BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Canada/Eastern BEGIN:DAYLIGHT DTSTART:20240310T030000 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:20240813T140522Z UID:350D17CE-1748-415F-A861-C1D400BB1317 DTSTART;TZID=Canada/Eastern:20240806T123000 DTEND;TZID=Canada/Eastern:20240806T133000 DESCRIPTION:Abstract: Real-time detection\, classification\, and identifica tion of aerosol particles are crucial in various industries and public hea lth areas. To overcome the limitations of existing particle analysis metho ds\, we investigated three categories of industrial-oriented techniques fo r both statistical monitoring and fingerprint detection. The first techniq ue is based on optical scattering\, which correlates particle information with scattered intensity. By employing polarization characterization and m ulti-angle measurement\, we have sufficiently classified different particl e types at the single-species level. Additionally\, to achieve high-throug hput particle characterization\, we developed imaging platforms for partic le detection. Utilizing polarization imaging and deep learning algorithms\ , we achieved a classification accuracy of ~95%. Finally\, we demonstrated a compact digital in-line holographic microscopy platform with an inertia l spectrometer for simultaneous measurement of two independent fingerprint parameters at the single-species level. Specifically\, by interrogating t he particle location and size captured with the platform\, particle mass d ensity can be estimated. Furthermore\, by employing Monte Carlo fitting to the Lorenz-Mie theory\, the refractive index of each particle can be extr acted from the interference patterns. The combination of mass density and optical density characterization unambiguously enhances the discriminatory power of the system\, especially when dealing with particles that exhibit similar mass densities but distinctive refractive indices or vice versa.\ n\n[]\n\nCo-sponsored by: Co-sponsored by National Research Council\, Cana da. Optonique. ETS Optica Student Chapter.\n\nSpeaker(s): Jingwen Li\, \n\ nAgenda: \n-\n- Introduction from the host (2 to 5 minutes)\n- Presentatio n (40 to 45 minutes)\n- Questions from the audience (5 to 10 minutes)\n- L unch and networking\n\nRoom: Room 1302\, Bldg: Building A\, 1100 Notre-Dam e St W\, Montréal\, Quebec\, Canada\, H3C 1K3 LOCATION:Room: Room 1302\, Bldg: Building A\, 1100 Notre-Dame St W\, Montr éal\, Quebec\, Canada\, H3C 1K3 ORGANIZER:matthew.t.posner@ieee.org SEQUENCE:61 SUMMARY:Particle monitoring and classification based on optical scattering and imaging analysis URL;VALUE=URI:https://events.vtools.ieee.org/m/429282 X-ALT-DESC:Description: <br /><p><span style="font-family: arial\, helvetic a\, sans-serif\; font-size: 12pt\;"><strong>Abstract: </strong>Real-time d etection\, classification\, and identification of aerosol particles are cr ucial in various industries and public health areas. To overcome the limit ations of existing particle analysis methods\, we investigated three categ ories of industrial-oriented techniques for both statistical monitoring an d fingerprint detection. The first technique is based on optical scatterin g\, which correlates particle information with scattered intensity. By emp loying polarization characterization and multi-angle measurement\, we have sufficiently classified different particle types at the single-species le vel. Additionally\, to achieve high-throughput particle characterization\, we developed imaging platforms for particle detection. Utilizing polariza tion imaging and deep learning algorithms\, we achieved a classification a ccuracy of ~95%. Finally\, we demonstrated a compact digital in-line holog raphic microscopy platform with an inertial spectrometer for simultaneous measurement of two independent fingerprint parameters at the single-specie s level. Specifically\, by interrogating the particle location and size ca ptured with the platform\, particle mass density can be estimated. Further more\, by employing Monte Carlo fitting to the Lorenz-Mie theory\, the ref ractive index of each particle can be extracted from the interference patt erns. The combination of mass density and optical density characterization unambiguously enhances the discriminatory power of the system\, especiall y when dealing with particles that exhibit similar mass densities but dist inctive refractive indices or vice versa.</span></p>\n<p><span style="font -family: arial\, helvetica\, sans-serif\; font-size: 12pt\;"><img src="htt ps://events.vtools.ieee.org/vtools_ui/media/display/afa78c2f-d1b3-472f-a14 6-6ce05643d995" alt="" width="277" height="388"></span></p>\n<p>&nbsp\;</p ><br /><br />Agenda: <br /><ul>\n<li style="list-style-type: none\;">\n<ul type="circle">\n<li>Introduction from the host (2 to 5 minutes)</li>\n<li >Presentation (40 to 45 minutes)</li>\n<li>Questions from the audience (5 to 10 minutes)</li>\n<li>Lunch and networking</li>\n</ul>\n</li>\n</ul> END:VEVENT END:VCALENDAR