Thesis worker - Disturbance Detection and Classification in Large Microwave Networks
Recently a successful study on “Disturbance Detection and Classification in Large Microwave Networks” was conducted . That study concluded that using convolutional neural networks operating on time series of received input power can classify disturbances of microwave links with good accuracy. As a result of  a set of tools that continuously detect, classify and display disturbances in live networks have been developed.
The goal of this thesis is to investigate means to improve the performance of the classification scheme currently employed. The potential of further improvement of performance and stability by introducing additional input features than the received input power will be studied. In addition, if time permits, analysis of correlation between classification and network performance as well as differences depending on the geographical conditions will be studied. Finally, a goal of the thesis project is to implement and deploy the developed solution in a live system for real time classification of links.
The thesis is suitable for 1 Master’s students. Planned starting date is January 2019
- Knowledge in data analytics and machine learning
- Strong programming and development skills in different environments including
- Tools for data analytics and machine learning
- Data pipeline and database engineering
- Docker based microservices
- Visualization using web technologies
- Basic understanding of micro wave link technology
Send in your application (CV, current grades and cover letter written in English collated into one document) as soon as possible.
Last day to apply: 22/11-2018.
If you have any questions, please contact recruiter Stefan Vasiljevic
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Primary country and city: Sweden (SE) || || Göteborg || Stud&YP
Job details: Researcher Job Stage 04