Energy efficient solutions for computing and sensing

Thesis event information

Date and time of the thesis defence

Place of the thesis defence

Auditorium IT116, Linnanmaa, remote connection:

Topic of the dissertation

Energy efficient solutions for computing and sensing

Doctoral candidate

Master of Science (Tech) Mehdi Safarpour

Faculty and unit

University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Center for Machine Vision and Signal Analysis

Subject of study

Computer Science


Professor Jari Nurmi, University of Tampere


Professor Olli Silvén, University of Oulu

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Reducing energy consumption of electronics devices

There are many electronic devices around us, but many of them depend on batteries to function. Often battery life is an issue. Progress in the field of more advanced batteries is slow. In this study, we provide solutions that reduce the power consumption of common electronic components and extend battery life. Emerging technologies, such as the Internet of Things (IoT), Deep Neural Network (DNN) based machine learning, and 6th generation wireless communications, impose substantial performance and energy efficiency demands for implementations. In answer to the requirements, this thesis focuses on improving the efficiency of selected energy hungry system sections, from signal acquisition to computing.
Last updated: 1.3.2023