An efficient parallel implementation of the Fast Multipole Method (FMM) combined with the fast Fourier transform (FFT) is presented in this paper. The good scaling properties of the FMM-FFT, combined with a smart parallelization strategy, has been shown to be very effective when using large parallel supercomputers. A challenging problem with more than 150 million unknowns has been solved, demonstrating that the proposed implementation of the FMM-FFT constitutes a real alternative to the more frequently used multilevel approaches, such as the Multilevel FMM (MLFMA). Even more importantly, we have achieved a high efficiency with 1,024 parallel processors, which indeed constitutes one of the better scalability results ever reached for a rigorous integral-equation electromagnetic solver.

Different industrial and medical situations require the non-invasive extraction of information from the inside of bodies. This is usually done through tomographic methods that generate images based on internal body properties. However, the image reconstruction involves a mathematical inverse problem, for which accurate resolution demands large computation time and capacity. In this paper we explore the use of Machine Learning to develop an accurate solver for reconstructing Electrical Impedance Tomography images in real-time. We compare the results with the Iterative Gauss-Newton and the Primal Dual Interior Point Method, which are both largely used and well-validated solvers. The approaches were compared from the qualitative as well as the quantitative viewpoints. The former was focused on correctly detecting the internal body features. The latter was based on accurately predicting internal property distributions. Experiments revealed that our approach achieved better accuracy and Cohen’s kappa coefficient (97.57% and 94.60% respectively) from the qualitative viewpoint. Moreover, it also obtained better quantitative metrics with a Mean Absolute Percentage Error of 18.28%. Experiments confirmed that Neural Networks algorithms can reconstruct internal body properties with high accuracy, so they would be able to replace more complex and slower alternatives.

We report a large enhancement of 1.7 in deuteron polarization up to values of 0.6 due to frequency modulation of the polarizing microwaves in a two liters polarized target using the method of dynamic nuclear polarization. This target was used during a deep inelastic polarized muon-deuteron scattering experiment at CERN. Measurements of the electron paramagnetic resonance absorption spectra show that frequency modulation gives rise to additional microwave absorption in the spectral wings. Although these results are not understood theoretically, they may provide a useful testing ground for the deeper understanding of dynamic nuclear polarization.Comment: 10 pages, including the figures coming in uuencoded compressed tar files in poltar.uu, which also brings cernart.sty and crna12.sty files neede