ε-bounded state estimation for time-delay systems with bounded disturbances

A new problem on ε-bounded functional state estimation for time-delay systems with unknown bounded disturbances is studied in this paper. In the presence of unknown bounded disturbances, the common assumption regarding the observers matching condition is no longer required. In this regard, instead of achieving asymptotic convergence for the observer error, the error is now required to converge exponentially within a ball with a small radius ε > 0. This means that the estimate converges exponentially within an ε-bound of the true value. A general observer that utilises multiple-delayed output and input information is proposed. Sufficient conditions for the existence of the proposed observer are first given. We then employ an extended Lyapunov-Krasovskii functional which combines the delay-decomposition technique with a triple-integral term to study the ε-convergence problem of the observer error system. Moreover, the obtained results are shown to be more effective than the existing results for the cases with no disturbances and/or no time delay. Three numerical examples are given to illustrate the obtained results.

Partial state estimation for linear systems with output and input time delays.

This paper deals with the problem of partial state observer design for linear systems that are subject to time delays in the measured output as well as the control input. By choosing a set of appropriate augmented Lyapunov-Krasovskii functionals with a triple-integral term and using the information of both the delayed output and input, a novel approach to design a minimal-order observer is proposed to guarantee that the observer error is ε-convergent with an exponential rate. Existence conditions of such an observer are derived in terms of matrix inequalities for the cases with time delays in both the output and input and with output delay only. Constructive design algorithms are introduced. Numerical examples are provided to illustrate the design procedure, practicality and effectiveness of the proposed observer.

Sub-lethal UV-C radiation induces callose, hydrogen peroxide and defence-related gene expression in Arabidopsis thaliana

Exposure of plants to UV-C irradiation induces gene expression and cellular responses that are commonly associated with wounding and pathogen defence, and in some cases can lead to increased resistance against pathogen infection. We examined, at a physiological, molecular and biochemical level, the effects of and responses to, sub-lethal UV-C exposure on Arabidopsis plants when irradiated with increasing dosages of UV-C radiation. Following UV-C exposure plants had reduced leaf areas over time, with the severity of reduction increasing with dosage. Severe morphological changes that included leaf glazing, bronzing and curling were found to occur in plants treated with the 1000 J·m(-2) dosage. Extensive damage to the mesophyll was observed, and cell death occurred in both a dosage- and time-dependent manner. Analysis of H2 O2 activity and the pathogen defence marker genes PR1 and PDF1.2 demonstrated induction of these defence-related responses at each UV-C dosage tested. Interestingly, in response to UV-C irradiation the production of callose (β-1,3-glucan) was identified at all dosages examined. Together, these results show plant responses to UV-C irradiation at much lower doses than have previously been reported, and that there is potential for the use of UV-C as an inducer of plant defence.

Convergence of object focused simultaneous estimation of optical flow and state dynamics

The purpose of this study is to prove the convergence of the simultaneous estimation of the optical flow and object state (SEOS) method. The SEOS method utilizes dynamic object parameter information when calculating optical flow in tracking a moving object within a video stream. Optical flow estimation for the SEOS method requires the minimization of an error function containing the object's physical parameter data. When this function is discretized, the Euler-Lagrange equations form a system of linear equations. The system is arranged such that its property matrix is positive definite symmetric, proving the convergence of the Gauss-Seidel iterative methods. The system of linear equations produced by SEOS can alternatively be resolved by Jacobi iterative schemes. The positive definite symmetric property is not sufficient for Jacobi convergence. The convergence of SEOS for a block diagonal Jacobi is proved by analysing the Euclidean norm of the Jacobi matrix. In this paper, we also investigate the use of SEOS for tracking individual objects within a video sequence. The illustrations provided show the effectiveness of SEOS for localizing objects within a video sequence and generating optical flow results.

Low-order estimation scheme for large-scale systems with interconnection delays

This paper deals with the practical aspects of reduced-order distributed functional state observers design for interconnected linear systems subject to time delays in the interconnections. Contrary to some estimation strategies which only take the ideal instantaneous output information into account, the proposed scheme incorporates output information that is inevitably encountered with time delays in the course of its transmission from the distanced subsystems. It is proved that such estimator possesses less restrictive existence conditions with the acceptance of measurement data from other interrelated subsystems. Upon the satisfaction of the established existence conditions, it will be demonstrated through a simple design procedure and simulation results that a feasible observer can be realized for a given numerical system.

Estimation and extrapolation of optimal treatment and testing strategies

We review recent developments in the estimation of an optimal treatment strategy or regime from longitudinal data collected in an observational study. We also propose novel methods for using the data obtained from an observational database in one health-care system to determine the optimal treatment regime for biologically similar subjects in a second health-care system when, for cultural, logistical, or financial reasons, the two health-care systems differ (and will continue to differ) in the frequency of, and reasons for, both laboratory tests and physician visits. Finally, we propose a novel method for estimating the optimal timing of expensive and/or painful diagnostic or prognostic tests. Diagnostic or prognostic tests are only useful in so far as they help a physician to determine the optimal dosing strategy, by providing information on both the current health state and the prognosis of a patient because, in contrast to drug therapies, these tests have no direct causal effect on disease progression. Our new method explicitly incorporates this no direct effect restriction.

