Compressive sensing meets time–frequency: An overview of recent advances in time–frequency processing of sparse signals
Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse s...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sejdić, Ervin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
14 |
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| Übergeordnetes Werk: |
Enthalten in: Modelling SARS-CoV-2 transmission in a UK university setting - Hill, Edward M. ELSEVIER, 2021, a review journal, Orlando, Fla |
|---|---|
| Übergeordnetes Werk: |
volume:77 ; year:2018 ; pages:22-35 ; extent:14 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.dsp.2017.07.016 |
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| 520 | |a Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. | ||
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10.1016/j.dsp.2017.07.016 doi GBV00000000000215A.pica (DE-627)ELV042856795 (ELSEVIER)S1051-2004(17)30166-5 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.75 bkl Sejdić, Ervin verfasserin aut Compressive sensing meets time–frequency: An overview of recent advances in time–frequency processing of sparse signals 2018transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. Compressive sensing Elsevier Time–frequency dictionary Elsevier Nonstationary signals Elsevier Sparse signals Elsevier Time–frequency analysis Elsevier Orović, Irena oth Stanković, Srdjan oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:77 year:2018 pages:22-35 extent:14 https://doi.org/10.1016/j.dsp.2017.07.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 77 2018 22-35 14 045F 620 |
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10.1016/j.dsp.2017.07.016 doi GBV00000000000215A.pica (DE-627)ELV042856795 (ELSEVIER)S1051-2004(17)30166-5 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.75 bkl Sejdić, Ervin verfasserin aut Compressive sensing meets time–frequency: An overview of recent advances in time–frequency processing of sparse signals 2018transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. Compressive sensing Elsevier Time–frequency dictionary Elsevier Nonstationary signals Elsevier Sparse signals Elsevier Time–frequency analysis Elsevier Orović, Irena oth Stanković, Srdjan oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:77 year:2018 pages:22-35 extent:14 https://doi.org/10.1016/j.dsp.2017.07.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 77 2018 22-35 14 045F 620 |
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10.1016/j.dsp.2017.07.016 doi GBV00000000000215A.pica (DE-627)ELV042856795 (ELSEVIER)S1051-2004(17)30166-5 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.75 bkl Sejdić, Ervin verfasserin aut Compressive sensing meets time–frequency: An overview of recent advances in time–frequency processing of sparse signals 2018transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. Compressive sensing Elsevier Time–frequency dictionary Elsevier Nonstationary signals Elsevier Sparse signals Elsevier Time–frequency analysis Elsevier Orović, Irena oth Stanković, Srdjan oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:77 year:2018 pages:22-35 extent:14 https://doi.org/10.1016/j.dsp.2017.07.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 77 2018 22-35 14 045F 620 |
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10.1016/j.dsp.2017.07.016 doi GBV00000000000215A.pica (DE-627)ELV042856795 (ELSEVIER)S1051-2004(17)30166-5 DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.75 bkl Sejdić, Ervin verfasserin aut Compressive sensing meets time–frequency: An overview of recent advances in time–frequency processing of sparse signals 2018transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. Compressive sensing Elsevier Time–frequency dictionary Elsevier Nonstationary signals Elsevier Sparse signals Elsevier Time–frequency analysis Elsevier Orović, Irena oth Stanković, Srdjan oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:77 year:2018 pages:22-35 extent:14 https://doi.org/10.1016/j.dsp.2017.07.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 77 2018 22-35 14 045F 620 |
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Compressive sensing meets time–frequency: An overview of recent advances in time–frequency processing of sparse signals |
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Compressive sensing meets time–frequency: An overview of recent advances in time–frequency processing of sparse signals |
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Modelling SARS-CoV-2 transmission in a UK university setting |
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compressive sensing meets time–frequency: an overview of recent advances in time–frequency processing of sparse signals |
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Compressive sensing meets time–frequency: An overview of recent advances in time–frequency processing of sparse signals |
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Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. |
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Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. |
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Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field. |
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Compressive sensing meets time–frequency: An overview of recent advances in time–frequency processing of sparse signals |
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Orović, Irena Stanković, Srdjan |
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