Due to its high spatial and temporal resolution, fetal magnetocardiography (fMCG) measurements have been used for fetal movement (FM) detection in several studies, which considered the changes in the amplitude and/or morphology of measured fMCG signals. Using source localization for fMCG measurements, we propose a novel method to fit a magnetic dipole moment to fetal heart signals and investigate the positional changes of magnetic dipole in order to detect FMs. We first split each fMCG recording into 6-s time windows. Then, the magnetic dipole location and orientation for each time window are estimated using our inverse solution model. Finally, the distance between magnetic dipole positions in adjacent time windows is computed. Also, we calculate the dot products of the normalized magnetic dipoles to monitor the orientational changes. We analyzed 28 fMCG measurements from 23 subjects to investigate accuracy of the dipole fitting results. For each dipole fit, our model described the measured data with a goodness-of-fit value over 97% and with a fitting error of less than 2%. We observed that magnetic dipole positions significantly moved for some time windows. The time points at which the significant movement was observed were correlated with the heart rate acceleration as well. In addition to identifying the time points of the movement, our method is capable of observing rotational movement checking orientation of the dipoles.

This paper presents a massively parallel implementation of a prominent network clustering algorithm, the structural clustering algorithm for networks (SCAN), on a graphical processing unit (GPU). SCAN is a fast and efficient clustering technique for finding hidden communities and isolating hubs/outliers within a network. However, for very large networks, it still takes considerable amount of time. With the introduction of massively parallel Compute Unified Device Architecture (CUDA) by Nvidia, applications properly employing GPUs are demonstrating high speed up. In current study, GPUSCAN, a CUDA based parallel implementation of SCAN, is presented. SCAN's computation steps have been carefully redesigned to run very efficiently on the GPU by transforming SCAN into a series of highly regular and independent concurrent operations. All intermediate data structures are created in the GPU to efficiently benefit from GPU's memory hierarchy. How these structures reformed and represented in the GPU memory hierarchy are illustrated. Now, through GPUSCAN, a large network or a batch of disjoint networks can be offloaded to the GPU for very fast and equivalent structural clustering. The performance of the GPU accelerated structural clustering has been shown to be much faster than the sequential CPU implementation. Both GPUSCAN and SCAN are tested on different size artificial and real-world networks. Results indicate that network becomes larger GPUSCAN significantly over performs SCAN. In tested datasets, speed-up of over 500-fold is achieved. For instance, calculating structural similarity and clustering of 5.5 million edges of the California road network in GPUSCAN is 513-fold faster than the serial version of SCAN.