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Community","2025-10-03T12:43:45.023Z","2025-10-03T12:43:47.107Z","2025-10-03T12:43:47.100Z","235",{"id":1325,"name":1326,"alternativeText":7,"caption":7,"width":1327,"height":1328,"formats":1329,"hash":1352,"ext":1131,"mime":1134,"size":1353,"url":1354,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1355,"updatedAt":1355},302,"190107IMTCFoutlines.jpg",1780,753,{"large":1330,"small":1336,"medium":1341,"thumbnail":1347},{"ext":1131,"url":1331,"hash":1332,"mime":1134,"name":1333,"path":7,"size":1334,"width":57,"height":1335},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/large_190107_IMTC_Foutlines_d671f7cebb.jpg","large_190107_IMTC_Foutlines_d671f7cebb","large_190107IMTCFoutlines.jpg",36.68,423,{"ext":1131,"url":1337,"hash":1338,"mime":1134,"name":1339,"path":7,"size":1340,"width":64,"height":435},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/small_190107_IMTC_Foutlines_d671f7cebb.jpg","small_190107_IMTC_Foutlines_d671f7cebb","small_190107IMTCFoutlines.jpg",16.78,{"ext":1131,"url":1342,"hash":1343,"mime":1134,"name":1344,"path":7,"size":1345,"width":71,"height":1346},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/medium_190107_IMTC_Foutlines_d671f7cebb.jpg","medium_190107_IMTC_Foutlines_d671f7cebb","medium_190107IMTCFoutlines.jpg",26.61,317,{"ext":1131,"url":1348,"hash":1349,"mime":1134,"name":1350,"path":7,"size":1351,"width":78,"height":1243},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_190107_IMTC_Foutlines_d671f7cebb.jpg","thumbnail_190107_IMTC_Foutlines_d671f7cebb","thumbnail_190107IMTCFoutlines.jpg",6.64,"190107_IMTC_Foutlines_d671f7cebb",69.47,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/190107_IMTC_Foutlines_d671f7cebb.jpg","2025-10-03T12:43:30.587Z",{"id":929,"variation":852,"button":1357},[1358],{"id":1359,"label":89,"size":90,"color":91,"style":7,"icon":92,"iconPosition":93,"url":1360,"newWindow":8,"downloadable":7,"shape":7},113,"https://ieeememstc.org/","-193",{"id":247,"name":1363,"description":41,"createdAt":1364,"updatedAt":1365,"publishedAt":1366,"url_path_id":1367,"logo":1368,"website":1398,"url_path":1403},"IEEE Sensors Journal","2025-10-07T18:27:56.676Z","2025-10-07T18:27:58.926Z","2025-10-07T18:27:58.918Z","236",{"id":1369,"name":1370,"alternativeText":7,"caption":7,"width":1371,"height":1372,"formats":1373,"hash":1394,"ext":17,"mime":18,"size":1395,"url":1396,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1397,"updatedAt":1397},305,"sensors-journal-25-years-hr_color.png",4280,953,{"large":1374,"small":1379,"medium":1384,"thumbnail":1389},{"ext":17,"url":1375,"hash":1376,"mime":18,"name":1377,"path":7,"size":1378,"width":57,"height":830},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/large_sensors_journal_25_years_hr_color_4f826a1bfe.png","large_sensors_journal_25_years_hr_color_4f826a1bfe","large_sensors-journal-25-years-hr_color.png",79.34,{"ext":17,"url":1380,"hash":1381,"mime":18,"name":1382,"path":7,"size":1383,"width":64,"height":65},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/small_sensors_journal_25_years_hr_color_4f826a1bfe.png","small_sensors_journal_25_years_hr_color_4f826a1bfe","small_sensors-journal-25-years-hr_color.png",35.7,{"ext":17,"url":1385,"hash":1386,"mime":18,"name":1387,"path":7,"size":1388,"width":71,"height":72},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/medium_sensors_journal_25_years_hr_color_4f826a1bfe.png","medium_sensors_journal_25_years_hr_color_4f826a1bfe","medium_sensors-journal-25-years-hr_color.png",55.68,{"ext":17,"url":1390,"hash":1391,"mime":18,"name":1392,"path":7,"size":1393,"width":78,"height":319},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_sensors_journal_25_years_hr_color_4f826a1bfe.png","thumbnail_sensors_journal_25_years_hr_color_4f826a1bfe","thumbnail_sensors-journal-25-years-hr_color.png",16.36,"sensors_journal_25_years_hr_color_4f826a1bfe",122.56,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/sensors_journal_25_years_hr_color_4f826a1bfe.png","2025-10-07T18:27:22.950Z",{"id":962,"variation":852,"button":1399},[1400],{"id":1401,"label":89,"size":90,"color":91,"style":7,"icon":92,"iconPosition":93,"url":1402,"newWindow":8,"downloadable":7,"shape":7},114,"https://ieee-sensors.org/ieee-sensors-journal/","-194",{"pagination":1405},{"page":5,"pageSize":155,"pageCount":5,"total":247},{"id":410,"heading":405,"pageHeader":1407,"sections":1409},{"id":410,"description":1408,"showPageHeader":8,"backgroundColor":107,"image":7},"Sunday, October 19, 2025",[1410,1421],{"id":574,"__component":1411,"componentVariation":1412,"styles":1413,"header":1415,"body":1418},"content.content","Content Image Left",{"id":155,"edgeTop":106,"edgeBottom":106,"background":1414,"containerWidth":7},"base-50",{"id":442,"heading":7,"prose":1416,"lead":7,"eyebrow":7,"badge":7,"componentVariation":1417,"containerWidth":7,"image":7},"\u003Cp>*Pre-Conference Tutorials are not included in full conference registration and require an additional payment to register to attend the sessions \u003Ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://2025.ieee-sensorsconference.org/registration\">here\u003C/a>.\u003C/p>","Heading Center",{"id":720,"title":1419,"body":1420,"containerWidth":7,"buttonGroup":7,"media":7},"Pre-Conference Tutorial Schedule","\u003Cfigure class=\"table\">\u003Ctable style=\"border:1px solid hsl(0, 0%, 90%);\">\u003Cthead>\u003Ctr>\u003Cth style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\">\u003Cstrong>PDT\u003C/strong>\u003C/th>\u003Cth style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\">Parq Salon C\u003C/th>\u003Cth style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\">Kitsilano A\u003C/th>\u003Cth style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\">Kitsilano B\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody>\u003Ctr>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\">8:30-10:00\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">Foundation Models for Wearable Sensor Data\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">Tensor Decompositions for Multidimensional Signal Processing - Part 1\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\"> \u003C/td>\u003C/tr>\u003Ctr>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\">10:00-10:30\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\" colspan=\"3\">Coffee Break\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\">10:30-12:00\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">Unlocking New Performance Horizons in Gas Sensing by Cross-Pollination between Contemporary Hardware and Mathematics\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">Tensor Decompositions for Multidimensional Signal Processing - Part 2\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">Large area E-skin with tomographic