Digital Signal and Data Analytics. 3 credits, 3 contact hours

Catalog description

Prerequisite: ECE 601 Linear Systems or equivalent. The theories of signals and transformations are introduced in this course. Their use in signal analytics and feature engineering is discussed. Big data applications in finance and Internet multimedia are emphasized. The course gives students the opportunity to gain hands-on experience on real world data processing and analytics. The representation of signals in the time and complex domains is covered. Z-transform is presented, and Laplace transform to Z-transform mapping techniques are studied. Fourier analysis tools for analog and discrete-time signals are developed and related to popular applications. The subspace methods and eigen decomposition of covariance are studied. Their use in data intensive applications including eigenface and portfolio design is discussed in depth. Design techniques for discrete-time (digital) filter (function) and filter bank (function set) are covered in this course. MATLAB proficiency is a requirement for course assignments.