Deep learning for compressive sensing : a ubiquitous systems perspective

Machidon, Alina Luminita (author), Pejović, Veljko (author)

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

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