Random signals and noise: a mathematical introduction
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
CRC Press
2007
|
| Schlagworte: | |
| Online-Zugang: | Table of contents only Publisher description Inhaltsverzeichnis |
| Beschreibung: | XIX, 216 S. graph. Darst. |
| ISBN: | 0849375541 9780849375545 |
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Datensatz im Suchindex
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| adam_text | RAN DOM SIGNALS AND NOISE A MATHEMATICA I INTRODUCTION SHIOMO ENGELBERG
JERUSALEM COLLEGE OF TECHNOLOGY ISRAEL LSSFS) CRC PRESS V^ * TAYLOR &
FRANCIS GROUP SS&TA RATEN LONDON NEW YORK CRC PRESS IS AN IMPRJNT OF THE
TAYLOR & FRANCIS CROUP, AN INFORMA BUSINESS CONTENTS PREFACE XVII
ELEMENTARY PROBABILITY THEORY 1.1 THE PROBABILITY FUNCTION 1.2 A BIT OF
PHILOSOPHY 1.3 THE ONE-DIMENSIONAL RANDOM VARIABLE . . . . 1.4 THE
DISCRETE RANDOM VARIABLE AND THE PMF 1.5 A BIT OF COMBINATORICS 1.5.1 AN
INTRODUCTORY EXAMPLE 1.5.2 A MORE SYSTEMATIC APPROACH 1.5.3 HOW MANY
WAYS CAN N DISTINCT ITEMS BE ORDERED? . 1.5.4 HOW MANY DISTINCT SUBSETS
OF N ELEMENTS ARE THERE? 1.5.5 THE BINOMIAL FORMULA 1.6 THE BINOMIAL
DISTRIBUTION 1.7 THE CONTINUOUS RANDOM VARIABLE, THE CDF, AND THE PDF
1.8 THE EXPECTED VALUE 1.9 TWO DIMENSIONAL RANDONI VARIABLES 1.9.1 THE
DISCRETE RANDOM VARIABLE AND THE PMF 1.9.2 THE CDF AND THE PDF . . . .
1.9.3 THE EXPECTED VALUE 1.9.4 CORRELATION 1.9.5 THE CORRELATION
COEFFICIENT . . 1.10 THE CHARACTERISTIC FUNCTION 1.11 GAUSSIAN RANDONI
VARIABLES 1.12 EXERCISES 1 1 1 2 3 4 4 5 6 C 7 7 9 12 17 18 19 20 21 21
22 24 26 AN INTRODUCTION TO STOCHASTIC PROCESSES 2.1 WHAT IS A
STOCHASTIC PROCESS? 2.2 THE AUTOCORRELATION FUNCTION 2.3 WHAT DOES THE
AUTOCORRELATION FUNCTION TEIL US? 2.4 THE EVENNESS OF THE
AUTOCORRELATION FUNCTION . . 2.5 TWO PROOFS THAT RXX(OE) RXX(R) 2.6
SOME EXAMPLES 2.7 EXERCISES 31 31 33 33 34 34 36 38 IX X RANDOM SIGNALS
AND NOISE: A MATHEMATICAL INTRODUCTION THE WEAK LAW OF LARGE NUMBERS 41
3.1 THE MARKOV INEQUALITY .^^V/ .RUE-I^ 3.2 CHEBYSHEV S INEQUALITY 42 3.3
A SIMPLE EXAMPLE 43 3.4 THE WEAK LAW OF LARGE NUMBERS 45 3.5 COTRELATED
RANDOM VARIABLES 47 3.6 DETECTING A CONSTANT SIGNAL IN THE PRESENCE OF
ADDITIVE NOISE 49 3.7 A METHOD FOR DETENNINING THE CDF OF A RANDOM
VARIABLE . 50 3.8 EXERCISES 51 4 THE CENTRAL LIMIT THEOREM 55 4.1 4.2
4.3 4.4 4.5 4.6 4.7 INTRODUCTION 55 THE PROOF OF THE CENTRAL LIMIT
THEOREM DETECTING A CONSTANT SIGNAL IN THE PRESENCE OF ADDITIVE NOISE
DETECTING A (PARTICULAR) NON-CONSTANT SIGNAL IN THE PRESENCE OF ADDITIVE
NOISE THE MONTE CARLO METHOD POISSON CONVERGENCE EXERCISES 5 EXTREMA AND
THE METHOD OF LAGRANGE MULTIPLIERS 5.1 THE DIRECTIONAL DERIVATIVE AND
THE GRADIENT . . . . 5.2 OVER-DETERMINED SYSTEMS 5.2.1 GENERAL THEORY
5.2.2 RECOVERING A CONSTANT FROM NOISY SAMPLES 5.2.3 RECOVERING A LINE
FROM NOISY SAMPLES 5.3 THE METHOD OF LAGRANGE MULTIPLIERS 5.3.1
STATEMENT OF THE RESULT 5.3.2 A PRELIMINARY RESULT 5.3.3 PROOF OF THE
METHOD 5.4 THE CAUCHY-SCHWARZ INEQUALITY 5.5 UNDER-DETERMINED SYSTEMS
5.6 EXERCISES 56 59 61 63 64 68 73 73 74 74 75 76 77 77 78 80 83 84 86
THE MATCHED FILTER FOR STATIONARY NOISE 89 6.1 WHITE NOISE 89 6.2
COLORED NOISE 91 6.3 THE AUTOCORRELATION MATRIX 96 6.4 THE EFFECT OF
SAMPLING MANY TIMES IN A FIXED INTERVAL ... 97 6.5 MORE ABOUT THE SIGNAL
TO NOISE RATIO 98 6.6 CHOOSING THE OPTIMAL SIGNAL FOR A GIVEN NOISE TYPE
100 6.7 EXERCISES 101 TABLE OF CONTENTS . , : . XI 7 FOURIER SERIES AND
TRANSFORMS . 105 7.1 THE FOURIER SERIES 105 7.2 THE FUNCTIONS E N (T)
SPAN*A PLAUSIBILITY ARGUMENT 108 7.3 THE FOURIER TRANSFORM 111 7.4 SOME
PROPERTIES OF THE FOURIER TRANSFORM 112 7.5 SOME FOURIER TRANSFORMS 115
7.6 A CONNECTION BETWEEN THE TIME AND FREQUENCY DOMAINS . . 119 7.7
PRESERVATION OF THE INNER PRODUCT 120 7.8 EXERCISES 121 8 THE
WIENER-KHINCHIN THEOREM AND APPLICATIONS 125 8.1 THE PERIODIC CASE 125
8.2 THE APERIODIC CASE 128 8.3 THE EFFECT OF FILTERING 129 8.4 THE
SIGNIFICANCE OF THE POWER SPECTRAL DENSITY 130 8.5 WHITE NOISE 131 8.6
LOW-PASS NOISE 131 8.7 LOW-PASS FILTERED LOW-PASS NOISE 132 8.8 THE
SCHOTTKY FORMULA FOR SHOT NOISE 133 8.9 A SEMI-PRACTICAL EXAMPLE 135
8.10 JOHNSON NOISE AND THE NYQUIST FORRNULA 138 8.11 WHY USE RMS
MEASUREMENTS 140 8.12 THE PRACTICAL RESISTOR AS A CIRCUIT ELEMENT 141
8.13 THE RANDOM TELEGRAPH SIGNAL*ANOTHET LOW-PASS SIGNAL . . 143 8.14
EXERCISES 144 9 SPREAD SPECTRUM 149 9.1 INTRODUCTION 149 9.2 THE
PROBABILISTIC APPROACH 150 9.3 A SPREAD SPECTRUM SIGNAL WITH NARROW BAND
NOISE 151 9.4 THE EFFECT OF MULTIPLE TRANSMITTED 153 9.5 SPREAD
SPECTRUM*THE DETERMINISTIC APPROACH 155 9.6 FINITE STATE MACHINES 156
9.7 MODULO TWO RECURRENCE RELATIONS 157 9.8 A SIMPLE EXAMPLE 158 9.9
MAXIMAL LENGTH SEQUENCES 158 9.10 DETERMINING THE PERIOD 160 9.11 AN
EXAMPLE 161 9.12 SOME CONDITIONS FOR MAXIMALITY 162 9.13 WHAT WE HAVE
