Bi0440 Linear and Adaptive Data Analysis

Faculty of Science
Autumn 2011
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.
Timetable
Tue 13:00–14:50 F01B1/709
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
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. Time domain and frequency domain analysis, Fourier series, Fourier transform, DTFT
  • P3: Z-transform, Stability.
  • P4: Linear filters, {AR, MA, ARMA} , {IIR, FIR}.
  • P5: Cumulative techniques, signal-to-noise ratio.
  • P6: Cumulative techniques.
  • P7: Random processes and time series models.
  • P8: Random processes and time series models.
  • P9: Adaptive processing of data. Linear prediction, optimal filtering.
  • P10: Autoregressive processes and linear prediciton - whitening filter. LMS algorithm.
  • P11: Adaptive filtering – RLS method.
  • P12: 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
  • SAYED, Ali H. Fundamentals of adaptive filtering. New York: Wiley-IEEE Press, 2003, wwwvii, 11. ISBN 0471461261. 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
Study Materials
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 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.
  • Enrolment Statistics (Autumn 2011, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2011/Bi0440