24,694 research outputs found
Estimation-based synthesis of H∞-optimal adaptive FIR filtersfor filtered-LMS problems
This paper presents a systematic synthesis procedure for H∞-optimal adaptive FIR filters in the context of an active noise cancellation (ANC) problem. An estimation interpretation of the adaptive control problem is introduced first. Based on this interpretation, an H∞ estimation problem is formulated, and its finite horizon prediction (filtering) solution is discussed. The solution minimizes the maximum energy gain from the disturbances to the predicted (filtered) estimation error and serves as the adaptation criterion for the weight vector in the adaptive FIR filter. We refer to this adaptation scheme as estimation-based adaptive filtering (EBAF). We show that the steady-state gain vector in the EBAF algorithm approaches that of the classical (normalized) filtered-X LMS algorithm. The error terms, however, are shown to be different. Thus, these classical algorithms can be considered to be approximations of our algorithm. We examine the performance of the proposed EBAF algorithm (both experimentally and in simulation) in an active noise cancellation problem of a one-dimensional (1-D) acoustic duct for both narrowband and broadband cases. Comparisons to the results from a conventional filtered-LMS (FxLMS) algorithm show faster convergence without compromising steady-state performance and/or robustness of the algorithm to feedback contamination of the reference signal
Subdivision surface fitting to a dense mesh using ridges and umbilics
Fitting a sparse surface to approximate vast dense data is of interest for many applications: reverse engineering, recognition and compression, etc. The present work provides an approach to fit a Loop subdivision surface to a dense triangular mesh of arbitrary topology, whilst preserving and aligning the original features. The natural ridge-joined connectivity of umbilics and ridge-crossings is used as the connectivity of the control mesh for subdivision, so that the edges follow salient features on the surface. Furthermore, the chosen features and connectivity characterise the overall shape of the original mesh, since ridges capture extreme principal curvatures and ridges start and end at umbilics. A metric of Hausdorff distance including curvature vectors is proposed and implemented in a distance transform algorithm to construct the connectivity. Ridge-colour matching is introduced as a criterion for edge flipping to improve feature alignment. Several examples are provided to demonstrate the feature-preserving capability of the proposed approach
Differences between Monte Carlo models for DIS at small-x and the relation to BFKL dynamics
The differences between two standard Monte Carlo models, Lepto and Ariadne,
for deep inelastic scattering at small-x is analysed in detail. It is shown
that the difference arises from a `unorthodox' suppression factor used in
Ariadne which replaces the normal ratio of parton densities. This gives rise to
a factor that qualitatively is similar to what one would expect from BFKL
dynamics for some observables like the energy flow and forward jets but not for
the 2+1 jet cross-section. It is also discussed how one could use the 2+1 jet
cross-section as a probe for BFKL dynamics.Comment: 10 pages Latex, 4 eps figure
Pre-integration lateral inhibition enhances unsupervised learning
A large and influential class of neural network architectures use
post-integration lateral inhibition as a mechanism for competition. We argue
that these algorithms are computationally deficient in that they fail to
generate, or learn, appropriate perceptual representations under certain
circumstances. An alternative neural network architecture is presented in which
nodes compete for the right to receive inputs rather than for the right to
generate outputs. This form of competition, implemented through pre-integration
lateral inhibition, does provide appropriate coding properties and can be used
to efficiently learn such representations. Furthermore, this architecture is
consistent with both neuro-anatomical and neuro-physiological data. We thus
argue that pre-integration lateral inhibition has computational advantages over
conventional neural network architectures while remaining equally biologically
plausible
A Bayesian Approach to Manifold Topology Reconstruction
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated
Multi-Jet Event Rates in Deep Inelastic Scattering and Determination of the Strong Coupling Constant
Jet event rates in deep inelastic ep scattering at HERA are investigated
applying the modified JADE jet algorithm. The analysis uses data taken with the
H1 detector in 1994 and 1995. The data are corrected for detector and
hadronization effects and then compared with perturbative QCD predictions using
next-to-leading order calculations. The strong coupling constant alpha_S(M_Z^2)
is determined evaluating the jet event rates. Values of alpha_S(Q^2) are
extracted in four different bins of the negative squared momentum
transfer~\qq in the range from 40 GeV2 to 4000 GeV2. A combined fit of the
renormalization group equation to these several alpha_S(Q^2) values results in
alpha_S(M_Z^2) = 0.117+-0.003(stat)+0.009-0.013(syst)+0.006(jet algorithm).Comment: 17 pages, 4 figures, 3 tables, this version to appear in Eur. Phys.
J.; it replaces first posted hep-ex/9807019 which had incorrect figure 4
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