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AL MOMENTO ESAURITO
The FFT in the 21st Century : Eigenspace Processing by James K. Beard (2010, Trade Paperback)
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Product Information
This book was undertaken to provide a text and reference on the theory and practice of the FFT and its common usage. This book is organized in only four chapters, and is intended as a tutorial on the use of the FFf and its trade space. The trade space of the FFT is the parameters in its usage and the relationships between them - the sampie rate, the total number of points or the interval over which processing occurs in a single FFf, the selectivity of tuning to a given frequency over signals out-of-band, and the bandwidth over which a signal appears. The examples given in this text are in FORTRAN 9512003. FORTRAN 2003 was frozen as a standard while this work was in progress. The listings given here are intended as an aid in understanding the FFT and associated algorithms such as spectral window weightings, with the goal of making the best of them more accessible to the reader. The code I use here provides a simple bridge between the material in the text and implementation in FORTRAN 2003, C++, Java, MATLAB ©, and other modem languages. The examples are sufficiently simple to be translated into older languages such as C and FORTRAN 77 if desired.
Product Identifiers
Publisher
Springer
ISBN-10
1441954104
ISBN-13
9781441954107
eBay Product ID (ePID)
109171307
Product Key Features
Author
James K. Beard
Format
Trade Paperback
Language
English
Topic
Transformations, Telecommunications, Electronics / General, Microwaves
Publication Year
2010
Type
Textbook
Genre
Technology & Engineering, Mathematics
Number of Pages
Xv, 216 Pages
Dimensions
Item Length
9.3in
Item Width
6.1in
Item Weight
16 Oz
Additional Product Features
Number of Volumes
1 Vol.
Lc Classification Number
Tk5102.9
Publication Name
Fft in the 21st Century : Eigenspace Processing
Table of Content
Dedication. Author. Preface. Foreword.The Fourier Transform. 1: Overview. 2: Conventions and Notations. 2.1. Complex Variables and the Complex Conjugate. 2.2. Vectors and Matrices. 2.3. Matrix Transpose and Hermitian. 2.4. Common Functions. 3: The Fourier Transform. 3.1. The Classical Fourier Transform. 3.2. Our First Encounter with the Dirac Delta Function. 3.3. Meaning, Usefulness, and Limitations of Formal Identities. 3.4. The Inverse Fourier Transform. 3.5. Parseval's Theorem. 3.6. Multivariate Fourier Transforms. 3.7. Fourier Transform Pairs. 3.8. The Hilbert Transform. 4: The Classical Fourier Series. 4.1. The Fourier Series. 4.2. Parseval's Theorem. 5: The Discrete Fourier Transform. 5.1. The Transform - A Trigonometric Identity. 5.2. Parseval's Theorem. 5.3. The Dirichlet Kernel. 5.4. DFT Pairs. 6: Gibb's Phenomenon. 7: Spatial and Matrix Representations and Interpretations. 7.1. The Continuous Fourier Transform. 7.2. The Classical Fourier Series. 7.3. The Discrete Fourier Transform. 8: Problems. 8.1. General. 8.2. Classical Fourier Transform. 8.3. Classical Fourier Series. 8.4. Discrete Fourier Transform. 8.5. Greater Time and Difficulty. 8.6. Project.Introduction to the Radix 2 FFT. 1: Historical Note. 2: Notations and Conventions. 3: Ordering the Bits in the Addresses. 4: Examples. 4.1. Simple DIF and DIT. 4.2. Multivariate FFT. 5: Problems. 5.1. General. 5.2. Greater Difficulty. 5.3. Project.The Reordering Problem and its Solutions. 1: Introduction. 2: Different types of Cooley-Tukey FFTs. 3: In-place, self reordering FFTs. 4: Conclusions. 4.1. Summary. 4.2. Execution Speeds. 4.3. Variable Radix Algorithms. 4.4. Multivariate FFTs. 5: Examples. 6: Problems. 6.1. General. 6.2. Greater Difficulty. 6.3. Project.Spectral Window Weightings. 1: Overview. 1.1. Base Concepts - The DFT Trade Space. 1.2. Continuous and Discrete Spectral Windows. 1.3. Sampled Continuous Spectral Windows. 1.4. Noise Bandwidth. 1.5. Array Efficiency. 1.6. Spectral Window Frequency Response. 2: Discrete Spectral Windows. 2.1. The Dolph-Chebychev Window. 2.2. Chebychev 2 Window. 2.3. Split Chebychev 2 Window for Monopulse. 2.4. Finite Impulse Response Filters. 3: Continuous Spectral Windows and Sampled Continuous Windows. 3.1. Sampled Continuous Window Functions as Discrete Windows. 3.2. Cosine Windows. 3.3. Continuous Extensions of the Dolph-Chebychev Window. 4: Two-Dimensional Window Weightings. 4.1. Planar Radar Antennas and Two-Dimensional DFTs. 4.2. The Two-Dimensional Dolph-Chebychev Weighting. 4.3. Two-Dimensional Chebychev 2 Window. 4.4. Monopulse with Split Two-Dime
Copyright Date
2004
Target Audience
Scholarly & Professional
Dewey Decimal
515/.2433
Dewey Edition
22
Illustrated
Yes
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