The fundamental concepts of multidimensional digital signal processing theory as well as several associated application areas are covered in this course. The course begins with an investigation of continuous-space signals and sampling theory in two or more dimensions. The multidimensional discrete Fourier transform is defined, and methods for its efficient calculation are discussed. The design and implementation of two-dimensional non-recursive linear filters are treated. The final part of the course examines the processing of signals carried by propagating waves. This section contains descriptions of computed tomography and related techniques and array signal processing. Several application oriented software projects are required.

Course prerequisites: 

525.414 Probability and Stochastic Processes for Engineers and 525.427 Digital Signal Processing or equivalents. Knowledge of linear algebra and MATLAB is helpful.

Course instructor: 

View Course Homepage(s) for this course.