Chebyshev Polynomial Approximation for Distributed Signal Processing

Unions of graph Fourier multipliers are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators to the high-dimensional signals collected by sensor networks. The proposed method features approximations of the graph Fourier multipliers by shifted Chebyshev polynomials, whose recurrence relations make them readily amenable to distributed computation. We demonstrate how the proposed method can be used in a distributed denoising task, and show that the communication requirements of the method scale gracefully with the size of the network.Comment: 8 pages, 5 figures, to appear in the Proceedings of the IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), June, 2011, Barcelona, Spai

On prefilters for digital FIR filter design

A new family of digital prefilter structures is introduced, based on the Dolph-Chebyshev function. These prefilters can be combined with appropriately designed "equalizer" filters based on equiripple methods, leading to efficient FIR digital filter designs. Design examples are included, demonstrating the simplicity of the resulting designs, as compared to conventional equiripple designs