Langfristiges Ziel des Projekts ist es, Methoden zur Erkennung spezifischer Nanoobjekte zu entwickeln und zu analysieren, indem datenintensive, verrauschte und artefaktbelastete Bildsequenzen aus Sensorsystemen analysiert werden, die mehreren erschwerenden Einflüssen ausgesetzt sind. Eine Herausforderung besteht darin, dass die Verarbeitung der Bildsequenzen in (weicher) Echtzeit bei minimalem Ressourcenverbrauch wie z.B. von Energie und Speicher, erfolgen sollte. Daher ist die mehrkriterielle Optimierung ein wichtiges Thema unserer Forschung. Unter anderem optimieren wir die Hardware-Effizienz, die Zeiteffizienz und die Erkennungsqualität bei unterschiedlichen Anforderungen. Im Mittelpunkt steht der Nachweis und die Quantifizierung biologischer Nanopartikel mit der plasmonunterstützten Mikroskopie von PAMONO-Sensoren. Zu den Themen gehören Aspekte der technologischen Entwicklung und biophysikalische Methoden zur Erkennung von Nano-Objekten in lebenswichtigen, realen Szenarien.
Basierend auf den Erfahrungen aus dem Projekt sollen Methoden und technische Konzepte in Bezug auf Robustheit und Anwendbarkeit entwickelt werden, um die am besten geeigneten Methoden zur schnellen und benutzerfreundlichen Anpassung der Sensorsoftware im Bereich der Qualitätskontrolle von Arzneimitteln zu identifizieren. Dies soll in Zusammenarbeit mit den Anwendungspartnern ARTES Biotechnology GmbH (ARTES) und Paul-Ehrlich-Institut (PEI) erreicht werden. Sie sind an der methodischen Entwicklung beteiligt und insbesondere für die anschließenden kommerziellen Aspekte des PAMONO-Sensors in den Bereichen Qualitätskontrolle bei der Herstellung von Impfstoffen (ARTES) und Qualitätskontrolle von Blutspenden und Blutprodukten (PEI) verantwortlich. Eine Herausforderung besteht darin, sich an veränderte Umgebungsbedingungen und damit an immer vielfältigere Eigenschaften von Bildern anzupassen. Zu diesem Zweck wollen wir Methoden untersuchen, die die Adaptivität von Deep-Feature-Learning-Ansätzen erhöhen und hochspezialisierte, effiziente Architekturen für DNNs (differentiable programming) entwickeln. Auf technischer Ebene konzentrieren wir uns auf die Entwicklung neuartiger Ansätze zur Oberflächenfunktionalisierung des PAMONO-Sensors und einer innovativen, adaptiven Biosensor/Aktoreinheit auf Basis des PAMONO-Sensors. Daher sind algorithmische Lösungen für die automatische Anpassung der beteiligten Aktoren und die automatische Überwachung des Sensorverhaltens der PAMONO-Einheit erforderlich. Als Ergebnis des Projekts wollen wir eine methodische und technische Lösung erreichen, die es ermöglicht, die PAMONO-Technologie zur Qualitätskontrolle von Arzneimitteln einzusetzen.
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