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Stochastic Processes - 105 Academic Year

本課程是由 國立陽明交通大學電機工程學系 提供。

This course intends to provide students with the necessary (fundamental and advanced) background on random processes.

Textbook：Athanasios Papoulis & S. Unnikrishna Pillai, Probability, Random Variables and Stochastic Processes. Fourth edition, Mc Graw Hill, 2002.

For perfect learning results, please buy textbooks!

Instructor(s) College of Electrical and Computer Engineering Prof. Po-Ning Chen
Course Credits 3 Credits
Academic Year 105 Academic Year
Level Graduate Student
Prior Knowledge None
Related Resources Course Video   Course Syllabus   Course Calendar  Course Handout  

WeekCourse ContentCourse VideoCourse Download
隨機過程課程介紹Watch OnlineMP4 Download
Chapter 9 General Concepts
9-1 Definitions (1/7)		Watch OnlineMP4 Download
9-1 Definitions (2/7)Watch OnlineMP4 Download
9-1 Definitions (3/7)Watch OnlineMP4 Download
9-1 Definitions (4/7)Watch OnlineMP4 Download
9-1 Definitions (5/7)Watch OnlineMP4 Download
9-1 Definitions (6/7)Watch OnlineMP4 Download
9-1 Definitions (7/7)Watch OnlineMP4 Download
9-2 Systems with Stochastic Inputs (1/3)Watch OnlineMP4 Download
9-2 Systems with Stochastic Inputs (2/3)Watch OnlineMP4 Download
9-2 Systems with Stochastic Inputs (3/3)Watch OnlineMP4 Download
9-3 The Power Spectrum (1/3)Watch OnlineMP4 Download
9-3 The Power Spectrum (2/3)Watch OnlineMP4 Download
9-3 The Power Spectrum (3/3)Watch OnlineMP4 Download
9-4 Discrete-Time ProcessesWatch OnlineMP4 Download
Chapter 10 Random Walks and Other Applications
10-3 Modulation (1/3)		Watch OnlineMP4 Download
10-3 Modulation (2/3)Watch OnlineMP4 Download
10-3 Modulation (3/3)Watch OnlineMP4 Download
10-4 Cyclostationary ProcessesWatch OnlineMP4 Download
10-5 Bandlimited Processes and Sampling TheoryWatch OnlineMP4 Download
10-6 Deterministic Signals in Noise
Appendix 10A The Poisson Sum Formula		Watch OnlineMP4 Download
Chapter 11 Spectral Representation
11-1 Factorization and Innovations		Watch OnlineMP4 Download
11-2 Finite-Order Systems and State Variables (1/3)Watch OnlineMP4 Download
11-2 Finite-Order Systems and State Variables (2/3)Watch OnlineMP4 Download
11-2 Finite-Order Systems and State Variables (3/3)Watch OnlineMP4 Download
11-3 Fourier Series and Karhunen-Lo`eve Expansions (1/2)Watch OnlineMP4 Download
11-3 Fourier Series and Karhunen-Lo`eve Expansions (2/2)Watch OnlineMP4 Download
11-4 Spectral Representation of Random Processes (1/2)Watch OnlineMP4 Download
11-4 Spectral Representation of Random Processes (2/2)Watch OnlineMP4 Download
Chapter 12 Spectrum Estimation
Ergodicity based on Shift-invariant Event		Watch OnlineMP4 Download
12-1 Ergodicity (1/3)Watch OnlineMP4 Download
12-1 Ergodicity (2/3)Watch OnlineMP4 Download
12-1 Ergodicity (3/3)Watch OnlineMP4 Download
12-2 Spectrum Estimation (1/3)Watch OnlineMP4 Download
12-2 Spectrum Estimation (2/3)Watch OnlineMP4 Download
12-2 Spectrum Estimation (3/3)Watch OnlineMP4 Download
Chapter 13 Mean Square Estimation
13-1 Introduction (1/2)		Watch OnlineMP4 Download
13-1 Introduction (2/2)Watch OnlineMP4 Download
13-2 Prediction (1/2)Watch OnlineMP4 Download
13-2 Prediction (2/2)Watch OnlineMP4 Download
 

課程目標

The course aims to provide the fundamentals of discrete time signal processing.

 

課程章節

章節 章節內容
Chapter 9: General Concepts9-1 Definitions 9-2 Systems with Stochastic Inputs 9-3 The Power Spectrum 9-4 Discrete-Time Processes
Chapter 10: Random Walks and Other Applications10-3 Modulation 10-4 Cyclostationary Processes 10-5 Bandlimited Processes and Sampling Theory 10-6 Deterministic Signals in Noise Appendix 10A The Poisson Sum Formula
Chapter 11: Spectral Representation 11-1 Factorization and Innovations 11-2 Finite-Order Systems and State Variables 11-3 Fourier Series and Karhunen-Lo´eve Expansions 11-4 Spectral Representation of Random Processes
Chapter 12: Spectrum Estimation12-1 Ergodicity 12-2 Spectrum Estimation
Chapter 13: Mean Square Estimation13-1 Introduction 13-2 Prediction (Partially, 12-3 Lattice Filters and Levinson’s Algorithm)


課程書目

Athanasios Papoulis & S. Unnikrishna Pillai, Probability, Random Variables and Stochastic Processes. Fourth edition, Mc Graw Hill, 2002.

 

評分標準

項目百分比
three quizzes25%
two midterm exam50%
final exam25%

本課程行事曆提供課程進度與考試資訊參考。

學期週次
參考課程進度

第一週
第二週
第三週

  • Chapter 9: General Concepts
    9-1 Definitions
第四週
  • 9-2 Systems with Stochastic Inputs
第五週
  • 9-2 Systems with Stochastic Inputs
    9-3 The Power Spectrum
第六週
  • 9-3 The Power Spectrum
    9-4 Discrete-Time Processes

    Chapter 10: Random Walks and Other Applications
    10-3 Modulation

第七週
  • 10-3 Modulation
    10-4 Cyclostationary Processes
第八週
  • 10-5 Bandlimited Processes and Sampling Theory
第九週
  • 第一次期中考
第十週
  • 10-5 Bandlimited Processes and Sampling Theory　
第十一週
  • 10-6 Deterministic Signals in Noise
    Appendix 10A The Poisson Sum Formula
第十二週
  • Chapter 11: Spectral Representation
    11-1 Factorization and Innovations
第十三週
  • 11-2 Finite-Order Systems and State Variables
    11-3 Fourier Series and Karhunen-Lo´eve Expansions
第十四週
  • 第二次期中考
第十五週
  • 11-3 Fourier Series and Karhunen-Lo´eve Expansions
第十六週
  • 11-4 Spectral Representation of Random Processes
第十七週
  • Chapter 12: Spectrum Estimation
    12-1 Ergodicity
    12-2 Spectrum Estimation
第十八週
  • 期末考

課程講義 Handout

參考授課進度
下載連接
Chapter 9 General ConceptsPDF
Chapter 10 Random Walks and Other ApplicationsPDF
Chapter 11 Spectral RepresentationPDF
Chapter 12 Spectrum EstimationPDF
Chapter 13 Mean Square EstimationPDF
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