訊號處理之數學方法及演算法(一)

This course is targeted for first-year graduate students (but students in their junior or senior year are strongly encouraged to register) who need to bridge the gap between introductory signal processing classes and the mathematics prevalent in contemporary signal processing research and practice, by providing a unified applied treatment of fundamental mathematics. Examples from different disciplines will be given throughout the course to illustrate the importance of the concepts in the course. The course serves as a lead-in to many courses offered/will be offered by the department, such as advanced signal processing, adaptive signal processing (with content containing the next generation algorithms for adaptive signal processing), MMASP-III: iterative algorithms and optimization (pending approval), detection and estimation (to be known as Detection and estimation: MMASP-II, pending approval), machine learning, and convex optimization.

Signals and systems, digital signal processing, probability. A good background in linear algebra, and probability and statistics is helpful, but not necessary.

http://cwww.ee.nctu.edu.tw/~cfung/courses/2017_2018/MMASP_I/ TA: Mykola Servetnyk (孫麥可) Office: ED 529 Office Hours: xx:xx – xx:xx, xxx or by appointment

-- Written and/or programming assignments (30%) -- Due time depending on assignments. Hand in before end of class. -- Each day will result in 50% reduction of the full grade. -- Class project (40%) -- Due date: Jan 10, 2017 at 23:59:59. Email presentation (ppt) and code (Matlab) in zip file to your TA. -- Oral presentation (15 min) per person, Q/A (5 min). Jan. 11, 2017. Time: 10:10-12:00 -- Credit given to students who ask “good” questions. -- Each day will result in 50% reduction of the full grade. -- Class Participation (10% at most) -- Given to students who actively participate in class, for example, answering or asking good questions. -- No credit will be given for simply showing up. -- Given to those whose grades are borderline -- Final (30%) -- Length: 2 hour exam. -- Scope: Comprehensive. -- Open first reference book, notes, and homework assignments.

T.K. Moon and W.C. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, 2000. Despite its breadth, this book contains numerous errors, but the authors have generously offered an errata sheet. E. Cinlar and R.J. Vanderbei, Mathematical Methods of Engineering Analysis, 2000. Can be downloaded at http://www.princeton.edu/~rvdb/506book/book.pdf S.M. Kay, Fundamentals of Statistical Signal Processing, Vol. I, Prentice Hall, 1993. D.G. Manolakis, V.K. Ingle, and S.M. Kogon, Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing, McGraw-Hill, 2000. R.J. Vanderbei, Linear Programming: Foundations and Extensions, 3rd Ed., Springer, 2008. J. Nocedal and S.J. Wright, Numerical Optimization, 2nd Ed., Springer, 2006.

地點

ED 302

時間

14:30 – 17:20, Wednesday, Fall 2017 (Pls. note the proposed change in time from Thu -> Wed)

聯絡方式

Prof. Carrson C. FUNG Office: ED 639, 03-573-1862 Office Hours: 10:10 – 11:00, Wednesday or by appointment