sensors\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\">12:00-13:30\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\" colspan=\"3\">\u003Cp>Lunch\u003C/p>\u003Cp>Parq Salon F\u003C/p>\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\">13:30-15:00\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">Modelling and simulations of biosensors: from analytical to machine learning approaches\u003Cbr> \u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">Terahertz Sensing Technology\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">From antifouling (nano-)coatings to smart biosensing in complex media\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\">15:00-15:30\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);text-align:center;\" colspan=\"3\">Coffee Break\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>15:30-17:00\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\" rowspan=\"2\">Sensor transform using Deep Learning: from technology to application\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">Advancing electrochemical field effect transistor(FET) biosensing technology: emphasis on clinical/field translation\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;text-align:center;\" colspan=\"1\">MEMS Micromirrors for Miniaturized Projection and 3D Sensing\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>17:00-17:30\u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;\"> \u003C/td>\u003Ctd style=\"border:1px solid hsl(0, 0%, 90%);padding:2px 3px;\"> \u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003C/figure>",{"id":219,"__component":1422,"componentVariation":1423,"contactsVariation":1424,"styles":7,"header":7,"sessionsGroup":1425},"content.sessions","Sessions Base","Card Contact Full",[1426],{"id":219,"groupTitle":7,"sessions":1427},[1428,1463,1493,1547,1583,1617,1665,1718,1773,1827],{"id":179,"session":1429},{"id":179,"title":1430,"teaser":1431,"body":1432,"createdAt":1433,"updatedAt":1434,"publishedAt":1435,"url_path_id":1436,"contacts":1437,"url_path":1462},"Large area E-skin with tomographic sensors","\u003Cp style=\"text-align:justify;\">Tomographic imaging based e-skin sensors offer a unique  solutions for  sensing for many applications. This includes artificial skins in robotics, medical application for interfacing with human skin.  Additionally this type of e-skin will have a key role in ever growing and emerging field of  flexible and  soft robots.  \u003C/p>","\u003Cp style=\"text-align:justify;\">In this tutorial we will show the application of tomographic imaging systems for e-skin application.  We cover the variety of sensing and imaging system, including resistivity, capacities, magnetic, acoustic and fiber optic based sensing. The tutorial will cover the sensor design, data collection and tactile performance analysis.  Data analysis, image reconstruction and multi-modality based e-skin will be covered in this tutorial. The tutorial will be interactive and the participant with a great deal of interest in image creation with deep learning or other methods should be able to participate in analysis activities during tutorial.\u003C/p>","2025-06-27T13:57:40.608Z","2025-06-30T20:57:37.755Z","2025-06-27T13:57:42.440Z","179",[1438],{"id":1439,"name":1440,"committee":7,"position":7,"affiliation":1441,"email":7,"biography":1442,"createdAt":1443,"updatedAt":1444,"url_path_id":1445,"contactPhoto":1446,"socialLinks":1460,"url_path":1461},96,"Manuch Soleimani","University of Bath","\u003Cp style=\"text-align:justify;\">Manuch Soleimani is currently a full Professor in Electronic and Electrical Engineering at the University of Bath, in the UK. He obtained a BSc degree in Electrical Engineering and an MSc in Biomedical Engineering. He received a PhD in Applied Mathematics in August 2005 from the University of Manchester, UK.  He worked as a post-doc research associate (PDRA) at the School of Materials in e-textiles of the University of Manchester, UK, between August 2005 and June 2007.  The example of research areas is in Multi-modality Tomography,   Inverse Problems, Machine learning and AI,  and Electrical and Electromagnetic Tomography. The application areas include: 1) Medical imaging, 2) Industrial process tomography, 3) Tomography for material characterization, and 4) Tomography for touch sensing. During the postdoc work in e-textiles, he developed an interest in pressure and force sensing and imaging on conductive textile using electrical impedance tomography (EIT), where he became one of the early adopters of EIT imaging for tactile sensing.  A work that has continued since and has been further extended to other sensing and imaging modalities.\u003C/p>","2025-06-27T13:56:57.275Z","2025-06-30T20:32:51.396Z","178",{"id":1447,"name":1448,"alternativeText":7,"caption":7,"width":1449,"height":823,"formats":1450,"hash":1456,"ext":17,"mime":18,"size":1457,"url":1458,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1459,"updatedAt":1459},247,"Screenshot 2025-06-27 112452.png",388,{"thumbnail":1451},{"ext":17,"url":1452,"hash":1453,"mime":18,"name":1454,"path":7,"size":1455,"width":1303,"height":689},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_Screenshot_2025_06_27_112452_b4855593a2.png","thumbnail_Screenshot_2025_06_27_112452_b4855593a2","thumbnail_Screenshot 2025-06-27 112452.png",41.96,"Screenshot_2025_06_27_112452_b4855593a2",46.03,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/Screenshot_2025_06_27_112452_b4855593a2.png","2025-06-27T15:25:21.350Z",[],"-148","-149",{"id":198,"session":1464},{"id":198,"title":1465,"teaser":1466,"body":1467,"createdAt":1468,"updatedAt":1469,"publishedAt":1470,"url_path_id":1471,"contacts":1472,"url_path":1492},"Sensor transform using Deep Learning: from technology to application ","\u003Cp style=\"text-align:justify;\">Sensor transform processes and converts raw data across different sensor modalities restructuring inputs into meaningful representations to enhance machine perception and decision-making. From remote sensing to autonomous systems, sensor transform has the potential to break down barriers between different sensor modalities, reshaping how we design and integrate intelligent systems for a more seamless and efficient future.\u003C/p>","\u003Cp style=\"text-align:justify;\">Sensor transform facilitates data conversion across modalities within heterogeneous sensor systems, enhancing decision support for applications in autonomous systems, remote sensing, industrial inspection, and medical imaging. The emergence of deep learning-based technologies offers a compelling alternative that enables systems to reduce reliance on expensive sensors while enhancing environmental awareness.