on the boundedness and compactness of operators of hankel type abstract boundedness and compactness results are obtained for commutators of multiplication operators with a general class of integral operators j of a critical homogeneity which are modeled on the bergman projection it is shown that the commutator m f j is bounded or compact on l p whenever the function is in an appropriately defined bmo or vmo space respectively special cases of the general results include commutators with the bergman projection in strictly pseudoconvex domains in c n and finite type domains in c 2 in addition it is shown that in the case of the bergman projection in a strictly pseudoconvex domain the sufficient condition is also necessary

solution of the h sub infinity optimal linear filtering problem for discrete time systems the solution of l sub 2 minimum variance and h sub infinity estimation problems is considered using a polynomial systems approach the results for the l sub 2 filtering problem which corresponds with wiener or kalman filtering prediction are first presented in polynomial matrix form attention then turns to the solution of the h sub infinity estimation problem for scalar systems numerous examples are presented to illustrate the computational procedures the two types of estimator are appropriate to very different estimation problems and the new h sub infinity devices should be valuable in certain application areas

a boundedness criterion for generalized calderon zygmund operators in 1965 a p calderon showed the l2 boundedness of the so called first calderon commutator this is one of the first examples of a non convolution operator associated to a kernel which satisfies certain size and smoothness properties comparable to those of the kernel of the hilbert transform these properties together with the l2 boundedness imply the lp boundedness for all p s in 1 oo many operators in analysis such as certain classes of pseudo differential operators and the cauchy integral operator on a curve are associated with kernels having these properties for these operators one of the major questions is if they are bounded on l2 we are going to give necessary and sufficient conditions for such an operator to be bounded on l2 they are essentially that the images of the function 1 under the actions of the operator and its adjoint both lie in bmo in the case of the aforementioned first commutator this can be checked by an integration by parts in the first section we present some basic notions and state the theorem which is proved in sections 2 and 3 in section 4 we show how to recover some classical results in sections 5 and 6 we construct a functional calculus for small perturbations of a and in section 7 we show a connection between the theory of calderon zygmund operators and kato s conjecture it is a pleasure to express our thanks to r r coifman and y meyer for suggesting many elegant simplifications in our proofs and most of the applications we also wish to thank stephen semmes for several pertinent remarks

h sub spl infin nonlinear filtering of discrete time processes this correspondence investigates the problem of h sub spl infin estimation of a discrete time nonlinear process an estimator which may be nonlinear is introduced so that an h sub spl infin norm like of what we call a generalized estimation error is guaranteed to be bounded by a prescribed level conditions for the existence of such an estimator and formulae for its derivation are obtained utilizing a discrete time analog of the hamilton jacobi inequality an approximate filter based on linearization is developed this filter relates to the extended kalman filter in the same way that the linear h sub spl infin filter relates to the kalman filter

carleson embedding theorem on convex finite type domains abstract this paper concerns certain geometric aspects of function theory on smoothly bounded convex domains of finite type in c n specifically we prove the carleson hormander inequality for this class of domains and provide examples of carleson measures improving a known result concerning such measures associated to bounded holomorphic functions

optimum performance levels for minimax filters predictors and smoothers abstract for both discrete and continuous time linear time varying systems we obtain the achievable performance levels for minimax filters predictors and smoothers in terms of the finite escape times of some related discrete and continuous time riccati equations our game theoretic approach also yields an alternative derivation for the corresponding minimax estimators which were first obtained in 1 they are all bayes estimators with respect to particular gaussian distributions and admit recursive structures

