EC454 Mathematical Algorithms for Signal Processing

Course Name: 

EC454 Mathematical Algorithms for Signal Processing


B.Tech (ECE)


Programme Specific Electives (PSE)

Credits (L-T-P): 

(3-1-0) 4


Mathematical Foundations–mathematical models, random variables and random processes, Markov and hidden Markov models. Representations and approximations - orthogonality, least squares, MMSE filtering, frequency domain optimal filtering, minimum norm solutions, Iterated reweighted least squares. Linear Operators –Operator norms, adjoint and transposes, geometry of linear equations, least squares and pseudo inverses, applications to linear models. Subspace methods – Eigen decomposition, KL transform and low rank approximation, Eigen filters, signal subspace techniques – MUSIC, ESPRIT. SVD – matrix structure, pseudo inverse and SVD, system identification using SVD, Total least squares, partial total least squares. Special matrices–Toeplitz matrices, optimal predictors and lattice filters, circulant matrices, properties.


Todd Moon and WC Stirling, Mathematical Methods and Algorithms for Signal Processing, Pearson Education, 2000
Steven, M. Kay, Modern spectral estimation: theory and application, Prentice Hall, 1988.


Electronics and Communication Engineering(ECE)

Contact us

Prof. N. Shekar V. Shet, Professor and Head, 
Department of ECE, NITK, Surathkal
P. O. Srinivasnagar,
Mangalore - 575 025 Karnataka, India.

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