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PRODID:IEEE vTools.Events//EN
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DTSTART:20250330T030000
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DTSTAMP:20251009T115128Z
UID:C5711DA7-064F-44CC-B4D4-34B13B5A7FB3
DTSTART;TZID=Europe/Brussels:20250828T140000
DTEND;TZID=Europe/Brussels:20250828T150000
DESCRIPTION:Distinguished Lecture (DL) by Prof. Xing-Chang Wei titled: “C
 an You Identify an Electromagnetic Photo? – Electromagnetic compatibilit
 y (EMC) Analysis Enhanced by Artificial Intelligence (AI).”\n\nAI has em
 erged as a powerful tool in electromagnetic analysis\, with notable applic
 ations in microwave and antenna design. The radiated near-field can be see
 n as an “electromagnetic photo” of an electromagnetic interference (EM
 I) source\, revealing information such as far-field radiation\, coupling\,
  and source characteristics. Since electromagnetic waves are invisible\, i
 nterpretation is difficult\, but AI’s image-recognition capabilities ena
 ble efficient extraction of these features for improved EMI analysis.\n\nS
 peaker(s): Wei\, \n\nBldg: KU Leuven\, Bruges Campus\, Spoorwegstraat 12\,
  Bruges\, West-Vlaanderen\, Belgium\, 8200\, Virtual: https://events.vtool
 s.ieee.org/m/506096
LOCATION:Bldg: KU Leuven\, Bruges Campus\, Spoorwegstraat 12\, Bruges\, Wes
 t-Vlaanderen\, Belgium\, 8200\, Virtual: https://events.vtools.ieee.org/m/
 506096
ORGANIZER:lirim.koraqi@kuleuven.be
SEQUENCE:45
SUMMARY:Distinguished Lecturer (DL) Talk by Prof. Xing-Chang Wei
URL;VALUE=URI:https://events.vtools.ieee.org/m/506096
X-ALT-DESC:Description: <br /><p><span style="mso-fareast-font-family: 'Tim
 es New Roman'\; mso-ligatures: none\; mso-fareast-language: EN-GB\;">Disti
 nguished Lecture (DL) by Prof. Xing-Chang Wei </span><span style="mso-fare
 ast-font-family: 'Times New Roman'\; mso-ligatures: none\; mso-fareast-lan
 guage: EN-GB\;">titled: <span style="font-family: 'Aptos'\,sans-serif\; ms
 o-bidi-font-family: Aptos\;">&ldquo\;Can You Identify an Electromagnetic P
 hoto? &ndash\; Electromagnetic compatibility (EMC) Analysis Enhanced by Ar
 tificial Intelligence (AI).&rdquo\;</span></span></p>\n<p><span style="fon
 t-size: 12.0pt\; font-family: 'Aptos'\,sans-serif\; mso-fareast-font-famil
 y: 'Times New Roman'\; mso-bidi-font-family: Aptos\; mso-ansi-language: EN
 -GB\; mso-fareast-language: EN-GB\; mso-bidi-language: AR-SA\;">AI has eme
 rged as a powerful tool in electromagnetic analysis\, with notable applica
 tions in microwave and antenna design. The radiated near-field can be seen
  as an &ldquo\;electromagnetic photo&rdquo\; of an electromagnetic interfe
 rence (EMI) source\, revealing information such as far-field radiation\, c
 oupling\, and source characteristics. Since electromagnetic waves are invi
 sible\, interpretation is difficult\, but AI&rsquo\;s image-recognition ca
 pabilities enable efficient extraction of these features for improved EMI 
 analysis. </span></p>
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