EECE 466: Digital Signal and Image Processing
Fall Term

Instructor: Prof. Rafeef Abugharbieh

 Fall 2014 course webpage

"The world of science and engineering is filled with signals: images from remote space probes, voltages generated by the heart and brain, radar and sonar echoes, seismic vibrations, and countless other applications. Digital Signal Processing is the science of using computers to understand these types of data. This includes a wide variety of goals: filtering, speech recognition, image enhancement, data compression, neural networks, and much more. DSP is one of the most powerful technologies that will shape science and engineering in the twenty-first century. Suppose we attach an analog-to-digital converter to a computer, and then use it to acquire a chunk of real world data. DSP answers the question: What next?". From the The Scientist and Engineer's Guide to Digital Signal Processing.

This course covers core topics in digital signal and image processing and their use in practical applications. Theoretical fundamentals will be reinforced by studying real life practical applications and carrying out hands on projects. The course will run in the form of lectures, tutorials/labs and hands on course projects.

Prerequisites: See more info here .

Objectives: This course will give students a solid understanding of digital signal and image processing including;

  • Digital signal/image representation and associated implications.

  • Advantages and limitations of digitization and fundamental tradeoffs.

  • Principles of signal/image generation and acquisition/capture.

  • The relationship between frequency and time/space representations.

  • Representation and processing in the temporal/spatial domain.

  • Representation and processing in the frequency domain, duality to time/spatial analysis.

  • Processing of multi-dimensional data such as 2D pictures, 3D medical images, videos etc.

  • Filtering techniques, e.g., for denoising, enhancement, and restoration.

  • Digital signal processing uses in real life practical applications.

  • Development of DSP applications in MATLAB and/or other software platforms.