450-6019/02 – Advanced Signal Processing Methods (PMZS)

Gurantor departmentDepartment of Cybernetics and Biomedical EngineeringCredits10
Subject guarantordoc. Ing. Radek Martinek, Ph.D.Subject version guarantordoc. Ing. Radek Martinek, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
MAR944 doc. Ing. Radek Martinek, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Combined Examination 28+0

Subject aims expressed by acquired skills and competences

Upon completion of this course, students should be able to: a) Implement sophisticated models describing the applications for the testing new and existing adaptive and non-adaptive signal processing methods (e.g. LMS, NLMS, RLS, QR-RSL, PCA, and ICA methods, etc.) b) Design, implement, and optimize advanced adaptive and non-adaptive signal processing methods. c) Classify and implement methods for the quality evaluation of proposed adaptive and non-adaptive systems for optimization purposes. d) Implement optimized methods for real hardware. e) Deploying optimized methods into a real environment. f) Feedback adjustments of mathematical models describing selected applications.

Teaching methods

Individual consultations
Project work


The aim of the course is to acquaint students with the design, implementation, optimization, and verification of systems using advanced signal processing methods (e.g. adaptive and non-adaptive algorithms) for the needs of technical cybernetics and biomedical engineering. Modern methods of signal processing have already proven themselves in many areas and industries today, with current practice suggesting that the same trend will continue in the future. Within the PMZS course students will learn the practical application of these methods in real-world applications.

Compulsory literature:

[1] Stearns, S. D., & Hush, D. R. (2016). Digital Signal Processing with Examples in MATLAB®. CRC Press. [2] Kehtarnavaz, N., & Kim, N. (2011). Digital signal processing system-level design using LabVIEW. Newnes. [3] Clark, C. L. (2005). LabVIEW digital signal processing. Tata McGraw-Hill Education. [4] Krishna, H. (2017). Digital signal processing algorithms: number theory, convolution, fast Fourier transforms, and applications. Routledge.

Recommended literature:

[1] Kumar, P. R., & Varaiya, P. (2015). Stochastic systems: Estimation, identification, and adaptive control. Society for industrial and applied mathematics. [2] Goodwin, G. C., & Sin, K. S. (2014). Adaptive filtering prediction and control. Courier Corporation. [3] Haykin, S. O. (2013). Adaptive filter theory. Pearson Higher Ed.

Way of continuous check of knowledge in the course of semester



Další požadavky na studenta

There are no additional requirements for the student.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

The subject is devoted mainly to: a) The use of adaptive and non-adaptive systems for processing and diagnosing biomedical signals. b) Using adaptive and non-adaptive systems for the needs of fifth generation (5G) networks and the Internet of Things (IoT). c) Signal processing and analysis using adaptive and non-adaptive systems for Smart Technology and Industry 4.0.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (P0714D150002) Cybernetics P English Ostrava Choice-compulsory type B study plan
2019/2020 (P0714D150002) Cybernetics K English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner