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Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals

This paper introduces an event-based methodology to perform arbitrary linear basis transformations that encompass a broad range of practically important signal transforms, such as the discrete Fourier transform (DFT) and the discrete wavelet transform (DWT). We present a complexity analysis of the p...

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Detalles Bibliográficos
Autores principales: Ieng, Sio-Hoi, Lehtonen, Eero, Benosman, Ryad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6006676/
https://www.ncbi.nlm.nih.gov/pubmed/29946231
http://dx.doi.org/10.3389/fnins.2018.00373
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author Ieng, Sio-Hoi
Lehtonen, Eero
Benosman, Ryad
author_facet Ieng, Sio-Hoi
Lehtonen, Eero
Benosman, Ryad
author_sort Ieng, Sio-Hoi
collection PubMed
description This paper introduces an event-based methodology to perform arbitrary linear basis transformations that encompass a broad range of practically important signal transforms, such as the discrete Fourier transform (DFT) and the discrete wavelet transform (DWT). We present a complexity analysis of the proposed method, and show that the amount of required multiply-and-accumulate operations is reduced in comparison to frame-based method in natural video sequences, when the required temporal resolution is high enough. Experimental results on natural video sequences acquired by the asynchronous time-based neuromorphic image sensor (ATIS) are provided to support the feasibility of the method, and to illustrate the gain in computation resources.
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spelling pubmed-60066762018-06-26 Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals Ieng, Sio-Hoi Lehtonen, Eero Benosman, Ryad Front Neurosci Neuroscience This paper introduces an event-based methodology to perform arbitrary linear basis transformations that encompass a broad range of practically important signal transforms, such as the discrete Fourier transform (DFT) and the discrete wavelet transform (DWT). We present a complexity analysis of the proposed method, and show that the amount of required multiply-and-accumulate operations is reduced in comparison to frame-based method in natural video sequences, when the required temporal resolution is high enough. Experimental results on natural video sequences acquired by the asynchronous time-based neuromorphic image sensor (ATIS) are provided to support the feasibility of the method, and to illustrate the gain in computation resources. Frontiers Media S.A. 2018-06-12 /pmc/articles/PMC6006676/ /pubmed/29946231 http://dx.doi.org/10.3389/fnins.2018.00373 Text en Copyright © 2018 Ieng, Lehtonen and Benosman. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Ieng, Sio-Hoi
Lehtonen, Eero
Benosman, Ryad
Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals
title Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals
title_full Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals
title_fullStr Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals
title_full_unstemmed Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals
title_short Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals
title_sort complexity analysis of iterative basis transformations applied to event-based signals
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6006676/
https://www.ncbi.nlm.nih.gov/pubmed/29946231
http://dx.doi.org/10.3389/fnins.2018.00373
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