Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects

The official published version of this article can be found at the link below.In this paper, the robust H∞ finite-horizon filtering problem is investigated for discrete time-varying stochastic systems with polytopic uncertainties, randomly occurred nonlinearities as well as quantization effects. The randomly occurred nonlinearity, which describes the phenomena of a nonlinear disturbance appearing in a random way, is modeled by a Bernoulli distributed white sequence with a known conditional probability. A new robust H∞ filtering technique is developed for the addressed Itô-type discrete time-varying stochastic systems. Such a technique relies on the forward solution to a set of recursive linear matrix inequalities and is therefore suitable for on-line computation. It is worth mentioning that, in the filtering process, the information of both the current measurement and the previous state estimate is employed to estimate the current state. Finally, a simulation example is exploited to show the effectiveness of the method proposed in this paper.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National 973 Program of China under Grant 2009CB320600, the National Natural Science Foundation of China under Grant 60974030, the Shanghai Natural Science Foundation of China under Grant 10ZR1421200, and the Alexander von Humboldt Foundation of Germany

Delay-dependent stabilization of stochastic interval delay systems with nonlinear disturbances

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this paper, a delay-dependent approach is developed to deal with the robust stabilization problem for a class of stochastic time-delay interval systems with nonlinear disturbances. The system matrices are assumed to be uncertain within given intervals, the time delays appear in both the system states and the nonlinear disturbances, and the stochastic perturbation is in the form of a Brownian motion. The purpose of the addressed stochastic stabilization problem is to design a memoryless state feedback controller such that, for all admissible interval uncertainties and nonlinear disturbances, the closed-loop system is asymptotically stable in the mean square, where the stability criteria are dependent on the length of the time delay and therefore less conservative. By using Itô's differential formula and the Lyapunov stability theory, sufficient conditions are first derived for ensuring the stability of the stochastic interval delay systems. Then, the controller gain is characterized in terms of the solution to a delay-dependent linear matrix inequality (LMI), which can be easily solved by using available software packages. A numerical example is exploited to demonstrate the effectiveness of the proposed design procedure.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany

Robust stability for stochastic Hopfield neural networks with time delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this paper, the asymptotic stability analysis problem is considered for a class of uncertain stochastic neural networks with time delays and parameter uncertainties. The delays are time-invariant, and the uncertainties are norm-bounded that enter into all the network parameters. The aim of this paper is to establish easily verifiable conditions under which the delayed neural network is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. By employing a Lyapunov–Krasovskii functional and conducting the stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the stability criteria. The proposed criteria can be checked readily by using some standard numerical packages, and no tuning of parameters is required. Examples are provided to demonstrate the effectiveness and applicability of the proposed criteria.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of German

Peripheral blood-derived mesenchymal stem cells demonstrate immunomodulatory potential for therapeutic use in horses

Previously, we showed that mesenchymal stem cells (MSC) can be mobilized into peripheral blood using electroacupuncture (EA) at acupoints, LI-4, LI-11, GV-14, and GV-20. The purpose of this study was to determine whether EA-mobilized MSC could be harvested and expanded in vitro to be used as an autologous cell therapy in horses. Peripheral blood mononuclear cells (PBMC) isolated from young and aged lame horses (n = 29) showed a marked enrichment for MSCs. MSC were expanded in vitro (n = 25) and administered intravenously at a dose of 50 x 106 (n = 24). Treatment resulted in significant improvement in lameness as assessed by the American Association of Equine Practitioners (AAEP) lameness scale (n = 23). MSCs exhibited immunomodulatory function by inhibition of lymphocyte proliferation and induction of IL-10. Intradermal testing showed no immediate or delayed immune reactions to MSC (1 x 106 to 1 x 104). In this study, we demonstrated an efficient, safe and reproducible method to mobilize and expand, in vitro, MSCs in sufficiently high concentrations for therapeutic administration. We confirm the immunomodulatory function of these cells in vitro. This non-pharmacological and non-surgical strategy for stem cell harvest has a broad range of biomedical applications and represents an improved clinically translatable and economical cell source for humans

