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

doi:10.1007/978-3-658-25326-4_74

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 proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

Abstract

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 language	English
Title of host publication	Bildverarbeitung für die Medizin 2019
Subtitle of host publication	Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck
Pages	339-344
DOIs	https://doi.org/10.1007/978-3-658-25326-4_74
Publication status	Published - 2019
Externally published	Yes

Publication series

Name	Informatik aktuell

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10.1007/978-3-658-25326-4_74

Cite this

Yayla, M., Toma, A., Lenssen, J. E., Shpacovitch, V., Chen, K.-H., Weichert, F., & Chen, J.-J. (2019). Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis. In Bildverarbeitung für die Medizin 2019: Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck (pp. 339-344). (Informatik aktuell). https://doi.org/10.1007/978-3-658-25326-4_74

@inproceedings{a4aa9467de3d4ebb838caff82a8e0944,

title = "Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis",

abstract = "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.",

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

year = "2019",

doi = "10.1007/978-3-658-25326-4_74",

language = "English",

series = "Informatik aktuell",

pages = "339--344",

booktitle = "Bildverarbeitung f{\"u}r die Medizin 2019",

}

Yayla, M, Toma, A, Lenssen, JE, Shpacovitch, V, Chen, K-H, Weichert, F & Chen, J-J 2019, Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis. in Bildverarbeitung für die Medizin 2019: Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck. Informatik aktuell, pp. 339-344. https://doi.org/10.1007/978-3-658-25326-4_74

Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis. / Yayla, M.; Toma, A.; Lenssen, J.E. et al.
Bildverarbeitung für die Medizin 2019: Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck. 2019. p. 339-344 (Informatik aktuell).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis

AU - Yayla, M.

AU - Toma, A.

AU - Lenssen, J.E.

AU - Shpacovitch, V.

AU - Chen, K.-H.

AU - Weichert, F.

AU - Chen, J.-J.

PY - 2019

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N2 - 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.

AB - 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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Yayla M, Toma A, Lenssen JE, Shpacovitch V, Chen KH, Weichert F et al. Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis. In Bildverarbeitung für die Medizin 2019: Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck. 2019. p. 339-344. (Informatik aktuell). doi: 10.1007/978-3-658-25326-4_74

Resource-Efficient Nanoparticle Classification Using Frequency Domain Analysis

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