Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis
Publication Type
Conference Paper
  • Mikail Yayla
  • Anas Toma
  • Jan Eric Lenssen
  • Victoria Shpacovitch
  • Kuan-Hsun Chen
  • Frank Weichert
  • Jian-Jia Chen

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.

Conference Title
BVM 2019
Conference Country
Conference Date
March 17, 2019 - March 19, 2019
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