Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Maja Lakicevic Zaric"'
Publikováno v:
MECO
The quick response code has been used in many applications nowadays. In this work, we examined the encoding performance when code image is highly under-sampled. The random under-sampling process is considered and motivated by the Compressive Sensing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4f3140cf281c415dcc6fc05c290f32a
https://doi.org/10.1109/meco49872.2020.9134345
https://doi.org/10.1109/meco49872.2020.9134345
Publikováno v:
MECO
The performance of gradient (steepest descent) and the threshold-based algorithms are observed in terms of the sparse signal reconstruction. The advantages of both methods are combined within the new approach used to recover all samples from randomly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::78d026550848454b33ece8629775a58c
https://doi.org/10.1109/meco.2018.8406090
https://doi.org/10.1109/meco.2018.8406090
Autor:
Stefan Vujović, Andjela Draganić, Maja Lakičević Žarić, Irena Orović, Miloš Daković, Marko Beko, Srdjan Stanković
Publikováno v:
Sensors, Vol 20, Iss 9, p 2602 (2020)
The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals’ recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applica
Externí odkaz:
https://doaj.org/article/6592e4f295cb4424b5221b0c76e34c68