1、1Application Form For Opening Graduate CoursesSchool (Department/Institute)：School of Information Science and EngineeringCourse Type: New Open Reopen Rename （Please tick in , the same below）Chinese 现 代 数 字 信 号 处 理Course Name English Advanced Digital Signal ProcessingCourse Number S004103 Type of Deg
2、ree Ph. D Master Total Credit Hours 54 In Class Credit Hours 54 Credit 3 Practice experiment Computer-using Hours 8Course Type P ublic Fundamental Major Fundamental M ajor Compulsory M ajor ElectiveSchool (Department)School of Information Science and EngineeringTerm AutumnExaminationA. Paper（ Open-b
3、ook Closed-book） B. O ral C. P aper-oral Combination D. Others Name Luxi Yang Professional Title ProfessorChiefLecturer E-mail Website http:/:80/scr2008-personal/c/S004103Teaching Language used in Course ChineseTeaching Material Websitehttp:/:80/scr2008-personal/c/S004103Applicable Range of Discipl
4、ine first-class disciplineName of First-Class DisciplineCommunications and Information EngineeringNumber of Experiment 4 Preliminary Courses Signals and Systems2Teaching Books Textbook Title Author Publisher Year of Publication Edition NumberMain Textbook Advanced Digital Signal Processing Luxi Yang
5、 Science Press 2007 1Digital Signal Processing Guangshu Hu Tsinghua University Press 2000 2Adaptive Filter Theory Simon HaykinPrentice Hall; Publishing House of Electronics Industry in China1998 3Main Reference BooksAdvanced Signal ProcessingXianda ZhangTsinghua University Press1995 1I. Course Intro
6、duction (including teaching goals and requirements) within 300 words:This course focuses on problems, algorithms, and solutions for processing signals in stationary and non-stationary environment. It will provide students with the basics of stochastic processes, estimation, transformation, spectral
7、analysis, optimal filtering and adaptive filtering techniques present in modern digital signal processing systems. The class is designed as an advanced statistical signal processing course in which students will build a strong foundation in approaching problems in such diverse areas as acoustic, son
8、ar, radar, multimedia and communications signal processing. Understanding of the theoretical foundations of advanced signal processing theory will be achieved through a combination of theoretical and computer-based homework assignments. The class meets for 4 lecture hours per week for 16 weeks.3II.
9、Teaching Syllabus (including the content of chapters and sections. A sheet can be attached): Chapter 1 Fundamentals of Discrete-time Signal processing1. Introduction to Digital Signals and Digital Signjal Processing (DSP) 2. Digital Filters3. Transforms for Digital Signals: a) z-Transform, b) DTFT,
10、c) DFT and FFT 4. Special Sequences and Special Filters: a)All-Pass, b) Minimum Phase, c) Linear Phase, d) Positive Semi-definiteChapter 2 Fundamentals of Stochastic Discrete-time Signal Analysis1. Random Processes 2. Filtering Random Processes 3. Spectral Factorization4. Special Types of Random Pro
11、cesses5. Basic orthogonal transforms: a) Orthogonal transforms in Hilbert space, b) K-L transform and principal component analysis, c) Discrete-time Cosine transform (DCT)6. Basic methods of parameter estimation: a) Principles of parameter estimation, b) Performance bounds, c) Sample mean and sample
12、 autocorrelation, d) Least squares (LS) estimation, e) Linear minimum mean squares estimation (LMMSE), f) Maximum likelihood (ML) estimation, g) Bayes estimationChapter 3 Linear Prediction and Lattice Filters1. Basic Model of Linear Prediction and the autocorrelation method 2. The equivalence betwee
13、n all-pole modeling of AR process and linear prediction3. Levinson-Durbin recursion algorithm 4. Step-up, step-down, and inverse recursion5. Schur recursion 6. Levinson recursion47. The covariance algorithm for linear prediction 8. Forward and backward linear prediction and Lattice filters9. The Bur
14、g recursion algorithmlinear prediction based on Lattice modeling 10. The modified covariance algorithm for linear predictionChapter 4 Linear Modeling of Random Sequences1. ARMA modeling of random sequences 2. AR modeling of random sequences3. MA modeling of random sequences4. Applications and exampl
15、es Chapter 5 Power spectrum estimation1. Classical methods 2. The minimum variance method 3. The maximum entropy method4. Parametric spectrum estimation 5. Comparison of several methods6. Subspace methods for frequency estimationChapter 6 Wiener filtering and Kalman filtering1. FIR Wiener filters: a
16、) FIR Wiener filtering, b) FIR Wiener linear prediction, c) Noise cancelling by FIR Wiener filters, d) FIR Wiener deconvolution -MMSE equalizer, e) FIR Wiener Lattice filters2. IIR Wiener filters: a) Noncausal IIR Wiener filtering, b) Noncausal IIR Wiener deconvolution, c) Causal IIR Wiener filterin
