BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:DAYLIGHT DTSTART:20250330T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20241027T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20241218T085651Z UID:AD04D9DC-C790-4582-83C1-F24495483CDE DTSTART;TZID=Europe/Paris:20241129T090000 DTEND;TZID=Europe/Paris:20241129T104500 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 A. Potyrailo\, \n\ nBordeaux\, Aquitaine\, France LOCATION:Bordeaux\, Aquitaine\, France ORGANIZER:maxime.guillot@u-bordeaux.fr SEQUENCE:16 SUMMARY:Impact of Materials\, Transducers\, and Contemporary Multivariate A nalytics On Design of Next-Generation Gas and Biological Sensors URL;VALUE=URI:https://events.vtools.ieee.org/m/454156 X-ALT-DESC:Description: <br /><p>Contemporary monitoring requirements of ga ses and liquids for demanding applications such as environmental surveilla nce\, medical diagnostics\, food and industrial safety\, biopharmaceutical process control\, homeland security\, and others push the limits of exist ing detection concepts where we may reach their fundamental performance li mits. These and other modern monitoring scenarios demand sensing with high er accuracy\, enhanced stability\, improved sensitivity\, and lower power consumption\; often all in unobtrusive formats and at low cost. We are dev eloping new generation of sensors that bridge the gap between existing and contemporary required capabilities. Our sensors utilize radio-frequency a nd optical detection principles and achieve required performance via syste m analytics. The system analytics is our methodology to deliver high perfo rmance sensing via new sensor design rules that include transducer with se veral uncorrelated outputs\, sensed environment (e.g. sensing film or sens ing volume) with diverse intrinsic properties detected by the transducer\, and multivariate signal processing algorithms (a.k.a. machine learning). We will illustrate the capabilities of these multivariable (multi-paramete r) sensors to quantify individual components in mixtures\, reject interfer ences\, and correct for environmental instabilities. Our multivariable sen sors when coupled with edge data analytics boost data analytics accuracy a nd reduce data analytics demands for computing and electrical power. Examp les of scenarios where such developed multivariable sensors are important include wearable and remotely deployed sensors\, autonomous robotics\, and home health. 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.</p> END:VEVENT END:VCALENDAR