Prediction Model for Microwave Radio Unit Testing

dc.contributor.authorBalasubramaniam, Sivasenapathi
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerLarsson-Edefors, Per
dc.contributor.supervisorSvensson, Lars
dc.date.accessioned2020-11-02T13:54:49Z
dc.date.available2020-11-02T13:54:49Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractDuring radio unit testing, the radio unit is tested in different test flows in order to ensure its conformance. But testing radio unit is time consuming and increases the unit’s production time. Prediction models are developed for the test flows to predict test points continuously by using the inputs from the previous test flows. Machine learning models presented in this thesis helps in the prediction of test point values before placing the radio unit in all testing stations. The developed prediction model helps in finding the measured value and their dependencies for failure. The model presented in this thesis helps to predict the future test point value and reduces the unit time to market. In addition, to obtain a more intuitive insight Graphical User Interface is built up to access the prediction model.sv
dc.identifier.coursecodeMPEESsv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/302026
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectPrediction modelsv
dc.subjectRegressorsv
dc.subjectRadio unitsv
dc.subjecttest flowssv
dc.subjecttest pointssv
dc.subjectDatasv
dc.subjectFeaturessv
dc.subjectModelsv
dc.subjectModellingsv
dc.titlePrediction Model for Microwave Radio Unit Testingsv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmeEmbedded electronic system design (MPEES), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Hämtar...
Bild (thumbnail)
Namn:
CSE 19-124 Balasubramaniam .pdf
Storlek:
3.94 MB
Format:
Adobe Portable Document Format
Beskrivning:
License bundle
Visar 1 - 1 av 1
Hämtar...
Bild (thumbnail)
Namn:
license.txt
Storlek:
1.14 KB
Format:
Item-specific license agreed upon to submission
Beskrivning: