Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis

M. Yayla, A. Toma, J.E. Lenssen, V. Shpacovitch, K.-H. Chen, F. Weichert, J.-J. Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


We present a method for resource-efficient classification of nanoparticles such as viruses in liquid or gas samples by analyzing Surface Plasmon Resonance (SPR) images using frequency domain features. The SPR images are obtained with the Plasmon Assisted Microscopy Of Nano-sized Objects (PAMONO) biosensor, which was developed as a mobile virus and particle detector. Convolutional neural network (CNN) solutions are available for the given task, but since the mobility of the sensor is an important factor, we provide a faster and less resource demanding alternative approach for the use in a small virus detection device. The execution time of our approach, which can be optimized further using low power hardware such as a digital signal processor (DSP), is at least 2:6 times faster than the current CNN solution while sacrificing only 1 to 2.5 percent points in accuracy.
Original languageEnglish
Title of host publicationBildverarbeitung für die Medizin 2019
Subtitle of host publicationAlgorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameInformatik aktuell


Dive into the research topics of 'Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis'. Together they form a unique fingerprint.

Cite this