nonlinear stability of traveling waves to a hyperbolic parabolic system modeling chemotaxis we prove nonlinear stability of traveling waves of arbitrary amplitudes to a hyperbolic parabolic system modeling repulsive chemotaxis in contrast to the previous related results where various sm

aggregation blowup and collapse the abc s of taxis in reinforced random walks in many biological systems movement of an organism occurs in response to a diffusible or otherwise transported signal and in its simplest form this can be modeled by diffusion equations with advection terms of the form first derived by patlak bull of math biophys 15 1953 pp 311 338 however other systems are more accurately modeled by random walkers that deposit a nondiffusible signal that modifies the local environment for succeeding passages in these systems one example of which is the myxobacteria the question arises as to whether aggregation is possible under suitable hypotheses on the transition rules and the production of a control species that modulates the transition rates davis probab theory related fields 84 1990 pp 203 229 has studied this question for a certain class of random walks and here we extend this analysis to the continuum limit of such walks we first derive several general classes of partial differential equations that depend on how the movement rules are

rotation invariant texture retrieval considering the scale dependence of gabor wavelet obtaining robust and efficient rotation invariant texture features in content based image retrieval field is a challenging work we propose three efficient rotation invariant methods for texture image retrieval using copula model based in the domains of gabor wavelet gw and circularly symmetric gw csgw the proposed copula models use copula function to capture the scale dependence of gw csgw for improving the retrieval performance it is well known that the kullback leibler distance kld is the commonly used similarity measurement between probability models however it is difficult to deduce the closed form of kld between two copula models due to the complexity of the copula model we also put forward a kind of retrieval scheme using the klds of marginal distributions and the kld of copula function to calculate the kld of copula model the proposed texture retrieval method has low computational complexity and high retrieval precision the experimental results on vistex and brodatz data sets show that the proposed retrieval method is more effective compared with the state of the art methods

global finite time stabilisation for a class of stochastic high order time varying nonlinear systems abstractin this paper the problem of global finite time stabilisation by state feedback is investigated for a class of stochastic high order nonlinear systems with time varying nonlinearities based on the generalised stochastic lyapunov theorem on finite time stability and by skillfully using the method of adding a power integrator a continuous state feedback controller is successfully constructed to guarantee that the closed loop system is globally finite time stable in probability finally several simulation examples are provided to illustrate the effectiveness of the proposed method

adding one power integrator a tool for global stabilization of high order lower triangular systems a new feedback design tool called adding a power integrator is introduced and used to solve the problem of global robust stabilization for a significant class of uncertain nonlinear systems that are of a lower triangular form but neither necessarily feedback linearizable fully or partially nor affine in the control input and therefore cannot be dealt with via conventional approaches sufficient conditions are presented under which a globally stabilizing smooth state feedback control law can be explicitly constructed using repeatedly the adding one power integrator technique this novel design technique permits removal of the feedback linearizability and linearity of the control input which have been two common assumptions so far in the literature of nonlinear control

state feedback control of high order stochastic nonlinear systems with siiss inverse dynamics this technical note considers a class of high order stochastic nonlinear systems with stochastic integral input to state stability siiss inverse dynamics and drift and diffusion terms depending upon the stochastic inverse dynamics and all the states a new design and analysis approach to the problem of state feedback global regulation is developed to guarantee that all the signals of the closed loop system are bounded almost surely and the states can be regulated to zero almost surely

a system of reaction diffusion equations arising in the theory of reinforced random walks we investigate the properties of solutions of a system of chemotaxis equations arising in the theory of reinforced random walks we show that under some circumstances finite time blow up of solutions is possible in other circumstances the solutions will decay to a spatially constant solution collapse we also give some intuitive arguments which demonstrate the possibility of the existence of aggregation piecewise constant solutions

the improved lasalle type theorems for stochastic functional differential equations abstract the main aim of this paper is to improve some results obtained by mao x mao the lasalle type theorems for stochastic functional differential equations nonlinear stud 7 2000 307 328 our new theorems give better results while conditions imposed are much weaker than in the paper mentioned above for example we need only the local lipschitz condition but neither the linear growth condition nor the bounded moment condition on the solutions to guarantee the existence and uniqueness of the global solution to the underlying stochastic functional differential equation sfde under the weaker conditions imposed in this paper we establish a generalised existence and uniqueness theorem which covers a wider class of nonlinear sfdes as demonstrated by the examples discussed in this paper moreover from our improved results follow some new criteria on the stochastic asymptotic stability for sfdes

image retrieval based on multi texton histogram this paper presents a novel image feature representation method called multi texton histogram mth for image retrieval mth integrates the advantages of co occurrence matrix and histogram by representing the attribute of co occurrence matrix using histogram it can be considered as a generalized visual attribute descriptor but without any image segmentation or model training the proposed mth method is based on julesz s textons theory and it works directly on natural images as a shape descriptor meanwhile it can be used as a color texture descriptor and leads to good performance the proposed mth method is extensively tested on the corel dataset with 15000 natural images the results demonstrate that it is much more efficient than representative image feature descriptors such as the edge orientation auto correlogram and the texton co occurrence matrix it has good discrimination power of color texture and shape features

further results on global adaptive stabilisation for a class of uncertain stochastic nonlinear systems this paper considers the global adaptive stabilisation for a class of uncertain stochastic nonlinear systems comparing with the existing relevant literature the systems under investigation allow more serious unknowns uncertainties more importantly by remarkably reducing the dimension of dynamic compensator for system unknowns from n to 2 and considerably diminishing the overparametrisation in the existing literature this paper proposes an improved design scheme for adaptive state feedback controllers it is turned out that with the designed controller in loop all the closed loop system states are globally bounded and the original system states converge to the origin both in the sense of probability one a simulation example is also given to demonstrate the effectiveness of the theoretical results

lightweight probabilistic texture retrieval this paper contemplates the framework of probabilistic image retrieval in the wavelet domain from a computational point of view we not only focus on achieving high retrieval rates but also discuss possible performance bottlenecks which might prevent practical application we propose a novel retrieval approach which is motivated by previous research work on modeling the marginal distributions of wavelet transform coefficients the building blocks of our work are the dual tree complex wavelet transform and a number of statistical models for the coefficient magnitudes image similarity measurement is accomplished by using closed form solutions for the kullback leibler divergences between the statistical models we provide an in depth computational analysis regarding the number of arithmetic operations required for similarity measurement and model parameter estimation the experimental retrieval results on a widely used texture image database show that we achieve competitive retrieval results at low computational cost

brief paper backstepping controller design for a class of stochastic nonlinear systems with markovian switching a more general class of stochastic nonlinear systems with irreducible homogenous markovian switching are considered in this paper as preliminaries the stability criteria and the existence theorem of strong solutions are first presented by using the inequality of mathematic expectation of a lyapunov function the state feedback controller is designed by regarding markovian switching as constant such that the closed loop system has a unique solution and the equilibrium is asymptotically stable in probability in the large the output feedback controller is designed based on a quadratic plus quartic form lyapunov function such that the closed loop system has a unique solution with the equilibrium being asymptotically stable in probability in the large in the unbiased case and has a unique bounded in probability solution in the biased case

a globally convergent and consistent method for estimating the shape parameter of a generalized gaussian distribution we propose a novel methodology for estimating the shape parameter of a generalized gaussian distribution ggd this new method is based on a simple estimating equation derived from a convex shape equation the estimating equation is completely independent of gamma and polygamma functions thus no lookup table interpolation or additional subroutine to evaluate these functions are required for real time implementations of the proposed method which is in contrast to all existing methods furthermore we establish that the shape equation has a unique global root on the positive real line and the newton raphson root finding algorithm converges to the unique global root from any starting point in a semi infinite interval thetamin more importantly we show that the sample based shape estimating equation has a unique global root with probability tending to one and the root is consistent for the true shape parameter finally we prove via fixed point arguments that with probability tending to one the newton raphson algorithm converges to the unique global root of the sample shape estimating equation from any starting point in thetamin some numerical experiments are also provided to demonstrate the global convergence and the excellent finite sample performance of the proposed method

asymptotic stability of traveling waves of a chemotaxis model with singular sensitivity abstract this paper establishes the nonlinear stability of traveling wave solutions to a chemotaxis model with singular or logarithmic sensitivity and its transformed parabolic hyperbolic system depending on the parameter signs we discuss the linear instability of traveling wave solutions using the spectral analysis and nonlinear asymptotic stability of traveling wave solutions with zero end state by the weighted energy estimates where the latter result solves the open question left in a previous work li and wang 2009 7

global optimization of wavelet domain hidden markov tree for image segmentation this work presents a global energy minimization method for multiscale image segmentation using convex optimization theory the construction of energy function is motivated by the intuition that the larger the entropy the less a priori information one has on the value of the random variables first we represent the wavelet domain hidden markov tree whmt model of the original image as a structured energy function which is proved convex in marginal distributions next we derive the maximum lower bound of the energy function through lagrange dual transform for the purpose of incorporating marginal constraints into optimization finally a modified belief propagation optimization algorithm is used to perform global energy minimization of the dual convex energy function experiments on real image segmentation problems demonstrate the superior performance of this new algorithm when compared with nonconvex ones

laplacian regularized d optimal design for active learning and its application to image retrieval in increasingly many cases of interest in computer vision and pattern recognition one is often confronted with the situation where data size is very large usually the labels are expensive and the challenge is thus to determine which unlabeled samples would be the most informative i e improve the classifier the most if they were labeled and used as training samples particularly we consider the problem of active learning of a regression model in the context of experimental design classical optimal experimental design approaches are based on least square errors over the measured samples only they fail to take into account the unmeasured samples in this paper we propose a novel active learning algorithm which operates over graphs our algorithm is based on a graph laplacian regularized regression model which simultaneously minimizes the least square error on the measured samples and preserves the local geometrical structure of the data space by constructing a nearest neighbor graph the geometrical structure of the data space can be described by the graph laplacian we discuss how results from the field of optimal experimental design may be used to guide our selection of a subset of data points which gives us the most amount of information experiments demonstrate its superior performance in comparison with conventional algorithms

a statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval abstract this paper presents a texture descriptor based on wavelet frame transforms at each position in the image and for each resolution level we consider both vertical and horizontal wavelet detail coefficients as the components of a bivariate random vector the magnitudes and angles of these vectors are computed at each level the empirical histogram of magnitudes is modeled by a generalized gamma distribution and the empirical histogram of angles is modeled by a different version of the von mises distribution that accounts for histograms with 2 modes each texture is characterized by few parameters a new distance is presented based on the kullback leibler divergence that allows giving relative importance to each model and to each resolution level this distance is later conveniently adapted to provide for rotation invariance by establishing equivalence classes over distributions of angles through a broad set of experiments on three different image databases we demonstrate that our new descriptor and distance measure can be successfully applied in the context of texture retrieval we compare our system to several relevant methods in this field in terms of retrieval performance and number of parameters used by each method we also include some classification tests in all the tests we obtain superior retrieval rates for a set of fewer parameters involved

lyapunov like techniques for stochastic stability the purpose of this paper is to study the stabilizability problem for control stochastic nonlinear systems driven by a wiener process sufficient conditions for the existence of stabilizing feedback laws that are smooth except possibly at the equilibrium point of the system are provided by means of stochastic lyapunov like techniques the notion of dynamic asymptotic stability in probability of control stochastic differential systems is introduced and the stabilization by means of dynamic controllers is studied

parametric generalized gaussian density estimation the primary objective of this paper is to compare the large sample as well as the small sample properties of different methods for estimating the parameters of a three parameter generalized gaussian distribution three estimators namely the moment method mm the maximum likelihood ml and the moment newton step mns estimators are considered the applicability of general asymptotic optimality results of the efficient ml and mns estimation techniques is studied in the generalized gaussian context the asymptotic normal distributions of the estimators are obtained the asymptotic relative superiority of the ml estimator or its variant the mns estimator over the moment method is studied in terms of asymptotic relative efficiency based on this study it is concluded that deviations from normality in the underlying distribution of the data necessitate the use of the efficient ml or mns methods in the small sample case a detailed comparative study of the estimators is made possible by extensive monte carlo simulations from this study it is concluded that the maximum likelihood method is found to be significantly superior for heavy tailed distributions in a region of the parameter space corresponding to the vicinity of the gaussian distribution the moment method compares well with the other methods further the mns estimator is shown to perform best for light tailed distributions the simulation results are shown to lend support to analytically derived asymptotic results for each of the methods

on explosions of solutions to a system of partial differential equations modelling chemotaxis a system of partial differential equations modelling chemotactic aggregation is analysed keller segel model conditions on the system of paramaters are given implying global existence of smooth solutions in two space dimensions and radially symmetric situations explosion of the bacteria concentration in finite time is shown for a class of initial values

gaussian copula multivariate modeling for texture image retrieval using wavelet transforms in the framework of texture image retrieval a new family of stochastic multivariate modeling is proposed based on gaussian copula and wavelet decompositions we take advantage of the copula paradigm which makes it possible to separate dependence structure from marginal behavior we introduce two new multivariate models using respectively generalized gaussian and weibull densities these models capture both the subband marginal distributions and the correlation between wavelet coefficients we derive as a similarity measure a closed form expression of the jeffrey divergence between gaussian copula based multivariate models experimental results on well known databases show significant improvements in retrieval rates using the proposed method compared with the best known state of the art approaches

output feedback regulation of stochastic nonlinear systems with stochastic iiss inverse dynamics this paper develops a unifying framework for output feedback regulation of stochastic nonlinear systems with more general stochastic inverse dynamics the contributions of this work are characterized by the following novel features 1 motivated by the concept of integral input to state stability iiss in deterministic systems and stochastic input to state stability siss using lyapunov function in stochastic systems a concept of stochastic integral input to state stability siiss using lyapunov function is first introduced two important properties of siiss are obtained i siiss is strictly weaker than siss using lyapunov function ii siiss is stronger than the minimum phase property however only under the minimum phase assumption there is no dynamic output feedback control law for global stabilization in probability 2 almost sure boundedness a reasonable and stronger concept than boundedness in probability is introduced the purpose of introducing the concept is to prove the boundedness and convergence of some signals in the closed loop control system 3 some important mathematical tools which play an essential role in the boundedness and convergence analysis of the closed loop system are established 4 a unifying framework is proposed to design a dynamic output feedback control law which drives the states to the origin almost surely while maintaining all the closed loop signals bounded almost surely

wavelet based texture retrieval using generalized gaussian density and kullback leibler distance we present a statistical view of the texture retrieval problem by combining the two related tasks namely feature extraction fe and similarity measurement sm into a joint modeling and classification scheme we show that using a consistent estimator of texture model parameters for the fe step followed by computing the kullback leibler distance kld between estimated models for the sm step is asymptotically optimal in term of retrieval error probability the statistical scheme leads to a new wavelet based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized gaussian density ggd and on the existence a closed form for the kld between ggds the proposed method provides greater accuracy and flexibility in capturing texture information while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates e g from 65 to 77 compared with traditional approaches while it retains comparable levels of computational complexity

stochastic stability properties of jump linear systems jump linear systems are defined as a family of linear systems with randomly jumping parameters usually governed by a markov jump process and are used to model systems subject to failures or changes in structure the authors study stochastic stability properties in jump linear systems and the relationship among various moment and sample path stability properties it is shown that all second moment stability properties are equivalent and are sufficient for almost sure sample path stability and a testable necessary and sufficient condition for second moment stability is derived the lyapunov exponent method for the study of almost sure sample stability is discussed and a theorem which characterizes the lyapunov exponents of jump linear systems is presented finally for one dimensional jump linear system it is proved that the region for delta moment stability is monotonically converging to the region for almost sure stability at delta down arrow 0 sup

a theory for multiresolution signal decomposition the wavelet representation multiresolution representations are effective for analyzing the information content of images the properties of the operator which approximates a signal at a given resolution were studied it is shown that the difference of information between the approximation of a signal at the resolutions 2 sup j 1 and 2 sup j where j is an integer can be extracted by decomposing this signal on a wavelet orthonormal basis of l sup 2 r sup n the vector space of measurable square integrable n dimensional functions in l sup 2 r a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi x this decomposition defines an orthogonal multiresolution representation called a wavelet representation it is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters wavelet representation lies between the spatial and fourier domains for images the wavelet representation differentiates several spatial orientations the application of this representation to data compression in image coding texture discrimination and fractal analysis is discussed

brief paper finite time stability and instability of stochastic nonlinear systems this paper presents a new definition of finite time stability for stochastic nonlinear systems this definition involves stability in probability and finite time attractiveness in probability an important lyapunov theorem on finite time stability for stochastic nonlinear systems is established a theorem extending the stochastic lyapunov theorem is also proved moreover an example and a lemma are presented to illustrate the scope of extension a useful inequality extended from bihari s inequality is derived which plays an important role in showing the lyapunov theorem finally a lyapunov theorem on finite time instability is proved which states that almost surely globally asymptotical stability is not equivalent to finite time stability for some stochastic systems two simulation examples are given to illustrate the theoretical analysis

adaptive color feature extraction based on image color distributions this paper proposes an adaptive color feature extraction scheme by considering the color distribution of an image based on the binary quaternion moment preserving bqmp thresholding technique the proposed extraction methods fixed cardinality fc and variable cardinality vc are able to extract color features by preserving the color distribution of an image up to the third moment and to substantially reduce the distortion incurred in the extraction process in addition to utilizing the earth mover s distance emd as the distance measure of our color features we also devise an efficient and effective distance measure comparing histograms by clustering chic moreover the efficient implementation of our extraction methods is explored with slight modification of the bqmp algorithm our extraction methods are equipped with the capability of exploiting the concurrent property of hardware implementation the experimental results show that our hardware implementation can achieve approximately a second order of magnitude improvement over the software implementation it is noted that minimizing the distortion incurred in the extraction process can enhance the accuracy of the subsequent various image applications and we evaluate the meaningfulness of the new extraction methods by the application to content based image retrieval cbir our experimental results show that the proposed extraction methods can enhance the average retrieval precision rate by a factor of 25 over that of a traditional color feature extraction method

state feedback h_ infty control for a class of nonlinear stochastic systems this paper discusses the h infty control problem for a class of nonlinear stochastic systems with both state and disturbance dependent noise by means of hamilton jacobi equations both infinite and finite horizon nonlinear stochastic h infty control designs are developed some results on nonlinear h infty control of deterministic systems are generalized to a stochastic setting we introduce some useful concepts such as zero state observability and zero state detectability which together with the stochastic lasalle invariance principle yield some valuable consequences in infinite horizon nonlinear stochastic h infty control

image retrieval based on shape similarity by edge orientation autocorrelogram abstract this paper introduces a new feature vector for shape based image indexing and retrieval this feature classifies image edges based on two factors their orientations and correlation between neighboring edges hence it includes information of continuous edges and lines of images and describes major shape properties of images this scheme is effective and robustly tolerates translation scaling color illumination and viewing position variations experimental results show superiority of proposed scheme over several other indexing methods averages of precision and recall rates of this new indexing scheme for retrieval as compared with traditional color histogram are 1 99 and 1 59 times respectively these ratios are 1 26 and 1 04 compared to edge direction histogram

global regulation for a class of stochastic high order nonlinear systems with multiple unknown control directions this paper concerns the global regulation for a class of stochastic high order nonlinear systems which admit more general system form than the existing works for details except for the serious sy

oscillatory traveling wave solutions to an attractive chemotaxis system abstract this paper investigates oscillatory traveling wave solutions to an attractive chemotaxis system the convective part of this system changes its type when crossing a parabola in the phase space the oscillatory nature of the traveling wave comes from the fact that one far field state is in the elliptic region and another in the hyperbolic region such traveling wave solutions are shown to be linearly unstable detailed construction of some traveling wave solutions is presented

sar amplitude probability density function estimation based on a generalized gaussian model in the context of remotely sensed data analysis an important problem is the development of accurate models for the statistics of the pixel intensities focusing on synthetic aperture radar sar data this modeling process turns out to be a crucial task for instance for classification or for denoising purposes in this paper an innovative parametric estimation methodology for sar amplitude data is proposed that adopts a generalized gaussian gg model for the complex sar backscattered signal a closed form expression for the corresponding amplitude probability density function pdf is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model specifically the recently proposed method of log cumulants molc is applied which stems from the adoption of the mellin transform instead of the usual fourier transform in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant for the developed gg based amplitude model the resulting molc estimates turn out to be numerically feasible and are also analytically proved to be consistent the proposed parametric approach was validated by using several real ers 1 xsar e sar and nasa jpl airborne sar images and the experimental results prove that the method models the amplitude pdf better than several previously proposed parametric models for backscattering phenomena

rapid transform based rotation invariant descriptor for texture classification under non ideal conditions rapid transform based descriptor is proposed for texture classification against rotation variations illumination variations as well as noise effect the proposed descriptor is based on the local circular neighborhood and the local feature vector is obtained by means of rapid transform the local feature vector is rotation invariant because of the cyclic shift invariance property of rapid transform combining several descriptors with different n r parameters the spatial multiscale is obtained feature selection approach is designed to improve the classification accuracy and reduce the computing cost the issue of noise is discussed and more robust descriptor based on rapid transform is introduced under noise condition texture classification experiments were carried out on the brodatz and outex databases and promising results are obtained from those experiments a rotation invariant descriptor is proposed for adapting non ideal conditions the proposed descriptor is based on rapid transform which is shift invariant feature selection approach is designed to improve the performance of the descriptor more robust descriptor is proposed under noise condition

practical output feedback risk sensitive control for stochastic nonlinear systems with stable zero dynamics this paper addresses the design problem of practical or satisfaction output feedback controls for stochastic strict feedback nonlinear systems in observer canonical form with stable zero dynamics under long term average tracking risk sensitive cost criteria the cost function adopted here is of the quadratic integral type usually encountered in practice rather than the quartic integral one used to avoid difficulty in control design and performance analysis of the closed loop system a sequence of coordinate diffeomorphisms is introduced to separate the zero dynamics from the entire system so that the transformed system has an appropriate form suitable for integrator backstepping design for any given risk sensitivity parameter and desired cost value by using the integrator backstepping methodology an output feedback control is constructively designed such that a the closed loop system is bounded in probability and b the long term average risk sensitive cost is upper bounded by the desired value in addition this paper does not require the uniform boundedness of the gain functions of the system noise furthermore an example is given to show the effectiveness of the theory

universal fuzzy models and universal fuzzy controllers for stochastic nonaffine nonlinear systems this paper investigates the universal fuzzy model and universal fuzzy controller problems for stochastic nonaffine nonlinear systems the underlying mechanism of stochastic fuzzy logic is first discussed and a stochastic generalized fuzzy model with new stochastic fuzzy rule base is then given based on their function approximation capability these kinds of stochastic generalized fuzzy models are shown to be universal fuzzy models for stochastic nonaffine nonlinear systems under some sufficient conditions an approach to stabilization controller design for stochastic nonaffine nonlinear systems is then developed through their stochastic generalized takagi sugeno t s fuzzy approximation models then the results of universal fuzzy controllers for two classes of stochastic nonlinear systems along with constructive procedures to obtain the universal fuzzy controllers are also provided respectively finally a numerical example is presented to illustrate the effectiveness of the proposed approach

rotation invariant texture retrieval with gaussianized steerable pyramids this paper presents a novel rotation invariant image retrieval scheme based on a transformation of the texture information via a steerable pyramid first we fit the distribution of the subband coefficients using a joint alpha stable sub gaussian model to capture their non gaussian behavior then we apply a normalization process in order to gaussianize the coefficients as a result the feature extraction step consists of estimating the covariances between the normalized pyramid coefficients the similarity between two distinct texture images is measured by minimizing a rotation invariant version of the kullback leibler divergence between their corresponding multivariate gaussian distributions where the minimization is performed over a set of rotation angles

on the modeling of dct and subband image data for compression image subband and discrete cosine transform coefficients are modeled for efficient quantization and noiseless coding quantizers and codes are selected based on laplacian fixed generalized gaussian and adaptive generalized gaussian models the quantizers and codes based on the adaptive generalized gaussian models are always superior in mean squared error distortion performance but generally by no more than 0 08 bit pixel compared with the much simpler laplacian model based quantizers and noiseless codes this provides strong motivation for the selection of pyramid codes for transform and subband image coding

on the efficient evaluation of probabilistic similarity functions for image retrieval probabilistic approaches are a promising solution to the image retrieval problem that when compared to standard retrieval methods can lead to a significant gain in retrieval accuracy however this occurs at the cost of a significant increase in computational complexity in fact closed form solutions for probabilistic retrieval are currently available only for simple probabilistic models such as the gaussian or the histogram we analyze the case of mixture densities and exploit the asymptotic equivalence between likelihood and kullback leibler kl divergence to derive solutions for these models in particular 1 we show that the divergence can be computed exactly for vector quantizers vqs and 2 has an approximate solution for gauss mixtures gms that in high dimensional feature spaces introduces no significant degradation of the resulting similarity judgments in both cases the new solutions have closed form and computational complexity equivalent to that of standard retrieval approaches

the method for image retrieval based on multi factors correlation utilizing block truncation coding abstract in this paper we proposed multi factors correlation mfc to describe the image structure element correlation sec gradient value correlation gvc and gradient direction correlation gdc at first the rgb color space image is converted to a bitmap image and a mean color component image utilizing the block truncation coding btc then three correlations will be used to extract the image feature the structure elements can effectively represent the bitmap which is generated by btc and sec can effectively denote the bitmap s structure and the correlation of the block in the bitmap gvc and gdc can effectively denote the gradient relation which is computed by a mean color component image formed by sec gvc and gdc the image feature vectors can effectively represent the image in the end the results demonstrate that the method has better performance than other image retrieval methods in the experiment

a method of block truncation coding for color image compression a basic color block truncation coding cbtc algorithm for color image compression is described a modification of the algorithm that reduces truncation errors is also described the block statistics related to cbtc methods are investigated some experimental results are given for a 256 256 pixel color image with 24 b pixel

finite time stabilisation of a class of stochastic nonlinear systems with dead zone input this paper addresses the problem of finite time stabilisation by state feedback for a class of stochastic nonlinear systems in strict feedback form remarkably the system under consideration posse

global dynamics of a hyperbolic parabolic model arising from chemotaxis we prove global existence and qualitative behavior of classical solutions for a hyperbolic parabolic system describing chemotaxis on bounded domains it is shown that classical solutions to the initial boundary value problem of the one dimensional model exist globally in time for large initial data and the solutions converge to constant equilibrium states exponentially in time which rigorously demonstrates the collapsing of cell populations in chemotaxis moreover similar results are established for the multidimensional model when the initial data are small

color texture image retrieval based on gaussian copula models of gabor wavelets color texture retrieval is a hot research area in image analysis in this paper we propose an efficient color texture retrieval method by using copula model based on gabor wavelets when gabor wavelets are used to decompose color image three types of dependence exist in the decomposed subbands of gabor wavelets color dependence scale dependence and direction dependence we analyze these dependencies and then capture them by using gaussian copula function four copula schemes are developed and accordingly four klds kullback leibler distances of the copula schemes are introduced for color texture image retrieval the evaluations of the proposed method are performed on several color texture databases including two large texture databases alot and stex experimental results demonstrate the proposed method has better performance than the state of the art retrieval methods highlightswe propose a color texture retrieval method by using gabor wavelet and copula model we use copula model to capture the dependencies of gabor wavelet and color channels four copula models are implemented based on gabor wavelet in color space our method shows better performance on large databases than other popular methods

fusion of color histogram and lbp based features for texture image retrieval and classification abstract the local binary pattern lbp operator and its variants play an important role as the image feature extractor in the textural image retrieval and classification the lbp based operator extracts the textural information of an image by considering the neighboring pixel values a single or join histogram can be derived from the lbp code which can be used as an image feature descriptor in some applications however the lbp based feature is not a good candidate in capturing the color information of an image making it is less suitable for measuring the similarity of color images with rich color information this work overcomes this problem by adding an additional color feature namely color information feature cif along with the lbp based feature in the image retrieval and classification systems the cif and lbp based feature adequately represent the color and texture features as documented in the experimental result the hybrid cif and lbp based feature presents a promising result and outperforms the existing methods over several image databases thus it can be a very competitive candidate in retrieval and classification application

well posedness of a 3d parabolic hyperbolic keller segel system in the sobolev space framework abstract we investigate global strong solution to a 3 dimensional parabolic hyperbolic system arising from the keller segel model we establish the global well posedness and asymptotic behavior in the energy functional setting precisely speaking if the initial difference between cell density and its mean is small in l 2 and the ratio of the initial gradient of the chemical concentration and the initial chemical concentration is also small in h 1 then they remain to be small in l 2 h 1 for all time moreover if the mean value of the initial cell density is smaller than some constant then the cell density approaches its initial mean and the chemical concentration decays exponentially to zero as t goes to infinity the proof relies on an application of fourier analysis to a linearized parabolic hyperbolic system and the smoothing effect of the cell density and the damping effect of the chemical concentration

wavelet based statistical signal processing using hidden markov models wavelet based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly gaussian these models are unrealistic for many real world signals we develop a new framework for statistical signal processing based on wavelet domain hidden markov models hmms that concisely models the statistical dependencies and non gaussian statistics encountered in real world signals wavelet domain hmms are designed with the intrinsic properties of the wavelet transform in mind and provide powerful yet tractable probabilistic signal models efficient expectation maximization algorithms are developed for fitting the hmms to observational signal data the new framework is suitable for a wide range of applications including signal estimation detection classification prediction and even synthesis to demonstrate the utility of wavelet domain hmms we develop novel algorithms for signal denoising classification and detection

the solvability of some chemotaxis systems abstract in this paper we study the local existence and uniqueness of classical solutions to a wide class of systems of chemotaxis equations these systems are essentially quasi linear strongly coupled partial differential equations we also study the maximal interval of existence in time of solutions the results are illustrated in application to a number of partial differential equation models arising in biology

output feedback control design for strict feedback stochastic nonlinear systems under a risk sensitive cost in this note we study the problem of output feedback control design for a class of strict feedback stochastic nonlinear systems under an infinite horizon risk sensitive cost criterion the controller designed can guarantee an arbitrary small long term average cost for arbitrary risk sensitivity parameter and achieve boundedness in probability for the closed loop system using the integrator backstepping methodology furthermore the controller preserves the equilibrium at the origin of the nonlinear system

filtering for texture classification a comparative study in this paper we review most major filtering approaches to texture feature extraction and perform a comparative study filtering approaches included are laws masks 1980 ring wedge filters dyadic gabor filter banks wavelet transforms wavelet packets and wavelet frames quadrature mirror filters discrete cosine transform eigenfilters optimized gabor filters linear predictors and optimized finite impulse response filters the features are computed as the local energy of the filter responses the effect of the filtering is highlighted keeping the local energy function and the classification algorithm identical for most approaches for reference comparisons with two classical nonfiltering approaches co occurrence statistical and autoregressive model based features are given we present a ranking of the tested approaches based on extensive experiments

