On nonlinear H∞ filtering for discrete-time stochastic systems with missing measurements

Copyright [2008] 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, the H∞ filtering problem is investigated for a general class of nonlinear discrete-time stochastic systems with missing measurements. The system under study is not only corrupted by state-dependent white noises but also disturbed by exogenous inputs. The measurement output contains randomly missing data that is modeled by a Bernoulli distributed white sequence with a known conditional probability. A filter of very general form is first designed such that the filtering process is stochastically stable and the filtering error satisfies H infin performance constraint for all admissible missing observations and nonzero exogenous disturbances under the zero-initial condition. The existence conditions of the desired filter are described in terms of a second-order nonlinear inequality. Such an inequality can be decoupled into some auxiliary ones that can be solved independently by taking special form of the Lyapunov functionals. As a consequence, a linear time-invariant filter design problem is discussed for the benefit of practical applications, and some simplified conditions are obtained. Finally, two numerical simulation examples are given to illustrate the main results of this paper

H-infinity state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays

This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the state estimation problem is investigated for a class of discrete time-delay nonlinear complex networks with randomly occurring phenomena from sensor measurements. The randomly occurring phenomena include randomly occurring sensor saturations (ROSSs) and randomly varying sensor delays (RVSDs) that result typically from networked environments. A novel sensor model is proposed to describe the ROSSs and the RVSDs within a unified framework via two sets of Bernoulli-distributed white sequences with known conditional probabilities. Rather than employing the commonly used Lipschitz-type function, a more general sector-like nonlinear function is used to describe the nonlinearities existing in the network. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that, for all probabilistic sensor saturations and sensor delays, the dynamics of the estimation error is guaranteed to be exponentially mean-square stable and the effect from the exogenous disturbances to the estimation accuracy is attenuated at a given level by means of an $H_{infty}$-norm. In terms of a novel Lyapunov–Krasovskii functional and the Kronecker product, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semidefinite programming method. A simulation example is provided to show the usefulness of the proposed state estimation conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 61028008, 61134009, 61104125 and 60974030, the Natural Science Foundation of Universities in Anhui Province of China under Grant KJ2011B030, and the Alexander von Humboldt Foundation of Germany

Quantized H-Infinity control for nonlinear stochastic time-delay systems with missing measurements

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the quantized H∞ control problem is investigated for a class of nonlinear stochastic time-delay network-based systems with probabilistic data missing. A nonlinear stochastic system with state delays is employed to model the networked control systems where the measured output and the input signals are quantized by two logarithmic quantizers, respectively. Moreover, the data missing phenomena are modeled by introducing a diagonal matrix composed of Bernoulli distributed stochastic variables taking values of 1 and 0, which describes that the data from different sensors may be lost with different missing probabilities. Subsequently, a sufficient condition is first derived in virtue of the method of sector-bounded uncertainties, which guarantees that the closed-loop system is stochastically stable and the controlled output satisfies H∞ performance constraint for all nonzero exogenous disturbances under the zero-initial condition. Then, the sufficient condition is decoupled into some inequalities for the convenience of practical verification. Based on that, quantized H∞ controllers are designed successfully for some special classes of nonlinear stochastic time-delay systems by using Matlab linear matrix inequality toolbox. Finally, a numerical simulation example is exploited to show the effectiveness and applicability of the results derived.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Leverhulme Trust of the U.K., the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 61028008, 61134009, 61104125, 60974030, and 61074016, and the Alexander von Humboldt Foundation of Germany

Tick-borne encephalitis virus induces chemokine RANTES expression via activation of IRF-3 pathway.

BACKGROUND: Tick-borne encephalitis virus (TBEV) is one of the most important flaviviruses that targets the central nervous system (CNS) and causes encephalitides in humans. Although neuroinflammatory mechanisms may contribute to brain tissue destruction, the induction pathways and potential roles of specific chemokines in TBEV-mediated neurological disease are poorly understood. METHODS: BALB/c mice were intracerebrally injected with TBEV, followed by evaluation of chemokine and cytokine profiles using protein array analysis. The virus-infected mice were treated with the CC chemokine antagonist Met-RANTES or anti-RANTES mAb to determine the role of RANTES in affecting TBEV-induced neurological disease. The underlying signaling mechanisms were delineated using RANTES promoter luciferase reporter assay, siRNA-mediated knockdown, and pharmacological inhibitors in human brain-derived cell culture models. RESULTS: In a mouse model, pathological features including marked inflammatory cell infiltrates were observed in brain sections, which correlated with a robust up-regulation of RANTES within the brain but not in peripheral tissues and sera. Antagonizing RANTES within CNS extended the survival of mice and reduced accumulation of infiltrating cells in the brain after TBEV infection. Through in vitro studies, we show that virus infection up-regulated RANTES production at both mRNA and protein levels in human brain-derived cell lines and primary progenitor-derived astrocytes. Furthermore, IRF-3 pathway appeared to be essential for TBEV-induced RANTES production. Site mutation of an IRF-3-binding motif abrogated the RANTES promoter activity in virus-infected brain cells. Moreover, IRF-3 was activated upon TBEV infection as evidenced by phosphorylation of TBK1 and IRF-3, while blockade of IRF-3 activation drastically reduced virus-induced RANTES expression. CONCLUSIONS: Our findings together provide insights into the molecular mechanism underlying RANTES production induced by TBEV, highlighting its potential importance in the process of neuroinflammatory responses to TBEV infection

