lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
1-D Convolutional Neural Networks for Signal Processing Applications
Linnaeus University, Faculty of Technology, Department of Building Technology.ORCID iD: 0000-0003-0530-9552
Show others and affiliations
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts. First of all, compact 1D CNNs can be efficiently trained with a limited dataset of 1D signals while the 2D deep CNNs, besides requiring 1D to 2D data transformation, usually need datasets with massive size, e.g., in the "Big Data" scale in order to prevent the well-known "overfitting" problem. 1D CNNs can directly be applied to the raw signal (e.g., current, voltage, vibration, etc.) without requiring any pre- or post-processing such as feature extraction, selection, dimension reduction …

Place, publisher, year, edition, pages
2019.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:lnu:diva-89756OAI: oai:DiVA.org:lnu-89756DiVA, id: diva2:1362698
Conference
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-10-21

Open Access in DiVA

No full text in DiVA

Search in DiVA

By author/editor
Abdeljaber, Osama
By organisation
Department of Building Technology
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 38 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf