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

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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: 3.3.2020