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Spectral Analysis of Signals Vital Source e-bog
Yanwei Wang, Jian Li og Petre Stoica
(2022)
Springer Nature
176,00 kr.
Leveres umiddelbart efter køb
Spectral Analysis of Signals Vital Source e-bog
Yanwei Wang, Jian Li og Petre Stoica
(2022)
Springer Nature
273,00 kr.
Leveres umiddelbart efter køb
Spectral Analysis of Signals Vital Source e-bog
Yanwei Wang, Jian Li og Petre Stoica
(2022)
Springer Nature
135,00 kr.
Leveres umiddelbart efter køb
Søgbar e-bog
Spectral Analysis of Signals Vital Source e-bog
Yanwei Wang, Jian Li og Petre Stoica
(2022)
Springer Nature
271,00 kr.
Leveres umiddelbart efter køb
Spectral Analysis of Signals
The Missing Data Case
Yanwei Wang, Jian Li og Petre Stoica
(2007)
Sprog: Engelsk
Springer International Publishing AG
323,00 kr.
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Detaljer om varen
- Vital Source 180 day rentals (fixed pages)
- Udgiver: Springer Nature (Maj 2022)
- Forfattere: Yanwei Wang, Jian Li og Petre Stoica
- ISBN: 9783031025259R180
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
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- Vital Source 365 day rentals (fixed pages)
- Udgiver: Springer Nature (Maj 2022)
- Forfattere: Yanwei Wang, Jian Li og Petre Stoica
- ISBN: 9783031025259R365
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
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Detaljer om varen
- Vital Source 90 day rentals (fixed pages)
- Udgiver: Springer Nature (Maj 2022)
- Forfattere: Yanwei Wang, Jian Li og Petre Stoica
- ISBN: 9783031025259R90
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
Licens varighed:
Online udgaven er tilgængelig: 90 dage fra købsdato.
Offline udgaven er tilgængelig: 90 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
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Online udgaven er tilgængelig: 90 dage fra købsdato.
Offline udgaven er tilgængelig: 90 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Vital Source searchable e-book (Fixed pages)
- Udgiver: Springer Nature (Maj 2022)
- Forfattere: Yanwei Wang, Jian Li og Petre Stoica
- ISBN: 9783031025259
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Paperback
- Udgiver: Springer International Publishing AG (December 2007)
- Forfattere: Yanwei Wang, Jian Li og Petre Stoica
- ISBN: 9783031013973
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.
Introduction.- Linear Source Separation.- Nonlinear Separation.- Final Comments.- Statistical Concepts.- Online Software and Data.
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