NOT DISCUSSED 163 9.14 EXERCISES 163 XII RANDOM SIGNALS AND NOISE: A
M&THEMATICAL INTRODUCTION 10 MORE ABOUT THE AUTOCORRELATION AND THE PSD
10.1 THE POSITIVITY OF THE AUTOCORRELATION 10.2 ANOTHER PROOF THAT R X
X(0) RXX(R) 10.3 ESTIMATING THE PSD 10.4 THE PROPERTIES OF THE
PERIODOGRAM . . . 10.5 EXERCISES 165 . 165 166 166 . 168 169 11 WIENER
FILTERS 11.1 A NON-CAUSAL SOLUTION 11.2 WHITE NOISE AND A LOW-PASS
SIGNAL 11.3 CAUSALITY, ANTI-CAUSALITY AND THE FOURIER TRANSFORM 11.4 THE
OPTIMAL CAUSAL FILTER 11.5 TWO EXAMPLES 11.5.1 WHITE NOISE AND A
LOW-PASS SIGNAL 11.5.2 LOW-PASS SIGNAL AND NOISE 11.6 EXERCISES 171 171
174 175 177 179 179 180 181 MATRIX MULTIPLICATION A A BRIEF OVERVIEW OF
LINEAR ALGEBRA A.L THE SPACE C N A.2 LINEAR INDEPENDENCE AND BASES A.3 A
PREUEMINARY RESULT A.4 THE DIMENSION OF C N . . . . A.5 LINEAR MAPPINGS
A.6 MATRICES A.7 SUMS OF MAPPINGS AND SUMS OF MATRICES A.8 THE
COMPOSITION OF LINEAR MAPPINGS* A.9 A VERY SPECIAL MATRIX A.10 SOLVING
SIMULTANEOUS LINEAR EQUATIONS A.LL THE INVERSE OF A LINEAR MAPPING A.12
INVERTIBILITY A.13 THE DETERMINANT*A TEST FOR INVERTIBILITY .... A.14
EIGENVECTORS AND EIGENVALUES A.15 THE INNER PRODUCT A.16 A SIMPLE PROOF
OF THE CAUCHY-SCHWARZ INEQUALITY A.17 THE HENNITIAN TRANSPOSE OF A
MATRIX A.18 SOME IMPORTANT PROPERTIES OF SELF-ADJOINT MATRICES A.19
EXERCISES 185 185 186 187 188 189 190 191 192 193 193 196 197 199 200
202 203 204 205 206 BIBLIOGRAPHY INDEX 209 212
|
| adam_txt |
RAN DOM SIGNALS AND NOISE A MATHEMATICA I INTRODUCTION SHIOMO ENGELBERG
JERUSALEM COLLEGE OF TECHNOLOGY ISRAEL LSSFS) CRC PRESS \V^ * TAYLOR &
FRANCIS GROUP SS&TA RATEN LONDON NEW YORK CRC PRESS IS AN IMPRJNT OF THE
TAYLOR & FRANCIS CROUP, AN INFORMA BUSINESS CONTENTS PREFACE XVII
ELEMENTARY PROBABILITY THEORY 1.1 THE PROBABILITY FUNCTION 1.2 A BIT OF
PHILOSOPHY 1.3 THE ONE-DIMENSIONAL RANDOM VARIABLE . . . . 1.4 THE
DISCRETE RANDOM VARIABLE AND THE PMF 1.5 A BIT OF COMBINATORICS 1.5.1 AN
INTRODUCTORY EXAMPLE 1.5.2 A MORE SYSTEMATIC APPROACH 1.5.3 HOW MANY
WAYS CAN N DISTINCT ITEMS BE ORDERED? . 1.5.4 HOW MANY DISTINCT SUBSETS
OF N ELEMENTS ARE THERE? 1.5.5 THE BINOMIAL FORMULA 1.6 THE BINOMIAL
DISTRIBUTION 1.7 THE CONTINUOUS RANDOM VARIABLE, THE CDF, AND THE PDF
1.8 THE EXPECTED VALUE 1.9 TWO DIMENSIONAL RANDONI VARIABLES 1.9.1 THE
DISCRETE RANDOM VARIABLE AND THE PMF 1.9.2 THE CDF AND THE PDF . . . .
