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随机信号分析(Random Signal Analysis 中英双语)

主讲教师:Quan-Quan Liang (梁泉泉)、Bin Yan (颜斌)、Jian-Jun Hao (郝建军)、Gao-Yang Zhu(朱高阳)、Lin Shao (邵林)

教师团队:共5

  • 梁泉泉
  • 颜斌
  • 朱高阳
  • 郝建军
  • 邵林
学校: 山东科技大学
开课院系: 电子信息工程学院
专业大类: 电子信息
开课专业: 通信工程
课程英文名称: Random Signal Processing
课程编号: 211121110502
学分: 2
课时: 32
Introduction (课程介绍)
The purpose of the course "random signal processing" is analyzing and utilizing the underlying structure in random signals. These types of signals are pervasive in communication, control and signal processing. The basic tools we utilize are probability models and statistical inference. Probability models are used to describe the uncertainty and structure in random signal. Based on this model, statistical estimation and hypothesis testing can be utilized to estimate signal parameters, filter signals and detect signals. For the major of Communication Engineering, this course is a prerequisite of the following courses: Principles of Communication, Wireless Communication, and Multimedia communication, Information Theory and Coding.
Teaching Team (教师团队)

梁泉泉

职称:副教授

单位:山东科技大学

部门:电子信息工程学院

职位:系主任

颜斌

职称:教授

单位:山东科技大学

部门: 电子信息工程学院

朱高阳

职称:讲师

单位:山东科技大学

部门:电子信息工程学院

职位:无

郝建军

职称:教授

单位:山东科技大学

部门:电子信息工程学院

邵林

职称:讲师

单位:山东科技大学

部门:电子信息工程学院

教学方法
  • Lecture (授课)

  • Experiments (实验)

  • Projects (课程小项目)

Textbooks and Reference Books (参考教材和资料)

Required Textbook:

  1. 李晓峰、李在铭、周宁、傅志中. 随机信号分析(第四版). 电子工业出版社,北京。

  2. Lathi. Modern digital and analog communicationsystems. (4th ) chapters 8 and 9. (We will follow closely the structure of the two chapters in this classical textbook, but will also add more materials when needed.)

Reference Textbooks:

  1. S. Miller, D. G. Childers. Probability and Random Processes, with Applications to Signal Processing and Communications. Elsevier, 2004.

  2. Steven Kay. Fundamentals of Statistical Signal Processing, vol 1: Estimation Theory, Vol 2: Detection Theory.

  3. Steven Kay. Intuitive Probability and Random Processes using MATLAB. Springer, 2006.

  4. Robert M. Gray and Lee D. Davisson. An Introduction to Statistical Signal Processing. (1st Edition), 2010, Cambridge University Press.

  5. P. Peebles. Probability, Random Variables, and Random Signal Principles. (4th edition). 2000, McGraw-Hill.

  6. J. A. Gubner. Probability and Random Processes for Electrical and Computer Engineers. 2006, Cambridge University Press.

  7. W. Woyczynski. A First Course in Statistics for Signal Analysis. 2011, Birkhäuser. 

  8. Stanford EE 278: Introduction to statistical signal processing. URL: http://web.stanford.edu/class/ee278/

  9. University of California Santa Cruz. Statistical signal processing. URL: https://itunes.apple.com/us/itunes-u/statistical-signal-processing/id547397278?mt=10

  10. Rice University. ELEC531, Statistical signal processing. Video lectures: https://mediacosmos.rice.edu/Playlist/Be5b8R7E

Schedule (课程安排)

Lectures
                                                                                                                                               

 

Lecture

 
 

Contents

 
 

Reading

 
 

1-2

 
 

Course  introduction, Review of probability theory (I)

 
 

Section  8.1

 
 

3-4

 
 

Review  of probability theory (II)

 
 

Section  8.1

 
 

5-6

 
 

Problem  session: Review of probability theory

 
 

Problems  8.1.x

 
 

7-8

 
 

Concept  of random variable, discrete random variable

 
 

Section  8.2

 
 

9-10

 
 

Continuous  random variable(1)

 
 

Section  8.2

 
 

11-12

 
 

Continuous  random variable (II)

 
 

Section  8.2

 
 

13-14

 
 

Statistical  averages(1):

 
 

Section  8.3

 
 

15-16

 
 

Statistical  averages(II):

 
 

Section  8.3

 
 

17-18

 
 

Correlation

 
 

Section  8.4

 
 

19-20

 
 

LMSE

 
 

Section  8.5

 
 

21-22

 
 

Sum  of random variables

 
 

Section  8.6

 
 

23-24

 
 

Central  Limit Theorem

 
 

Section  8.7

 
 

25-26

 
 

Concept  of random processes and Auto-correlation Function (ACF)

 
 

Section  9.1

 
 

27-28

 
 

Classification  of R.P.

