教材分析 MIT机电工程与计算机科学系【本科生课程】6.011.通信，控制，和信号处理导论.Introduction.to.Communication,.Control,.and.Signal.Processing[www.ed2kers.com].pdfVIP

Introduction to Communication, Control, and Signal Processing As taught in: Spring 2010 Spectral shaping of a white-noise signal. (Image by MIT OpenCourseWare. Courtesy of Prof. Alan Oppenheim and Prof. George Verghese.) Instructors: Prof. Alan V. Oppenheim Prof. George Verghese MIT Course Number: 6.011 Level: Undergraduate Course Highlights This course features a complete set of course notes, Signals, Systems and Inference. Course Description This course examines signals, systems and inference as unifying themes in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization; least-mean square error estimation; Wiener filtering; hypothesis testing; detection; matched filters. Syllabus Course Meeting Times Lectures: 2 sessions / week, 1 hour / session Recitations: 2 sessions / week, 1 hour / session Objectives and Outcomes 6.011 objectives and outcomes (PDF) Prerequisites The essential prerequisites (not co-requisites) for this course are mastery of 6.003 and 6.041 (or equivalents), and of the 18.03 material related to solving linear, time-invariant systems of first-order differential equations using eigenvalues and eigenvectors. You should certainly not plan on taking 6.011 if you got less than a C in 6.003 or 6.041. If you did get a C (and maybe even if you got a higher grade than that), some further remedial work in the corresponding subject is important, starting immediately. If you are unsteady with 6.003, 6.041, or the relevant 18.03 material, you can be sure that 6.011 will expose that fact, sooner or later. The lectures, recitations, and homework in 6.011 will expand on signals, systems and probabilistic m