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content

Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results.

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part II: proofs of results

In this companion article to "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content" [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption.

An optimized mean variance estimation method for uncertainty quantification of wind power forecasts

A statistical optimized technique for rapid development of reliable prediction intervals (PIs) is presented in this study. The mean-variance estimation (MVE) technique is employed here for quantification of uncertainties related with wind power predictions. In this method, two separate neural network models are used for estimation of wind power generation and its variance. A novel PI-based training algorithm is also presented to enhance the performance of the MVE method and improve the quality of PIs. For an in-depth analysis, comprehensive experiments are conducted with seasonal datasets taken from three geographically dispersed wind farms in Australia. Five confidence levels of PIs are between 50% and 90%. Obtained results show while both traditional and optimized PIs are hypothetically valid, the optimized PIs are much more informative than the traditional MVE PIs. The informativeness of these PIs paves the way for their application in trouble-free operation and smooth integration of wind farms into energy systems. © 2014 Elsevier Ltd. All rights reserved.

Insights into the evolution of a cryptic radiation of bats: dispersal and ecological radiation of Malagasy Miniopterus (Chiroptera: Miniopteridae)

The past decade has seen a proliferation of new species of Miniopterus bats (family Miniopteridae) recognized from Madagascar and the neighboring Comoros archipelago. The interspecific relationships of these taxa, their colonization history, and the evolution of this presumed adaptive radiation have not been sufficiently explored. Using the mitochondrial cytochrome-b gene, we present a phylogeny of the Malagasy members of this widespread Old World genus, based on 218 sequences, of which 82 are new and 136 derived from previous studies. Phylogenetic analyses recovered 18 clades, which divide into five primary lineages: (1) M. griveaudi; (2) M. mahafaliensis, M. sororculus and X3; (3) M. majori, M. gleni and M. griffithsi; (4) M. brachytragos; M. aelleniA, and M. aelleniB; and (5) M. manavi and M. petersoni recovered as sister species, which were in turn linked to a group comprising M. egeri and five genetically distinct populations referred to herein as P3, P4, P5, P6 and P7. Beast analysis indicated that the initial divergence within the Malagasy Miniopterus radiation took place 4.5 Myr; most species diverged between 4 and 2.5 Myr, and a secondary period was between 1.25 and 1 Myr. DNA K2P-distances between recognized taxa ranged from 12.9% to 2.5% and intraspecific variation was less than 1.8%. Of the 18 identified clades, Latin binomials are only associated with 11, which indicates much greater differentiation than currently recognized for Malagasy Miniopterus. These data are placed in a context of the dispersal history of this genus on the island and patterns of ecological diversity.

Blind estimation of MIMO relay channels

In this paper, we integrate two blind source separation (BSS) methods to estimate the individual channel state information (CSI) for the source-relay and relay-destination links of three-node two-hop multiple-input multiple-output (MIMO) relay systems. In particular, we propose a first-order Z-domain precoding technique for the blind estimation of the relay-destination channel matrix, while an algorithm based on the constant modulus and mutual information properties is developed to estimate the source-relay channel matrix. Compared with training-based MIMO relay channel estimation approaches, our algorithm has a better bandwidth efficiency as no bandwidth is wasted for sending the training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm. © 2014 IEEE.

Channel estimation for frequency-selective two-way MIMO relay systems

In this paper, we investigate the channel estimation problem for two-way multiple-input multiple-output (MIMO) relay communication systems in frequency-selective fading environments. We propose a superimposed channel training algorithm to estimate the individual channel state information (CSI) of the first-hop and second-hop links for two-way MIMO relay systems with frequency-selective fading channels. In this algorithm, a relay training sequence is superimposed on the received signals at the relay node to assist the estimation of the second-hop channel matrices. The optimal structure of the source and relay training sequences is derived to minimize the meansquared error (MSE) of channel estimation. We also derive the optimal power allocation between the source and relay training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm.

Optical coherence tomography : age estimation of Calliphora vicina pupae in vivo?

Necrophagous blowfly pupae are valuable contributors to the estimation of post-mortem interval, should an accurate age estimate be obtained. At present, this is reliant on a combination of rearing and destructive methods conducted on preserved samples, including morphological observation and gene expression analyses. This study demonstrates the use of optical coherence tomography (OCT) as a tool for in vivo morphological observation and pupal age estimation. Using a Michelson OCT microscope, alive and preserved four and ten-day old Calliphora vicina pupae were scanned in different orientations. Two and three-dimensional images were created. Morphological characteristics such as the brain, mouthparts and legs were identifiable in both living and preserved samples, with distinct differences noted between the two ages. Absorption of light by the puparium results in a vertical resolution of 1-2 mm, preventing observation of deeper tissues. The use of contrast agents or a longer wavelength laser would improve the images obtainable. At present, the data suggests OCT provides a primary view of external and internal morphology, which can be used to distinguish younger and older pupae for further analysis of age and PMI estimation.