Window Functions and Their Applications in Signal Processing
Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal...
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| Sprache: | Englisch |
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Boca Raton
CRC Press
2014
Taylor & Francis Taylor & Francis Group |
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| ISBN: | 9781466515833, 9781138076136, 1138076139, 146651583X |
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| Abstract | Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture radar |
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| AbstractList | Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture radar Window functions-otherwise known as weighting functions, tapering functions, or apodization functions-are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing.Comprehensively reviewing previous research and recent developments, this book:Provides suggestions on how to choose a window function for particular applicationsDiscusses Fourier analysis techniques and pitfalls in the computation of the DFTIntroduces window functions in the continuous-time and discrete-time domainsConsiders two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture radar “Window functions-otherwise known as weighting functions, tapering functions, or apodization functions-are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture radar” Window functions-otherwise known as weighting functions, tapering functions, or apodization functions-are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture radar. Window functions-otherwise known as weighting functions, tapering functions, or apodization functions-are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processingpresents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture radar |
| Author | Prabhu, K. M. M |
| Author_xml | – sequence: 1 fullname: Prabhu, K. M. M |
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| Keywords | continuous-time domains Side Lobe Level MSLL Rectangular Window Minimum Stopband Attenuation Fir Filter Flat Top Window discrete-time domains spectral analysis Window Functions Blackman Window biomedical signal analysis finite impulse response filter Continuous Time Signal Frequency Domain Plots Frequency Response Plot Side Lobe Blackman Tukey Method data windows digital signal processing Welch Periodogram PSD Estimate Kaiser-Bessel window Prolate Spheroidal Wave Functions Hamming Window Asymptotic Decay Rate Periodogram Estimate discrete-time windows PSLL ISAR Image Fourier analysis time-domain implementation discrete Fourier transform Hann Window synthetic aperture radar Lag Window Main Lobe Widths frequency-domain implementation Lower Side Lobe Levels |
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| Notes | Includes bibliographical references and index |
| OCLC | OCN: 1031048741 862821699 |
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| PublicationDate | 2014 [2014] 2013 2018-09-03 2013-10-21 |
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| Snippet | “Window functions-otherwise known as weighting functions, tapering functions, or apodization functions-are mathematical functions that are zero-valued outside... Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside... Window functions-otherwise known as weighting functions, tapering functions, or apodization functions-are mathematical functions that are zero-valued outside... |
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| SubjectTerms | Communications engineering / telecommunications Computer Engineering Computer science COMPUTERSCIENCE Computing and Information Technology Digital and Wireless Communication digital filtering Digital Signal Processing Digital techniques Electrical Electrical engineering ElectricalEngineering Electronics and communications engineering Energy technology and engineering ENG INFORMATIONSCIENCE Mobile & Wireless Communications radar SCI-TECH Short Time Fourier Transform Signal processing Signal processing -- Digital techniques spectral analysis speech processing STM Technology, Engineering, Agriculture, Industrial processes Telephone technology TJKT1 Mobile phone technology window functions |
