| Course title | Modern Methods in Signal Processing |
|---|---|
| Course code | ITE/MMZ |
| Organizational form of instruction | Lecture + Lesson |
| Level of course | Master |
| Year of study | 2 |
| Semester | Summer |
| Number of ECTS credits | 5 |
| Language of instruction | Czech |
| Status of course | Compulsory-optional |
| Form of instruction | Face-to-face |
| Work placements | Course does not contain work placement |
| Recommended optional programme components | None |
| Lecturer(s) |
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| Course content |
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Lecture topics: - Overview of biomedical signals: action potential, ECG, EEG, PCG, CP, human voice - Repeating the fundamentals of digital signal processing - Quadratic criteria for comparing signals - Optimal filters in terms of quadratic criteria - Multi-sensor signals and beamforming methods - Principal component analysis - Blind separation: Independent component analysis - Tensor decompositions and their applications - Compressed sensing Exercises: - Audio recording, ECG recording, data import into Matlab, visualization - Removing artifacts from ECG using filters - Isoline drift problem in ECG - Analysis of covariance matrix of EEG signals - Detection of QRS complex and P wave in ECG, detection of EEG rhythms - Synchronized averaging - Adaptive LMS and RLS algorithms and the estimation of direction of arrival - Characteristics of delay-and-sum beamformer - ECG/EEG reconstruction using PCA and ICA - CP and INDSCAL tensor decomposition - Compressed sensing - simulation
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| Learning activities and teaching methods |
Lecture, Practicum
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| Learning outcomes |
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The course introduces students to selected advanced signal processing methods. The exercises will include case studies and examples from biomedical and acoustic signal processing.
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| Prerequisites |
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unspecified
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| Assessment methods and criteria |
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Combined examination
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| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester |
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