 
 

Section  9.2

 
 

29-30

 
 

Power  Spectrum Density(I)

 
 

Section  9.3

 
 

31-32

 
 

Power  Spectrum Density(II): Examples

 
 

Section  9.3

 
 

33-34

 
 

Transmission  of R.P. through Linear systems

 
 

Section  9.4

 
 

35-36

 
 

Wiener  Filtering

 
 

Section  9.5

 
 

37-38

 
 

Course  Review

 
 

Problems  9.x.x

 
 

39-40

 
 

Advanced  Topics: Kalman Filter

 
 

Steven Kay. Fundamentals of Statistical Signal Processing, Volume I.

 
 

41-42

 
 

Advanced  Topics: Monte Carlo Simulation I

 
 

Course  Note

 
 

43-44

 
 

Advanced  Topics: Markov Chain Monte Carlo

 
 

Course  Note

 
 

45-46

 
 

Advanced  Topics: Signal Detection

 
 

Course  Note

 


Syllabus- Lab (实验大纲)

Aim and Purpose

"Random Signal Processing (RSP)” is a fundamental course for the major Electronic and Communication. This lab is an inseparable part of this course.

As a fundamental course for students majored in communication and electronics, RSP deals with modelling of random signals, parameter estimation, signal estimation and signal detection problems. All these problems have strong engineering background and areextracted from realistic engineering applications.

Through study of this course and related labs, students should be able to get a basic understanding of information extraction from random signals. Furthermore, they will also get familiar with recent advances in communication and signal processing. Through the “learning via practicing” approach, we anticipate that our students can get the basic training needed for their future engineering work, being rigorous and also innovative.

 A series of experiments are designed in this lab, coordinating closely with the discussion of theoretical topics in class. In addition, we also considered the course design and latter courses when designing our experiments. These experiments will help our students to digest what they have learned in class; they will help students to think creatively by doing some innovation-based projects. Realization of signal estimation and detection is extensively used in this lab, which is proved to be very useful for practical works.

In addition to Matlab based experiments,some experiments are designed to use the hardware platform in signal processing lab. This lab will lay a solid foundation for later courses, including course project and practices in multimedia communication and network security.

List of experiments

We need totally 8 hours on these experiments, including4 hours of basic experiments and 4 hours of more complicated and comprehensive experiments.

There are also two innovation-based experiments which are optional. A detailed introduction to all experiments is listed in the following table.

                                                                               

 

No.

 
 

Title

 
 

Hours

 
 

Contents

 
 

Type

 
 

1

 
 

Generation of random variables

 
 

2

 
 

1.         Generate samples from discrete random variable

 

2.         Generate samples from continuous random variable

 

3.         Rayleigh distributed samples

 
 

Verification

 

Non-optional

 
 

2

 
 

Histogram processing in image processing

 
 

2

 
 

1.         Histogram of an image

 

2.         Histogram equalization

 

3.         Histogram specification

 
 

Verification

 

Non-optional

 
 

3

 
 

Phase locked loop using least square estimation

 
 

2

 
 

1.         Generate sinusoidal signal

 

2.         Estimation of frequency and phase based on Gauss-Newton  iteration

 
 

Verification

 

Non-optional

 
 

4

 
 

Estimation of  frequencies from superimposed signals

 
 

2

 
 

1.       Estimate  frequency for a single sinusoidal signal

 

2.       EM algorithm  and iterations

 

3.       Estimate  frequencies from superimposed sinusoidals

 
 

Innovative

 

Non-optional

 
 

5

 
 

Vehicle tracking  based on Kalman filtering

 
 

2

 
 

1.       Getting  started with state space model

 

2.       Simulate  noisy observations

 

3.       State  estimation based on Kalman filtering

 
 

Innovative

 

Non-optional

 
 

6

 
 

Image denoising

 
 

2

 
 

1.       Mean filter

 

2.       Median  filter

 

3.       Implementation  of median filter and mean filter

 
 

Innovative

 

Optional

 
 

7

 
 

Voice activity  detection

 
 

2

 
 

1.       Short term  energy feature of voiced and non-voiced speech

 

2.       Adaptive  threshold

 
 

Innovative

 

Optional

 
教学资源
课程章节 | 文件类型   | 修改时间 | 大小 | 备注
1.1 课程简介 Introduction to Random Signal Analysis
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2023-09-09 186.12MB
 
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2023-09-09 474.70MB
 
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2023-09-09 760.55KB
 
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2023-09-09 1.05MB
 
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2023-09-09 336.74MB
 
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2023-09-09 643.53KB
 
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2023-09-09 334.79MB
 
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2023-09-09 623.05KB
 
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2023-09-09 526.31KB
 
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2023-09-09 176.17MB
 
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2023-09-09 175.67KB
 
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2023-09-09 458.55KB
 
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2023-09-09 351.64KB
 
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2023-09-09 526.28KB
 
作业
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2023-09-09 --
 
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2023-09-09 --
1.2 概率论复习 Review of Probability I
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2023-09-09 1.29MB
1.3 概率论复习 Review of Probability II
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2021-07-06 143.41MB
 
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2021-07-06 47.77MB
 
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2021-07-06 53.18MB
 
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2021-07-06 72.31MB
 
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2021-07-06 625.12KB
 
作业
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2021-07-06 --
1.4 Concept of R.V. and Discrete R.V.
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2023-09-09 33.12MB
 
