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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03449naa a2200373 4500 | |
| 001 | oai:lup.lub.lu.se:4e317d24-e651-4aa8-b746-0d1dfa93f99e | |
| 003 | SwePub | |
| 008 | 161118s2016 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://lup.lub.lu.se/record/4e317d24-e651-4aa8-b746-0d1dfa93f99e2 URI |
| 024 | 7 | a https://doi.org/10.1109/TSP.2016.25915132 DOI |
| 040 | a (SwePub)lu | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a art2 swepub-publicationtype |
| 072 | 7 | a ref2 swepub-contenttype |
| 100 | 1 | a Behjat, Hamidu Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH4 aut0 (Swepub:lu)eit-hbj |
| 245 | 1 0 | a Signal-Adapted Tight Frames on Graphs |
| 264 | 1 | c 2016 |
| 300 | a 13 s. | |
| 520 | a The analysis of signals on complex topologies modeled by graphs is a topic of increasing importance. Decompositions play a crucial role in the representation and processing of such information. Here, we propose a new tight frame design that is adapted to a class of signals on a graph. The construction starts from a prototype Meyer-type system of kernels with uniform subbands. The ensemble energy spectral density is then defined for a given set of signals defined on the graph. The prototype design is then warped such that the resulting subbands capture the same amount of energy for the signal class. This approach accounts at the same time for graph topology and signal features. The proposed frames are constructed for three different graph signal sets and are compared with non-signal-adapted frames. Vertex localization of a set of resulting atoms is studied. The frames are then used to decompose a set of real graph signals and are also used in a setting of signal denoising. The results illustrate the superiority of the designed signal-adapted frames, over frames blind to signal characteristics, in representing data and in denoising. | |
| 650 | 7 | a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Signalbehandling0 (SwePub)202052 hsv//swe |
| 650 | 7 | a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Signal Processing0 (SwePub)202052 hsv//eng |
| 653 | a filter design | |
| 653 | a signal processing on graphs | |
| 653 | a Spectral graph theory | |
| 653 | a tight frames | |
| 700 | 1 | a Richter, Ulrikeu Lund University,Lunds universitet,Integrativ neurofysiologi,Forskargrupper vid Lunds universitet,Integrative Neurophysiology,Lund University Research Groups4 aut0 (Swepub:lu)esx-urt |
| 700 | 1 | a Van De Ville, Dimitriu Swiss Federal Institute of Technology4 aut |
| 700 | 1 | a Sörnmo, Leifu Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH4 aut0 (Swepub:lu)tde-lso |
| 710 | 2 | a Avdelningen för Biomedicinsk teknikb Institutionen för biomedicinsk teknik4 org |
| 773 | 0 | t IEEE Transactions on Signal Processingg 64:22, s. 6017-6029q 64:22<6017-6029x 1053-587X |
| 856 | 4 | u http://dx.doi.org/10.1109/TSP.2016.2591513y FULLTEXT |
| 856 | 4 8 | u https://lup.lub.lu.se/record/4e317d24-e651-4aa8-b746-0d1dfa93f99e |
| 856 | 4 8 | u https://doi.org/10.1109/TSP.2016.2591513 |
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