window, according to scale parameter in time-scale domain, the wavelet transform is capable of multiresolution analysis and defines various functions according to the application environments. In this paper, to detect features of signal we analyzed wavelet the spectrum by using the bases function of the fourier transform.
Å°¿öµå(Keyword) wavelet transform   time-scale   window   multiresolution  
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