1.9.3 THE EXPECTED VALUE 1.9.4 CORRELATION 1.9.5 THE CORRELATION
COEFFICIENT . . 1.10 THE CHARACTERISTIC FUNCTION 1.11 GAUSSIAN RANDONI
VARIABLES 1.12 EXERCISES 1 1 1 2 3 4 4 5 6 C 7 7 9 12 17 18 19 20 21 21
22 24 26 AN INTRODUCTION TO STOCHASTIC PROCESSES 2.1 WHAT IS A
STOCHASTIC PROCESS? 2.2 THE AUTOCORRELATION FUNCTION 2.3 WHAT DOES THE
AUTOCORRELATION FUNCTION TEIL US? 2.4 THE EVENNESS OF THE
AUTOCORRELATION FUNCTION . . 2.5 TWO PROOFS THAT RXX(OE) \RXX(R)\ 2.6
SOME EXAMPLES 2.7 EXERCISES 31 31 33 33 34 34 36 38 IX X RANDOM SIGNALS
AND NOISE: A MATHEMATICAL INTRODUCTION THE WEAK LAW OF LARGE NUMBERS 41
3.1 THE MARKOV INEQUALITY .^^V/'.RUE-I^ 3.2 CHEBYSHEV'S INEQUALITY 42 3.3
A SIMPLE EXAMPLE 43 3.4 THE WEAK LAW OF LARGE NUMBERS 45 3.5 COTRELATED
RANDOM VARIABLES 47 3.6 DETECTING A CONSTANT SIGNAL IN THE PRESENCE OF
ADDITIVE NOISE 49 3.7 A METHOD FOR DETENNINING THE CDF OF A RANDOM
VARIABLE . 50 3.8 EXERCISES 51 4 THE CENTRAL LIMIT THEOREM 55 4.1 4.2
4.3 4.4 4.5 4.6 4.7 INTRODUCTION 55 THE PROOF OF THE CENTRAL LIMIT
THEOREM DETECTING A CONSTANT SIGNAL IN THE PRESENCE OF ADDITIVE NOISE
DETECTING A (PARTICULAR) NON-CONSTANT SIGNAL IN THE PRESENCE OF ADDITIVE
NOISE THE MONTE CARLO METHOD POISSON CONVERGENCE EXERCISES 5 EXTREMA AND
THE METHOD OF LAGRANGE MULTIPLIERS 5.1 THE DIRECTIONAL DERIVATIVE AND
THE GRADIENT . . . . 5.2 OVER-DETERMINED SYSTEMS 5.2.1 GENERAL THEORY
5.2.2 RECOVERING A CONSTANT FROM NOISY SAMPLES 5.2.3 RECOVERING A LINE
FROM NOISY SAMPLES 5.3 THE METHOD OF LAGRANGE MULTIPLIERS 5.3.1
STATEMENT OF THE RESULT 5.3.2 A PRELIMINARY RESULT 5.3.3 PROOF OF THE
METHOD 5.4 THE CAUCHY-SCHWARZ INEQUALITY 5.5 UNDER-DETERMINED SYSTEMS
5.6 EXERCISES 56 59 61 63 64 68 73 73 74 74 75 76 77 77 78 80 83 84 86
THE MATCHED FILTER FOR STATIONARY NOISE 89 6.1 WHITE NOISE 89 6.2
COLORED NOISE 91 6.3 THE AUTOCORRELATION MATRIX 96 6.4 THE EFFECT OF
SAMPLING MANY TIMES IN A FIXED INTERVAL . 97 6.5 MORE ABOUT THE SIGNAL
TO NOISE RATIO 98 6.6 CHOOSING THE OPTIMAL SIGNAL FOR A GIVEN NOISE TYPE
100 6.7 EXERCISES 101 TABLE OF CONTENTS . , : .\ XI 7 FOURIER SERIES AND
TRANSFORMS . 105 7.1 THE FOURIER SERIES 105 7.2 THE FUNCTIONS E N (T)
SPAN*A PLAUSIBILITY ARGUMENT 108 7.3 THE FOURIER TRANSFORM 111 7.4 SOME
PROPERTIES OF THE FOURIER TRANSFORM 112 7.5 SOME FOURIER TRANSFORMS 115
7.6 A CONNECTION BETWEEN THE TIME AND FREQUENCY DOMAINS . . 119 7.7
PRESERVATION OF THE INNER PRODUCT 120 7.8 EXERCISES 121 8 THE
WIENER-KHINCHIN THEOREM AND APPLICATIONS 125 8.1 THE PERIODIC CASE 125
8.2 THE APERIODIC CASE 128 8.3 THE EFFECT OF FILTERING 129 8.4 THE
SIGNIFICANCE OF THE POWER SPECTRAL DENSITY 130 8.5 WHITE NOISE 131 8.6
LOW-PASS NOISE 131 8.7 LOW-PASS FILTERED LOW-PASS NOISE 132 8.8 THE
SCHOTTKY FORMULA FOR SHOT NOISE 133 8.9 A SEMI-PRACTICAL EXAMPLE 135
8.10 JOHNSON NOISE AND THE NYQUIST FORRNULA 138 8.11 WHY USE RMS
MEASUREMENTS 140 8.12 THE PRACTICAL RESISTOR AS A CIRCUIT ELEMENT 141
8.13 THE RANDOM TELEGRAPH SIGNAL*ANOTHET LOW-PASS SIGNAL . . 143 8.14
EXERCISES 144 9 SPREAD SPECTRUM 149 9.1 INTRODUCTION 149 9.2 THE
