Change search
ReferencesLink to record
Permanent link

Direct link
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]
Keyword [sv]
Teknik, Machine learning, Sparse coding, Over-complete dictionary, Feature extraction, Signal decomposition
URN: urn:nbn:se:ltu:diva-53528Local ID: a89deda9-ce5e-44c3-a296-0dfd365307c4OAI: diva2:1026902
Subject / course
Student thesis, at least 30 credits
Educational program
Engineering Physics and Electrical Engineering, master's level
Validerat; 20130822 (global_studentproject_submitter)Available from: 2016-10-04 Created: 2016-10-04Bibliographically approved

Open Access in DiVA

fulltext(1042 kB)0 downloads
File information
File name FULLTEXT02.pdfFile size 1042 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Albertsson, Kim

Search outside of DiVA

GoogleGoogle Scholar
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

ReferencesLink to record
Permanent link

Direct link