Magneto-Hydrodynamics of Population III Star Formation

Jet driving and fragmentation process in collapsing primordial cloud are studied using three-dimensional MHD nested grid simulations. Starting from a rotating magnetized spherical cloud with the number density of n=10^3 cm^-3, we follow the evolution of the cloud up to the stellar density n=10^22 cm^-3. We calculate 36 models parameterizing the initial magnetic and rotational energies (\gamma_0, \beta_0). In the collapsing primordial clouds, the cloud evolutions are characterized by the ratio of the initial rotational to magnetic energy, \gamma_0/\beta_0. The Lorentz force significantly affects the cloud evolution when \gamma_0 > \beta_0, while the centrifugal force is more dominant than the Lorentz force when \beta_0 > \gamma_0. When the cloud rotates rapidly with angular velocity of \Omega_0 > 10^-17 (n/10^3 cm^-3)^2/3 s^-1 and \beta_0 > \gamma_0, fragmentation occurs before the protostar is formed, but no jet appears after the protostar formation. On the other hand, a strong jet appears after the protostar formation without fragmentation when the initial cloud has the magnetic field of B_0 > 10^-9 (n/10^3 cm^-3)^2/3 G and \gamma_0 > \beta_0. Our results indicate that proto-Population III stars frequently show fragmentation and protostellar jet. Population III stars are therefore born as binary or multiple stellar systems, and they can drive strong jets, which disturb the interstellar medium significantly, as well as in the present-day star formation, and thus they may induce the formation of next generation stars.Comment: 37 pages, 10 figures, Submitted to ApJ, For high resolution figures, see http://astro3.sci.hokudai.ac.jp/~machida/astro-ph.pd

SUMO Modification Stabilizes Enterovirus 71 Polymerase 3D To Facilitate Viral Replication.

Accumulating evidence suggests that viruses hijack cellular proteins to circumvent the host immune system. Ubiquitination and SUMOylation are extensively studied posttranslational modifications (PTMs) that play critical roles in diverse biological processes. Cross talk between ubiquitination and SUMOylation of both host and viral proteins has been reported to result in distinct functional consequences. Enterovirus 71 (EV71), an RNA virus belonging to the family Picornaviridae, is a common cause of hand, foot, and mouth disease. Little is known concerning how host PTM systems interact with enteroviruses. Here, we demonstrate that the 3D protein, an RNA-dependent RNA polymerase (RdRp) of EV71, is modified by small ubiquitin-like modifier 1 (SUMO-1) both during infection and in vitro Residues K159 and L150/D151/L152 were responsible for 3D SUMOylation as determined by bioinformatics prediction combined with site-directed mutagenesis. Also, primer-dependent polymerase assays indicated that mutation of SUMOylation sites impaired 3D polymerase activity and virus replication. Moreover, 3D is ubiquitinated in a SUMO-dependent manner, and SUMOylation is crucial for 3D stability, which may be due to the interplay between the two PTMs. Importantly, increasing the level of SUMO-1 in EV71-infected cells augmented the SUMOylation and ubiquitination levels of 3D, leading to enhanced replication of EV71. These results together suggested that SUMO and ubiquitin cooperatively regulated EV71 infection, either by SUMO-ubiquitin hybrid chains or by ubiquitin conjugating to the exposed lysine residue through SUMOylation. Our study provides new insight into how a virus utilizes cellular pathways to facilitate its replication. IMPORTANCE: Infection with enterovirus 71 (EV71) often causes neurological diseases in children, and EV71 is responsible for the majority of fatalities. Based on a better understanding of interplay between virus and host cell, antiviral drugs against enteroviruses may be developed. As a dynamic cellular process of posttranslational modification, SUMOylation regulates global cellular protein localization, interaction, stability, and enzymatic activity. However, little is known concerning how SUMOylation directly influences virus replication by targeting viral polymerase. Here, we found that EV71 polymerase 3D was SUMOylated during EV71 infection and in vitro Moreover, the SUMOylation sites were determined, and in vitro polymerase assays indicated that mutations at SUMOylation sites could impair polymerase synthesis. Importantly, 3D is ubiquitinated in a SUMOylation-dependent manner that enhances the stability of the viral polymerase. Our findings indicate that the two modifications likely cooperatively enhance virus replication. Our study may offer a new therapeutic strategy against virus replication

Density distributions for trapped one-dimensional spinor gases

We numerically evaluate the density distribution of a spin-1 bosonic condensate in its ground state within a modifed Gross-Pitaevskii theory, which is obtained by the combination of the exact solution of the corresponding integrable model with the local density approximation. Our study reveals that atoms in the m_F = 0 state are almost completely suppressed for the anti-ferromagnetic interactions in both weakly and strongly interacting regimes, whereas all three components remain non-vanishing for ferromagnetic interactions. Specially, when the system is in the Tonks-Girardeau (TG) regime, obvious Fermi-like distribution emerges for each component. We also discuss the possible deviation of the spatial distribution from the Fermi-like distribution when the spin-spin interaction is strong enough.Comment: 6 pages, 3 figures, version to be published in Phys. Rev.