Single-Course Danish 10 ECTS

Digital Signal Processing

Overall Course Objectives

The aim of the course is to enable the student to master the following disciplines: linear systems, digital signal processing, and application of digital signal processors.

Thus the student achieves understanding and application knowledge about the fundamental methods and signal processing algorithms for electronic products, it-systems, communication systems, and medical systems.

Learning Objectives

  • Classify and characterise discrete time signals and systems.
  • Analyse discrete time signals and discrete time systems in time-, z-, and frequency domain.
  • Apply Discrete Time Fourier Transformation on discrete time signals and review the characteristics of the transformed signal.
  • Analyse a sampling system in terms of required sampling frequency and quantization levels for a predefined Signal to Quantization Noise ratio and coding scheme.
  • Include anti-aliasing filter in sampling system and relate impact with required sampling frequency, modify sampling system to accommodate for over sampling, up-sampling, down-sampling and design systems for multi-rate systems.
  • Design digital filters, FIR and IIR, and characterise the filter properties.
  • Describe and analyse linear electronics systems.
  • Apply theory for spectral analysis of signals – both continuous time and discrete time.
  • Utilise functions and tool-boxes from Matlab to implement digital signal processing

Course Content

Introduction to and application of Matlab in signal processing.

Classification of signals in time and frequency, continuous- or discrete time.

Impulse- and frequency response.


Sampling (A/D) and reconstruction of signals such as e.g. speech (D/A).

Fourier series, Fourier transform, DFT, DTFT.

FFT applications.

Linear systems.

Advantages of digital signal processing against analog signal processing.

Oversampling, up-sampling, down-sampling and combinations in terms of multi-rate systems.

Design digital filters, FIR and IIR, and characterise the filter properties.

Computational efficient spectral analysis methods.

Recommended prerequisites


Teaching Method

Lectures and exercises.


Faggruppe: AI, matematik og software (50); Institut for Fotonik (50)
Elektroteknologi: 3. semester

Matlab will be used from the very first lecture so the student should make sure to have access to Matlab on their laptops at the semester start.

See course in the course database.





13 weeks


Engineering Technology


DTU Ballerup Campus

Course code 62743
Course type Graduate Engineer

15.000,00 DKK