PROBABILISTIC APPROACH 150 9.3 A SPREAD SPECTRUM SIGNAL WITH NARROW BAND
NOISE 151 9.4 THE EFFECT OF MULTIPLE TRANSMITTED 153 9.5 SPREAD
SPECTRUM*THE DETERMINISTIC APPROACH 155 9.6 FINITE STATE MACHINES 156
9.7 MODULO TWO RECURRENCE RELATIONS 157 9.8 A SIMPLE EXAMPLE 158 9.9
MAXIMAL LENGTH SEQUENCES 158 9.10 DETERMINING THE PERIOD 160 9.11 AN
EXAMPLE 161 9.12 SOME CONDITIONS FOR MAXIMALITY 162 9.13 WHAT WE HAVE
NOT DISCUSSED 163 9.14 EXERCISES 163 XII RANDOM SIGNALS AND NOISE: A
M&THEMATICAL INTRODUCTION 10 MORE ABOUT THE AUTOCORRELATION AND THE PSD
10.1 THE "POSITIVITY" OF THE AUTOCORRELATION 10.2 ANOTHER PROOF THAT R X
X(0) \RXX(R)\ 10.3 ESTIMATING THE PSD 10.4 THE PROPERTIES OF THE
PERIODOGRAM . . . 10.5 EXERCISES 165 . 165 166 166 . 168 169 11 WIENER
FILTERS 11.1 A NON-CAUSAL SOLUTION 11.2 WHITE NOISE AND A LOW-PASS
SIGNAL 11.3 CAUSALITY, ANTI-CAUSALITY AND THE FOURIER TRANSFORM 11.4 THE
OPTIMAL CAUSAL FILTER 11.5 TWO EXAMPLES 11.5.1 WHITE NOISE AND A
LOW-PASS SIGNAL 11.5.2 LOW-PASS SIGNAL AND NOISE 11.6 EXERCISES 171 171
174 175 177 179 179 180 181 MATRIX MULTIPLICATION A A BRIEF OVERVIEW OF
LINEAR ALGEBRA A.L THE SPACE C N A.2 LINEAR INDEPENDENCE AND BASES A.3 A
PREUEMINARY RESULT A.4 THE DIMENSION OF C N . . . . A.5 LINEAR MAPPINGS
A.6 MATRICES A.7 SUMS OF MAPPINGS AND SUMS OF MATRICES A.8 THE
COMPOSITION OF LINEAR MAPPINGS* A.9 A VERY SPECIAL MATRIX A.10 SOLVING
SIMULTANEOUS LINEAR EQUATIONS A.LL THE INVERSE OF A LINEAR MAPPING A.12
INVERTIBILITY A.13 THE DETERMINANT*A TEST FOR INVERTIBILITY . A.14
EIGENVECTORS AND EIGENVALUES A.15 THE INNER PRODUCT A.16 A SIMPLE PROOF
OF THE CAUCHY-SCHWARZ INEQUALITY A.17 THE HENNITIAN TRANSPOSE OF A
MATRIX A.18 SOME IMPORTANT PROPERTIES OF SELF-ADJOINT MATRICES A.19
EXERCISES 185 185 186 187 188 189 190 191 192 193 193 196 197 199 200
202 203 204 205 206 BIBLIOGRAPHY INDEX 209 212 |
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| dewey-full | 621.382/2 |
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| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Book |
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| id | DE-604.BV022377067 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:09:42Z |
| indexdate | 2024-07-09T20:56:18Z |
| institution | BVB |
| isbn | 0849375541 9780849375545 |
| language | English |
| lccn | 2006299669 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015586121 |
| oclc_num | 76806977 |
| open_access_boolean | |
| owner | DE-92 DE-523 |
| owner_facet | DE-92 DE-523 |
| physical | XIX, 216 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Engelberg, Shlomo Verfasser aut Random signals and noise a mathematical introduction Shlomo Engelberg Boca Raton, Fla. [u.a.] CRC Press 2007 XIX, 216 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Filtres numériques (Mathématiques) Processamento de sinais larpcal Processos estocásticos larpcal Processus stochastiques Signal, Théorie du (Télécommunications) Teoria da comunicação larpcal Théorie mathématique de la communication Traitement du signal - Mathématiques Signal theory (Telecommunication) Statistical communication theory Stochastisches Signal (DE-588)4140374-5 gnd rswk-swf Signaltheorie (DE-588)4054945-8 gnd rswk-swf Rauschen (DE-588)4048606-0 gnd rswk-swf Signaltheorie (DE-588)4054945-8 s Rauschen (DE-588)4048606-0 s Stochastisches Signal (DE-588)4140374-5 s DE-604 http://www.loc.gov/catdir/toc/fy0705/2006299669.