a cubature h filter and its square root version in this paper we present a nonlinear state estimation algorithm based on the fusion of an extended h eh f and a cubature kalman filter ckf the resulting estimator is called a cubature h filter the recently developed ckf is a gaussian approximation of a bayesian filter and its performance over non gaussian noises may degrade in contrast the h filter is capable of estimating the states of linear systems with non gaussian noises and the extended h filter eh f can estimate the states of non linear and non gaussian systems similar to the h filter an eh f also does not make any assumptions about the statistics of the process or measurement noise but it does require jacobians during the state estimation of nonlinear systems which degrade the overall performance when the nonlinearities are severe the cubature h filter is developed to have the desirable features of both ckf and eh f for numerical accuracy a square root version of the cubature h filter is developed using j unitary transform

morrey boundedness and compactness characterizations of integral commutators with singular kernel on strictly pseudoconvex domains in cn abstract we prove the morrey boundedness and compactness characterization of integral commutators with singular kernel of calderon zygmund type on pseudo convex domains in c n our results extend some of the ones of krantz li in 16 17

fast rank adaptive subspace tracking and applications high computational complexity and inadequate parallelism have deterred the use of subspace based algorithms in real time systems we proposed a new class of fast subspace tracking fst algorithms that overcome these problems by exploiting the matrix structure inherent in multisensor processing these algorithms simultaneously track an orthonormal basis for the signal subspace and preserve signal eigenstructure information while requiring only o nr operations per update where n is the number of channels and r is the effective rank because of their low computational complexity these algorithms have applications in both recursive and block data processing because they preserve the signal eigenstructure as well as compute an orthonormal basis for the signal subspace these algorithms may be used in a wide range of sensor array applications including bearing estimation beamforming and recursive least squares we present a detailed description of the fst algorithm and its rank adaptive variation ra fst as well as a number of enhancements we also demonstrate the fst s rapid convergence properties in a number of application scenarios

commutators of singular integrals and fractional integral operators the commutators of integral and differential operators are very important tools in harmonic analysis and pde recently the third author of this paper gave a survey front math china 2011 6 5 821 833 on some late results and put forward a few problems on commutators of many important operators in harmonic analysis in this paper we present some results about the commutators of singular integrals and fractional integrals with rough or variable kernels which can be seen as a supplement of the aforementioned survey

robust spl hscr sub spl infin filtering for continuous time varying uncertain systems with deterministic input signals many dynamical systems involve not only process and measurement noise signals but also parameter uncertainty and known input signals when spl lscr sub 2 or spl hscr sub spl infin filters that were designed based on a nominal model of the system are applied the presence of parameter uncertainty will not only affect the noise attenuation property of the filter but also introduce a bias proportional to the known input signal and the latter may be very appreciable we introduce a finite horizon robust spl hscr sub spl infin filtering method that provides a guaranteed spl hscr sub spl infin bound for the estimation error in the presence of both parameter uncertainty and a known input signal this method is developed by using a game theoretic approach and the results generalize those obtained for cases without parameter uncertainty or without a known input signal it is also demonstrated via an example that the proposed method provides significantly improved signal estimates

linear time algorithms for testing the realisability of line drawings of curved objects this paper shows that the semantic labelling of line drawings of curved objects with piecewise c 3 surfaces is solvable in linear time this result is robust to changes in the assumptions on object shape when all vanishing points are known a different linear time algorithm exists to solve the labelling problem furthermore in both cases all legally labelled line drawings of curved objects are shown to be physically realisable however when some but not all of the vanishing points are known when the drawing is an orthographic projection of a scene containing parallel lines or when we wish to minimise the number of phantom junctions the labelling problem becomes np hard the introduction of collinearity constraints also renders the labelling problem np complete except in the case when all vanishing points are known 1999 elsevier science b v all rights reserved

compactness for the commutators of singular integral operators with rough variable kernels abstract let t be the singular integral operator with variable kernel defined by t f x p v r n x x y x y n f y d y where x y is homogeneous of degree zero in the second variable y and s n 1 x z d z 0 for any x r n in this paper the authors prove that if l r n l q s n 1 for some q 2 n 1 n then the commutator generated by a cmo r n function and t and the associated lacunary maximal operator are compact on l 2 r n the associated continuous maximal commutator is also considered

