Sparse stochastic processes

We cover the theory and applications of sparse stochastic processes (SSP). SSP are solutions of differential equations driven by non-Gaussian innovations. They admit a parsimonious representation in a wavelet basis and are relevant to coding, compressed sensing, and biomedical imaging.

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Signals and systems I (for MT)

Introduction of the basic concepts and mathematical tools for the characterization of signals and for the analysis and design of linear systems (filters or transmission channels). Application of these techniques to signal processing and communications.

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Signals and systems I (for SV)

Introduction of the basic concepts and mathematical tools for the analysis and characterization of signals, the design of processing algorithms, and the linear modeling of systems for students in the life sciences. Application of these techniques to signal processing and communications.

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Signals and systems II (for MT)

This course is an introduction to the theory of discrete linear time invariant systems. Their properties and fundamental characteristics are discussed as well as the fundamental tools that are used to study and design them (Fourier transform, Z transform).

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Signals and systems II (for SV)

This course is an introduction to the theory of discrete linear time invariant systems. Their properties and fundamental characteristics are discussed as well as the fundamental tools that are used to study and design them (Fourier transform, Z transform).

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Image processing I

Introduction to the basic techniques of image processing. Introduction to the development of image-processing software and to prototyping in JAVA. Application to real-world examples in industrial vision and biomedical imaging.

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Image processing II

Study of advanced image processing; mathematical imaging. Development of image-processing software and prototyping in JAVA; application to real-world examples in industrial vision and biomedical imaging.

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