东南大学研究生课程管理规定.docx
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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 NameEnglishAdvanced Digital Signal ProcessingCourse NumberS004103Type of Degree Ph. DMas
2、terTotal Credit Hours54In Class Credit Hours54Credit3PracticeexperimentComputer-using Hours8Course TypePublic Fundamental Major Fundamental Major Compulsory Major ElectiveSchool (Department)School of Information Science and EngineeringTermAutumnExaminationA. Paper( Open-book Closed-book) B. Oral C.
3、Paper-oral Combination D. Others NameLuxi YangProfessional TitleProfessorChiefLecturerE-Websitehttp:/:80/scr2008-personal/c/S004103Teaching Language used in CourseChineseTeaching Material Websitehttp:/:80/scr2008-personal/c/S004103Applicable Range of Disciplinefirst-class disciplineName of First-Cla
4、ss DisciplineCommunications and Information EngineeringNumber of Experiment4Preliminary CoursesSignals and Systems2Teaching BooksTextbook TitleAuthorPublisherYear of PublicationEdition NumberMain TextbookAdvanced Digital Signal ProcessingLuxi YangScience Press20071Digital Signal ProcessingGuangshu H
5、uTsinghua University Press20002Adaptive Filter TheorySimon HaykinPrentice Hall; Publishing House of Electronics Industry in China19983Main Reference BooksAdvanced Signal ProcessingXianda ZhangTsinghua University Press19951I.Course Introduction (including teaching goals and requirements) within 300 w
6、ords: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 analysis, optimal filtering and adaptive filtering techniques pr
7、esent 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, sonar, radar, multimedia and communications signal processing. Unde
8、rstanding 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. Teaching Syllabus (including the content of chapters and section
9、s. 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, c) DFT and FFT 4. Special Sequences and Special Filters: a)All-P
10、ass, 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 Processes5. Basic orthogonal transforms: a) Orthogonal transforms i
11、n 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 autocorrelation, d) Least squares (LS) estimation, e) Linear mi
12、nimum 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 between all-pole modeling of AR process and linear prediction3. Levins
13、on-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 Burg recursion algorithmlinear prediction based on Lattice modeling
14、 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 examples Chapter 5 Power spectrum estimation1. Classical methods 2. Th
15、e 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) FIR Wiener filtering, b) FIR Wiener linear prediction, c) Nois
16、e 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 filtering, d) Causal IIR Wiener linear prediction3. Discret time Kalman
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