| TableOfContents | 3.3.10 Raised-Cosine Family -- 3.3.11 Blackman Window -- 3.3.12 Optimized Blackman Window -- 3.3.13 Blackman-Harris Window -- 3.3.14 Parabolic Window -- 3.3.15 Papoulis Window -- 3.3.16 Tukey Window -- 3.3.17 Parzen (Jackson) Window -- 3.3.18 Dolph-Chebyshev Window -- 3.3.19 Kaiser's Modified Zeroth-Order Bessel Window Function Family -- 3.3.20 Kaiser's Modified First-Order Bessel Window Function Family -- 3.4 Rate of Fall-Off Side-Lobe Level -- 3.4.1 Theorem -- 3.4.2 Side-Lobe Fall-Off Rate in the Time-Domain -- 3.5 Comparison of Windows -- References -- 4. Performance Comparison of Data Windows -- 4.1 Definition of Window Parameters -- 4.2 Computation of Window Parameters -- 4.3 Discussion on Window Selection -- References -- 5. Discrete-TimeWindows and Their Figures of Merit -- 5.1 Different Classes of Windows -- 5.2 Discrete-Time Windows -- 5.2.1 Rectangular (Box Car) Window -- 5.2.2 Triangular (Bartlett) Window -- 5.2.3 Cosαx Window Family -- 5.2.4 Hann Window -- 5.2.5 Truncated Taylor Family of Windows -- 5.2.6 Hamming Window -- 5.2.7 Sum-Cosine Window -- 5.2.8 Raised-Cosine Window Family -- 5.2.9 Blackman Window -- 5.2.10 Optimized Blackman Window -- 5.2.11 Tukey Window -- 5.2.12 Blackman-Harris Window -- 5.2.13 Nuttall Window Family -- 5.2.14 Flat-Top Window -- 5.2.15 Parabolic Window -- 5.2.16 Riemann Window -- 5.2.17 Poisson Window -- 5.2.18 Gaussian Window -- 5.2.19 Cauchy Window -- 5.2.20 Hann-Poisson Window -- 5.2.21 Papoulis (Bohman) Window -- 5.2.22 Jackson (Parzen) Window -- 5.2.23 Dolph-Chebyshev Window -- 5.2.24 Modified Zeroth-Order Kaiser-Bessel Window Family -- 5.2.25 Modified First-Order Kaiser-Bessel Window Family -- 5.2.26 Saramäki Window Family -- 5.2.27 Ultraspherical Window -- 5.2.28 Odd and Even-Length Windows -- 5.3 Figures of Merit -- 5.4 Time-Bandwidth Product -- 5.5 Applications of Windows 5.5.1 FIR Filter Design Using Windows -- 5.5.2 Spectral Analysis -- 5.5.3 Window Selection for Spectral Analysis -- References -- 6. Time-Domain and Frequency-Domain Implementations of Windows -- 6.1 Time-Domain Implementation -- 6.2 A Programmable Windowing Technique -- 6.3 Computational Error in Time and Frequency-Domains -- 6.4 Canonic Signed Digit Windowing -- 6.4.1 Window 1 -- 6.4.2 Window 2 -- 6.4.3 Window 3 -- 6.4.4 Window 4 -- 6.4.5 Window 5 -- 6.4.6 Window 6 -- 6.4.7 Window 7 -- 6.4.8 Window 8 -- 6.4.9 Window 9 -- 6.4.10 Window 10 -- 6.4.11 Window 11 -- 6.4.12 Window 12 -- 6.4.13 Window 13 -- 6.4.14 Window 14 -- 6.5 Modified Zeroth-Order Kaiser-Bessel Window Family -- 6.6 Summary -- References -- 7. FIR Filter Design Using Windows -- 7.1 Ideal Filters -- 7.1.1 Lowpass Filter -- 7.1.2 Highpass Filter -- 7.1.3 Bandpass Filter -- 7.1.4 Bandstop Filter -- 7.2 Linear Time Invariant Systems -- 7.3 FIR Filters -- 7.3.1 Advantages of FIR Filters -- 7.4 IIR Filters -- 7.4.1 Properties of IIR Filters -- 7.5 Structure of an FIR Filter -- 7.5.1 Filter Specifications -- 7.6 FIR Filter Design -- 7.6.1 Linear-Phase Filters -- 7.6.2 Types of FIR Filters -- 7.6.3 Frequency Response of Type 1 FIR Filter -- 7.6.4 Design Procedure for Filters -- 7.7 Kaiser-Bessel Windows for FIR Filter Design -- 7.7.1 Filter Design Using Kaiser-Bessel Zeroth-Order (I0-Sinh) Window -- 7.7.2 Filter Design Using Kaiser-Bessel First-Order (I1-Cosh) Window -- 7.8 Design of Differentiator by Impulse Response Truncation -- 7.9 Design of Hilbert Transformer Using Impulse Response Truncation -- References -- 8. Application of Windows in Spectral Analysis -- 8.1 Nonparametric Methods -- 8.1.1 Periodogram PSD Estimator -- 8.1.2 Modified