SGN-25006 Vector Space Methods for Signal and Image Processing, 5 cr
Additional information
Suitable for postgraduate studies.
Person responsible
Alessandro Foi
Lessons
| Implementation | Period | Person responsible | Requirements |
| SGN-25006 2019-01 | 3 |
Alessandro Foi |
Learning Outcomes
After completing this course, the student will - be able to model and solve signal processing problems through a wide range of vector-space methods - be able to extend the geometrical intuition from familiar 2D and 3D to very high-dimensional settings within a rigorous framework - be able to implement a wide range of fixed and adaptive signal transforms and understand their general properties - master the geometrical structures inherent to basic signal processing techniques, including color transforms, convolution filters, sliding window methods. - be able to design and implement consistent multi-dimensional differential operators for discrete signals - be analyze the propagation of signal distortions (e.g., noise) across transformations
Instructions for students on how to achieve the learning outcomes
Measures of learning: Exam and mandatory Matlab exercises/mini-project. The grade is determined by the exam and execution of the Matlab exercises/mini-project.
Assessment scale:
Numerical evaluation scale (0-5)
Study material
| Type | Name | Author | ISBN | URL | Additional information | Examination material |
| Book | A wavelet tour of signal processing: the sparse way | S. Mallat | No | |||
| Book | Frames for undergraduates | D. Han et al. | No | |||
| Journal | A unified framework for bases, frames, subspace bases, and subspace frames | G. Farnebäck | No | |||
| Lecture slides | slides and software examples from the lectures | A. Foi | Yes |
Correspondence of content
There is no equivalence with any other courses