Objectives and competences

Teaching objectives

1. Promote the interest in signal and image processing (PSI) area, concerning the acquisition processes, conditioning, filtering, analysis, and representation of relevant information, highlighting the main transforms used for processing and extracting patterns.
2. Recognize the characteristics and the specificities of some types of biosignals and medical images.

Expected results
The student should be able to:

1. Express themselves verbally and in written language about PSI problems through appropriate language and terminology;
2. Create and represent, in Matlab® environment, the signals and images in the original and transformed domains;
3. Interpret the signals and images spectral representation;
4. Design and implement digital filters;
5. Recognize the characteristics of some biosignals and medical images.

Teaching Methodologies

Theoretical and practical classes with students’ permanent involvement.
In this course unit, learning is based on student’s active participation whose practical demonstration is carried out in all classes with signal and image processing concrete examples that culminate in a final practical project and exam, covering in this way all the proposed learning topics that are evaluated in the respective assessment components.

Syllabus

1. Digital Signal Processing (PDS) introduction
   1. Signals and Discrete Systems
      - Signals and Discrete Systems characterization and representation;
   2. Continuous Signal Sampling
      - Sampling Theorem
      - Sampled signal reconstruction
      - Aliasing
      - Interpolation and decimation
   3. Z-transform
      - Definition and proprieties
      - Convergence Region
      - The Z-transform inversion
   4. Discrete Fourier transform (FFT)
      - Definition and properties
      - Linear convolution using the DFT
      - Fast Fourier transform (FFT)
      - Inverse discrete Fourier transform
   5. Discrete Wavelet transform (DWT)
      - Definition and properties
      - Wavelet Families
      - Inverse Discrete Wavelet transform
   6. Digital Filters
      - Characteristics
      - FIR and IIR digital filters design
2. Digital Image Processing (PDI)
   - Digital Image concept
   - Image representation and modeling
   - Image enhancement techniques and filters
   - Image restoration and reconstruction
   - Spectral representation – 2D DFT and 2D DWT application in image
3. Biosignals and Medical Images applications