A Deep Learning Approach for Artifact Suppression in MEMS-based Airborne Ultrasonic Transceivers

Alessandra Fusco, Martin Krueger, Lorenzo Servadei, Robert Wille

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

1 Scopus citations

Abstract

In this study, we introduce a denoising autoencoder as a solution to mitigate artifacts in MEMS-based airborne ultrasound transceivers. The autoencoder employs a deep neural network architecture to learn a robust representation of ultrasonic echo signals produced by reflecting targets, effectively eliminating unwanted noise. Experimental pulsed-echo ultrasonic recordings are used both in the development and evaluation of the model. Our results highlight the denoising autoencoder's ability to isolate echoes, even in complex scenarios where noise artifacts are prevalent. Evaluation based on the Mean Square Error (MSE) criterion reaffirms the success of the proposed method in effectively removing noise artifacts without suppressing the essential echo signals, as evidenced by the low MSE value of 7.89

Original languageEnglish
Title of host publicationIUS 2023 - IEEE International Ultrasonics Symposium, Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9798350346459
DOIs
StatePublished - 2023
Event2023 IEEE International Ultrasonics Symposium, IUS 2023 - Montreal, Canada
Duration: 3 Sep 20238 Sep 2023

Publication series

NameIEEE International Ultrasonics Symposium, IUS
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Conference

Conference2023 IEEE International Ultrasonics Symposium, IUS 2023
Country/TerritoryCanada
CityMontreal
Period3/09/238/09/23

Keywords

  • Airborne Ultrasound
  • Deep Learning
  • Echo Detection
  • MEMS
  • Ultrasonic Transceivers

Fingerprint

Dive into the research topics of 'A Deep Learning Approach for Artifact Suppression in MEMS-based Airborne Ultrasonic Transceivers'. Together they form a unique fingerprint.

Cite this