\u003Cbr>This tutorial will explore the fundamentals and applications of sensor transform, focusing on both traditional methods and deep learning-based approaches. We will examine how sensor transform enables cross-modal data conversion, enhancing perception in fields like remote sensing, industrial inspection, and autonomous systems. Key topics include optical-to-SAR and MFL-to-laser transformations, along with the role of deep learning in improving accuracy and adaptability. We will also discuss challenges such as data alignment, real-time processing, and generalization across different sensor types, highlighting solutions for seamless integration without additional hardware modifications.\u003C/p>","2025-06-27T14:01:43.306Z","2025-06-30T20:58:02.968Z","2025-06-27T14:01:45.549Z","182",[1473,1483],{"id":1474,"name":1475,"committee":7,"position":7,"affiliation":1476,"email":7,"biography":1477,"createdAt":1478,"updatedAt":1479,"url_path_id":1480,"contactPhoto":7,"socialLinks":1481,"url_path":1482},97,"Henry Leung","University of Calgary","\u003Cp style=\"text-align:justify;\">Henry Leung is the Schulich Industrial Chair Professor of the Department of Electrical and Software Engineering of the University of Calgary. Before joining U of C, he was with the Department of National Defence (DND) of Canada as a defence scientist. His current research interests include information fusion, machine learning, IoT, data analytics, robotics, signal, and image processing. He is an associate editor of various journals such as Scientific Reports, IEEE Emerging Topics on Circuits and Systems and Journal of Sensors.  He is the editor of the Springer book series on “Information Fusion and Data Science”. He is a Fellow of IEEE, SPIE, Engineering Institute of Canada (EIC) and Canadian Academy of Engineering (CAE).\u003C/p>","2025-06-27T13:58:45.610Z","2025-06-30T20:36:13.184Z","180",[],"-150",{"id":1115,"name":1484,"committee":7,"position":7,"affiliation":1485,"email":7,"biography":1486,"createdAt":1487,"updatedAt":1488,"url_path_id":1489,"contactPhoto":7,"socialLinks":1490,"url_path":1491},"Hongqi Zhang","Northeastern University at Qinhuangdao, China","\u003Cp style=\"text-align:justify;\">Hongqi Zhang received the B.S. degree from Northeastern University at Qinhuangdao, China, in 2014. He received the M.S. degree and Ph.D. degree with School of Control Science and Engineering, Dalian University of Technology, Dalian, China. He is currently a Postdoctoral Associate with the Department of Electrical and Software Engineering, University of Calgary. His research interests include information fusion, machine learning and deep learning with particular focus on applications in pipeline and remote sensing. \u003C/p>","2025-06-27T14:00:07.983Z","2025-06-30T20:36:51.135Z","181",[],"-151","-152",{"id":155,"session":1494},{"id":155,"title":1495,"teaser":1496,"body":1497,"createdAt":1498,"updatedAt":1499,"publishedAt":1500,"url_path_id":1501,"contacts":1502,"url_path":1546},"Terahertz Sensing Technology","\u003Cp style=\"text-align:justify;\">The illustrious history of terahertz (THz) imaging and sensing is nearly 50 years long. During this time, photonic and electronic THz technology has developed a lot, but it has not been able yet to bridge the famous THz gap between electronic and photonic devices. In the THz range of frequencies, a low photon energy (smaller than the room temperature thermal energy) makes the development of efficient THz lasers to be a challenge. And the cutoff frequency and maximum frequency of operation of field effect and bipolar transistors struggles to reach one THz. \u003C/p>","\u003Cp style=\"text-align:justify;\">Figure 1 shows the state of THz photonic and optoelectronic technologies competing for applications in THz sensing. Also shown is the expected order of magnitude performance improvement that could be achieved using synchronized arrays of active “plasmonic unit cells” – plasmonic crystals. TeraFETs – short-channel Si CMOS, SOI, FINFETs, GaAs-based and GaN-based HEMTs – operating in a new “plasmonic” regime – form unit cells of such plasmonic crystals. TeraFETs have the potential to become a dominant THz electronics technology. As seen from Fig. 1, deep submicron Si CMOS TeraFET circuits could support THz sensing, which is the key to a dramatic cost reduction of the THz technology deployment. \u003C/p>\u003Cp style=\"text-align:justify;\">Many theoretical and modeling papers and a few experimental papers have revealed the enormous potential of the TeraFET plasmonic crystal technology. Reaching this potential requires understanding of new counterintuitive physics of TeraFET plasmonic crystals. This physics involves the propagating, decaying, or growing waves of the electron density – plasma waves” - similar to water and sound waves driven by wind. \u003C/p>\u003Cp style=\"text-align:justify;\">THz sensing technology has found applications in industrial controls, the detection of biological and chemical hazardous agents, biology, medicine (including cancer diagnostics), detection of mines and explosives, providing security in buildings, airports, and other public spaces, radioastronomy, space research, and hardware cyber security (detecting tampered with or defective VLSI non-destructive and with or without bias). \u003C/p>","2025-06-27T14:03:42.666Z","2025-06-30T20:58:41.563Z","2025-06-27T14:03:46.122Z","184",[1503],{"id":1504,"name":1505,"committee":7,"position":7,"affiliation":1506,"email":7,"biography":1507,"createdAt":1508,"updatedAt":1509,"url_path_id":1510,"contactPhoto":1511,"socialLinks":1544,"url_path":1545},99,"Michael Shur","Rensselaer Polytechnic Institute","\u003Cp style=\"text-align:justify;\">Dr. Michael Shur is the Patricia W. and C. Sheldon Roberts Professor of Solid State Electronics and a Professor of Physics, Applied Physics, and Astronomy at Rensselaer Polytechnic Institute. He is also a co-founder of Sensor Electronics Technology, Inc. and Electronics of the Future, Inc.\u003C/p>\u003Cp style=\"text-align:justify;\">A Life Fellow of the U.S. National Academy of Inventors, IEEE, APS, ECS, Optica, and SPIE, Dr. Shur is also a Fellow of AAAS, IOP, and IET. His recognitions include IEEE, IET, and ECS Awards, the Tibbetts Award for Technology Commercialization, the Senior Humboldt Research Award, RPI Research Awards, multiple Best Paper Awards, and honorary doctorates from St. Petersburg Technical University and the University of Vilnius.\u003C/p>\u003Cp style=\"text-align:justify;\">Dr. Shur serves as an IEEE EDS and IEEE Sensors Council Distinguished Lecturer and is a Foreign Member of the Lithuanian Academy of Sciences.