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Robust filtering for uncertain linear systems with delayed states and outputs
Copyright [2002] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Deals with the robust filtering problem for uncertain linear systems with delayed states and outputs. Both time-invariant and time-varying cases are considered. For the time-invariant case, an algebraic Riccati matrix inequality approach is proposed to design a robust H∞ filter such that the filtering process remains asymptotically stable for all admissible uncertainties, and the transfer function from the disturbance inputs to error state outputs satisfies the prespecified H∞ norm upper bound constraint. We establish the conditions under which the desired robust H ∞ filters exist, and derive the explicit expression of these filters. For the time-varying case, we develop a differential Riccati inequality method to design the robust filters. A numerical example is provided to demonstrate the validity of the proposed design approac
Guest editorial: Memetic computing in the presence of uncertainties
Copyright @ Springer-Verlag 2010.The Guest Editors acknowledge the research support by the Academy of Finland, Akatemiatutkija 130600, Algorithmic
Design Issues in Memetic Computing, and by the UK Engineering and Physical Sciences Research Council (EPSRC) Project: Evolutionary Algorithms for Dynamic Optimisation Problems, under Grant EP/E060722/1
Field-ionization threshold and its induced ionization-window phenomenon for Rydberg atoms in a short single-cycle pulse
We study the field-ionization threshold behavior when a Rydberg atom is
ionized by a short single-cycle pulse field. Both hydrogen and sodium atoms are
considered. The required threshold field amplitude is found to scale
\emph{inversely} with the binding energy when the pulse duration becomes
shorter than the classical Rydberg period, and, thus, more weakly bound
electrons require larger fields for ionization. This threshold scaling behavior
is confirmed by both 3D classical trajectory Monte Carlo simulations and
numerically solving the time-dependent Schr\"{o}dinger equation. More
surprisingly, the same scaling behavior in the short pulse limit is also
followed by the ionization thresholds for much lower bound states, including
the hydrogen ground state. An empirical formula is obtained from a simple
model, and the dominant ionization mechanism is identified as a nonzero spatial
displacement of the electron. This displacement ionization should be another
important mechanism beyond the tunneling ionization and the multiphoton
ionization. In addition, an "ionization window" is shown to exist for the
ionization of Rydberg states, which may have potential applications to
selectively modify and control the Rydberg-state population of atoms and
molecules
SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion
Active depth cameras suffer from several limitations, which cause incomplete
and noisy depth maps, and may consequently affect the performance of RGB-D
Odometry. To address this issue, this paper presents a visual odometry method
based on point and line features that leverages both measurements from a depth
sensor and depth estimates from camera motion. Depth estimates are generated
continuously by a probabilistic depth estimation framework for both types of
features to compensate for the lack of depth measurements and inaccurate
feature depth associations. The framework models explicitly the uncertainty of
triangulating depth from both point and line observations to validate and
obtain precise estimates. Furthermore, depth measurements are exploited by
propagating them through a depth map registration module and using a
frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D
reprojection errors, independently. Results on RGB-D sequences captured on
large indoor and outdoor scenes, where depth sensor limitations are critical,
show that the combination of depth measurements and estimates through our
approach is able to overcome the absence and inaccuracy of depth measurements.Comment: IROS 201
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
This work proposes a robust visual odometry method for structured
environments that combines point features with line and plane segments,
extracted through an RGB-D camera. Noisy depth maps are processed by a
probabilistic depth fusion framework based on Mixtures of Gaussians to denoise
and derive the depth uncertainty, which is then propagated throughout the
visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are
used to model the uncertainties of the feature parameters and pose is estimated
by combining the three types of primitives based on their uncertainties.
Performance evaluation on RGB-D sequences collected in this work and two public
RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth
fusion framework and combining the three feature-types, particularly in scenes
with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34
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