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X-ORIGINAL-URL:https://www.ieeegdl.org/
X-WR-CALNAME:IEEE SecciÃ³n Guadalajara
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UID:MEC-b4a528955b84f584974e92d025a75d1f@ieeegdl.org
DTSTART:20190220T003000Z
DTEND:20190220T020000Z
DTSTAMP:20190216T211600Z
CREATED:20190216
LAST-MODIFIED:20190216
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:Conferencia Graph Signal Processing: Distributed Graph Filters
DESCRIPTION:Graph signal processing extends the field of classical\nsignal processing to data with an irregular structure, which\ncan be characterized by means of a graph. Such data\nappears for instance in sensor, social, traffic, and brain\nnetworks, to name a few. One of the cornerstones of the field\nof graph signal processing are graph filters, direct analogues\nof time-domain filters, but intended for signals defined on\ngraphs.\nIn this talk, we first introduce the frequency domain related\nto a graph. This gives rise to a so-called graph Fourier\ntransform, which allows us to define graph filters. We give an\noverview of the graph filtering problem and specify a number\nof interesting applications, such as denoising, interpolating,\nanalyzing and classifying signals over a graph. Further, we\nlook at the family of finite impulse response (FIR) and infinite\nimpulse response (IIR) graph filters and show how they can\nbe implemented in a distributed manner.\n
URL:https://posgrados.iteso.mx/web/general/detalle;jsessionid=1b06706bda6781104f02b8f199c5?group_id=14863560&fbclid=IwAR3dPoCYK0GyNVsUWB4KRy71SIC8PE_zgeivgRRal10M8ChLRhQ3ugDOe64
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