A signal theoretic introduction to random processes
A fresh introduction to random processes utilizing signal theory By incorporating a signal theory basis, A Signal Theoretic Introduction to Random Processes presents a unique introduction to random processes with an emphasis on the important random phenomena encountered in the electronic and commu...
| Other Authors: | |
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| Format: | eBook |
| Language: | Inglés |
| Published: |
Hoboken, New Jersey :
Wiley
2016.
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| Edition: | 1st edition |
| Series: | New York Academy of Sciences
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| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629948006719 |
Table of Contents:
- Title Page; Copyright Page; About the Author; Contents; Preface; Chapter 1 A Signal Theoretic Introduction to Random Processes; 1.1 INTRODUCTION; 1.2 MOTIVATION; 1.2.1 Usefulness of Randomness; 1.2.2 Engineering; 1.3 BOOK OVERVIEW; Chapter 2 Background: Mathematics; 2.1 INTRODUCTION; 2.2 SET THEORY; 2.2.1 Basic Definitions; 2.2.2 Infinity; 2.2.3 Supremum and Infimum; 2.3 FUNCTION THEORY; 2.3.1 Function Definition; 2.3.2 Common Functions; 2.3.3 Function Properties; 2.4 MEASURE THEORY; 2.4.1 Sigma Algebra; 2.4.2 Measure; 2.4.3 Lebesgue Measure; 2.5 MEASURABLE FUNCTIONS
- 2.5.1 Simple or Elementary Functions 2.6 LEBESGUE INTEGRATION; 2.6.1 The Lebesgue Integral; 2.6.2 Demarcation of Signal Space; 2.6.3 Miscellaneous Results; 2.7 CONVERGENCE; 2.7.1 Dominated and Monotone Convergence; 2.8 LEBESGUE-STIELTJES MEASURE; 2.8.1 Lebesgue-Stieltjes Measure: Monotonic Function Case; 2.8.2 Lebesgue-Stieltjes Measure: Decreasing Function; 2.8.3 Lebesgue-Stieltjes Measure: General Case; 2.9 LEBESGUE-STIELTJES INTEGRATION; 2.9.1 Motivation; 2.9.2 Lebesgue-Stieltjes Integral; 2.9.3 Lebesgue-Stieltjes Integrals: Specific Cases; 2.10 MISCELLANEOUS RESULTS; 2.11 PROBLEMS
- APPENDIX 2.A PROOF OF THEOREM 2.1 APPENDIX 2.B PROOF OF THEOREM 2.2; APPENDIX 2.C PROOF OF THEOREM 2.7; APPENDIX 2.D PROOF OF THEOREM 2.8; APPENDIX 2.E PROOF OF THEOREM 2.10; Chapter 3 Background: Signal Theory; 3.1 INTRODUCTION; 3.2 SIGNAL ORTHOGONALITY; 3.2.1 Signal Decomposition; 3.2.2 Generalization; 3.2.3 Example: Hermite Basis Set; 3.3 THEORY FOR DIRICHLET POINTS; 3.3.1 Existence of Dirichlet Points; 3.4 DIRAC DELTA; 3.5 FOURIER THEORY; 3.5.1 Fourier Series; 3.5.2 Fourier Transform; 3.5.3 Inverse Fourier Transform; 3.5.4 Parsevalś Theorem; 3.6 SIGNAL POWER; 3.6.1 Sinusoidal Basis Set
- 3.6.2 Arbitrary Basis Set 3.7 THE POWER SPECTRAL DENSITY; 3.7.1 Energy Spectral Density; 3.7.2 Power Spectral Density: Sinusoidal Basis Set; 3.8 THE AUTOCORRELATION FUNCTION; 3.8.1 Definition of the Autocorrelation Function; 3.9 POWER SPECTRAL DENSITY-AUTOCORRELATION FUNCTION; 3.9.1 Relationships for Alternative Autocorrelation Function; 3.10 RESULTS FOR THE INFINITE INTERVAL; 3.10.1 Average Power; 3.10.2 The Power Spectral Density; 3.10.3 Integrated Spectrum; 3.10.4 Time Averaged Autocorrelation Function; 3.10.5 Power Spectral Density-Autocorrelation Relationship
- 3.11 CONVERGENCE OF FOURIER COEFFICIENTS 3.11.1 Periodic Signal Case; 3.11.2 Convergence of Fourier Coefficients to Zero; 3.12 Cramerś Representation and Transform; 3.12.1 Miscellaneous Mathematical Results; 3.12.2 Cramer Representation and Transform; 3.12.3 Initial Approach to the Cramer Transform; 3.12.4 The Cramer Transform; 3.12.5 Miscellaneous Results; 3.12.6 Transform of Common Signals; 3.12.7 Change in Transform; 3.12.8 Linear Filtering; 3.12.9 Integrated Spectrum, Spectrum, and Power Spectrum; 3.12.10 Cramer Transform of Standard Signals; 3.13 PROBLEMS
- APPENDIX 3.A PROOF OF THEOREM 3.5
