BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20241103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20241120T154423Z UID:3E165A78-2837-42ED-A3F3-158DB8CCFBC7 DTSTART;TZID=America/New_York:20241119T193000 DTEND;TZID=America/New_York:20241119T203000 DESCRIPTION:Exploring the Potential of Deep-Learning and Machine-Learning i n Dual-Band Antenna Design\n\nKeywords\nAntennas\, Dual Band\, Computation al Modeling\, Mathematical Models\, Antenna Arrays\, Training\, Accuracy\, Machine Learning\, Deep Learning\, Antenna Design\, Dual Band Antennas\, AI Antenna\, Deep Learning\, Antenna Design\, Dual Band Antenna\, Dual Ban d Antenna Design\, Neural Network\, Support Vector Machine\, Design Proces s\, Machine Learning Models\, Machine Learning Techniques\, Design Paramet ers\, Internet Of Things\, Reflection Coefficient\, Operating Frequency\, Regression Problem\, Residual Network\, Wireless Communication Systems\, I nternet Of Things Applications\, Modern Communication\, Antenna Performanc e\, Residual Neural Network\, Electromagnetic Simulation\, Multiple Output s\, Neurons In Layer\, Hidden Layer\, Internet Of Things Devices\, Res Net Model\, Horizontal Stripes\, Vertical Stripes\, Simulation Technology\, T raditional Antenna\nAbstract\nThis article presents an in-depth exploratio n of machine learning (ML) and deep learning (DL) for the optimization and design of dual-band antennas in Internet of Things (IoT) applications. Du al-band antennas\, which are essential for the functionality of current an d forthcoming flexible wireless communication systems\, face increasing co mplexity and design challenges as demands and requirements for IoT-connect ed devices become more challenging. The study demonstrates how artificial intelligence (AI) can streamline the antenna design process\, enabling cus tomization for specific frequency ranges or performance characteristics wi thout exhaustive manual tuning. By utilizing ML and DL tools\, this resear ch not only enhances the efficiency of the design process but also achieve s optimal antenna performance with significant time savings. The integrati on of AI in antenna design marks a notable advancement over traditional me thods\, offering a systematic approach to achieving dual-band functionalit y tailored to modern communication needs. We approached the antenna design as a regression problem\, using the reflection coefficient\, operating fr equency\, bandwidth\, and voltage standing wave ratio as input parameters. The ML and DL models then are used to predict the corresponding design pa rameters for the antenna by using 1\,000 samples\, from which 700 are allo cated for training and 300 for testing. This effectiveness of this approac h is demonstrated through the successful application of various ML techniq ues\, including Fine Gaussian Support Vector Machines (SVM)\, as well as R egressor and Residual Neural Networks (ResNet) with different activation f unctions\, to optimize the design of a dual-band T-shaped monopole antenna \, thereby substantiating AI's transformative potential in antenna design. \n\nAuthors:\n\nRIDA GADHAFI 1 (Senior Member\, IEEE)\, ABIGAIL COPIACO 1 (Member\, IEEE)\,\nYASSINE HIMEUR 1 (Senior Member\, IEEE)\, KIYAN AFSARI 2 (Member\, IEEE)\,\nHUSAMELDIN MUKHTAR 1 (Senior Member\, IEEE)\, KHALIDA GHANEM 3\,4\,\nAND WATHIQ MANSOOR 1 (Senior Member\, IEEE)\n\nAgenda: \n7 :30 Tea and Coffee\n\n7:45 Paper discussion\n\n8:30 End\n\n6 Flagstone Dr\ , Hudson\, New Hampshire\, United States\, 03051\, Virtual: https://events .vtools.ieee.org/m/442040 LOCATION:6 Flagstone Dr\, Hudson\, New Hampshire\, United States\, 03051\, Virtual: https://events.vtools.ieee.org/m/442040 ORGANIZER:baris.kazar@oracle.com SEQUENCE:7 SUMMARY:AI Talks with Coffee/Tea No:V: Exploring the Potential of Deep-Lear ning and Machine-Learning in Dual-Band Antenna Design URL;VALUE=URI:https://events.vtools.ieee.org/m/442040 X-ALT-DESC:Description: <br /><p>Exploring the Potential of Deep-Learning a nd Machine-Learning in Dual-Band Antenna Design</p>\n<p>Keywords<br>Antenn as\, Dual Band\, Computational Modeling\, Mathematical Models\, Antenna Ar rays\, Training\, Accuracy\, Machine Learning\, Deep Learning\, Antenna De sign\, Dual Band Antennas\, AI Antenna\, Deep Learning\, Antenna Design\, Dual Band Antenna\, Dual Band Antenna Design\, Neural Network\, Support Ve ctor Machine\, Design Process\, Machine Learning Models\, Machine Learning Techniques\, Design Parameters\, Internet Of Things\, Reflection Coeffici ent\, Operating Frequency\, Regression Problem\, Residual Network\, Wirele ss Communication Systems\, Internet Of Things Applications\, Modern Commun ication\, Antenna Performance\, Residual Neural Network\, Electromagnetic Simulation\, Multiple Outputs\, Neurons In Layer\, Hidden Layer\, Internet Of Things Devices\, Res Net Model\, Horizontal Stripes\, Vertical Stripes \, Simulation Technology\, Traditional Antenna<br>Abstract<br>This article presents an in-depth exploration of machine learning (ML) and deep learni ng (DL) for the optimization and design of dual-band antennas in Internet of Things (IoT) applications. Dual-band antennas\, which are essential for the functionality of current and forthcoming flexible wireless communicat ion systems\, face increasing complexity and design challenges as demands and requirements for IoT-connected devices become more challenging. The st udy demonstrates how artificial intelligence (AI) can streamline the anten na design process\, enabling customization for specific frequency ranges o r performance characteristics without exhaustive manual tuning. By utilizi ng ML and DL tools\, this research not only enhances the efficiency of the design process but also achieves optimal antenna performance with signifi cant time savings. The integration of AI in antenna design marks a notable advancement over traditional methods\, offering a systematic approach to achieving dual-band functionality tailored to modern communication needs. We approached the antenna design as a regression problem\, using the refle ction coefficient\, operating frequency\, bandwidth\, and voltage standing wave ratio as input parameters. The ML and DL models then are used to pre dict the corresponding design parameters for the antenna by using 1\,000 s amples\, from which 700 are allocated for training and 300 for testing. Th is effectiveness of this approach is demonstrated through the successful a pplication of various ML techniques\, including Fine Gaussian Support Vect or Machines (SVM)\, as well as Regressor and Residual Neural Networks (Res Net) with different activation functions\, to optimize the design of a dua l-band T-shaped monopole antenna\, thereby substantiating AI's transformat ive potential in antenna design.</p>\n<p>&nbsp\;</p>\n<p>Authors:</p>\n<p> RIDA GADHAFI 1 (Senior Member\, IEEE)\, ABIGAIL COPIACO 1 (Member\, IEEE)\ ,<br>YASSINE HIMEUR 1 (Senior Member\, IEEE)\, KIYAN AFSARI 2 (Member\, IE EE)\,<br>HUSAMELDIN MUKHTAR 1 (Senior Member\, IEEE)\, KHALIDA GHANEM 3\,4 \,<br>AND WATHIQ MANSOOR 1 (Senior Member\, IEEE)</p><br /><br />Agenda: < br /><p>7:30 Tea and Coffee</p>\n<p>7:45 Paper discussion</p>\n<p>8:30 End </p> END:VEVENT END:VCALENDAR