Mathematical foundations for signal processing communications and networking ebook

About this book series This book series presents monographs about fundamental topics and trends in signal processing, communications and networking in the field of information technology.

Electronic ISSN Print ISSN View all book titles. Publish with us Submission guidelines. Policies and ethics. Good knowledge of Python and its scientific packages Numpy, Scipy. Master the right tools to tackle advanced signal and data processing problems Develop an intuitive understanding of signal processing through a geometrical approach Get to know the applications that are of interest today Learn about topics that are at the forefront of signal processing research Identify and implement the algorithm best suited to solve a given convex optimisation problem Assess the computational cost and numerical stability of a numerical solver.

Collect data. Write a scientific or technical report. Use a work methodology appropriate to the task. Demonstrate the capacity for critical thinking Use both general and domain specific IT resources and tools. Vetterli, J. Kovacevic and V.

Press, Exercise, TP. Project, other. Study plans Coursebooks Mathematical foundations of signal processing. Summary Signal processing tools are presented from an intuitive geometric point of view which is at the heart of all modern signal processing techniques. From Euclid to Hilbert: Linear Algebra Fundamentals for Representation Theory vector spaces; Hilbert spaces; approximations, projections and decompositions; bases and frames; linear operators; adjoint; generalized inverses; matrix representations; computational aspects Sampling and Interpolation sampling and interpolation with normal and non orthogonal vectors, sequences and functions; sampling and interpolation of bandlimited sequences and functions Polynomial and Spline Approximation Legendre and Chebyshev polynomials; Lagrange interpolation; minimax approximation; Taylor expansions; B-splines Regularized Inverse Problems regularized convex optimisation; Tikhonov regularisation; penalised basis pursuit; proximal algorithms; pseudo-differential operators and L-splines; representer theorems for continuous inverse problems with Tikhonov penalties.

Recommended courses Signals and Systems Important concepts to start the course Good knowledge of linear algebra concepts.