\u003C/p>","2025-06-27T14:02:50.571Z","2025-06-30T20:32:22.260Z","183",{"id":1512,"name":1513,"alternativeText":7,"caption":7,"width":1514,"height":1515,"formats":1516,"hash":1540,"ext":1131,"mime":1134,"size":1541,"url":1542,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1543,"updatedAt":1543},249,"Michael Shur .jpg",957,1130,{"large":1517,"small":1523,"medium":1528,"thumbnail":1534},{"ext":1131,"url":1518,"hash":1519,"mime":1134,"name":1520,"path":7,"size":1521,"width":1522,"height":57},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/large_Michael_Shur_1e8ed35e07.jpg","large_Michael_Shur_1e8ed35e07","large_Michael Shur .jpg",61.78,847,{"ext":1131,"url":1524,"hash":1525,"mime":1134,"name":1526,"path":7,"size":1527,"width":1335,"height":64},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/small_Michael_Shur_1e8ed35e07.jpg","small_Michael_Shur_1e8ed35e07","small_Michael Shur .jpg",23.22,{"ext":1131,"url":1529,"hash":1530,"mime":1134,"name":1531,"path":7,"size":1532,"width":1533,"height":71},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/medium_Michael_Shur_1e8ed35e07.jpg","medium_Michael_Shur_1e8ed35e07","medium_Michael Shur .jpg",41.08,635,{"ext":1131,"url":1535,"hash":1536,"mime":1134,"name":1537,"path":7,"size":1538,"width":1539,"height":689},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_Michael_Shur_1e8ed35e07.jpg","thumbnail_Michael_Shur_1e8ed35e07","thumbnail_Michael Shur .jpg",4.55,132,"Michael_Shur_1e8ed35e07",64.55,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/Michael_Shur_1e8ed35e07.jpg","2025-06-27T20:45:16.518Z",[],"-153","-154",{"id":351,"session":1548},{"id":351,"title":1549,"teaser":1550,"body":1551,"createdAt":1552,"updatedAt":1553,"publishedAt":1554,"url_path_id":1555,"contacts":1556,"url_path":1582},"Advancing electrochemical field effect transistor(FET) biosensing technology: emphasis on clinical/field translation","\u003Cp style=\"text-align:justify;\">Field-effect transistor (FET)-based sensors offer high sensitivity, rapid detection and compatibility with large-scale semiconductor fabrication, making them ideal for point-of-care diagnostics. Recent advances leverage one- and two-dimensional materials with high surface area-to-volume ratios and excellent charge carrier mobility, greatly enhancing sensing performance. Notable examples include MoS₂, metal dichalcogenides, graphene and MXenes, which have demonstrated superior sensitivity. These nanostructured materials are driving the next generation of electrochemical FET sensors with improved efficiency and scalability. \u003C/p>","\u003Cp style=\"text-align:justify;\">This tutorial will focus on the advancement in the electrochemical field effect transistor technology towards detection of biomarkers in clinical samples and for monitoring of contaminants in food and water samples with emphasis on the adoption of machine learning algorithms for mitigating the device to device variability and overcoming the background noise of non-specific proteins/ contaminants in physiological/environmental samples. The ultimate limit of detection of these sensors will be established from the physical principles guiding the antibody-antigen binding which will lead to their systematic design approach. Discussion will be augmented by case studies of these sensors applied on patient cohorts targeting cancer and infectious diseases and on environmental water and food samples, highlighting the existing challenges related to their exploitation in near-field and resource limited settings. \u003C/p>","2025-06-27T14:05:23.951Z","2025-06-30T20:59:00.993Z","2025-06-27T14:05:26.511Z","186",[1557],{"id":1558,"name":1559,"committee":7,"position":7,"affiliation":1560,"email":7,"biography":1561,"createdAt":1562,"updatedAt":1563,"url_path_id":1564,"contactPhoto":1565,"socialLinks":1580,"url_path":1581},100,"Chirasree RoyChaudhuri","Indian Institute of Engineering Science and Technology","\u003Cp>Chirasree RoyChaudhuri is a Professor in the Department of Electronics and Telecommunication Engineering at Indian Institute of Engineering Science and Technology, Shibpur, India. Her fields of interest include Biological and Chemical sensors and signal processing for healthcare and environmental monitoring. She has explored various 2D and 3D nanomaterials for developing point-of-care electrochemical transistor devices. She is a senior member of IEEE and NASI(National Academy of Science, India). She has published over 100 papers in different international journals and conference proceedings, written seven book chapters, has four patents and is the Associate Editor of IEEE Sensors journal. She is a recipient of various young engineer and young scientist awards of Indian National academies of Engineering and Science and has also received the Women Excellence award by Department of Science and Technology, India. She had received fellowship from Humboldt Foundation for a young faculty exchange program at RWTH Aachen, Germany. Recently her profile has been featured in the book titled Women in STEM: Vanguards of India@75, published by Confederation of Indian Industries.\u003C/p>","2025-06-27T14:04:51.805Z","2025-10-06T18:52:49.455Z","185",{"id":1566,"name":1567,"alternativeText":7,"caption":7,"width":1568,"height":1569,"formats":1570,"hash":1576,"ext":1131,"mime":1134,"size":1577,"url":1578,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1579,"updatedAt":1579},303,"passport_photo.jpg",177,188,{"thumbnail":1571},{"ext":1131,"url":1572,"hash":1573,"mime":1134,"name":1574,"path":7,"size":1575,"width":379,"height":689},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_passport_photo_45cd4dc0c4.jpg","thumbnail_passport_photo_45cd4dc0c4","thumbnail_passport_photo.jpg",5.13,"passport_photo_45cd4dc0c4",6.96,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/passport_photo_45cd4dc0c4.jpg","2025-10-06T18:52:45.800Z",[],"-155","-156",{"id":229,"session":1584},{"id":229,"title":1585,"teaser":1586,"body":1587,"createdAt":1588,"updatedAt":1589,"publishedAt":1590,"url_path_id":1591,"contacts":1592,"url_path":1616},"Modelling and simulations of biosensors: from analytical to machine learning approaches","\u003Cp style=\"text-align:justify;\">Biosensors can be broadly classified into different types based on the method used for signal transduction, including electrochemical, optical, thermal, piezoelectric and magnetic biosensors. Due to a vast area of possibilities, in this tutorial, I will focus my attention on electrochemical biosensors and I will discuss the underlying physical and electrical principles of operations and their areas of applications. \u003C/p>","\u003Cp style=\"text-align:justify;\">Using simulations and modelling is the most cost, time and resource effective method to test new devices and explain physical, chemical and electrical concepts. To explain the operation of such electrochemical biosensor devices, I will present different simulations methods and approaches. This tutorial will cover various simulation techniques covering analytical and numerical methods and how these approaches are used to train neural networks. I will discuss the advantages and disadvantages of each of these methods and summarise the current state of the art in modelling of biosensors. I will use practical examples and case study emphasising how hybrid modelling strategies can bridge the gap between physics-based and data-driven models.