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Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- Glossary of Symbols
- Acronyms and Abbreviations
- Part I Background
- Part II Uniform Quantization
- Part III Floating–Point Quantization
- Part IV Quantization in Signal Processing, Feedback Control, and Computations
- Part V Applications of Quantization Noise Theory
- Part VI Quantization of System Parameters
- APPENDICES
- A Perfectly Bandlimited Characteristic Functions
- B General Expressions of the Moments of the Quantizer Output, and of the Errors of Sheppard's Corrections
- C Derivatives of the Sinc Function
- D Proofs of Quantizing Theorems III and IV
- E Limits of Applicability of the Theory — Caveat Reader
- F Some Properties of the Gaussian PDF and CF
- G Quantization of a Sinusoidal Input
- H Application of the Methods of Appendix G to Distributions other than Sinusoidal
- I A Few Properties of Selected Distributions
- J Digital Dither
- K Roundoff Noise in Scientific Computations
- L Simulating Arbitrary–Precision Fixed–Point and Floating–Point Roundoff in Matlab
- M The First Paper on Sampling–Related Quantization Theory
- Bibliography
- Index
A - Perfectly Bandlimited Characteristic Functions
from APPENDICES
Published online by Cambridge University Press: 06 July 2010
- Frontmatter
- Contents
- Preface
- Acknowledgments
- Glossary of Symbols
- Acronyms and Abbreviations
- Part I Background
- Part II Uniform Quantization
- Part III Floating–Point Quantization
- Part IV Quantization in Signal Processing, Feedback Control, and Computations
- Part V Applications of Quantization Noise Theory
- Part VI Quantization of System Parameters
- APPENDICES
- A Perfectly Bandlimited Characteristic Functions
- B General Expressions of the Moments of the Quantizer Output, and of the Errors of Sheppard's Corrections
- C Derivatives of the Sinc Function
- D Proofs of Quantizing Theorems III and IV
- E Limits of Applicability of the Theory — Caveat Reader
- F Some Properties of the Gaussian PDF and CF
- G Quantization of a Sinusoidal Input
- H Application of the Methods of Appendix G to Distributions other than Sinusoidal
- I A Few Properties of Selected Distributions
- J Digital Dither
- K Roundoff Noise in Scientific Computations
- L Simulating Arbitrary–Precision Fixed–Point and Floating–Point Roundoff in Matlab
- M The First Paper on Sampling–Related Quantization Theory
- Bibliography
- Index
Summary
We have seen when discussing the sampling theorem in Section 2.2 that the conditions of the theorem are exactly met only if the signal being sampled is perfectly bandlimited. This is rarely the case, since perfect bandlimitedness implies that the signal cannot be time–limited. Such a signal can be easily defined mathematically, but measured signals are always time–limited, so the condition of the sampling theorem can be met only approximately. While the sinc function wave is theoretically bandlimited, its time truncated versions are not, so the sampling theorem can be applied only approximately. However, sampling theory proved to be very powerful despite its approximate applicability.
The situation is similar with the quantizing theorems. Bandlimitedness of the CF would imply that the PDF is not amplitude–limited. Since measured signals are always amplitude–limited, the quantization theorems can be applied only approximately. Similarly to the sampling theorem, this does not prevent the quantizing theorems from being very powerful in many applications.
Most distributions that are applied in practice, like the Gaussian, exponential or chi–squared are not bandlimited. This fact does not prevent the application of the quantizing theorems if the quantum step size is significantly smaller than the standard deviation. Nevertheless, it is of interest to investigate the question whether there are distributions whose characteristic functions are perfectly bandlimited, similarly to the sinc function. In the following paragraphs we will discuss some examples of distributions whose CFs are perfectly bandlimited.
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- Information
- Quantization NoiseRoundoff Error in Digital Computation, Signal Processing, Control, and Communications, pp. 589 - 596Publisher: Cambridge University PressPrint publication year: 2008