constraints between distant lines in the labelling of line drawings of polyhedral scenes the machine interpretation of line drawings has applications both in vision and geometric modelling this paper extends the classic technique of assigning semantic labels to lines subject to junction constraints by introducing new constraints often between distant lines these include generic constraints between lines lying on a path in the drawing as well as preference constraints between the labellings of pairs of junctions lying on parallel lines such constraints are essential to avoid an exponential number of legal labellings of drawings of objects with non trihedral vertices the strength of these constraints is demonstrated by their ability to identify the unique correct labelling of many drawings of polyhedral objects with tetrahedral vertices these new constraints also allowed us to deduce a general polyhedral junction constraint for the case when there is no limit on the number of faces which can meet at a junction

arc and path consistence revisited mackworth and freuder have analyzed the time complexity of several constraint satisfaction algorithms 5 we present here new algorithms for arc and path consistency and show that the arc consistency algorithm is optimal in time complexity and of the same order space complexity as the earlier algorithms a refined solution for the path consistency problem is proposed however the space complexity of the path consistency algorithm makes it practicable only for small problems these algorithms are the result of the synthesis techniques used in image a general constraint satisfaction system and local consistency methods 3

estimates for the bergman and szego kernels in mathbf c 2 the purpose of this paper some of whose conclusions were announced in nrsw is to study the bergman and szegd projection operators on pseudoconvex domains q of finite type in c2 the results we obtain are of three kinds i the size estimates of the bergman and szeg6 kernels and their derivatives ii the cancellation properties of those kernels expressed in terms of the actions of these operators on suitable bump functions these properties have an interest in their own right but are also crucial in iii below iii the sharp mapping properties of these operators on functions spaces such as lp the nonisotropic sobolev spaces and the nonisotropic lipschitz spaces which are naturally attached to the geometry of the boundary this also leads ultimately to sharp results in the regularity properties of solutions of the equation du f in q and 9bu f on the boundary let us describe these matters in more detail our starting points are certain basic geometric constructs associated to q a metric p defined in terms of the vector fields x1 and x2 which are the real and imaginary parts of the tangential levi vector field on d 2 and a function a p 8 a polynomial in 8 for p e d 2 which represents the higher levi invariant attached to d 9 the significance of these to the geometry of d q can be understood from the following facts the ball b p 8 c d q centered at p of radius 8 in the metric p when viewed in the appropriate coordinates has width 8 in the complex tangential directions but its width is a p 8 in the complementary direction thus the volume of the ball is 82a p 8 among other crucial properties

reduction operations in fuzzy or valued constraint satisfaction in the constraint satisfaction problem csp knowledge about a relation on n variables is given in the form of constraints on subsets of the variables a solution to the csp is simply an instantiation of the n variables which satisfies all the constraints the fuzzy constraint satisfaction problem fcsp is a generalisation of the csp to the case in which the constraints are not categorical but represent preferences in the form of fuzzy sets the fcsp is then an optimisation problem in the space of all possible instantiations of the n variables it has potentially many more applications in real world problems than the csp when the aim is to maximise the value of the most violated constraint then the fcsp can be solved by solving a logarithmic number of csps fuzzy k consistency can even be established by an algorithm whose worst case complexity is identical to that of an optimal k consistency algorithm for csps when the operator used to aggregate constraint values is strictly monotonic as is the case in max csp for example the classic operation of arc consistency has to be completely redefined we show that an ordered version of arc consistency exists for all fcsps with a strictly monotonic aggregation operator and that in the case of max csp it can be established by an algorithm whose worst case complexity is identical to that of the optimal arc consistency algorithm for csps neighbourhood substitution in csps can be generalised to fuzzy neighbourhood substitution in fcsps whether the aggregation operator is strictly monotonic or idempotent we give an algorithm for applying fuzzy neighbourhood substitutions until convergence based on the corresponding algorithm for csps

