Bi0440 Linear and Adaptive Data Analysis

Faculty of Science
Autumn 2016
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Schwarz, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: doc. Ing. Daniel Schwarz, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 19. 9. to Sun 18. 12. Tue 12:00–14:50 F01B1/709
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
There is a considerable increase in the amount of data, which represent processes, events and activities in living systems, together with the rapid developments in digital technology which allow us to acquire, transmit and store the data. Thus, there is also an increase in the importance of methods for digital signal processing and analysis. The goal of signal processing is to enhance signal components in noisy measurements or to transform measured data sets such that new features become visible. At the end of the course students should be able to understand and explain linear and adaptive techniques for signal processing and analysis. Students should be also able to design and use own linear system for denoising in the measured data and for suppression of distortion in the measured data.
Syllabus
  • P1: Signals, time series, data. Classification and properties of signals. Sampling theorem. Aliasing. Quantization.
  • P2: Systems: classification, examples, properties, superposition, causality, stability, LTI, convolution, impulse response - i.e. system description in time domain.
  • P3: Systems: frequency domain analysis, Fourier series, band-pass filters, Fourier transform, DTFT.
  • P4: Sampling and aliasing in detail.
  • P5: Linear filters, Z-transform, Stability.
  • P6: Linear filters, {AR, MA, ARMA} , {IIR, FIR}.
  • P7: Cumulative techniques, signal-to-noise ratio.
  • P8: Cumulative techniques.
  • P9: Random processes and time series models.
  • P10: Adaptive processing of data. Linear prediction, optimal filtering. LMS algorithm.
  • P11: Autoregressive processes and linear prediction - whitening filter. LMS filter variations.
  • P12: Adaptive filtering – RLS method.
  • P13: Time-frequency analysis with the use of wavelet transform. Nonlinear filtering for smoothing.
Literature
  • DEVASAHAYAM, Suresh R. Signals and systems in biomedical engineering : signal processing and physiological systems modeling. 1st ed. New York: Kluwer Academic/Plenum Publishers, 2000, xvi, 337. ISBN 0306463911. info
  • DRONGELEN, Wim van. Signal processing for neuroscientists : introduction to the analysis of physiological signals. Amsterdam: Academic Press, 2007, ix, 308. ISBN 9780123708670. info
  • Wavelets and their applications. Edited by Michel Misiti. London: ISTE, 2006, 330 s. ISBN 9781905209316. info
Teaching methods
lectures combined with practising on computers with the use of mathematical system Matlab
Assessment methods
oral examination
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.
  • Enrolment Statistics (Autumn 2016, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2016/Bi0440