![]() The term graph signal processing was coined a decade ago in the seminal works of, ,, and. Graphs are versatile, able to model irregular interactions, easy to interpret, and endowed with a corpus of mathematical results, rendering them natural candidates to serve as the basis for a theory of processing signals in more irregular domains. Graph SP (GSP) generalizes SP tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. With the digitalization of the modern world and the increasing pervasiveness of data-collection mechanisms, information of interest in current applications oftentimes arises in non-Euclidean, irregular domains. Indeed, the last 75 years have shown how SP has made an impact in areas such as communications, acoustics, sensing, image processing, and control, to name a few. Signal processing (SP) excels at analyzing, processing, and inferring information defined over regular (first continuous, later discrete) domains such as time or space. Moura, Antonio Ortega, David I Shuman 40msp04-leus-opener-3262906 Below you can see an example.Graph Signal Processing History, development, impact, and outlook Then click the "Parse references" button to link references to papers in PapersWithCode and annotate the results. First, you’ll need at least one record in the cell that has results (see image below for an example). How do I add referenced results? If a table has references, you can use the parse references feature to get more results from other papers. When editing multiple results from the same table you can click the "Change all" button to copy the current value to all other records from that table. ![]()
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