@article{GoldhackerKeckIgeletal., author = {Goldhacker, Markus and Keck, P. and Igel, A. and Lang, Elmar Wolfgang and Tom{\´e}, Ana Maria}, title = {A multi-variate blind source separation algorithm}, series = {Computer Methods and Programs in Biomedicine}, volume = {151}, journal = {Computer Methods and Programs in Biomedicine}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1872-7565}, doi = {10.1016/j.cmpb.2017.08.019}, pages = {91 -- 99}, abstract = {Background and objective The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation. Methods The proposed methodology follows an algebraic approach, which is common to space, temporal or spatiotemporal blind source separation algorithms. More specifically, the algebraic approach relies on singular value decomposition techniques, which avoids computationally costly and numerically instable matrix inversion. The method is equally applicable to correlation matrices determined from second order correlations or by considering fourth order correlations. Results The resulting algorithms are applied to fMRI data sets either to extract the underlying fMRI components or to extract connectivity maps from resting state fMRI data collected for a dynamic functional connectivity analysis. Intriguingly, our algorithm shows increased spatial specificity compared to common approaches, while temporal precision stays similar. Conclusion The study presents a novel spatiotemporal blind source separation algorithm, which is both robust and avoids parameters that are difficult to fine tune. Applied on experimental data sets, the new method yields highly confined and focused areas with least spatial extent in the retinotopy case, and similar results in the dynamic functional connectivity analyses compared to other blind source separation algorithms. Therefore, we conclude that our novel algorithm is highly competitive and yields results, which are superior or at least similar to existing approaches.}, language = {en} } @article{LangMonkman, author = {Lang, A. and Monkman, Gareth J.}, title = {An analysis of the electrical capacitance between two conducting spheres}, series = {Journal of Electrostatics}, volume = {108}, journal = {Journal of Electrostatics}, publisher = {Elsevier}, doi = {10.1016/j.elstat.2020.103518}, abstract = {Many modern composites consist of dielectric polymer matrices containing embedded spherical particles. These particles can be electrically conducting and often have magnetic properties. In order to accurately model and simulate such materials, precise calculation of the electrical capacitance between identically sized spheres is required. This is of particular relevance at microscopic dimensions where many smart material-based micro devices are concerned. This may appear trivial for a small number of particles. However, many methods or their analysis appear to be questionable or not applicable at reduced dimensions. In this work, the various methods of analysis are scrutinized before being compared with both simulation and direct measurements. It is surprising to note that of the many works investigated only 3 are in close agreement with both simulation and measurement. Graphical abstract This paper deals with the precise calculation of the electrical capacitance between two conducting spheres of equal, or near equal size.The calculations have been verified by both experimental measurements and simulation. Algorithms in MatLab (Octave) format have been included in the supporting information.}, language = {en} }