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Signal and Image Processing

Code 13516
Year 1
Semester S1
ECTS Credits 6
Workload TP(60H)
Scientific area Biomedical Sciences
Entry requirements N/A
Mode of delivery Face-to-face
Work placements Not applicable
Learning outcomes - To understand the Continuous and Discrete Time Sinals characterisitics.
- To understand the Continuous and Discrete Time Sinals represented on the Frequency domain.
- To project Analogue and Discrete Filters.
- To Process and Analyse random signals.
- To understand the Image Processing and Analysis techniques.
- To understand the information classification and recognition models.
- To understand the signal modulation and demodulation models.

At the end of this UC the student must be able to:
- Recognise the characteristics of Analog and Discrete Time signals .
- To represent the Frequency of Analog and Discrete signals.
- To know the Analog and Discrete Time Filters.
- Be able to analyse and process Random Signals.
- Be able to analyse and process Images.
- Charcaterize and Recognize Information.
Syllabus 1. Continuous and Discrete signals.(Signal representation, Linear and Time Invariant Sistems, Time and frequency representation, Sampling)
2. Filters(Bode diagrams, Continuous signals filters, Digital signals filters – IIR and FIR)
3. Random Signals (Random signal concept, Stochastic signals, Ergodic Process, Stationary signals, Correlation and Powers spectrum functions, Wiener Filters, Kalman Filter)
4. Image Processing and Analysis( Image acquisition and representation, Spatial convolution, Image Filtering, Bidimensional Transforms, Image spectrum analysis, Bidimensional filtering, Binary and multilevel morphology) (Basis of Image Analysis, Edge detectors, Image segmentation, Image description, bidimensional estimators filters (Wiener and Kalman)
5. Pattern Recognition(Signal and Image Characterization, Classification Techniques).
Main Bibliography 1 S. Haykin and B. Van Veen, Signals and Systems, John Wiley & Sons, New Jersey, 2nd ed, 2003
2 M. H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley & Sons, NY, 1996
3 W. K. Pratt, Digital Image Processing, John Wiley & Sons, Inc.,3rd ed, 2001
4 L. G. Shapiro and George C. Stockman, Computer Vision, Prentice Hall, Upper Saddle River, New Jersey, 2001
5 J.G. Proakis and D.G. Manolakis, Digital Signal Processing: Principles, Algorithms, Prentice Hall, New Jersey, 4th ed, 1996
6 A. V. Oppenheim and A. S. Willsky, Signals & Systems, Prentice Hall, Upper Saddle River, New Jersey, 2nd ed, 1997
7 J. W. Woods, Multidimensional Signal, Image and Video Processing and Coding, Acad Press, 2006
8 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley Interscience, 2nd ed, 2000
9 S. Theodoridis, K. Koutroumbas, Pattern Recognition, Academic Press, 4th ed, 2009
10 B. Girod, R. Rabenstein, and A. Stenger, Signals and Systems, John Wiley & Sons, 2001
Teaching Methodologies and Assessment Criteria 1) Theorectical-Practical classes based on the Signal and Image processing and analysis concepts,
complemented with Matlab aplications examples .
2) Theorectical-Practical classes for project design and implementation:
- Analogic Filter
- Digital Filter using microprocessors
- Image Analysis and recognition system)
3) Individual presentation on the state of the art of related subject
4) Course Evaluation:

NF=0.6 NE+0.4 NT
NE- Mark of the Written Proof (Test or Exam)•Approval requires NE>=9.5 out of 20
NT- Resulting mark from the weighted mean of the different laboratory projects and presentation •Approval
requires NT>=9.5 out of 20
Minimum attendance 50%
Language Portuguese. Tutorial support is available in English.
Last updated on: 2020-01-14

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