Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Towards self-learning sensors: Matching pursuit with dictionary learning on FPGA
2013 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A general approach for sparse signal decomposition is matching pursuit with an over-complete dictionary of features. It has been demonstrated that this gives efficient codes when applied to images and acoustic signals. This thesis implements an on-line version of matching pursuit with Hebbian dictionary learning, which is theoretically able to process vibration and acoustic emission signals for condition monitoring of, for example, bearings. The implementation is done in C and critical parts are identified and implemented in VHDL with the goal of synthesising those parts on an FPGA. The C implementation can handle data in two formats, both fixed and floating point and is verified to be functioning in both cases. The VHDL components are verified in simulation. The maximum processing speed possible for the designed system is expected be on the order of one million samples per second, 1 Msps.

Place, publisher, year, edition, pages
2013. , 35 p.
Keyword [en]
Technology
Keyword [sv]
Teknik, Machine learning, Sparse coding, Over-complete dictionary, Feature extraction, Signal decomposition
Identifiers
URN: urn:nbn:se:ltu:diva-53528Local ID: a89deda9-ce5e-44c3-a296-0dfd365307c4OAI: oai:DiVA.org:ltu-53528DiVA: diva2:1026902
Subject / course
Student thesis, at least 30 credits
Educational program
Engineering Physics and Electrical Engineering, master's level
Examiners
Note
Validerat; 20130822 (global_studentproject_submitter)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

Open Access in DiVA

fulltext(1042 kB)46 downloads
File information
File name FULLTEXT02.pdfFile size 1042 kBChecksum SHA-512
4304ae6bace5d45b78b049d7123acdd7762ed87e53d4925ca00f750788cfc2e400c4468c7832ec873464eb6a37860e5d86205e8caa855404f1b813fd7cb4f534
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Albertsson, Kim

Search outside of DiVA

GoogleGoogle Scholar
Total: 46 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 62 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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