The scientific basis of acupuncture for veterinary pain management: A review based on relevant literature from the last two decades

The practice of acupuncture is becoming increasingly popular in veterinary medicine, especially as a method of providing pain relief. Originally based on principles derived from centuries of observation, conventional scientific mechanisms of action for acupuncture as a pain-relieving modality have recently been elucidated. Acupuncture points allow access to multiple regions of the body via the peripheral nervous system and its connection with the central nervous system. Local, segmental (spinal), and suprasegmental (brain) effects of acupuncture involve enhanced release of pain-relieving endogenous substances (e.g., opioids) and mitigated release of pain-inducing substances (e.g., inflammatory cytokines). In addition, there is evidence that acupuncture can induce positive neurochemical and cytoarchitectural change in the central nervous system via the phenomenon of neuroplasticity. Electroacupuncture is considered the most effective type of acupuncture delivery, allowing for more potent and long-lasting pain relief than is achieved via other methods (e.g., dry needling). The purpose of this review article is to summarize the relevant scientific literature from the last two decades relating to the physiological mechanisms of action of acupuncture as a pain-relieving modality. Keywords: Animal, Acupuncture, Electroacupuncture, Pain, Veterinary medicine

The scientific basis of acupuncture for veterinary pain management: A review based on relevant literature from the last two decades

The practice of acupuncture is becoming increasingly popular in veterinary medicine, especially as a method of providing pain relief. Originally based on principles derived from centuries of observation, conventional scientific mechanisms of action for acupuncture as a pain-relieving modality have recently been elucidated. Acupuncture points allow access to multiple regions of the body via the peripheral nervous system and its connection with the central nervous system. Local, segmental (spinal) and suprasegmental (brain) effects of acupuncture involve enhanced release of pain-relieving endogenous substances (e.g., opioids) and mitigated release of pain-inducing substances (e.g., inflammatory cytokines). In addition, there is evidence that acupuncture can induce positive neurochemical and cytoarchitectural change in the central nervous system via the phenomenon of neuroplasticity. Electroacupuncture is considered the most effective type of acupuncture delivery, allowing for more potent and long-lasting pain relief than is achieved via other methods (e.g., dry needling). The purpose of this review article is to summarize the relevant scientific literature from the last two decades relating to the physiological mechanisms of action of acupuncture as a pain-relieving modality

Text-Dependent Speaker Identification Based on Input/Output HMMs: An Empirical Study

In this paper, we explore the Input/Output HMM (IOHMM) architecture for a substantial problem, that of text-dependent speaker identification. For subnetworks modeled with generalized linear models, we extend the IRLS algorithm to the M-step of the corresponding EM algorithm. Experimental results show that the improved EM algorithm yields significantly faster training than the original one. In comparison with the multilayer perceptron, the dynamic programming technique and hidden Markov models, we empirically demonstrate that the IOHMM architecture is a promising way to text-dependent speaker identification. Keywords: Speaker Identification, Input/Output HMM, EM algorithm, temporal processing 1 Introduction Speaker identification task is to classify an unlabeled voice token as belonging to one of a set of N reference speakers [1]. It is a very hard problem since a speaker's voice changes in time. There have been extensive studies in this field based upon conventional techniques of spe..

Speaker identification based on the time-delay Hierarchical Mixture of Experts

In this paper, we explore the Hierarchical Mixture of Experts (HME) architecture for a substantial problem, that of text-dependent speaker identification. For a specific multi-way classification, we propose a generalized Bernolli density instead of the multinomial logit density. Time-delay technique is also introduced to HME for spatio-temporal processing. Using the proposed density and the time-delay HME along with the EM algorithm, we show that the system has a satisfactory performance and yields significantly fast training.EI