\u003C/p>","2025-06-27T14:08:17.357Z","2025-06-30T20:41:00.526Z","2025-06-27T14:08:19.315Z","188",[1593],{"id":1156,"name":1594,"committee":7,"position":7,"affiliation":1595,"email":7,"biography":1596,"createdAt":1597,"updatedAt":1598,"url_path_id":1599,"contactPhoto":1600,"socialLinks":1614,"url_path":1615},"Vihar Georgiev","University of Glasgow","\u003Cp style=\"text-align:justify;\">I am a Professor of Nanoelectronics, the leader of the DeepNano Group at University of Glasgow and an EPSRC UKRI Innovation Fellow. I am also a Visiting Professor at TU Vienna and Member of IET, IEEE Senior Member and a Fellow of Higher Education Academy (FHEA). My research group is part of the Glasgow Computational Engineering Centre (GCEC). The group’s focus is on modelling and simulations of electronic devices for optoelectronics, biosensors and quantum applications by combining not only analytical and numerical approaches but also machine learning and artificial intelligence methods. We collaborate with academic groups and companies not only in the UK but also from USA, China, South Korea, India, Japan, Austria, Switzerland, Spain, France and Italy.\u003C/p>\u003Cp style=\"text-align:justify;\">I have more than 15 years of experience of developing numerical solvers and machine learning methods that are used for modelling and simulations of various semiconductor devices, such as nanowire transistors, tunnelling FETs, molecular flash memories and bio-sensors not only on IV and III-V semiconductors but also molecules, carbon nanotubes and Josephson’s junctions. Since my appointment in 2015, I have secured funding of around £2.0M as a PI and around £10.0M as a co-PI. Apart from my academic achievements I was a Quantum Simulation and Business Development Adviser at Semiwise and VP of Business Development and Quantum Technology at Advanced Microelectronics Associates.\u003C/p>\u003Cp style=\"text-align:justify;\">I am a member of the EPSRC College, member of the Royal Society grant committees and reviews proposals for the European Commission. I am an associate editor of the IET Electronic Letters journal (2018–), guest editor of the Journal of Computational Electronics (2022) and IEEE Access (2019). At University of Glasgow, I am the Chair of the Industry Advisory Board.\u003C/p>","2025-06-27T14:07:04.226Z","2025-06-30T20:33:20.178Z","187",{"id":1601,"name":1602,"alternativeText":7,"caption":7,"width":1287,"height":1603,"formats":1604,"hash":1610,"ext":1131,"mime":1134,"size":1611,"url":1612,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1613,"updatedAt":1613},248,"Vihar Georgiev.jpg",420,{"thumbnail":1605},{"ext":1131,"url":1606,"hash":1607,"mime":1134,"name":1608,"path":7,"size":1609,"width":65,"height":689},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_Vihar_Georgiev_3a50a296ae.jpg","thumbnail_Vihar_Georgiev_3a50a296ae","thumbnail_Vihar Georgiev.jpg",3.44,"Vihar_Georgiev_3a50a296ae",15.93,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/Vihar_Georgiev_3a50a296ae.jpg","2025-06-27T17:47:00.734Z",[],"-157","-158",{"id":448,"session":1618},{"id":448,"title":1619,"teaser":1620,"body":1621,"createdAt":1622,"updatedAt":1623,"publishedAt":1624,"url_path_id":1625,"contacts":1626,"url_path":1664},"Foundation Models for Wearable Sensor Data","\u003Cp style=\"text-align:justify;\">Foundation models are rapidly transforming the analysis of wearable sensor data by enabling robust, generalizable learning from massive unlabeled datasets. Originally developed for language and vision, these models are now being adapted to time-series data from wearable devices—offering new capabilities in health monitoring, activity recognition, and digital phenotyping.\u003C/p>","\u003Cp style=\"text-align:justify;\">This tutorial provides a comprehensive overview of emerging approaches for applying foundation models to large-scale wearable sensor data. We will cover self-supervised learning techniques for extracting transferable representations, strategies for multimodal sensor fusion, and the empirical effects of scaling data, compute, and model parameters. Participants will be introduced to state-of-the-art architectures and training paradigms, including masking-based pretraining and few-shot learning. Case studies will illustrate how these models improve imputation, extrapolation, and classification performance in real-world sensing contexts.\u003C/p>\u003Cp style=\"text-align:justify;\">Designed for researchers and engineers in sensor analytics, mobile health, and machine learning, the session combines technical foundations with practical examples. Attendees will gain actionable insights into building scalable and sample-efficient models for longitudinal, multimodal sensor data—and explore the frontier of general-purpose models for personalized sensing.\u003C/p>","2025-06-27T14:09:47.861Z","2025-06-30T20:41:26.988Z","2025-06-27T14:09:50.427Z","190",[1627],{"id":1198,"name":1628,"committee":7,"position":7,"affiliation":1476,"email":7,"biography":1629,"createdAt":1630,"updatedAt":1631,"url_path_id":1632,"contactPhoto":1633,"socialLinks":1662,"url_path":1663},"Sayeh Bayat","\u003Cp style=\"text-align:justify;\">Dr. Sayeh Bayat is an Assistant Professor in Biomedical and Geomatics Engineering at the University of Calgary, where she directs the Healthy City Lab. Her research sits at the intersection of AI, sensor technologies, and health systems engineering, with a focus on developing human-centred, data-enabled tools to monitor behaviour, assess cognitive health, and support aging in place. She specializes in digital phenotyping, machine learning, and context-aware sensing using multimodal real-world data—from GPS and IMUs to smartphones and wearables. Her work has led to innovations in dementia care, smart mobility, and AI-driven clinical decision support, and has been featured in international media including BBC News, The Telegraph, and The New York Times. Dr. Bayat collaborates with clinicians, engineers, and industry partners across Canada, the U.S., and Europe. She holds a Ph.D. in Biomedical Engineering and a BASc in Engineering Science (Aerospace Major), both from the University of Toronto.\u003C/p>","2025-06-27T14:09:06.958Z","2025-07-14T01:44:48.405Z","189",{"id":1634,"name":1635,"alternativeText":7,"caption":7,"width":1636,"height":1636,"formats":1637,"hash":1658,"ext":1131,"mime":1134,"size":1659,"url":1660,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1661,"updatedAt":1661},260,"Sayeh-Bayat.jpg",3286,{"large":1638,"small":1643,"medium":1648,"thumbnail":1653},{"ext":1131,"url":1639,"hash":1640,"mime":1134,"name":1641,"path":7,"size":1642,"width":57,"height":57},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/large_Sayeh_Bayat_9e86006ce1.jpg","large_Sayeh_Bayat_9e86006ce1","large_Sayeh-Bayat.jpg",90.52,{"ext":1131,"url":1644,"hash":1645,"mime":1134,"name":1646,"path":7,"size":1647,"width":64,"height":64},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/small_Sayeh_Bayat_9e86006ce1.jpg","small_Sayeh_Bayat_9e86006ce1","small_Sayeh-Bayat.jpg",27.39,{"ext":1131,"url":1649,"hash":1650,"mime":1134,"name":1651,"path":7,"size":1652,"width":71,"height":71},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/medium_Sayeh_Bayat_9e86006ce1.jpg","medium_Sayeh_Bayat_9e86006ce1","medium_Sayeh-Bayat.jpg",54.43,{"ext":1131,"url":1654,"hash":1655,"mime":1134,"name":1656,"path":7,"size":1657,"width":689,"height":689},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_Sayeh_Bayat_9e86006ce1.jpg","thumbnail_Sayeh_Bayat_9e86006ce1","thumbnail_Sayeh-Bayat.jpg",4.63,"Sayeh_Bayat_9e86006ce1",832.28,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/Sayeh_Bayat_9e86006ce1.jpg","2025-07-14T01:44:41.637Z",[],"-159","-160",{"id":580,"session":1666},{"id":580,"title":1667,"teaser":1668,"body":1669,"createdAt":1670,"updatedAt":1671,"publishedAt":1672,"url_path_id":1673,"contacts":1674,"url_path":1717},"MEMS Micromirrors for Miniaturized Projection and 3D Sensing","\u003Cp style=\"text-align:justify;\">MEMS micromirrors have been proposed and adopted in many different laser-beam-scanning (LBS) applications , starting from barcode reading, and ranging to both medical, consumer and automotive applications, such as miniaturized projection, augmented-reality (AR) headsets and LiDAR/3D sensing. The main architecture adopted in these fields consists in two-dimensional scanning of a laser source using either a bi-axial device or a couple of mono-axial ones.\u003C/p>","\u003Cp style=\"text-align:justify;\">As these applications increase in complexity, requiring more and more accurate scanning devices with ever increasing field-of-view, the challenges inherent in the optical device fabrication, sensing and control of the mirror inclination, synchronization and cross-talk of the two axes, become increasingly challenging.\u003Cbr>The tutorial will provide an in depth discussion about the most relevant trends and the most successful technologies used in the field, focusing in particular on recent advancements in the use of piezoelectric technologies for both actuation and sensing of the mirror inclination. Starting from a historical perspective on the evolution of MEMS scanners, the tutorial will then address the most prominent challenges faced in sensing and control, both at the mechanical and the sensing electronic and system level.\u003C/p>","2025-06-27T14:12:35.897Z","2025-06-30T20:59:28.305Z","2025-06-27T14:12:37.708Z","192",[1675],{"id":1297,"name":1676,"committee":7,"position":7,"affiliation":1677,"email":7,"biography":1678,"createdAt":1679,"updatedAt":1680,"url_path_id":1681,"contactPhoto":1682,"socialLinks":1715,"url_path":1716},"Paolo Frigerio","Politecnico di Milano","\u003Cp style=\"text-align:justify;\">Paolo Frigerio received the Ph.D. degree in information technology from Politecnico di Milano, Italy, in 2022. During his Ph.D. he has worked on the development of control circuits and systems for STMicroelectronics micromirrors. He is currently an Assistant Professor at Politecnico di Milano since 2022. His research interests include micro-electromechanical systems (MEMS), micromirrors, temperature sensors, and inertial sensors such as whole-angle gyroscopes, as well as the related analog electronics and digital control. He is the author of more than 20 publications and 2 patents in the field of MEMS sensors and actuators.\u003C/p>","2025-06-27T14:10:31.839Z","2025-07-01T12:41:03.085Z","191",{"id":1683,"name":1684,"alternativeText":7,"caption":7,"width":1685,"height":1686,"formats":1687,"hash":1711,"ext":1131,"mime":1134,"size":1712,"url":1713,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1714,"updatedAt":1714},253,"frigerio_pic.jpg",992,1361,{"large":1688,"small":1694,"medium":1700,"thumbnail":1706},{"ext":1131,"url":1689,"hash":1690,"mime":1134,"name":1691,"path":7,"size":1692,"width":1693,"height":57},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/large_frigerio_pic_c6965daaf7.jpg","large_frigerio_pic_c6965daaf7","large_frigerio_pic.jpg",61.18,729,{"ext":1131,"url":1695,"hash":1696,"mime":1134,"name":1697,"path":7,"size":1698,"width":1699,"height":64},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/small_frigerio_pic_c6965daaf7.jpg","small_frigerio_pic_c6965daaf7","small_frigerio_pic.jpg",21.11,364,{"ext":1131,"url":1701,"hash":1702,"mime":1134,"name":1703,"path":7,"size":1704,"width":1705,"height":71},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/medium_frigerio_pic_c6965daaf7.jpg","medium_frigerio_pic_c6965daaf7","medium_frigerio_pic.jpg",38.74,547,{"ext":1131,"url":1707,"hash":1708,"mime":1134,"name":1709,"path":7,"size":1710,"width":1401,"height":689},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_frigerio_pic_c6965daaf7.jpg","thumbnail_frigerio_pic_c6965daaf7","thumbnail_frigerio_pic.jpg",4.21,"frigerio_pic_c6965daaf7",100.78,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/frigerio_pic_c6965daaf7.jpg","2025-07-01T12:40:59.652Z",[],"-161","-162",{"id":617,"session":1719},{"id":617,"title":1720,"teaser":1721,"body":1722,"createdAt":1723,"updatedAt":1724,"publishedAt":1725,"url_path_id":1726,"contacts":1727,"url_path":1772},"Unlocking New Performance Horizons in Gas Sensing by Cross-Pollination between Contemporary Hardware and Mathematics","\u003Cp style=\"text-align:justify;\">Contemporary gas-monitoring demands push existing gas sensor designs to their fundamental limits in “3S” requirements of Sensitivity, Selectivity, and Stability. The origin of these limits is in the single-output (e.g., resistance, light intensity) sensor designs, also known as zero-order sensors. Any zero-order sensor is affected by chemical background and sensor drift that cannot be distinguished from the response to an analyte. \u003C/p>","\u003Cp style=\"text-align:justify;\">This tutorial will bridge the gap between existing and required gas detection capabilities by single-output sensors, sensor arrays, and traditional analytical instruments. Inspired by mathematics behind the first- and second-order traditional analytical instruments, next-generation gas sensors are being designed on four pillars: (1) physical transducer with excitation to operate with independent variables, (2) sensing material with different responses to multiple volatiles under variable excitation, (3) excitation to achieve independent responses, (4) data analytics for multi-gas differentiation, rejection of interferences, and drift self-correction. Next-generation sensors are attractive in scenarios when traditional analytical instruments cannot be used because of their size, power, and periodic maintenance requirements.  