72,709 research outputs found
Solutions of large-scale electromagnetics problems involving dielectric objects with the parallel multilevel fast multipole algorithm
Fast and accurate solutions of large-scale electromagnetics problems involving homogeneous dielectric objects are considered. Problems are formulated with the electric and magnetic current combined-field integral equation and discretized with the Rao-Wilton-Glisson functions. Solutions are performed iteratively by using the multi-level fast multipole algorithm (MLFMA). For the solution of large-scale problems discretized with millions of unknowns, MLFMA is parallelized on distributed-memory architectures using a rigorous technique, namely, the hierarchical partitioning strategy. Efficiency and accuracy of the developed implementation are demonstrated on very large problems involving as many as 100 million unknowns
Reciprocity Calibration for Massive MIMO: Proposal, Modeling and Validation
This paper presents a mutual coupling based calibration method for
time-division-duplex massive MIMO systems, which enables downlink precoding
based on uplink channel estimates. The entire calibration procedure is carried
out solely at the base station (BS) side by sounding all BS antenna pairs. An
Expectation-Maximization (EM) algorithm is derived, which processes the
measured channels in order to estimate calibration coefficients. The EM
algorithm outperforms current state-of-the-art narrow-band calibration schemes
in a mean squared error (MSE) and sum-rate capacity sense. Like its
predecessors, the EM algorithm is general in the sense that it is not only
suitable to calibrate a co-located massive MIMO BS, but also very suitable for
calibrating multiple BSs in distributed MIMO systems.
The proposed method is validated with experimental evidence obtained from a
massive MIMO testbed. In addition, we address the estimated narrow-band
calibration coefficients as a stochastic process across frequency, and study
the subspace of this process based on measurement data. With the insights of
this study, we propose an estimator which exploits the structure of the process
in order to reduce the calibration error across frequency. A model for the
calibration error is also proposed based on the asymptotic properties of the
estimator, and is validated with measurement results.Comment: Submitted to IEEE Transactions on Wireless Communications,
21/Feb/201
A Fast and Accurate Algorithm for Spherical Harmonic Analysis on HEALPix Grids with Applications to the Cosmic Microwave Background Radiation
The Hierarchical Equal Area isoLatitude Pixelation (HEALPix) scheme is used
extensively in astrophysics for data collection and analysis on the sphere. The
scheme was originally designed for studying the Cosmic Microwave Background
(CMB) radiation, which represents the first light to travel during the early
stages of the universe's development and gives the strongest evidence for the
Big Bang theory to date. Refined analysis of the CMB angular power spectrum can
lead to revolutionary developments in understanding the nature of dark matter
and dark energy. In this paper, we present a new method for performing
spherical harmonic analysis for HEALPix data, which is a central component to
computing and analyzing the angular power spectrum of the massive CMB data
sets. The method uses a novel combination of a non-uniform fast Fourier
transform, the double Fourier sphere method, and Slevinsky's fast spherical
harmonic transform (Slevinsky, 2019). For a HEALPix grid with pixels
(points), the computational complexity of the method is , with an initial set-up cost of . This compares
favorably with runtime complexity of the current methods
available in the HEALPix software when multiple maps need to be analyzed at the
same time. Using numerical experiments, we demonstrate that the new method also
appears to provide better accuracy over the entire angular power spectrum of
synthetic data when compared to the current methods, with a convergence rate at
least two times higher
Lattice QCD thermodynamics at finite chemical potential and its comparison with Experiments
We compare higher moments of baryon numbers measured at the RHIC heavy ion
collision experiments with those by the lattice QCD calculations. We employ the
canonical approach, in which we can access the real chemical potential regions
avoiding the sign problem. In the lattice QCD simulations, we study several
fits of the number density in the pure imaginary chemical potential, and
analyze how these fits affects behaviors at the real chemical potential. In the
energy regions between =19.6 and 200 GeV, the susceptibility
calculated at is consistent with experimental data at , while the kurtosis shows similar behavior with that of the
experimental data in the small regions . The
experimental data at 11.5 shows quite different behavior. The
lattice result in the deconfinement region,, is far from
experimental data
Short-term fire front spread prediction using inverse modelling and airborne infrared images
A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.Peer ReviewedPostprint (author's final draft
Soft Consistency Reconstruction: A Robust 1-bit Compressive Sensing Algorithm
A class of recovering algorithms for 1-bit compressive sensing (CS) named
Soft Consistency Reconstructions (SCRs) are proposed. Recognizing that CS
recovery is essentially an optimization problem, we endeavor to improve the
characteristics of the objective function under noisy environments. With a
family of re-designed consistency criteria, SCRs achieve remarkable
counter-noise performance gain over the existing counterparts, thus acquiring
the desired robustness in many real-world applications. The benefits of soft
decisions are exemplified through structural analysis of the objective
function, with intuition described for better understanding. As expected,
through comparisons with existing methods in simulations, SCRs demonstrate
preferable robustness against noise in low signal-to-noise ratio (SNR) regime,
while maintaining comparable performance in high SNR regime
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