5 accepted papers
This paper presents a framework for perfect reconstruction two-channel critically-sampled graph filter banks with spectral domain sampling. Graph signals have a unique characteristic: sampling in the vertex and graph spectral domains are generally different, in contrast to classical signal processin…
This paper addresses a polyphase <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> structure of spectral graph wavelets and filter banks. We consider two-channel critically sampled graph filter banks. In classical signal processing, polyphase stru…
This paper addresses the problem of finding optimal sensor placement, i.e., determining F sensor positions from N possible locations. We propose a sensor selection method based on the localization operator of graph signal processing. This method can select sensors while considering the localizations…
We consider the problem of selecting optimal sensor placements. The proposed approach is based on the sampling theorem of graph signals. We choose sensors that maximize the graph cut-off frequency, i.e., the most informative sensors for predicting the values on unselected sensors. We study the exist…
This paper presents a design method of critically sampled graph wavelet transforms (CSGWTs) utilizing real-valued biorthogonal linear-phase wavelets for regular signals. Their filter characteristics are equivalent to those of biorthogonal linear-phase wavelets and can be expressed by real-valued clo…