EE 120: SIGNALS AND SYSTEMS Department: Electrical Engineering and Computer Sciences Instructor: Dr. Babak Ayazifar (EECS Faculty) Credit Units: 4 Prerequisites: EE 20N (Introductory Signals and Systems) Math 53 (Multivariate Calculus) Math 54 (Linear Algebra and Differential Equations) Course Structure: • Lecture hours per week (Lec): 4 • Discussion (recitation) section hours per week (D): 1 (not mandatory) • Instructor office hours, focused on group- based problem solving (OH): 2 (not mandatory) Course Components: • Problem Sets/Homework (HW) • Pop quizzes (Q) • Exams (E) Textbooks: • Required: o E. A. Lee and P. Varaiya, Structure and Interpretation of Signals and Systems, Addison-Wesley, 2003.  Review of: • Appendix A (sets and functions) • Appendix B (complex numbers) • Chapter 1 (Signals and Systems) • Chapter 2 (Defining Signals and Systems)  Extended coverage of: • Chapter 7 (Frequency Domain) • Chapter 8 (Frequency Response) • Chapter 9 (Filtering) • Chapter 10 (The Four Fourier Transforms)  In-depth first-time coverage of: • Chapter 11 (Sampling and Reconstruction) • Chapter 12 (Stability) • Chapter 13 (Laplace and Z Transforms) • Chapter 14 (Composition and Feedback Control)  Miscellaneous: • Chapter 5 (Linear Systems/State-Space Analysis) • Recommended:o A. V. Oppenheim, A. S. Willsky, and S. H. Nawab, Signals and Systems, Prentice-Hall, 1997. o H. P. Hsu, Schaum’s Outline of Signals and Systems, McGraw-Hill, 1995. o S. H. Strogatz, Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering, Addison-Wesley, 1994. RELATIONSHIP TO ABET PROGRAM OUTCOMES: Students who complete this course successfully meet the following ABET program outcomes: (a), (b), (c), (e), (g), (i), (k). EE 120 continues the tradition of its lower-division prerequisite course EE 20N by requiring students to apply a fundamental knowledge of mathematics, science and engineering to not only solve electrical and computer engineering problems, but also to recognize the multidisciplinary reach of the topics and techniques emphasized in the course. Students learn modern skills, techniques and engineering tools. They refine their skills in back-of-the-envelope and design-oriented analysis. They learn to model signals and systems, with an eye toward design. Problem sets and exams are designed to probe a thorough understanding of fundamental concepts and to de-emphasize rote algebraic manipulation. Exam problems insist on refined and to-the-point responses; limited space is allocated for each problem to encourage students to think more clearly and logically, and to articulate their responses efficiently and without clutter. The various components of the course encourage students to think graphically and to draw, before they reach for mathematical formulae. Lectures, discussion sections, and instructor’s office hours promote group work by engaging the students in a collaborative learning environment where they divide into groups of 3-5 students and discuss the solutions to various problems. In discussion sections and office hours, students present their solutions to their peers on the board; they generate peer discussion by asking each other questions and assisting each other toward solutions. Recognizing that team work is an integral part of engineering practice, the homework policy encourages collaborative groups of up to five students. Lectures also cultivate group learning. For almost every example problem the students encounter in lecture, they take time to discuss the solution among themselves, explaining concepts and learning about alternative solutions from their peers.COURSE LEARNING OBJECTIVES AND OUTCOMES: This course trains students for an intermediate level of fluency with signals and systems in both continuous time and discrete time, in preparation for more advanced subjects in digital signal processing (including audio, image and video processing), communication theory, and system theory, control, and robotics. Upon successful completion, a student should: • Classify systems based on their properties: in particular, understand and exploit the implications of linearity, time-invariance, causality, memory, and bounded-input, bounded-out (BIBO) stability. Coverage: Lec, D, HW, Q, E, OH Student Success: Excellent • Know the principles of vector spaces, including how to relate the concepts of basis, dimension, inner product, and norm to signals. Coverage: Lec, D, HW, Q, E, OH Student Success: Very Good • Learn to treat signals as vectors in a vector space and ascribe geometry to that space by defining an appropriate inner product—in both discrete-time and continuous-time, and for both periodic and aperiodic signals. For example, treat the set of periodic discrete-time signals having period p as a vector space, and define an inner product for that space. Coverage: Lec, D, HW, Q, E, OH Student Success: Excellent • Know how to analyze, design, approximate, and manipulate signals using vector-space concepts. For example, o know how to project a signal onto another signal, and exploit signal orthogonality to develop  Fourier series expansions of periodic discrete-time and continuous-time signals—that is, decompose a signal in terms of complex exponentials in the frequency domain;  know how to interpret and plot the Fourier series coefficients of a signal and understand what they mean in terms of the frequency content of the signal;  find a best approximation for a signal using a strict subset of the basis signals in the appropriate vector space—that is, approximate the signal when the number of basis signals is smaller than the dimensionality of the signal space. understand that a signal’s energy is the 2-norm of a signal, that the 2-norm is defined according to an appropriate inner product, and that signal energy can be expressed in the time and frequency domains via Parseval’s relation. Coverage: Lec, D, HW, Q, E, OH Student Success: Very Good • Determine Fourier transforms for continuous-time and discrete-time signals (or impulse-response functions), and understand how to interpret and plot Fourier transform magnitude and phase functions. o Understand the discontinuous nature of the Fourier transform of a signal that is not absolutely summable or integrable. o Understand