Intendierte Lernergebnisse
This course covers fundamentals of digital signal processing (digitale Signalverarbeitung). We start from the Fourier transform of continuous time signals, which leads to both sampling and the Fourier domain description of discrete time signals, including the discrete and fast Fourier transform. In a second segment, we explore the analysis, implementation, and design of discrete time systems, including finite and infinite impulse response filters. A final section of the course then addresses random signals, their characterisation, and the estimation of statistical quantities such as the auto- and cross-correlation functions, as well as of power spectral density. The course is accompanied by application examples and an analysis & design exercise which, together with an exam, contributes towards the assessment of this course.
Lehrmethodik
Lecture approx 24 unitsand e-learning approx 6 units.
Inhalt/e
Frequency Domain RepresentationSampling TheoremDisrecte Time Fourier TransformDFT and FFTDiscrete-Time Convolutionz-TransformImplementation of Discrete Time SystemsDigital Filter Properties and DesignRandom SignalsQuantisationLinear TransformsMultirate Systems and Filter BanksEfficient ImplementationsOrthogonal Frequency Division MultiplexingLink auf weitere Informationenhttps://www.strath.ac.uk/staff/weissstephandr/