By the end of the tutorial, attendees will have a good understanding of design rules for building such next-generation sensors and will relate to how these sensors may be utilized in their envisioned applications.\u003C/p>","2025-06-27T14:15:24.229Z","2025-06-30T20:59:47.384Z","2025-06-27T14:15:26.969Z","194",[1728],{"id":1243,"name":1729,"committee":7,"position":7,"affiliation":1730,"email":7,"biography":1731,"createdAt":1732,"updatedAt":1733,"url_path_id":1734,"contactPhoto":1735,"socialLinks":1770,"url_path":1771},"Radislav A. Potyrailo","GE Vernova Advanced Research","\u003Cp style=\"text-align:justify;\">Radislav Potyrailo is a Senior Principal Scientist at GE Vernova Advanced Research leading the growth of GE Vernova’s sensing technologies for gas, chemical, and physical detection.  He has an Optoelectronics degree from Kyiv Polytechnic Institute (1985) and a PhD in Analytical Chemistry from Indiana University, Bloomington, IN (1998).  Upon joining GE in 1998, Dr. Potyrailo has been directing programs on designs of physical transducers, materials with multi-response mechanisms to ambient environments, data analytics, and system engineering. Dr. Potyrailo has been serving as a technical lead on GE R&D programs transitioned to GE businesses or GE partners for commercialization. Examples include optical multi-parameter chemical sensor for GE Water, wireless gas sensors for GE Oil & Gas, multi-parameter oil sensor for GE Renewable Energy, and GE Ventures start-up company on radio-frequency sensors. Dr. Potyrailo has been serving as a PI on programs funded by AFRL, ARPA-E, DARPA, DHS, DOE, DTRA, JPEO, NIH, NIOSH, NETL, and TSWG. Dr. Potyrailo is the initiator of the First Gordon Research Conference on Combinatorial and High Throughput Materials Science and serves as an editor of the Springer-Nature book series “Integrated Analytical Systems”. He is the North America Chair of International Society for Olfaction and Chemical Sensing and is the Chair of the Device Working Group of the MEMS and Sensors Industry Group. Dr. Potyrailo has 160+ granted US Patents and many publications (Google Scholar h-index 55). He has delivered 15+ keynote/plenary lectures and numerous invited talks at National and International Meetings. He is a recipient of the Prism Award by SPIE/Photonics Media (2011) and the AMA Innovation Award (2021).  Dr. Potyrailo is SPIE Fellow (2011) and recent IEEE Fellow (2023), covering the whole electromagnetic spectrum of his sensors. Dr. Potyrailo has been appointed as a Distinguished Lecturer of the IEEE Sensors Council for the period 2024-2026.\u003C/p>","2025-06-27T14:14:41.976Z","2025-06-30T20:34:53.986Z","193",{"id":1736,"name":1737,"alternativeText":7,"caption":7,"width":1738,"height":1739,"formats":1740,"hash":1765,"ext":1131,"mime":1134,"size":1766,"url":1767,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1768,"updatedAt":1769},251,"Radislav Potyrailo-Oct 2024.jpg",3074,3512,{"large":1741,"small":1747,"medium":1753,"thumbnail":1759},{"ext":1131,"url":1742,"hash":1743,"mime":1134,"name":1744,"path":7,"size":1745,"width":1746,"height":57},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/large_Radislav_Potyrailo_Oct_2024_8aa55394e2.jpg","large_Radislav_Potyrailo_Oct_2024_8aa55394e2","large_Radislav Potyrailo-Oct 2024.jpg",90.39,875,{"ext":1131,"url":1748,"hash":1749,"mime":1134,"name":1750,"path":7,"size":1751,"width":1752,"height":64},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/small_Radislav_Potyrailo_Oct_2024_8aa55394e2.jpg","small_Radislav_Potyrailo_Oct_2024_8aa55394e2","small_Radislav Potyrailo-Oct 2024.jpg",25.79,437,{"ext":1131,"url":1754,"hash":1755,"mime":1134,"name":1756,"path":7,"size":1757,"width":1758,"height":71},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/medium_Radislav_Potyrailo_Oct_2024_8aa55394e2.jpg","medium_Radislav_Potyrailo_Oct_2024_8aa55394e2","medium_Radislav Potyrailo-Oct 2024.jpg",53.06,656,{"ext":1131,"url":1760,"hash":1761,"mime":1134,"name":1762,"path":7,"size":1763,"width":1764,"height":689},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_Radislav_Potyrailo_Oct_2024_8aa55394e2.jpg","thumbnail_Radislav_Potyrailo_Oct_2024_8aa55394e2","thumbnail_Radislav Potyrailo-Oct 2024.jpg",3.9,136,"Radislav_Potyrailo_Oct_2024_8aa55394e2",1201.76,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/Radislav_Potyrailo_Oct_2024_8aa55394e2.jpg","2025-06-30T12:28:47.421Z","2025-06-30T12:29:14.290Z",[],"-163","-164",{"id":635,"session":1774},{"id":635,"title":1775,"teaser":1776,"body":1777,"createdAt":1778,"updatedAt":1779,"publishedAt":1780,"url_path_id":1781,"contacts":1782,"url_path":1826},"From antifouling (nano-)coatings to smart biosensing in complex media","\u003Cp style=\"text-align:justify;\">Despite outstanding performance in laboratory environments, only a limited number of novel biosensor concepts transition into practical point-of-care (POC) or on-site detection devices. Key obstacles include limited robustness, cost constraints, and biofouling in complex biological samples. A major unmet challenge lies in engineering antifouling biofunctional surfaces that simultaneously support high biorecognition efficiency, resist biofouling, and allow scalable, reproducible sensing performance. \u003C/p>","\u003Cp style=\"text-align:justify;\">This tutorial presents recent advances in antifouling strategies for biosensing applications, focusing on: (i) molecular mechanisms of biofouling to inform rational surface design; (ii) advanced tools for characterizing ultrathin antifouling (nano)coatings; (iii) biofunctionalization; (iv) integrated approaches for on-chip polymer synthesis using microfluidics; and (v) examples of antifouling label-free optical, electrochemical, and piezoelectric biosensors in clinical and environmental applications. Special attention will be given to emerging hydrophilic materials, including zwitterionic and non-ionic polymer brushes, and their remarkable antifouling behavior. The tutorial will also highlight challenges in accurately quantifying fouling levels, particularly when using standard techniques such as SPR. Interactive discussion will address how to optimize biofunctionalization without compromising antifouling properties, and how molecular insights can guide the scalable fabrication of robust biosensing platforms. Ultimately, next-generation antifouling coatings may enable reliable, high-performance POC diagnostics with real-world impact, from environmental surveillance to precision medicine.