BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260403T023524Z UID:AAE138E0-8EF8-494F-AF11-E4C8AEEB99CB DTSTART;TZID=America/Los_Angeles:20260402T170000 DTEND;TZID=America/Los_Angeles:20260402T181500 DESCRIPTION:[]\n\nThe precise manipulation and assembly of micro- and nano- objects hold immense promise for advancing various industries\, from healt hcare to nanotechnology. However\, the robust and independent manipulation of multiple particles remains a significant challenge due to the global i nfluence of coupled external fields. This presentation will delve into the development of innovative solutions to independently and simultaneously c ontrol multiple micro- and nanoparticles in fluid suspensions using shared electric fields. By leveraging adaptive robust motion control and ensembl e control systems\, we demonstrate how to efficiently manipulate multiple micro- and nanowires using a simple electrode setup in a liquid environmen t. In addition\, I will introduce a learning-based autofocus (AF) and 3D p osture estimation scheme that enhances the tracking and manipulation of mi cro- and nanowires. This approach integrates convolutional neural networks for precise identification of focal distances and inclination angles\, en abling the accurate tracking of multiple moving objects in a three-dimensi onal microfluidic environment. Through transfer learning\, we extend the v ersatility of this system to wires of various materials\, achieving high a ccuracy and efficiency in comparison to traditional methods. These advance ments in AF and pose estimation lay the groundwork for automated micro- an d nano-object manipulation with wide-reaching applications in material sci ence\, nanorobotics\, and targeted drug delivery. Finally\, I will briefly touch on our ongoing work with Fine-Tuning Hybrid Dynamics\, a generaliza ble physics-informed neural network model\, applied to vehicle dynamics bu t adaptable to other complex dynamic systems. These innovations offer a co mprehensive solution to the challenges of autonomous control and manipulat ion of micro- and nanoparticles\, paving the way for next-generation funct ional nanodevice assembly and advancements in neuromorphic computing.\n\nK aiyan Yu earned her B.S. degree in Intelligent Science and Technology from Nankai University\, China\, and a Ph.D. in Mechanical and Aerospace Engin eering from Rutgers University\, USA. She joined Binghamton University in 2018 as an Assistant Professor in the Department of Mechanical Engineering . Her research focuses on autonomous robotic systems\, motion planning\, m echatronics\, and automation science. In 2022\, she received the NSF CAREE R Award and currently holds positions as Associate Editor for IEEE Transac tions on Automation Science and Engineering\, IEEE Robotics and Automation Letters\, IFAC Mechatronics\, Frontiers in Robotics and AI\, the IEEE Rob otics and Automation Society Conference Editorial Boards and the ASME Dyna mic Systems and Control Division Conference Editorial Boards.\n\nVirtual: https://events.vtools.ieee.org/m/549221 LOCATION:Virtual: https://events.vtools.ieee.org/m/549221 ORGANIZER:srazanaq@uwo.ca SEQUENCE:9 SUMMARY:IEEE IAS Lecture: Learning-Based Approaches for 3D Manipulation of Micro- and Nano-Objects URL;VALUE=URI:https://events.vtools.ieee.org/m/549221 X-ALT-DESC:Description: <br /><p><br><img style="display: block\; margin-le ft: auto\; margin-right: auto\;" src="https://events.vtools.ieee.org/vtool s_ui/media/display/06e0bce6-c310-46f1-b63a-22d828ed4fda" alt="" width="756 " height="86"></p>\n<p><span style="font-size: 12.0pt\; line-height: 107%\ ; font-family: 'Calibri'\,sans-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: SimSun\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: minor-latin\; mso-bidi-font-family: 'Times New Roma n'\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fare ast-language: ZH-CN\; mso-bidi-language: AR-SA\;">The precise manipulation and assembly of micro- and nano-objects hold immense promise for advancin g various industries\, from healthcare to nanotechnology. However\, the ro bust and independent manipulation of multiple particles remains a signific ant challenge due to the global influence of coupled external fields. This presentation will delve into the development of innovative solutions to i ndependently and simultaneously control multiple micro- and nanoparticles in fluid suspensions using shared electric fields. By leveraging adaptive robust motion control and ensemble control systems\, we demonstrate how to efficiently manipulate multiple micro- and nanowires using a simple elect rode setup in a liquid environment. In addition\, I will introduce a learn ing-based autofocus (AF) and 3D posture estimation scheme that enhances th e tracking and manipulation of micro- and nanowires. This approach integra tes convolutional neural networks for precise identification of focal dist ances and inclination angles\, enabling the accurate tracking of multiple moving objects in a three-dimensional microfluidic environment. Through tr ansfer learning\, we extend the versatility of this system to wires of var ious materials\, achieving high accuracy and efficiency in comparison to t raditional methods. These advancements in AF and pose estimation lay the g roundwork for automated micro- and nano-object manipulation with wide-reac hing applications in material science\, nanorobotics\, and targeted drug d elivery. Finally\, I will briefly touch on our ongoing work with Fine-Tuni ng Hybrid Dynamics\, a generalizable physics-informed neural network model \, applied to vehicle dynamics but adaptable to other complex dynamic syst ems. These innovations offer a comprehensive solution to the challenges of autonomous control and manipulation of micro- and nanoparticles\, paving the way for next-generation functional nanodevice assembly and advancement s in neuromorphic computing.</span></p>\n<p class="MsoNormal" style="mso-m argin-top-alt: auto\; margin-bottom: 8.0pt\; line-height: 115%\;"><img src ="https://events.vtools.ieee.org/vtools_ui/media/display/13e65dcf-76dd-42e a-8834-932e88c83d82" width="227" height="288"></p>\n<p class="MsoNormal" s tyle="mso-margin-top-alt: auto\; margin-bottom: 8.0pt\; line-height: 115%\ ;">&nbsp\;</p>\n<p><span class="il"><span style="font-size: 12.0pt\; line- height: 107%\; font-family: 'Calibri'\,sans-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: SimSun\; mso-fareast-theme-font: mi nor-fareast\; mso-hansi-theme-font: minor-latin\; mso-bidi-theme-font: min or-latin\; color: #222222\; background: white\; mso-ansi-language: EN-US\; mso-fareast-language: ZH-CN\; mso-bidi-language: AR-SA\;">Kaiyan</span></ span><span style="font-size: 12.0pt\; line-height: 107%\; font-family: 'Ca libri'\,sans-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font- family: SimSun\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-f ont: minor-latin\; mso-bidi-theme-font: minor-latin\; color: #222222\; bac kground: white\; mso-ansi-language: EN-US\; mso-fareast-language: ZH-CN\; mso-bidi-language: AR-SA\;">&nbsp\;<span class="il">Yu</span>&nbsp\;earned her B.S. degree in Intelligent Science and Technology from Nankai Univers ity\, China\, and a Ph.D. in Mechanical and Aerospace Engineering from Rut gers University\, USA. She joined Binghamton University in 2018 as an Assi stant Professor in the Department of Mechanical Engineering. Her research focuses on autonomous robotic systems\, motion planning\, mechatronics\, a nd automation science. In 2022\, she received the NSF CAREER Award and cur rently holds positions as Associate Editor for&nbsp\;<em>IEEE Transactions on Automation Science and Engineering</em>\,&nbsp\;<em>IEEE Robotics and Automation Letters</em>\,&nbsp\;<em>IFAC Mechatronics</em>\,&nbsp\;<em>Fro ntiers in Robotics and AI</em>\, the IEEE Robotics and Automation Society Conference Editorial Boards and the ASME Dynamic Systems and Control Divis ion Conference Editorial Boards.</span></p> END:VEVENT END:VCALENDAR