Title:  Advanced Methods of Signal Processing 

Code:  MZS 

Ac.Year:  2016/2017 

Sem:  Summer 

Curriculums:  

Language of Instruction:  Czech 

Credits:  6 

Completion:  credit+exam (written) 

Type of instruction:  Hour/sem  Lectures  Seminar Exercises  Laboratory Exercises  Computer Exercises  Other 

Hours:  39  0  0  26  0 

 Exams  Tests  Exercises  Laboratories  Other 

Points:  70  0  0  30  0 



Guarantor:  Jan Jiří, prof. Ing., CSc. (DBME) 

Lecturer:  Jan Jiří, prof. Ing., CSc. (DBME) 
Faculty:  Faculty of Electrical Engineering and Communication BUT 

Department:  Department of Biomedical Engineering FEEC BUT 

 Learning objectives: 

  Becoming familiar with advanced methods of digital signal processing and their application in practice. Design and testing of systems for advanced signal processing, aiming at optimum and adaptive noise reduction, identification and modelling of systems, reconstruction and restoration, analysis and classification of signals and images.  Description: 

  Formalised inverse filtering and restoration of signals. Wiener filter, constrained deconvolution and further higher restoration approaches. Kalman filtering, scalar and vector formulation, system modelling based on Kalman filtering. Adaptive filtering and identification, algorithms of adaptive filters, typical applications of adaptive filtering. Multirate systems. Nonlinear filtering: polynomial filters, rank filters, homomorphic filtering and deconvolution, nonlinear matched filters. Signal processing by neural networks. Timefrequency analysis, wavelet transform and its applications. Concept of multidimensional signal and spectrum, 2D and 3D Fourier transform, discrete unitary multidimensional transforms. Applications in formalised image processing: restoration approaches, tomographic reconstruction from projections, 3D reconstruction from stereo data.  Knowledge and skills required for the course: 

  The course knowledge on the Bachelor´s degree level is requested, namely on digital signal processing.  Learning outcomes and competencies: 

  Understanding of advanced methods of signal processing and analysis, ability to utilise and modify them, and to design and verify a realisation aimed at a particular practical task.  Syllabus of lectures: 


 Formalised inverse filtering and restoration of signals. Wiener filter: classical and discrete formulation
 Constrained deconvolution, deconvolution with impulse response optimisation, maximum posteriorprobability method
 Kalman filtering, scalar and vector formulation, system modelling based on Kalman filtering
 Concept of adaptive filtering and identification, algorithms of adaptive filtering
 Typical applications of adaptive filtering: system identification and modelling, linear adaptive prediction, adaptive noise and interference suppression
 Multirate systems of digital signal processing, multirate filter banks
 Nonlinear filtering: polynomial filters, rank filters, homomorphic filtering and deconvolution, nonlinear matched filters
 Signal processing by neural networks: learning neuronal filters and classifiers, restoration by feedback neuronal networks
 Timefrequency analysis, wavelet transform and its applications in processing and compression of signals
 Concept of multidimensional signal and spectrum, 2D and 3D Fourier transform. Discrete unitary 2D transforms: cosine, Hadamard and Walsh, Haar and 2D wavelet tr.
 Applications of signaltheorybased approaches to formalised image processing: restitution and restoration approaches, formalised image segmentation
 Tomographic methods of image reconstruction from 1D projections
 Motion analysis and 3D reconstruction from stereo data
 Syllabus of computer exercises: 


 Simulation of discrete Wiener filter and evaluation of efficiency in stationary case
 Simulation of a 3rd order Kalman filter and comparison with the above Wiener filter in stationary environment
 Simulation of adaptive filters of RLS and LMS type as applied to a system modelling. Comparison of both results in stationary and slowly varying environment
 Wavelet transform: application to analysis and denoising of a signal, verification of compression ability
 Restoration of blurred and noisy image by pseudoinversion and by 2D classical Wiener filter  comparison of results
 2D image reconstruction from tomographic data (1D projections) via frequency domain  evaluation of artefacts
 Learning 2D neuronal filter: applied for texture analysis. Comparison with featureoriented classification
 Fundamental literature: 


 Madisetti, V.K., Williams, D.B. (ed.): The Digital Signal Processing Handbook, CRC & IEEE Press, USA, 1998, ISBN 0849385725
 Vích, R., Smékal, Z.: Číslicové filtry, Academia Praha 2000, ISBN 802000761X
 Mulgrew, B., Grant, P., Thompson, J.: Digital Signal Processing  Concepts & Applications, MacMillan Press Ltd., UK, 1999, ISBN 0333745310
 Banks, S., Signal Processing, Image Processing and Pattern Recognition, Prentice Hall Int., UK, Ltd., 1990
 Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall Int. Edit., 1989
 Jan, J.: Číslicová filtrace, analýza a restaurace signálů, Nakl. VUT Brno 1997, ISBN 8021408162
 Gonzales, R.C., Wintz, P.: Digital Image Processing, 2nd ed., AddisonWesley Publ. Comp., 1987
 Pratt, W.K.: Digital Image Processing, 2nd ed., J. Wiley & Sons, 1991
 Rosenfeld, A., Kak, A.C.: Digital Picture Processing, 2nd. edit., Academic Press, 1982
 Schalkoff, R.J.: Digital Image Processing and Computer Vision, J. Wiley & Sons, 1989
 Study literature: 


 Jan, J.: Číslicová filtrace, analýza a restaurace signálů (Digital Signal Filtering, Analysis and Restoration  in Czech), TU Brno Publ., Brno 1997, ISBN 8021408162, second edition, 2001
 Jan, J.: Digital Signal Filtering, Analysis and Restoration (English edition), IEE London, United Kingdom 2000, 407+14 pp., ISBN 0 85296 760 8
 Gonzales, R.C., Wintz, P.: Digital Image Processing, 2nd ed., AddisonWesley Publ. Comp., 1987
 Controlled instruction: 

  Active participation in the computerlab tutorials is checked, the minimum participation is 4 out of 7 tutorials, can not be substituted.  Progress assessment: 

  Active participation in the computerlab tutorials and solving of given problems will be evaluated (maximum 30 points total).  Exam prerequisites: 

  Obtaining at least 15 points from the computerlabs.  
