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thela038 och filjo653

TDP032 project

An extention of the randomized singular value decomposition (SVD) algorithm to estimate the SVD of a shifted data matrix without explicitly constructing the matrix in the memory. With no loss in the accuracy of the original algorithm, the extended algorithm provides for a more efficient way of matrix factorization. The algorithm facilitates the low-rank approximation and principal component analysis (PCA) of off-center data matrices. When applied to different types of data matrices, our experimental results confirm the advantages of the extensions made to the original algorithm.

Imperative programming language

A bot for inviting users to communities and rooms in Matrix

Foreman plugin to read data from the LiU-IT MaskinDB system

Development of the Lua-based LiU Flight Test app for automated excitation manoeuvres using JETI Model RC systems.