Robust H∞ filtering for markovian jump systems with randomly occurring nonlinearities and sensor saturation: The finite-horizon case

This article is posted with the permission of IEEE - Copyright @ 2011 IEEEThis paper addresses the robust H∞ filtering problem for a class of discrete time-varying Markovian jump systems with randomly occurring nonlinearities and sensor saturation. Two kinds of transition probability matrices for the Markovian process are considered, namely, the one with polytopic uncertainties and the one with partially unknown entries. The nonlinear disturbances are assumed to occur randomly according to stochastic variables satisfying the Bernoulli distributions. The main purpose of this paper is to design a robust filter, over a given finite-horizon, such that the H∞ disturbance attenuation level is guaranteed for the time-varying Markovian jump systems in the presence of both the randomly occurring nonlinearities and the sensor saturation. Sufficient conditions are established for the existence of the desired filter satisfying the H∞ performance constraint in terms of a set of recursive linear matrix inequalities. Simulation results demonstrate the effectiveness of the developed filter design scheme.This work was supported in part by the National Natural Science Foundation of China under Grants 61028008, 60825303, and 61004067, National 973 Project under Grant 2009CB320600, the Key Laboratory of Integrated Automation for the Process Industry (Northeastern University) from the Ministry of Education of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K., under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Variance-constrained control for uncertain stochastic systems with missing measurements

Copyright [2005] 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.In this paper, we are concerned with a new control problem for uncertain discrete-time stochastic systems with missing measurements. The parameter uncertainties are allowed to be norm-bounded and enter into the state matrix. The system measurements may be unavailable (i.e., missing data) at any sample time, and the probability of the occurrence of missing data is assumed to be known. The purpose of this problem is to design an output feedback controller such that, for all admissible parameter uncertainties and all possible incomplete observations, the system state of the closed-loop system is mean square bounded, and the steady-state variance of each state is not more than the individual prescribed upper bound. We show that the addressed problem can be solved by means of algebraic matrix inequalities. The explicit expression of the desired robust controllers is derived in terms of some free parameters, which may be exploited to achieve further performance requirements. An illustrative numerical example is provided to demonstrate the usefulness and flexibility of the proposed design approach

Compact printed multiband antenna with independent setting suitable for fixed and reconfigurable wireless communication systems

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This paper presents the design of a low-profile compact printed antenna for fixed frequency and reconfigurable frequency bands. The antenna consists of a main patch, four sub-patches, and a ground plane to generate five frequency bands, at 0.92, 1.73, 1.98, 2.4, and 2.9 GHz, for different wireless systems. For the fixed-frequency design, the five individual frequency bands can be adjusted and set independently over the wide ranges of 18.78%, 22.75%, 4.51%, 11%, and 8.21%, respectively, using just one parameter of the antenna. By putting a varactor (diode) at each of the sub-patch inputs, four of the frequency bands can be controlled independently over wide ranges and the antenna has a reconfigurable design. The tunability ranges for the four bands of 0.92, 1.73, 1.98, and 2.9 GHz are 23.5%, 10.30%, 13.5%, and 3%, respectively. The fixed and reconfigurable designs are studied using computer simulation. For verification of simulation results, the two designs are fabricated and the prototypes are measured. The results show a good agreement between simulated and measured results

Robust finite-horizon filtering for stochastic systems with missing measurements

Copyright [2005] 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.In this letter, we consider the robust finite-horizon filtering problem for a class of discrete time-varying systems with missing measurements and norm-bounded parameter uncertainties. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. An upper bound for the state estimation error variance is first derived for all possible missing observations and all admissible parameter uncertainties. Then, a robust filter is designed, guaranteeing that the variance of the state estimation error is not more than the prescribed upper bound. It is shown that the desired filter can be obtained in terms of the solutions to two discrete Riccati difference equations, which are of a form suitable for recursive computation in online applications. A simulation example is presented to show the effectiveness of the proposed approach by comparing to the traditional Kalman filtering method

Application of serious games to sport, health and exercise

Use of interactive entertainment has been exponentially expanded since the last decade. Throughout this 10+ year evolution there has been a concern about turning entertainment properties into serious applications, a.k.a "Serious Games". In this article we present two set of Serious Game applications, an Environment Visualising game which focuses solely on applying serious games to elite Olympic sport and another set of serious games that incorporate an in house developed proprietary input system that can detect most of the human movements which focuses on applying serious games to health and exercise

Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography using Radially Symmetric Expansion Functions

Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an emerging computed biomedical imaging modality that exploits optical contrast and ultrasonic detection principles. Iterative image reconstruction algorithms that are based on discrete imaging models are actively being developed for OAT due to their ability to improve image quality by incorporating accurate models of the imaging physics, instrument response, and measurement noise. In this work, we investigate the use of discrete imaging models based on Kaiser-Bessel window functions for iterative image reconstruction in OAT. A closed-form expression for the pressure produced by a Kaiser-Bessel function is calculated, which facilitates accurate computation of the system matrix. Computer-simulation and experimental studies are employed to demonstrate the potential advantages of Kaiser-Bessel function-based iterative image reconstruction in OAT

Prevalence and risk factors of sarcopenia among adults living in nursing homes

Objectives: Sarcopenia is a progressive loss of skeletal muscle and muscle function, with significant healthand disability consequences for older adults. We aimed to evaluate the prevalence and risk factors ofsarcopenia among older residential aged care adults using the European Working Group on Sarcopeniain Older People (EWGSOP) criteria.Study design: A cross-sectional study design that assessed older people (n = 102, mean age 84.5 ± 8.2 years)residing in 11 long-term nursing homes in Australia.Main outcome measurements: Sarcopenia was diagnosed from assessments of skeletal mass index bybioelectrical impedance analysis, muscle strength by handheld dynamometer, and physical performanceby the 2.4 m habitual walking speed test. Secondary variables where collected to inform a risk factoranalysis.Results: Forty one (40.2%) participants were diagnosed as sarcopenic, 38 (95%) of whom were categorizedas having severe sarcopenia. Univariate logistic regression found that body mass index (BMI) (Oddsratio (OR) = 0.86; 95% confidence interval (CI) 0.78–0.94), low physical performance (OR = 0.83; 95% CI0.69–1.00), nutritional status (OR = 0.19; 95% CI 0.05–0.68) and sitting time (OR = 1.18; 95% CI 1.00–1.39)were predictive of sarcopenia. With multivariate logistic regression, only low BMI (OR = 0.80; 95% CI0.65–0.97) remained predictive.Conclusions: The prevalence of sarcopenia among older residential aged care adults is very high. Inaddition, low BMI is a predictive of sarcopenia