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2023-09-09 7.96MB
 
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2023-09-09 2.88MB
 
作业
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2023-09-09 --
1.5 Continuous R. V. - Part 1
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2023-09-09 11.53MB
 
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2023-09-09 11.53MB
 
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2023-09-09 34.78MB
 
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2023-09-09 2.21MB
1.6 Continuous R.V. - Part 2
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2023-09-09 188.42MB
 
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2023-09-09 8.00MB
 
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2023-09-09 29.52MB
 
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2023-09-09 150.07MB
 
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2023-09-09 1.53MB
1.7 习题讲解 Problem Session
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2023-09-09 9.47MB
 
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2023-09-09 4.75MB
2.1 统计平均1 Statistical Average I: Expectation and Moments
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2023-09-09 330.71MB
 
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2023-09-09 320.95MB
 
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2023-09-09 352.07MB
 
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2023-09-09 371.32MB
 
文档
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2023-09-09 2.74MB
 
作业
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2023-09-09 --
2.2 统计平均2 Statistical Average II: Estimating Probability
视频
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2023-09-09 473.17MB
 
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2023-09-09 1.32MB
2.3 自相关 Correlation
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2023-09-09 339.90MB
 
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2023-09-09 406.13MB
 
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2023-09-09 352.85MB
 
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2023-09-09 1.19MB
2.4 最小均方误差估计 MMSE Estimation
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2023-09-09 470.23MB
 
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2023-09-09 437.90MB
 
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2023-09-09 467.98MB
 
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2023-09-09 432.70MB
2.5 Central Limit Theorem
视频
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2023-09-09 386.70MB
 
文档
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2023-09-09 1.43MB
2.6 Sum of Random Variable
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2023-09-09 25.19MB
 
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2023-09-09 28.41MB
 
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2023-09-09 28.75MB
 
文档
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2023-09-09 1.98MB
3.1 随机变量到随机过程 From Random Variable to Random Process
视频
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2023-09-09 403.67MB
 
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2023-09-09 417.26MB
 
文档
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2023-09-09 2.69MB
3.2 随机过程类型 Classification of R.P.
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2023-09-09 178.74MB
 
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2023-09-09 67.61MB
 
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2023-09-09 970.35KB
3.3 平稳随机过程 Stationary R. P.
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2023-09-09 98.05MB
 
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2023-09-09 115.64MB
 
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2023-09-09 82.19MB
 
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2023-09-09 76.78MB
3.4 随机过程的各态历经性
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2023-09-09 74.55MB
 
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2023-09-09 78.34MB
3.5 功率谱密度 Power Spectral Density
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2023-09-09 369.32MB
 
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2023-09-09 194.54MB
 
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2023-09-09 369.71MB
 
文档
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2023-09-09 1.70MB
 
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2023-09-09 3.55MB
3.6 高斯随机过程 Gaussian Random Process
视频
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2023-09-09 85.67MB
 
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2023-09-09 102.78MB
3.7 白噪声 White noise
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2023-09-09 97.34MB
 
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2023-09-09 82.54MB
 
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2023-09-09 85.36MB
 
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2023-09-09 82.40MB
3.8 习题讲解
视频
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2023-09-07 383.92MB
4.1 多随机信号关系 Multiple Random Process: Cross Correlation Function
视频
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2023-09-09 334.38MB
 
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2023-09-09 346.12MB
 
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2023-09-09 419.57MB
 
文档
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2023-09-09 2.02MB
4.2 随机过程的微分和积分
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2023-09-09 83.82MB
 
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2023-09-09 92.64MB
4.3 随机过程通过线性时不变系统
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2023-09-09 26.11MB
 
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2023-09-09 202.08KB
 
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2023-09-09 368.54MB
 
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2023-09-09 3.55MB
4.5 习题例题讲解-课本第五章
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2021-07-06 323.43MB
 
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2021-07-06 202.65MB
 
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2021-07-06 111.88MB
5.1 窄带随机过程的表示方法
视频
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2023-09-09 113.54MB
 
文档
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2023-09-09 2.10MB
5.2 解析信号与Hilbert变换
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2023-09-09 115.58MB
 
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2023-09-09 101.37MB
5.4 窄带噪声中的正弦信号
文档
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2022-12-06 1.30MB
5.5 窄带噪声的包络和相位分布
视频
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2023-09-09 130.31MB
6.1 Markov链
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2023-09-09 9.01MB
6.2 泊松过程
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2023-09-09 5.21MB
7.1.2 Discrete random variable
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2020-03-12 567.83KB
7.1.3 Continuous random variable
文档
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2020-03-23 511.73KB
7.1.4 Your work
文档
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2023-09-09 315.50KB
7.2.1 实验指导
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7.3.1 实验指导
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7.4.2 实现及仿真测试
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7.5.2 实现及仿真测试
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9.1 Exam paper from previous lectures
文档
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2023-09-09 200.64KB
 
视频
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2023-09-09 115.93MB
Lectures (课程章节)
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