237 research outputs found
Coherent and radiative couplings through 2D structured environments
We study coherent and radiative interactions induced among two or more
quantum units, by coupling them to two-dimensional lattices acting as
structured environments. This model can be representative of atoms trapped near
photonic crystal slabs, trapped ions in Coulomb crystals or to surface acoustic
waves on piezoelectric materials, cold atoms on state-dependent optical
lattices, or even circuit QED architectures, to name a few. We compare coherent
and radiative contributions for the isotropic and directional regimes of
emission into the lattice, for infinite and finite lattices, highlighting their
differences and existing pitfalls, e.g. related to long-time or large-lattice
limits. We relate the phenomenon of directionality of emission with
linear-shaped isofrequency manifolds in the dispersion relation, showing a
simple way to disrupt it. For finite lattices, we study further details as the
scaling of resonant number of lattice modes for the isotropic and directional
regimes, and relate this behavior with known van Hove singularities in the
infinite lattice limit. Further we export the understanding of emission
dynamics with the decay of entanglement for two quantum, atomic or bosonic,
units coupled to the 2D lattice. We analyze in some detail completely
subradiant configurations of more than two atoms, which can occur in the finite
lattice scenario, in contrast with the infinite lattice case. Finally we
demonstrate that induced coherent interactions for dark states are zero for the
finite lattice.Comment: 10 page
Structured least squares problems and robust estimators
Cataloged from PDF version of article.A novel approach is proposed to provide robust and
accurate estimates for linear regression problems when both the
measurement vector and the coefficient matrix are structured and
subject to errors or uncertainty. A new analytic formulation is developed
in terms of the gradient flow of the residual norm to analyze
and provide estimates to the regression. The presented analysis
enables us to establish theoretical performance guarantees to compare
with existing methods and also offers a criterion to choose the
regularization parameter autonomously. Theoretical results and
simulations in applications such as blind identification, multiple
frequency estimation and deconvolution show that the proposed
technique outperforms alternative methods in mean-squared error
for a significant range of signal-to-noise ratio values
Sensor array signal processing : two decades later
Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg
Efficient Beamspace Eigen-Based Direction of Arrival Estimation schemes
The Multiple SIgnal Classification (MUSIC) algorithm developed in the late 70\u27s was the first vector subspace approach used to accurately determine the arrival angles of signal wavefronts impinging upon an array of sensors. As facilitated by the geometry associated with the common uniform linear array of sensors, a root-based formulation was developed to replace the computationally intensive spectral search process and was found to offer an enhanced resolution capability in the presence of two closely-spaced signals. Operation in beamspace, where sectors of space are individually probed via a pre-processor operating on the sensor data, was found to offer both a performance benefit and a reduced computationa1 complexi ty resulting from the reduced data dimension associated with beamspace processing. Little progress, however, has been made in the development of a computationally efficient Root-MUSIC algorithm in a beamspace setting. Two approaches of efficiently arriving at a Root-MUSIC formulation in beamspace are developed and analyzed in this Thesis. In the first approach, a structura1 constraint is placed on the beamforming vectors that can be exploited to yield a reduced order polynomial whose roots provide information on the signal arrival angles. The second approach is considerably more general, and hence, applicable to any vector subspace angle estimation algorithm. In this approach, classical multirate digital signal processing is applied to effectively reduce the dimension of the vectors that span the signal subspace, leading to an efficient beamspace Root-MUSIC (or ESPRIT) algorithm. An auxiliaay, yet important, observation is shown to allow a real-valued eigenanalysis of the beamspace sample covariance matrix to provide a computational savings as well as a performance benefit, particularly in the case of correlated signal scenes. A rigorous theoretical analysis, based upon derived large-sample statistics of the signal subspace eigenvectors, is included to provide insight into the operation of the two algorithmic methodologies employing the real-valued processing enhancement. Numerous simulations are presented to validate the theoretical angle bias and variance expressions as well as to assess the merit of the two beamspace approaches
Inconsistency transmission and variance reduction in two-stage quantile regression
International audienceIn this paper, we propose a new variance reduction method for quantile regressions with endogeneity problems, for alpha-mixing or m-dependent covariates and error terms. First, we derive the asymptotic distribution of two-stage quantile estimators based on the fitted-value approach under very general conditions. Second, we exhibit an inconsistency transmission property derived from the asymptotic representation of our estimator. Third, using a reformulation of the dependent variable, we improve the efficiency of the two-stage quantile estimators by exploiting a tradeoff between an inconsistency confined to the intercept estimator and a reduction of the variance of the slope estimator. Monte Carlo simulation results show the fine performance of our approach. In particular, by combining quantile regressions with first-stage trimmed least-squares estimators, we obtain more accurate slope estimates than 2SLS, 2SLAD and other estimators for a broad set of distributions. Finally, we apply our method to food demand equations in Egypt
Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation
This work provides novel robust and regularized algorithms for parameter estimation with applications in vehicle tractive force prediction and mass estimation. Given a large record of real world data from test runs on public roads, recursive algorithms adjusted the unknown vehicle parameters under a broad variation of statistical assumptions for two linear gray-box models
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