an optimal k consistency algorithm abstract this paper generalizes the arc consistency algorithm of mohr and henderson 4 and the path consistency algorithm of han and lee 2 to a k consistency algorithm arc consistency and path consistency being 2 consistency and 3 consistency respectively the algorithm is a development of freuder s synthesis algorithm 1 it simultaneously establishes i consistency for each 1 i k it has worst case time and space complexity which is optimal when k is a constant and almost optimal for all other values of k in the case that all order i constraints exist for all 1 i n this algorithm is a solution to the consistent labeling problem with almost optimal worst case time and space complexity

maintaining reversible dac for max csp abstract we introduce an exact algorithm for maximizing the number of satisfied constraints in an overconstrained csp max csp the algorithm which can also solve weighted csp probabilistic csp and other similar problems is based on directed arc inconsistency counts dac the usage of dac increases the lower bound of branch and bound based algorithms for max csp improving their efficiency originally dac were defined following a static variable ordering in this paper we relax this condition showing how dac can be defined from a directed constraint graph these new graph based dac can be effectively used for lower bound computation interestingly any directed constraint graph of the considered problem is suitable for dac computation so the selected graph can change dynamically during search aiming at optimizing the exploitation of directed arc inconsistencies in addition directed arc inconsistencies are maintained during search propagating the effect of value pruning with these new elements we present the pfc maintaining reversible dac algorithm pfc mrdac a natural successor of pfc dac for max csp we provide experimental evidence for the superiority of pfc mrdac on random and real overconstrained csp instances including problems with weighted constraints

game theory approach to discrete h sub spl infin filter design a finite horizon discrete h sub spl infin filter design with a linear quadratic lq game approach is presented the exogenous inputs composed of the hostile noise signals and system initial condition are assumed to be finite energy signals with unknown statistics the design criterion is to minimize the worst possible amplification of the estimation error signals in terms of the exogenous inputs which is different from the classical minimum variance estimation error criterion for the modified wiener or kalman filter design the approach can show how far the estimation error can be reduced under an existence condition on the solution to a corresponding riccati equation a numerical example is given to compare the performance of the h sub spl infin filter with that of the conventional kalman filter

bilinear operators on herz type hardy spaces the authors prove that bilinear operators given by finite sums of products of calderon zygmund operators on rn are bounded from hk11 q1 hk22 q2 into hk p q if and only if they have vanishing moments up to a certain order dictated by the target space herehk q are homogeneous herztype hardy spaces with 1 p 1 p1 1 p2 0 pi 1 q 1 q1 1 q2 1 q1 q2 1 q 1 2 and n qi i as an application they obtain that the commutator of a calderon zygmund operator with a bmo function maps a herz space into itself

compactness of riesz transform commutator on stratified lie groups abstract let g be a stratified lie group and x j 1 j n a basis for the left invariant vector fields of degree one on g let j 1 n x j 2 be the sub laplacian on g the jth riesz transform on g is defined by r j x j 1 2 1 j n in this paper we provide a concrete construction of the twisted truncated sector which is related to the pointwise lower bound of the kernel of r j on g then we obtain the characterisation of compactness of the commutators of r j with a function b vmo g the space of functions with vanishing mean oscillation on g

h filtering for nonuniformly sampled systems a markovian jump systems approach abstract this paper is concerned with the design of an h filter for a sampled data system where the measurement is sampled nonuniformly and randomly but the input is updated uniformly at a fast rate the process of nonuniform measurement sampling is modeled using a markov chain therefore the discrete time system together with the markov chain is a markovian jump system a mode dependent full order h filter is constructed sufficient lmi conditions are derived to ensure stochastic stability and h disturbance attenuation for the error system the case of unknown measurement sampling probabilities is also considered finally the effectiveness of the proposed approach is demonstrated through simulation results and a comparison with a general time varying h filter

