Project: Compressed Sensing

Master data
Compressed Sensing
Description: Um ein analoges physikalisches Signal und die gesamte enthaltene Information digital zu erfassen, ist es nötig, die Parameter der Analog-zu-Digital-Umsetzung passend zu wählen. So ist beispielsweise die Nyquist-Frequenz die Untergrenze für die Abtastfrequenz für nahezu alle Signalarten. In neueren Publikationen kommen Techniken namens 'Compressive Sampling' oder 'Compressed Sensing' zur Sprache, die nach einer Abtastung weit unterhalb der Nyquist-Frequenz weitere Möglichkeiten bietet: Unter gewissen Voraussetzungen kann das Signal perfekt rekonstruiert werden. Sind diese Voraussetzungen nicht erfüllt kann der Fehler nach der Rekonstruktion dennoch in definierten Schranken gehalten werden. Für die Rekonstruktion werden aufwändige Methoden der Optimierung ('Operations Research') angewandt.
Keywords: Compressive Sampling, Nyquist Theorem, Compressed Sensing
Compressive Sampling
Description: CS - Compressive Sampling / Compressed Sensing In order to acquire analog physical signals and all contained information the analog-to-digital-conversion needs to be designed adequately. Presently the Nyquist frequency is the mandatory minimum sampling frequency for almost every type of signal. In recent publications a technique, named Compressive Sampling or Compressed Sensing, came up that allows sampling of a signal far below the Nyquist frequency. In this way the contained information can never be acquired completely, according to Nyquist's and Shannon's theories. The new methods allow exact signal reconstruction from sub-Nyquist sampled data with high probability. Even higher is the probability of reconstructing the signal within defined error bounds. These reconstruction methods lie in the field of optimization theory and operations research. Most signals meet the necessary preconditions for compressive sampling and so many applications can gain from the technique. Technology Review also considers Compressed Sensing for image capturing devices such as cameras and medical scanners as one of the 10 Emerging Technologies 2007. The ground breaking results from recent publications on this field can have a huge impact on current `known facts' in information theory, as well as in signal processing and (multimedia) communication technologies. Since this topic startet evolving over the last few years it was mostly covered on a mathematical basis. An applicational point of view from engineering is missing and will be target for research.
Keywords: Nyquist Theorem, Compressed Sensing, Compressive Sampling
Short title: n.a.
Period: 01.04.2007 - 31.03.2010
Contact e-mail: alexander.onic@uni-klu.ac.at
Homepage: -

Employees
Employees Role Time period
Mario Huemer
Research activities
To visiting card
(internal)
  • Project leader
  • 01.04.2007 - 31.03.2010
Alexander Onic
Research activities
To visiting card
(internal)
  • Research staff
  • Contact person
  • 01.04.2007 - 31.03.2010
  • 01.04.2007 - 31.03.2010

Assignment
Organisational unit
Fakultät für Technische Wissenschaften
  Institut für Vernetzte und Eingebettete Systeme
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Austria
  -993640
   kornelia.lienbacher@aau.at
https://nes.aau.at/
To organisation

Categorisation
Project type Current focus of work
Funding type Other
Research type
  • Fundamental research
  • Applied research
Subject areas
  • 11 - Mathematics, Computer Sciences *
  • 2533 - Signal processing (analogous, digital) *
Research Cluster No research Research Cluster selected
Gender aspects 0%
Project focus
  • Science to Science (Quality indicator: n.a.)
Classification raster of the assigned organisational units:
working groups No working group selected

Funding
No available funding programs

Cooperations
No partner organisations selected