A Fast DOA Estimation Algorithm Based on Polarization MUSIC

A fast DOA estimation algorithm developed from MUSIC, which also benefits from the processing of the signals' polarization information, is presented. Besides performance enhancement in precision and resolution, the proposed algorithm can be exerted on various forms of polarization sensitive arrays, without specific requirement on the array's pattern. Depending on the continuity property of the space spectrum, a huge amount of computation incurred in the calculation of 4-D space spectrum is averted. Performance and computation complexity analysis of the proposed algorithm is discussed and the simulation results are presented. Compared with conventional MUSIC, it is indicated that the proposed algorithm has considerable advantage in aspects of precision and resolution, with a low computation complexity proportional to a conventional 2-D MUSIC

Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC $\sim 1$ for simple data sets and AUC $\sim 0.85$ for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online

Disk Accretion onto Magnetized Neutron Stars: The Inner Disk Radius and Fastness Parameter

It is well known that the accretion disk around a magnetized compact star can penetrate inside the magnetospheric boundary, so the magnetospheric radius \ro does not represent the true inner edge \rin of the disk; but controversies exist in the literature concerning the relation between \ro and \rin. In the model of Ghosh & Lamb, the width of the boundary layer is given by \delta=\ro-\rin\ll\ro, or \rin\simeq\ro, while Li & Wickramasinghe recently argued that \rin could be significantly smaller than \ro in the case of a slow rotator. Here we show that if the star is able to absorb the angular momentum of disk plasma at \ro, appropriate for binary X-ray pulsars, the inner disk radius can be constrained by 0.8\lsim \rin/\ro\lsim 1, and the star reaches spin equilibrium with a relatively large value of the fastness parameter ($\sim 0.7-0.95$). For accreting neutron stars in low-mass X-ray binaries (LMXBs), \ro is generally close to the stellar radius \rs so that the toroidal field cannot transfer the spin-up torque efficiently to the star. In this case the critical fastness parameter becomes smaller, but \rin is still near \ro.Comment: 7 pages, 2 figures, to appear in Ap