Compact representation of biomedical signals.

Sparsity decreases computations, memory usage, and data communications. It is required by increasingly spohisticated information processing devices. The increase in biomedical measurement techniques (EEG, fMRI, EMG, MRI, Magnetic Resonance Spectroscopy (MRS), near-infrared,...) for diagnosis and follow-up of human diseases, strongly requires compression in order to keep the dataflow tractable. To extract the relevant information, the biomedical data sets should be represented sparsely in a context-dependent basis. In general however, this basis is not known. Research in computer algebra in comparable sparse models and techniques provides a foundation for cross-fertilization. The goal of this projects is: 1. To identify appropriate bases to represent each of the different biomedical signals sparsely and compute the coefficients in this representation, and 2. To estimate how the compact representations of the exact noise free signal perform in a noisy environment.

Keywords:Wireless transmission, Compressed sensing, Sparse models, Sparse interpolation algorithm, Computer algebra, Robot kinematics, Biomedical digital signal processing, Scientific computing

Disciplines:Modelling, Biological system engineering, Signal processing