Sparse recovery algorithms for streaming and multidimensional signals
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
University of Oulu, L10
Topic of the dissertation
Sparse recovery algorithms for streaming and multidimensional signals
Doctoral candidate
M. Sc. Uditha Wijewardhana
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Radio Technologies
Subject of study
Wireless Communications
Opponent
Docent Mikko Vehkaperä, Aalto University
Custos
D.Sc Marian Codreanu, University of Oulu
Energy-efficient sensor networks
As the world is moving toward the era of big data, when a system will accumulate and process animmense amount of information, the cost and complexity of the acquisition and processing of highdimensional data is a critical issue to be addressed. In this respect, compressive sensing (CS), withits capability of utilizing sub-Nyquist sampling to recover a signal of interest, may play a vital rolein addressing this problem. In this thesis, we develop sparse recovery algorithms to reconstructstreaming signals and multi-dimensional signals from compressive measurements.
Last updated: 1.3.2023