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0628/2006299669-d.html Publisher description GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015586121&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Engelberg, Shlomo Random signals and noise a mathematical introduction Filtres numériques (Mathématiques) Processamento de sinais larpcal Processos estocásticos larpcal Processus stochastiques Signal, Théorie du (Télécommunications) Teoria da comunicação larpcal Théorie mathématique de la communication Traitement du signal - Mathématiques Signal theory (Telecommunication) Statistical communication theory Stochastisches Signal (DE-588)4140374-5 gnd Signaltheorie (DE-588)4054945-8 gnd Rauschen (DE-588)4048606-0 gnd |
| subject_GND | (DE-588)4140374-5 (DE-588)4054945-8 (DE-588)4048606-0 |
| title | Random signals and noise a mathematical introduction |
| title_auth | Random signals and noise a mathematical introduction |
| title_exact_search | Random signals and noise a mathematical introduction |
| title_exact_search_txtP | Random signals and noise a mathematical introduction |
| title_full | Random signals and noise a mathematical introduction Shlomo Engelberg |
| title_fullStr | Random signals and noise a mathematical introduction Shlomo Engelberg |
| title_full_unstemmed | Random signals and noise a mathematical introduction Shlomo Engelberg |
| title_short | Random signals and noise |
| title_sort | random signals and noise a mathematical introduction |
| title_sub | a mathematical introduction |
| topic | Filtres numériques (Mathématiques) Processamento de sinais larpcal Processos estocásticos larpcal Processus stochastiques Signal, Théorie du (Télécommunications) Teoria da comunicação larpcal Théorie mathématique de la communication Traitement du signal - Mathématiques Signal theory (Telecommunication) Statistical communication theory Stochastisches Signal (DE-588)4140374-5 gnd Signaltheorie (DE-588)4054945-8 gnd Rauschen (DE-588)4048606-0 gnd |
| topic_facet | Filtres numériques (Mathématiques) Processamento de sinais Processos estocásticos Processus stochastiques Signal, Théorie du (Télécommunications) Teoria da comunicação Théorie mathématique de la communication Traitement du signal - Mathématiques Signal theory (Telecommunication) Statistical communication theory Stochastisches Signal Signaltheorie Rauschen |
| url | http://www.loc.gov/catdir/toc/fy0705/2006299669.html http://www.loc.gov/catdir/enhancements/fy0628/2006299669-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015586121&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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