Applied signal processing

• use common Fourier transform pairs and properties to determine the Fourier transform of complex analog and digital signals.
• relate spectra of periodic and aperiodic analog and digital signals and plot these using correct physical units such as Hertz and Volt.
• determine the auto- and cross correlation functions of analog and digital random signals.
• analyze zero-pole diagrams to determine the causality and stability of linear time-invariant systems.
• use the z-transform to calculate the impulse response and the transfer function of a linear time-invariant system.
• determine the quantization errors in analog-to-digital conversion and model error sources in filters due to finite word length.
• design simple low-pass, high-pass, band-pass, band-stop and notch filters using pole-zero placement, simple windowing functions, filter transformation rules and the signal processing toolbox in Matlab.
• find the power density spectra of random signals using non-parametric and parametric spectral estimation methods.
• apply signal processing techniques to signals emanating from biological systems (ECG, ultrasound) and design procedures to estimate some parameters such as heart rate, blood velocity and profile of blood flow.
• in own words give examples of signal processing techniques applied in various applications such as telecommunications, radar and sonar and biomedical systems.
• Communicate proficiently the signal processing topics in English.

Classification of signals. Analytic signals. Use of the fast fourier transform (FFT). Analysis of random signals. Correlations functions, power and cross spectra. Errors in analog to digital conversion. Digital filters and their error sources. Simple signal measures. Modulation of analog and digital signals. Matched filter. Spectral estimation. Parametric models. Use of signal processing software (Matlab). Processing of biomedical signals. Exercises.

02402/02403/02405/31605/22050, 1. Linear systems or basic signal analysis and introductory statistics.
2. Students should have prior knowledge in Matlab.

Lectures and exercises

The courses 22050, 22051, and 22052 constitute a specialization track in signal processing and should be taken in said order.

Minimum: 5.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.