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Deep learning for compressive sensing : a ubiquitous systems perspective
ID
Machidon, Alina Luminita
(
Author
),
ID
Pejović, Veljko
(
Author
)
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MD5: 91C2153E5713F1D55C26FA547304C762
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https://link.springer.com/article/10.1007/s10462-022-10259-5
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Abstract
Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning from the compressed samples. While the CS–DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has any light been shed on practical issues towards bringing the CS–DL to real world implementations in the ubiquitous computing domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS–DL efficient, outline major trends in the CS–DL research space, and derive guidelines for the future evolution of CS–DL within the ubiquitous computing domain.
Language:
English
Keywords:
neural networks
,
deep learning
,
compressive sensing
,
ubiquitous computing
Work type:
Article
Typology:
1.02 - Review Article
Organization:
FRI - Faculty of Computer and Information Science
Publication status:
Published
Publication version:
Version of Record
Year:
2023
Number of pages:
Str. 3619-3658
Numbering:
Vol. 56, iss. 4
PID:
20.500.12556/RUL-155349
UDC:
004.8
ISSN on article:
0269-2821
DOI:
10.1007/s10462-022-10259-5
COBISS.SI-ID:
129878019
Publication date in RUL:
27.03.2024
Views:
245
Downloads:
83
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Record is a part of a journal
Title:
Artificial intelligence review
Shortened title:
Artif. intell. rev.
Publisher:
Springer Nature
ISSN:
0269-2821
COBISS.SI-ID:
15632133
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Secondary language
Language:
Slovenian
Keywords:
nevronske mreže
,
globoko učenje
,
kompresijsko zaznavanje
,
vseprisotno računalništvo
Projects
Funder:
ARRS - Slovenian Research Agency
Project number:
N2-0136
Name:
Povečanje učinkovitosti uporabe virov na pametnih telefonih s pomočjo približnega računanja
Funder:
ARRS - Slovenian Research Agency
Project number:
J2-3047
Name:
Kontekstno odvisno približno računanje na mobilnih napravah
Funder:
ARRS - Slovenian Research Agency
Project number:
P2-0098
Name:
Računalniške strukture in sistemi
Funder:
ARRS - Slovenian Research Agency
Project number:
P2-0426
Name:
Digitalna preobrazba za pametno javno upravljanje
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