Digital Signal Processing

• 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

Introduction to and application of Matlab in signal processing.

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

Impulse- and frequency response.

z-transformation.

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.

01964/31022/62736/62735

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.