Compressive Sensing

2022/2023

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

Lectures with exercises.

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

Exams

Name of examCompressive Sensing
Type of exam
Written or oral exam
ECTS5
AssessmentPassed/Not Passed
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleKomprimeret signal-/dataanalyse og syntese
Module code22KMTK2COSE
Module typeCourse
Duration1 semester
SemesterSpring
ECTS5
Language of instructionDanish and English
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module

Organisation

Study BoardStudy Board of Mathematical Sciences
DepartmentDepartment of Mathematical Sciences
FacultyThe Faculty of Engineering and Science