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Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis

  • Mikail YaylaEmail author
  • Anas Toma
  • Jan Eric Lenssen
  • Victoria Shpacovitch
  • Kuan-Hsun Chen
  • Frank Weichert
  • Jian-Jia Chen
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

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.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Mikail Yayla
    • 1
    Email author
  • Anas Toma
    • 1
  • Jan Eric Lenssen
    • 2
  • Victoria Shpacovitch
    • 3
  • Kuan-Hsun Chen
    • 1
  • Frank Weichert
    • 2
  • Jian-Jia Chen
    • 1
  1. 1.Department of Computer Science XIITU Dortmund UniversityDortmundDeutschland
  2. 2.Department of Computer Science VIITU Dortmund UniversityDortmundDeutschland
  3. 3.Leibniz-Institute for Analytical Science ISAS e.V.DortmundDeutschland

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