\u003C/p>","2025-06-27T14:17:08.812Z","2025-06-30T20:42:52.811Z","2025-06-27T14:17:11.225Z","196",[1783],{"id":1277,"name":1784,"committee":7,"position":7,"affiliation":1785,"email":7,"biography":1786,"createdAt":1787,"updatedAt":1788,"url_path_id":1789,"contactPhoto":1790,"socialLinks":1824,"url_path":1825},"Hana Vaisocherová - Lísalová","FZU – Institute of Physics of the Czech Academy of Sciences","\u003Cp style=\"text-align:justify;\">Hana Vaisocherová-Lísalová leads research in biosensors, bioinspired materials, and functional (nano)coatings, with a focus on understanding biomolecular interactions at interfaces under near-native conditions. She earned her Ph.D. in Biophysics from Charles University in Prague and broadened her expertise during a postdoctoral fellowship in chemical engineering at the University of Washington in Seattle. In 2019, she launched an interdisciplinary bio-interface research program at the FZU – Institute of Physics of the Czech Academy of Sciences, where she currently leads a team developing antifouling surfaces and advanced biosensing platforms. Over the past six years, while raising her three daughters - Helen, Adéla, and Justýna - she has built a productive research group, sharpened her leadership and mentoring capabilities, and received recognitions, including the 2022 Werner von Siemens Award, the 2024 ICBZM Mid-career Award at Cornell University, and the 2025 Biomaterials Science Emerging Investigator Award.  Her contributions include over 50 world-recognized publications, three book chapters, and six patents. Her work has attracted more than 2800 citations (h-index 25, Web of Science), particularly in the areas of SPR and QCM biosensor development and antifouling surfaces. Beyond her research, Hana is active in science communication and in initiatives supporting women in science and academia in the Czech Republic. Her work bridges fundamental science and real-world application, contributing to the development of robust biosensing technologies for medical diagnostics, food safety and security.\u003C/p>","2025-06-27T14:16:35.039Z","2025-06-30T20:35:39.693Z","195",{"id":1791,"name":1792,"alternativeText":7,"caption":7,"width":1793,"height":1794,"formats":1795,"hash":1820,"ext":1131,"mime":1134,"size":1821,"url":1822,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1823,"updatedAt":1823},246,"Hana-1024x942.jpg",1024,942,{"large":1796,"small":1802,"medium":1808,"thumbnail":1814},{"ext":1131,"url":1797,"hash":1798,"mime":1134,"name":1799,"path":7,"size":1800,"width":57,"height":1801},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/large_Hana_1024x942_8cfc2a9ba4.jpg","large_Hana_1024x942_8cfc2a9ba4","large_Hana-1024x942.jpg",79.37,920,{"ext":1131,"url":1803,"hash":1804,"mime":1134,"name":1805,"path":7,"size":1806,"width":64,"height":1807},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/small_Hana_1024x942_8cfc2a9ba4.jpg","small_Hana_1024x942_8cfc2a9ba4","small_Hana-1024x942.jpg",27.54,460,{"ext":1131,"url":1809,"hash":1810,"mime":1134,"name":1811,"path":7,"size":1812,"width":71,"height":1813},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/medium_Hana_1024x942_8cfc2a9ba4.jpg","medium_Hana_1024x942_8cfc2a9ba4","medium_Hana-1024x942.jpg",51.62,690,{"ext":1131,"url":1815,"hash":1816,"mime":1134,"name":1817,"path":7,"size":1818,"width":1819,"height":689},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_Hana_1024x942_8cfc2a9ba4.jpg","thumbnail_Hana_1024x942_8cfc2a9ba4","thumbnail_Hana-1024x942.jpg",5.95,170,"Hana_1024x942_8cfc2a9ba4",83.33,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/Hana_1024x942_8cfc2a9ba4.jpg","2025-06-27T15:20:48.258Z",[],"-165","-166",{"id":266,"session":1828},{"id":325,"title":1829,"teaser":1830,"body":41,"createdAt":1831,"updatedAt":1832,"publishedAt":1833,"url_path_id":1834,"contacts":1835,"url_path":1873},"Tensor Decompositions for Multidimensional Signal Processing","\u003Cp style=\"text-align:justify;\">This introductory tutorial addresses problems of storing and/or multilinear processing of very large multidimensional  data  arrays  (tensors)  that could possibly result from multi-sensor measurements. After  introducing  basic  tensor  operations,  we  will  cover  both  theoretical and computational aspects of two classic tensor decompositions: Canonical Polyadic Decomposition (CPD) and Tucker Decomposition (TD). We will also introduce the concept of Tensor Networks, with particular emphasis on the relatively recent Tensor Train Decomposition (TTD).  \u003C/p>","2025-08-25T20:04:03.408Z","2025-08-25T20:05:29.276Z","2025-08-25T20:04:05.682Z","223",[1836],{"id":1837,"name":1838,"committee":7,"position":7,"affiliation":1839,"email":7,"biography":1840,"createdAt":1841,"updatedAt":1841,"url_path_id":1842,"contactPhoto":1843,"socialLinks":1871,"url_path":1872},119,"Sherif S. Sherif","University of Manitoba, Winnipeg, Canada","\u003Cp style=\"text-align:justify;\">Sherif S. Sherif is Professor of Electrical & Computer Engineering, and Director of the Biomedical Engineering Graduate Program, at the University of Manitoba, Winnipeg, Canada. His research interests include Digital Image Processing  (M.Sc., University of Wisconsin-Madison)  and  Optics (Ph.D., University of Colorado at Boulder).  Before joining the University of Manitoba, he held research positions at the University of Oxford,  Imperial College London, and the National Research Council Canada (NRC).  He was also a Lecturer in Applied Optics (Assistant Professor) at the University of Kent,  United Kingdom.  He is the author or co-author of over 130 scientific publications, including five patents.   \u003C/p>","2025-08-25T20:03:06.880Z","222",{"id":1844,"name":1845,"alternativeText":7,"caption":7,"width":1846,"height":1847,"formats":1848,"hash":1867,"ext":1131,"mime":1134,"size":1868,"url":1869,"previewUrl":7,"provider":21,"provider_metadata":7,"createdAt":1870,"updatedAt":1870},285,"Sherif Sherif.jpg",893,990,{"small":1849,"medium":1855,"thumbnail":1861},{"ext":1131,"url":1850,"hash":1851,"mime":1134,"name":1852,"path":7,"size":1853,"width":1854,"height":64},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/small_Sherif_Sherif_7d8302b1a4.jpg","small_Sherif_Sherif_7d8302b1a4","small_Sherif Sherif.jpg",36.65,451,{"ext":1131,"url":1856,"hash":1857,"mime":1134,"name":1858,"path":7,"size":1859,"width":1860,"height":71},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/medium_Sherif_Sherif_7d8302b1a4.jpg","medium_Sherif_Sherif_7d8302b1a4","medium_Sherif Sherif.jpg",83.02,677,{"ext":1131,"url":1862,"hash":1863,"mime":1134,"name":1864,"path":7,"size":1865,"width":1866,"height":689},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/thumbnail_Sherif_Sherif_7d8302b1a4.jpg","thumbnail_Sherif_Sherif_7d8302b1a4","thumbnail_Sherif Sherif.jpg",5.15,141,"Sherif_Sherif_7d8302b1a4",145.76,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sensors25/Sherif_Sherif_7d8302b1a4.jpg","2025-08-25T20:03:00.309Z",[],"-187","-188",{"data":1875,"meta":1876},{"id":410,"heading":405,"createdAt":411,"updatedAt":412,"publishedAt":413,"url_path_id":414,"url_path":406,"contentType":128},{},1778853425460]