Gäller från och med: Autumn 2014
Beslutad av: FN1/Anders Gustafsson
Datum för fastställande: 2015-03-27
Avdelning: Electrical and Information Technology
Kurstyp: Gemensam kurs, avancerad nivå och forskarnivå
Kursen ges även på avancerad nivå med kurskod: EITN55
Undervisningsspråk: English
The course gives basic knowledge in statistical signal processing and treats the theory of independent and principal components, together with applications in signal separation. In most areas where sensor systems are used, the received signals contain undesired components or several superposed useful signals, which affects the transmission of information negatively. The course in Signal Separation deals with methods to separate the received signals in a general context, only using the information contained in the received signals. The traditional approaches to analyse, filter, compress and separate a combination of signals by means of second order statistics (e.g. correlation based methods) are extended to include higher order statistics (e.g. higher than second order moments). This leads to the concept of independent components in contrast to principal components.
Kunskap och förståelse
För godkänd kurs skall doktoranden
Färdighet och förmåga
För godkänd kurs skall doktoranden
Värderingsförmåga och förhållningssätt
För godkänd kurs skall doktoranden be able to comprehend literature as well as standards in this area
The following items are treated in the course: linear representation of multivariate data, random vectors and independence, higher order moments, gradients and optimization, learning rules for non-constrained and constrained optimization, estimation theory for signal separation, methods of least-squares and maximum likelihood, information theory, entropy cumulants, definition of Independent Components (ICA) and Principal Components (PCA), differences and similarities between PCA and ICA, methods for estimation of ICA: ICA by maximization of non-Gaussianity, ICA by maximum likelihood estimation, ICA by minimization of mutual information, ICA by nonlinear decorrelation and nonlinear PCA. Applications: acoustic signal separation and deconvolution, feature extraction from multivariate data, artifact identification from Electroencephalography (EEG) and Magnetoencephalography (MEG), prediction of time series data by using ICA.
Hyvärinen, A., Karhunen, J. & Oja, E.: INDEPENDENT COMPONENT ANALYSIS. J. Wiley, 2001. ISBN 047140540X.
Undervisningsformer: Föreläsningar, laborationer, övningar
Examinationsformer: Skriftlig tentamen, inlämningsuppgifter.
Two compulsory hand-in tasks are part of the examination
Betygsskala: Underkänd, godkänd
Examinator:
Förkunskapskrav: - ESS040 Digital signal processing OR ETI265 Signal processing in multimedia OR EITF15 Digital signal processing - theory and applications, or an equivalent course
Förutsatta förkunskaper: Fundamental mathematics, statistics and stochastic processes
Course coordinator: Nedelko Grbic nedelko.grbic@eit.lth.se
Kursansvariga:
Hemsida: http://www.eit.lth.se/index.php?ciuid=868&L=