Periodogram PSD Estimator -- 8.1.3 Spectral Analysis Using Kaiser-Bessel Window -- 8.1.4 Bartlett Periodogram -- 8.1.5 Welch Periodogram Method Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Preface -- Acknowledgments -- Abbreviations -- 1. Fourier Analysis Techniques for Signal Processing -- 1.1 Review of Basic Signals and Systems -- 1.1.1 Basic Continuous-Time Signals -- 1.1.2 Basic Discrete-Time Signals -- 1.1.3 System and Its Properties -- 1.1.4 LTI Systems -- 1.2 Continuous-Time Fourier Transform -- 1.2.1 Properties of the CTFT -- 1.2.2 Examples of CTFT -- 1.3 Discrete-Time Fourier Transform -- 1.3.1 Properties of DTFT -- 1.3.2 Examples of DTFT -- 1.4 Z-Transform -- 1.4.1 Examples of z-Transform -- 1.5 Discrete Fourier Transform -- 1.5.1 Properties of the DFT -- 1.5.2 Examples of DFT -- 1.6 Fast Fourier Transform -- 1.6.1 Decimation-in-Time FFT (DIT-FFT) -- 1.6.1.1 Computational Savings -- 1.6.1.2 In-Place Computation -- 1.6.2 Decimation-in-Frequency FFT (DIF-FFT) -- 1.6.3 Inverse DFT from FFT -- 1.6.4 Linear Convolution Using DIT-FFT and DIF-FFT -- References -- 2. Pitfalls in the Computation of DFT -- 2.1 Sampling, Reconstruction, and Aliasing -- 2.1.1 WKS Sampling Theorem -- 2.1.2 Reconstruction of Continuous-Time Signals from Discrete-Time Samples -- 2.2 Frequency Leakage Effect -- 2.2.1 Zero Leakage Case -- 2.2.2 Maximum Leakage Case -- 2.3 DFT as a Filter Bank -- 2.4 Picket-Fence Effect or Scalloping Loss -- 2.5 Zero-Padding and Frequency Resolution -- 2.5.1 Zero-Padding -- 2.5.2 Frequency Resolution -- References -- 3. Review of Window Functions -- 3.1 Introduction -- 3.2 Characteristics of a Window Function -- 3.3 List of Windows -- 3.3.1 Rectangular (Box Car) Window -- 3.3.2 Triangular (Bartlett) Window -- 3.3.3 Cos(x) Window -- 3.3.4 Hann (Raised-Cosine) Window -- 3.3.5 Truncated Taylor Family -- 3.3.6 Hamming Window -- 3.3.7 Cos3(x) Window -- 3.3.8 Sum-Cosine Window -- 3.3.9 Cos4(x) Window 8.1.6 Blackman-Tukey Method -- 8.1.7 Daniel Periodogram -- 8.1.8 Application of the FFT to the Computation of a Periodogram -- 8.1.9 Short-Time Fourier Transform -- 8.1.10 Conclusions -- References -- 9. Applications of Windows -- 9.1 Windows in High Range Resolution Radars -- 9.1.1 HRR Target Profiling -- 9.1.2 Simulation Results -- 9.2 Effect of Range Side Lobe Reduction on SNR -- 9.2.1 Introduction -- 9.2.2 Loss Factor -- 9.2.3 Weighting Function -- 9.2.4 Results and Discussions -- 9.3 Window Functions in Stretch Processing -- 9.4 Application of Window Functions in Biomedical Signal Processing -- 9.4.1 Biomedical Signal Processing -- 9.4.2 FIR Filtering of Biomedical Signals -- 9.4.3 Moving Average Filtering of Biomedical Signals -- 9.4.4 QRS Detection in ECG Based on STFT -- 9.5 Audio Denoising Using the Time-Frequency Plane -- 9.5.1 Time-Frequency Plane -- 9.5.2 Audio Denoising Using Time-Frequency Plane -- 9.5.3 Block Thresholding -- 9.5.4 Effect of Windows -- 9.6 Effect of Windows on Linear Prediction of Speech -- 9.6.1 Linear Prediction Coder -- 9.6.2 Line Spectral Frequencies -- 9.6.3 LSF Variation due to Windows -- 9.7 Application of Windows in Image Processing -- 9.7.1 Windows for ISAR Images -- 9.7.2 Experimental Analysis -- 9.7.3 Results and Conclusions -- 9.8 Windows to Improve Contrast Ratio in Imaging Systems -- 9.8.1 Experimental Analysis -- 9.8.2 Results and Conclusions -- References -- Index |
| Title | Window Functions and Their Applications in Signal Processing |
| URI | https://www.taylorfrancis.com/books/9781315216386 https://cir.nii.ac.jp/crid/1130282271986091008 https://directory.doabooks.org/handle/20.500.12854/31192 https://library.oapen.org/handle/20.500.12657/41686 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=1378838 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781351832274 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9781466515840&uid=none |
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