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A signal theoretic introduction to random processes / / Roy M. Howard
| A signal theoretic introduction to random processes / / Roy M. Howard |
| Autore | Howard Roy M. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (742 p.) |
| Disciplina | 003.54 |
| Soggetto topico |
Signal processing
Signal theory (Telecommunication) Stochastic processes Random noise theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-04679-3
1-119-04678-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910460758303321 |
Howard Roy M.
|
||
| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
A signal theoretic introduction to random processes / / Roy M. Howard
| A signal theoretic introduction to random processes / / Roy M. Howard |
| Autore | Howard Roy M. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (742 p.) |
| Disciplina | 003.54 |
| Collana | New York Academy of Sciences |
| Soggetto topico |
Signal processing
Signal theory (Telecommunication) Stochastic processes Random noise theory |
| ISBN |
1-119-04679-3
1-119-04678-5 |
| Classificazione |
547.1
003/.54 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910797205803321 |
|
Howard Roy M.
|
||
| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
A signal theoretic introduction to random processes / / Roy M. Howard
| A signal theoretic introduction to random processes / / Roy M. Howard |
| Autore | Howard Roy M. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2016 |
| Descrizione fisica | 1 online resource (742 p.) |
| Disciplina | 003.54 |
| Collana | New York Academy of Sciences |
| Soggetto topico |
Signal processing
Signal theory (Telecommunication) Stochastic processes Random noise theory |
| ISBN |
1-119-04679-3
1-119-04678-5 |
| Classificazione |
547.1
003/.54 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910811443403321 |
|
Howard Roy M.
|
||
| Hoboken, New Jersey : , : Wiley, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||