BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Australia/Sydney BEGIN:DAYLIGHT DTSTART:20261004T030000 TZOFFSETFROM:+1000 TZOFFSETTO:+1100 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=10 TZNAME:AEDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20260405T020000 TZOFFSETFROM:+1100 TZOFFSETTO:+1000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4 TZNAME:AEST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260610T021853Z UID:B8A213CF-DA45-4DB7-9796-14E36629A929 DTSTART;TZID=Australia/Sydney:20260630T110000 DTEND;TZID=Australia/Sydney:20260630T120000 DESCRIPTION:Gas sensing is becoming critical for applications ranging from gas speciation of indoor and outdoor air-quality to monitoring of energy-a ssets\, precision agriculture\, noninvasive medical diagnostics\, and secu rity. However\, conventional legacy gas sensors rely on single-output desi gns that struggle with drift and detection of low-concentration analytes i n chemically complex environments. In this lecture we will present a roadm ap toward next-generation multi-output gas sensors: inspired by proven tra ditional analytical instruments such as gas chromatography and spectroscop y\, we will show how their underlying mathematical principles can be trans lated into new sensing designs to improve selectivity\, stability\, and re liability. The central focus of this lecture is understanding of importanc e of hardware–analytics co-design. Machine learning (ML) cannot fully su cceed when sensor outputs contain insufficient independent information. Th us\, emerging multi-output sensor architectures - enabled by independent e xcitation variables\, advances in electronics\, and edge analytics - creat e the information richness needed for multi-analyte discrimination\, drift correction\, and robust calibration. Using multiple examples from researc h teams worldwide\, we will connect foundational principles to practical d esign strategies and illustrate commercialization pathways. We will provid e a systems-level understanding of how to design multi-output gas sensors to achieve trusted performance in chemically complex environments.\n\nSpea ker(s): Radislav\, \n\nRoom: 6.105\, Bldg: Faculty of Engineering and Info rmation Sciences\, University of Wollongong\, School of Engineering\, Woll ongong\, New South Wales\, Australia LOCATION:Room: 6.105\, Bldg: Faculty of Engineering and Information Science s\, University of Wollongong\, School of Engineering\, Wollongong\, New So uth Wales\, Australia ORGANIZER:Subhas.Mukhopadhyay@mq.edu.au SEQUENCE:10 SUMMARY:Hardware–Analytics Co-Design for Trust-Enhanced Gas Sensing URL;VALUE=URI:https://events.vtools.ieee.org/m/563074 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-bottom: 0c m\; text-align: justify\; line-height: normal\;"><span lang="EN-US" style= "font-size: 12.0pt\; font-family: 'Times New Roman'\,serif\;">Gas sensing is becoming critical for applications ranging from gas speciation of indoo r and outdoor air-quality<span style="mso-spacerun: yes\;">&nbsp\; </span> to monitoring of energy-assets\, precision agriculture\, noninvasive medic al diagnostics\, and security. However\, conventional legacy gas sensors r ely on single-output designs that struggle with drift and detection of low -concentration analytes in chemically complex environments. In this lectur e we will present a roadmap toward next-generation multi-output gas sensor s: inspired by proven traditional analytical instruments such as gas chrom atography and spectroscopy\, we will show how their underlying mathematica l principles can be translated into new sensing designs to improve selecti vity\, stability\, and reliability. The central focus of this lecture is u nderstanding of importance of hardware&ndash\;analytics co-design. Machine learning (ML) cannot fully succeed when sensor outputs contain insufficie nt independent information. Thus\, emerging multi-output sensor architectu res - enabled by independent excitation variables\, advances in electronic s\, and edge analytics - create the information richness needed for multi- analyte discrimination\, drift correction\, and robust calibration. Using multiple examples from research teams worldwide\, we will connect foundati onal principles to practical design strategies and illustrate commercializ ation pathways. We will provide a systems-level understanding of how to de sign multi-output gas sensors to achieve trusted performance in chemically complex environments.<span style="mso-spacerun: yes\;">&nbsp\;&nbsp\; </s pan></span></p> END:VEVENT END:VCALENDAR