17、g, d) Causal IIR Wiener linear prediction3. Discret time Kalman filtering and ApplicationsChapter 7 Adaptive filtering51. Adaptive direct-form FIR filters: a) Steepest Descent algorithm, b) Least-Mean-Square (LMS) algorithm, c) Properties of the LMS, d) Normalized and frequency-domain LMS, e) LMS-Ne
18、wton algorithm, f) Transform-domain LMS algorithm, g) Affine projection algorithm, h) Gradient adaptive lattice methods, i) Adaptive joint process estimator2. Recursive least squares adaptive algorithms: a) Three type of RLS algorithms, b) Properties of RLS 3. Applications of adaptive filtering Chap
19、ter 8 Multi-rate Digital Signal Processing and Filter Banks1. The sampling rate alteration: a) Factor-of-M down-sampling, b) Factor-of-L up-sampling, c) Fractional sampling rate alteration2. Cascade equivalence of the basic sampling rate alteration devices3. Multistage design of Decimator and Interp
20、olator4. The polyphase decomposition: a) The decomposition, b) FIR filter structures based on the polyphase decomposition, c) Efficient implementation of Decimator and Interpolator5. Digital filter banks: a) Uniform DFT filter banks and their polyphase implementations, b) Lth-band filters, c) Two-ch
21、annel filter banks and their optimal design, L-channel filter banks (Cosine-modulated filter banks)III. Teaching Schedule:Week Course Content Teaching Method1 Chapter 1 Fundamentals of Discrete-time Signal processing Lecture2 Chapter 2 Fundamentals of Stochastic Discrete-time Signal Analysis #1 Lect
22、ure3 Chapter 2 Fundamentals of Stochastic Discrete-time Signal Analysis #2 Lecture4Chapter 2 Fundamentals of Stochastic Discrete-time Signal Analysis #3Chapter 3 Linear Prediction and Lattice Filters #1Lecture5 Chapter 3 Linear Prediction and Lattice Filters #2 Lecture6 Chapter 3 Linear Prediction a
23、nd Lattice Filters #3 Lecture7 Chapter 4 Linear modeling of Digital Random Signals Lecture6Note: 1.Above one, two, and three items are used as teaching Syllabus in Chinese and announced on the Chinese website of Graduate School. The four and five items are preserved in Graduate School.2. Course term
24、s: Spring, Autumn , and Spring-Autumn term. 3. The teaching languages for courses: Chinese, English or Chinese-English. 4. Applicable range of discipline: public, first-class discipline, second-class discipline, and third-class discipline. 5. Practice includes: experiment, investigation, research re
25、port, etc. 6. Teaching methods: lecture, seminar, practice, etc. 7. Examination for degree courses must be in paper. 8. Teaching material websites are those which have already been announced. 9. Brief introduction of chief lecturer should include: personal information (date of birth, gender, degree
26、achieved, professional title), research direction, teaching and research achievements. (within 100-500 words) 8 Problem Solving and Computer Projects Analysis Lecture and Seminar9 Chapter 5 Power spectrum estimation #1 Lecture10 Chapter 5 Power spectrum estimation #2 Lecture11 Chapter 5 Power spectr
27、um estimation #3 Lecture12 Chapter 6 Wiener filtering and Kalman filtering #1 Lecture13 Chapter 6 Wiener filtering and Kalman filtering #2 Lecture14 Chapter 7 Adaptive filtering #1 Lecture15 Chapter 7 Adaptive filtering #2 Lecture16Chapter 7 Adaptive filtering #3Chapter 8 Multi-rate Digital Signal P
28、rocessing and Filter Banks #1Lecture17 Chapter 8 Multi-rate Digital Signal Processing and Filter Banks #2 Lecture18 Problem Solving and Computer Projects Analysis Lecture and Seminar7IV. Brief Introduction of Chief lecturer:Luxi Yang, male, was born in 1964. He received the M.S. and Ph. D. degree in
29、 electrical engineering, from the Southeast University, Nanjing, China, in 1990 and 1993, respectively. Since 1993, he has been with the Department of Radio Engineering, Southeast University, where he is currently a Professor of information systems and communications and the director of Digital Sign
30、al Processing Division, and also served as a doctoral students advisor. His current research interests include signal processing for wireless communications, MIMO communications, cooperative relaying systems, and statistical signal processing. He is the author or coauthor of two published books and
31、more than 160 journal papers, and holds 20 patents. Prof. Yang received the first- and second-class prizes of Science and Technology Progress Awards of the State Education Ministry of China for 3 times, and the first-class prizes of Science and Technology Progress Awards of Jiang-su Province of Chin
32、a for 2 times. He is currently a Member of Signal Processing Committee of Chinese Institute of Electronics, Chapter Chair of Signal Processing, IEEE Nanjing Section.V. Lecturer Information (include chief lecturer)LecturerDiscipline(major)OfficePhone NumberEmail Address PostcodeLuxi YangSignal and Information Processing83792481 School of Information Science and Engineering, Sotheast University, Nanjing, Jiang-su 210096, China2100968
