---
res:
  bibo_abstract:
  - <jats:p>A mobile system that can detect viruses in real time is urgently needed,
    due to the combination of virus emergence and evolution with increasing global
    travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy
    of Nano-sized Objects) represents a viable technology for mobile real-time detection
    of viruses and virus-like particles. It could be used for fast and reliable diagnoses
    in hospitals, airports, the open air, or other settings. For analysis of the images
    provided by the sensor, state-of-the-art methods based on convolutional neural
    networks (CNNs) can achieve high accuracy. However, such computationally intensive
    methods may not be suitable on most mobile systems. In this work, we propose nanoparticle
    classification approaches based on frequency domain analysis, which are less resource-intensive.
    We observe that on average the classification takes 29    μ   s per image for
    the Fourier features and 17    μ   s for the Haar wavelet features. Although the
    CNN-based method scores 1–2.5 percentage points higher in classification accuracy,
    it takes 3370    μ   s per image on the same platform. With these results, we
    identify and explore the trade-off between resource efficiency and classification
    performance for nanoparticle classification of images provided by the PAMONO sensor.</jats:p>@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Mikail
      foaf_name: Yayla, Mikail
      foaf_surname: Yayla
  - foaf_Person:
      foaf_givenName: Anas
      foaf_name: Toma, Anas
      foaf_surname: Toma
  - foaf_Person:
      foaf_givenName: Kuan-Hsun
      foaf_name: Chen, Kuan-Hsun
      foaf_surname: Chen
  - foaf_Person:
      foaf_givenName: Jan Eric
      foaf_name: Lenssen, Jan Eric
      foaf_surname: Lenssen
  - foaf_Person:
      foaf_givenName: Victoria
      foaf_name: Shpacovitch, Victoria
      foaf_surname: Shpacovitch
  - foaf_Person:
      foaf_givenName: Roland
      foaf_name: Hergenröder, Roland
      foaf_surname: Hergenröder
  - foaf_Person:
      foaf_givenName: Frank
      foaf_name: Weichert, Frank
      foaf_surname: Weichert
  - foaf_Person:
      foaf_givenName: Jian-Jia
      foaf_name: Chen, Jian-Jia
      foaf_surname: Chen
  bibo_doi: 10.3390/s19194138
  bibo_issue: '19'
  bibo_volume: 19
  dct_date: 2019^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1424-8220
  dct_language: eng
  dct_publisher: MDPI AG@
  dct_title: Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited
    Platforms@
...