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:20260610T020954Z UID:192704D6-30E0-48AE-8699-2021FE844569 DTSTART;TZID=Australia/Sydney:20260701T103000 DTEND;TZID=Australia/Sydney:20260701T120000 DESCRIPTION:Contemporary monitoring requirements of gases and liquids for d emanding applications such as environmental surveillance\, medical diagnos tics\, food and industrial safety\, biopharmaceutical process control\, ho meland security\, and others push the limits of existing detection concept s where we may reach their fundamental performance limits. These and other modern monitoring scenarios demand sensing with higher accuracy\, enhance d stability\, improved sensitivity\, and lower power consumption\; often a ll in unobtrusive formats and at low cost. We are developing new generatio n of sensors that bridge the gap between existing and contemporary require d capabilities. Our sensors utilize radio-frequency and optical detection principles and achieve required performance via system analytics. The syst em analytics is our methodology to deliver high performance sensing via ne w sensor design rules that include transducer with several uncorrelated ou tputs\, sensed environment (e.g. sensing film or sensing volume) with dive rse intrinsic properties detected by the transducer\, and multivariate sig nal processing algorithms (a.k.a. machine learning). We will illustrate th e capabilities of these multivariable (multi-parameter) sensors to quantif y individual components in mixtures\, reject interferences\, and correct f or environmental instabilities. Our multivariable sensors when coupled wit h edge data analytics boost data analytics accuracy and reduce data analyt ics demands for computing and electrical power. Examples of scenarios wher e such developed multivariable sensors are important include wearable and remotely deployed sensors\, autonomous robotics\, and home health. In thes e and many other scenarios\, high-performance advantages of traditional ma ture instruments are cancelled by application-specific requirements that d emand unobtrusive form factors\, low or no power consumption\, no maintena nce\, and continuous operation.\n\nSpeaker(s): Radislav\, \n\nRoom: G65 Th e Forum\, Bldg: 9WW\, Macquarie University\, North Ryde\, Sydney\, New Sou th Wales\, Australia\, 2109 LOCATION:Room: G65 The Forum\, Bldg: 9WW\, Macquarie University\, North Ryd e\, Sydney\, New South Wales\, Australia\, 2109 ORGANIZER:Subhas.Mukhopadhyay@mq.edu.au SEQUENCE:13 SUMMARY:Design of Next-Generation Gas and Biological Sensors: Impact of Mat erials\, Transducers\, and Contemporary Multivariate Analytics URL;VALUE=URI:https://events.vtools.ieee.org/m/563071 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\;">Contemporary monitoring requirements of gases and liquids for demanding applications s uch as environmental surveillance\, medical diagnostics\, food and industr ial safety\, biopharmaceutical process control\, homeland security\, and o thers push the limits of existing detection concepts where we may reach th eir fundamental performance limits.<span style="mso-spacerun: yes\;">&nbsp \; </span>These and other modern monitoring scenarios demand sensing with higher accuracy\, enhanced stability\, improved sensitivity\, and lower po wer consumption\; often all in unobtrusive formats and at low cost. We are developing new generation of sensors that bridge the gap between existing and contemporary required capabilities.<span style="mso-spacerun: yes\;"> &nbsp\; </span>Our sensors utilize radio-frequency and optical detection p rinciples and achieve required performance via system analytics. The syste m analytics is our methodology to deliver high performance sensing via new sensor design rules that include transducer with several uncorrelated out puts\, sensed environment (e.g. sensing film or sensing volume) with diver se intrinsic properties detected by the transducer\, and multivariate sign al processing algorithms (a.k.a. machine learning). We will illustrate the capabilities of these multivariable (multi-parameter) sensors to quantify individual components in mixtures\, reject interferences\, and correct fo r environmental instabilities.<span style="mso-spacerun: yes\;">&nbsp\; </ span>Our multivariable sensors when coupled with edge data analytics boost data analytics accuracy and reduce data analytics demands for computing a nd electrical power. Examples of scenarios where such developed multivaria ble sensors are important include wearable and remotely deployed sensors\, autonomous robotics\, and home health.<span style="mso-spacerun: yes\;">& nbsp\; </span>In these and many other scenarios\, high-performance advanta ges of traditional mature instruments are cancelled by application-specifi c requirements that demand unobtrusive form factors\, low or no power cons umption\, no maintenance\, and continuous operation.</span></p> END:VEVENT END:VCALENDAR