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SUMMARY:Two applications of sparse and low-rank signal modelling
DTSTART:20140327T133000
DTEND:20140327T143000
DTSTAMP:20260506T044840Z
UID:92a0fac72b414e698cac5388569d90a45680908c32087509fcf60db8
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Pavel Rajmic\, SPLAB\, Brno University of Technology\, Cze
 ch Republic\nBio: Pavel Rajmic has been employed as researcher at the Facu
 lty of Electrical Engineering and Communication\, Brno University of Techn
 ology since 2004. He is a member of the team SPLab dealing with digital mu
 ltimedia signals processing at the Department of Telecommunications. In 20
 01\, he worked as a statistician in analyzing large-scale data coming from
  the longitudinal psychological research project ELSPAC at the Institute f
 or Research on Children\, Youth and Family by the Faculty of Social Studie
 s MUNI Brno.\nIn this talk\, I will present two topics recently addressed 
 by my students. The first problem falls into audio processing and the seco
 nd one to image/video processing.\nAudio recordings are sometimes affected
  by defects or even loss of information caused either by obsolete carriers
  (LP\, magnetic tapes) or signal transmission drop-outs. The problem refer
 red to as "audio inpainting" aims at recovering the information in such si
 gnal segments. Historically\, these problems were solved by interpolation 
 approaches based mostly on autoregressive modelling of partial harmonics. 
 Since most natural signals are sparse with respect to some time-frequency 
 transform\, sparse signal priors were utilized in developing related optim
 ization programs. We will compare the old methods with the sparsity-based 
 approach and discuss promising new results based on structured sparsity.\n
 Perfusion MRI is a diagnostic method in medicine used mainly for diagnosin
 g carcinoma and cardiovascular diseases. Herein\, a contrast agent is inje
 cted in the patient and its concentration then tracked via MRI during time
 . The signal captured this way from the affected area can be approximated 
 by the lognormal distribution curve. The standard way of obtaining the MR 
 measurements is very slow and does not comply with today’s challenging r
 equirements. We propose using compressed sensing to acquire much less coef
 ficients\, having minimal effect on the signal reconstruction. The approac
 h is based on the assumption that the data can be well approxiamted as a s
 um of low-rank matrix and a matrix sparse in row spectrum.
LOCATION:ELG120
STATUS:CONFIRMED
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