Applied Signal Processing

Common signal processing methods include a) filter analysis, design and structures, b) frequency-transformations, c) multirate-systems, d) correlation and frequency spectrum, e) modeling and estimation of real-world stochastic processes.
In this course the focus is on implementation of both single methods and systems based on these methods. The implementations will be based on high-level programming (i.e. C, C++, Matlab) for both offline and real time program structures. For some tasks real time implementation on a signal processor may be included.

The students will learn to use signal processing tools and algorithms to implement common modules in signal processing and communications. They will learn to design complete systems by combining such modules and be familiar with a typical complete development process from idea/concept to a tested system. They will also learn to document the work by writing a final report.

Knowledge objectives: understand the relationship between theory and algorithms for signal processing and practical implementation by programming and/or programming tools; understand how to construct more complex systems; be able to plan a development project; and know the principles and structure for project documentation.

Skills objectives: be able to use general development tools (C, Matlab) in order to implement signal processing algorithms and more complex systems; and to be able to do systematic quality control and debugging in development work.
The course will give a general competence to be able to plan and carry through a development project through teamwork; and to document the development and experimental work in a written report.

The course consists of a set of tasks, each based on a real-world signal and several methods. Each task starts with introductory lectures, after which the students shall fulfil the task groupwise. In some cases, each group shall implement a system based on several tasks. Systems and tasks will be chosen from speech- or image-processing, digital communication, and so on. Portfolio assessment is the basis for the grade in the course. The portfolio includes a final written exam (50%) and exercises (50%). For both portfolio elements, a minimum result of 40% is required to pass. The results for the parts are given in %-scores, while the entire portfolio is assigned a letter grade. If there is a re-sit examination, the examination form may be changed from written to oral.