Aims & Topics of the Workshop

The cross fertilization between numerical linear algebra and digital signal processing has been very fruitful in the last decades. The interaction between them has been growing, leading to many new algorithms.

Numerical linear algebra tools, such as eigenvalue and singular value decomposition and their higher--extension, least squares, total least squares, recursive least squares, regularization, orthogonality and projections, are the kernels of powerful and numerically robust algorithms.

The goal of this workshop is to bring together researcher in numerical linear algebra and digital signal processing to discuss new efficient and reliable numerical linear algebra tools for signal processing applications.

Areas and topics of interest for the workshop include (but are not limited to):

• Singular value and eigenvalue decompositions, including applications.
• Toeplitz, Cauchy, Vandermonde and semiseparable matrices, including special algorithms and architectures.
• Recursive least squares in digital signal processing.
• Updating and downdating techniques in linear algebra and signal processing.
• Stability and sensitivity analysis of special recursive least squares problems.

• Biomedical signal processing applications.
• Adaptive filters.
• Remote sensing.
• Acoustic echo cancellation.
• Blind signal separation and multiuser detection.
• Multidimensional harmonic retrieval and direction of arrival estimation.
• Applications in wireless communications.
• Applications in pattern analysis and statistical modeling.
• Sensor array processing.