stabilization of stochastic nonlinear systems driven by noise of unknown covariance this paper poses and solves a new problem of stochastic nonlinear disturbance attenuation where the task is to make the system solution bounded by a monotone function of the supremum of the covariance of the noise this is a natural stochastic counterpart of the problem of input to state stabilization in the sense of sontag 1989 our development starts with a set of new global stochastic lyapunov theorems for an exemplary class of stochastic strict feedback systems with vanishing nonlinearities where the equilibrium is preserved in the presence of noise we develop an adaptive stabilization scheme based on tuning functions that requires no a priori knowledge of a bound on the covariance next we introduce a control lyapunov function formula for stochastic disturbance attenuation finally we address optimality and solve a differential game problem with the control and the noise covariance as opposing players for strict feedback systems the resulting isaacs equation has a closed form solution

stochastic linear quadratic regulators with indefinite control weight costs ii in part i of this paper s chen x li and x zhou siam j control optim 36 1998 pp 1685 1702 an optimization model of stochastic linear quadratic regulators lqrs with indefinite control cost weighting matrices is proposed and studied in this sequel the problem of solving lqr models with system diffusions dependent on both state and control variables which is left open in part i is tackled first the solvability of the associated stochastic riccati equations sres is studied in the normal case namely all the state and control weighting matrices and the terminal matrix in the cost functional are nonnegative definite with at least one positive definite which in turn leads to an optimal state feedback control of the lqr problem in the general indefinite case the problem is decomposed into two optimal lqr problems one with a forward dynamics and the other with a backward dynamics the well posedness and solvability of the original lqr problem are then obtained by solving these two subproblems and an optimal control is explicitly constructed examples are presented to illustrate the results

output feedback stabilization of stochastic high order nonlinear systems under weaker conditions under the weaker conditions on the power order and the nonlinear functions this paper investigates the output feedback stabilization problem for a class of stochastic high order nonlinear systems based on the backstepping design method and homogeneous domination technique the closed loop system can be proved to be globally asymptotically stable in probability

stochastic linear quadratic regulators with indefinite control weight costs this paper considers optimal minimizing control of stochastic linear quadratic regulators lqrs the assumption that the control weight costs must be positive definite inherited from the determ

backstepping controller design for nonlinear stochastic systems under a risk sensitive cost criterion this paper develops a methodology for recursive construction of optimal and near optimal controllers for strict feedback stochastic nonlinear systems under a risk sensitive cost function criterion the design procedure follows the integrator backstepping methodology and the controllers obtained guarantee any desired achievable level of long term average cost for a given risk sensitivity parameter theta furthermore they lead to closed loop system trajectories that are bounded in probability and in some cases asymptotically stable in the large these results also generalize to nonlinear systems with strongly stabilizable zero dynamics a numerical example included in the paper illustrates the analytical results

mean square stabilisation of complex oscillatory regimes in nonlinear stochastic systems abstracta problem of stabilisation of the randomly forced periodic and quasiperiodic modes for nonlinear dynamic systems is considered for this problem solution we propose a new theoretical approach to consider these modes as invariant manifolds of the stochastic differential equations with control the aim of the control is to provide the exponential mean square ems stability for these manifolds a general method of the stabilisation based on the algebraic criterion of the ems stability is elaborated a constructive technique for the design of the feedback regulators stabilising various types of oscillatory regimes is proposed a detailed parametric analysis of the problem of the stabilisation for stochastically forced periodic and quasiperiodic modes is given an illustrative example of stochastic hopf system is included to demonstrate the effectiveness of the proposed technique

image representation using 2d gabor wavelets this paper extends to two dimensions the frame criterion developed by daubechies for one dimensional wavelets and it computes the frame bounds for the particular case of 2d gabor wavelets completeness criteria for 2d gabor image representations are important because of their increasing role in many computer vision applications and also in modeling biological vision since recent neurophysiological evidence from the visual cortex of mammalian brains suggests that the filter response profiles of the main class of linearly responding cortical neurons called simple cells are best modeled as a family of self similar 2d gabor wavelets we therefore derive the conditions under which a set of continuous 2d gabor wavelets will provide a complete representation of any image and we also find self similar wavelet parametrization which allow stable reconstruction by summation as though the wavelets formed an orthonormal basis approximating a tight frame generates redundancy which allows low resolution neural responses to represent high resolution images