Content, progress and pedagogy of the module

Learning objectives

Knowledge

• must have knowledge of compressed (sparse) representation of signals/data in one and two dimensions
• must have knowledge of the concepts measurement matrix and dictionary
• must have knowledge of hardware realizations at block level, which use compressive representation of signals/data (e.g. multi-coset and random demodulator architectures)
• must have knowledge of the relation between compressed representation and classical representation of signals/data
• must have knowledge of key concepts and methods within compressed signal/data representation
• must have knowledge of formulation of signal/data reconstruction as different types of optimization problems (e.g. Greedy Pursuit and Orthogonal Matching Pursuit)

Skills

• must be able to apply compressed signal/data representation in analysis- and/or synthesis-related applications
• must be able to simulate and assess the quality of signals/data which are represented in compressed form

Competences

• must be able to assess when compressed signal/data representation is appropriate
• must be able to formulate the basic elements for a given signal/data type and assess the signal/data quality in relation to the number of signal/data components

Type of instruction

As described in §17.

Extent and expected workload

This is a 5 ECTS course module and the work load is expected to be 150 hours for the student.

Exam

Prerequisite for enrollment for the exam

• In order to participate in the course evaluation, students must have actively participated in course progress by way of one or several independent oral and/or written contributions.

Exams