Robust discrete-time minimum-variance filtering

The bounded-variance filtered estimation of the state of an uncertain, linear, discrete-time system, with an unknown norm-bounded parameter matrix, is considered. An upper bound on the variance of the estimation error is found for all admissible systems, and estimators are derived that minimize the latter bound. We treat the finite-horizon, time-varying case and the infinite-time case, where the nominal system model is time invariant. In the special stationary case, where it is known that the uncertain system is time invariant, we provide a robust filter for all uncertainties that still keep the system asymptotically stable     

-like control for nonlinear stochastic systems

In this paper we develop a H_∞-type theory, from the dissipation point of view, for a large class of time-continuous stochastic nonlinear systems. In particular, we introduce the notion of stochastic dissipative systems analogously to the familiar notion of dissipation associated with deterministic systems and utilize it as a basis for the development of our theory. Having discussed certain properties of stochastic dissipative systems, we consider time-varying nonlinear systems for which we establish a connection between what is called the L₂-gain property and the solution to a certain Hamilton–Jacobi inequality (HJI), that may be viewed as a bounded real lemma for stochastic nonlinear systems. The time-invariant case with infinite horizon is also considered, where for this case we synthesize a worst case-based stabilizing controller. Stability in this case is taken to be in the mean-square sense. In the stationary case, the problem of robust state feedback control is considered in the case of norm-bounded uncertainties. A solution is then derived in terms of linear matrix inequalities.     

Discrete-time H ∞ and H 2 control with structured disturbances and probability-relaxed requirements

This paper presents a method that reduces the conservatism inherent in the disturbance representation in standard H _∞ theory. It addresses several discrete-time control synthesis problems where the disturbances acting on the system are structured by a convex family of linear filters (‘signal polytope’) and where the system disturbance attenuation level attained over the signal polytope is ensured to (just) a prescribed probability. This setup enables realistic multi-feature disturbance delimiting while accounting for uncertainty in the disturbance model itself by allowing probability waivers. The core of the probability aspects of the proposed solutions is the search for a truncated signal polytope which provides both the required probability and the best robust disturbance attenuation level. Many examples are given, including one of an aircraft output-feedback control with a polytope of low-pass filters representing different wind phenomena. The examples demonstrate that addressing realistic disturbances results in better control designs (hence better performance) and that a small certainty waiver can yield a large performance gain.     

ICRAM-1: Model for International Construction Risk Assessment

This paper presents a risk assessment model for international construction projects. The International Construction Risk Assessment Model (ICRAM-1) assists the user in evaluating the potential risk involved in expanding operations in an international market by analyzing risk at the macro (or country environment), market, and project levels. The paper discusses some of the existing models for country risk assessment, presents potential risk indicators at the macro, market, and project levels, and explains the ICRAM-1 methodology through an applied example. The ICRAM-1 provides a structured approach for evaluating the risk indicators involved in an international construction operation and is designed to examine a specific project in a foreign country. It can be used as a tool to quantify the risk involved in an international construction investment as one of the preliminary steps in project evaluation. Four main results are obtained from the ICRAM-1 analysis: (1) High-risk indicators; (2) impact of country environment on a specific project; (3) impact of market environment on a specific project; and (4) overall project risk.     

Space-dependent color gamut mapping: a variational approach.

Gamut mapping deals with the need to adjust a color image to fit into the constrained color gamut of a given rendering medium. A typical use for this tool is the reproduction of a color image prior to its printing, such that it exploits best the given printer/medium color gamut, namely the colors the printer can produce on the given medium. Most of the classical gamut mapping methods involve a pixel-by-pixel mapping and ignore the spatial color configuration. Recently proposed spatial-dependent approaches for gamut mapping are either based on heuristic assumptions or involve a high computational cost. In this paper, we present a new variational approach for space-dependent gamut mapping. Our treatment starts with the presentation of a new measure for the problem, closely related to a recent measure proposed for Retinex. We also link our method to recent measures that attempt to couple spectral and spatial perceptual measures. It is shown that the gamut mapping problem leads to a quadratic programming formulation, guaranteed to have a unique solution if the gamut of the target device is convex. An efficient numerical solution is proposed with promising results.     

A simple solution to the singular linear minimum-variance estimation problem

A simple expression is obtained for the transfer function matrix of the minimum-variance estimator for the states of a linear stationary continuous-time right invertible system whose output is measured perfectly. Using thes-domain approach, it is shown that the optimal estimator first finds the driving noise input that achieves, when applied on the minimum-phase image model of the system, an output spectrum which is identical to the measurement spectrum. This input is then applied on a state-space representation of the minimum-phase image to produce the optimal estimate.     

Multiple-viewpoint projection holograms synthesized by spatially incoherent correlation with broadband functions

We present a theoretical framework for recording and reconstructing incoherent correlation holograms of real-existing three-dimensional scenes observed from multiple viewpoints. This framework is demonstrated by generating and reconstructing a modified Fresnel hologram as well as a new correlation hologram called a protected correlation hologram. The reconstructed scene obtained from the protected correlation hologram has a significantly improved transverse resolution for the far objects in the scene compared to the modified Fresnel hologram. Additionally, the three-dimensional information encoded into the protected correlation hologram is scrambled by a secretive point spread function and thus the hologram can be used for encrypting the observed scene. The proposed holography methods are demonstrated by both simulations and experiments.     

The zeros of linear optimal control systems and their role in high feedback gain stability design

The finite zeros of the open-loop transfer function matrix of the optimal controller of the multiinput time-invariant regulator problem are found to be the eigenvalues of a negative real matrix. The infinite zeros of the transfer function matrix are all real. Using the root loci technique recently developed for multivariable systems, these properties of the zeros provide means of designing stable, not necessarily optimal, high feedback gain systems under significant parameter uncertainty.