robust maximum likelihood bearing estimation in contaminated gaussian noise presents a maximum likelihood ml direction of arrival doa estimation algorithm which is robust against outliers and distributional uncertainties in the gaussian noise the algorithm performs much better than the gaussian ml algorithm when the underlying noise distribution deviates from the assumed gaussian while still performing almost as well in the pure gaussian noise as with the gaussian ml estimation it is capable of handling coherent signals as well as single snapshot cases the authors analyze the performance of the new algorithm using the variance expression derived from influence function if and then present a resolution test procedure for determining whether a given doa estimation algorithm can resolve two dominant sources with very close doas for a given confidence level using the test one can also check the presence of the reflected path having its doa exactly the negative of the direct path doa

single snapshot robust direction finding the paper presents a novel approach for recursively estimating the directions of arrival of incident signals as measurements are received along a sensor array using a single snapshot and without any statistical assumptions the proposed method employs a robust performance criterion which is based on worst case gain minimization the criterion aims to reduce the estimation error induced by worst case amplitude and phase perturbations as well as additive noise in the array model an algorithm that guarantees the criterion within a first order approximation is developed and shown to converge moreover instead of using a trial and error method to find a constant minimum worst case gain the minimum worst case gain is updated as each sensor measurement is processed a step by step implementation of the algorithm is presented and its computational complexity is analyzed the performance of the new approach is evaluated by simulating the estimation algorithm for a linear array and comparing its performance with that of an existing single snapshot algorithm

second order extended h_ infty filter for nonlinear discrete time systems using quadratic error matrix approximation in this paper the second order extended soe h filter for nonlinear discrete time systems is derived based on an approximation to the quadratic error matrix the solution is obtained by the game theory approach it is shown that the result bears a strong resemblance to the soe kalman filter when the performance bound goes to infinity an example of vehicle state tracking is simulated to compare the performances of the soe kalman filter the first order extended and the soe h filter noises with unknown bias are injected into both process dynamics and measurements the results show that the soe h filter has the smallest state tracking error

mixed norm h 2 h regulation and estimation the discrete time case abstract a discrete time h2 static output feedback design problem involving a constraint on h disturbance attenuation is addressed and state space formulae are derived the dual problem of discrete time dynamic estimation with an h error bound is also addressed these results are analogous to results obtained previously for the continuous time problem

maximum likelihood localization of multiple sources by alternating projection an algorithm referred to as apm for computing the maximum likelihood estimator of the locations of simple sources in passive sensor arrays is presented the algorithm is equally applicable to the case of coherent signals and to the case of a single snapshot the algorithm is iterative the maximum of the likelihood function is computed by successive approximations the convergence of the algorithm to the global maximum is demonstrated for a variety of scenarios the key to this global convergence is the initialization scheme

comparing robustness of the kalman h_ infty and ufir filters this paper provides a comparative analysis for robustness of the kalman filter kf h infty filter derived using the game theory and unbiased finite impulse response ufir filter which ignores the noise statistics and initial values a comparison is provided for gaussian models by studying the effects of errors and disturbing factors on the bias correction gain it is shown that the rule of thumb of optimal filtering in terms of accuracy text ufir typically does not hold in the real world implying errors in the noise statistics mismodeling temporary uncertainties and difficulties in filter tuning to optimal mode under such conditions the filters are related to each other as text kf lessgtr h infty a justification of this statement is provided analytically and confirmed by simulations and experimentally based on two state polynomial and harmonic models

cyclic consistency a local reduction operation for binary valued constraints valued constraint satisfaction provides a general framework for optimisation problems over finite domains it is a generalisation of crisp constraint satisfaction allowing the user to express preferences between solutions consistency is undoubtedly the most important tool for solving crisp constraints it is not only a family of simplification operations on problem instances it also lies at the heart of intelligent search techniques g kondrak p van beek artificial intelligence 89 1997 365 387 and provides the key to solving certain classes of tractable constraints p g jeavons d a cohen m c cooper artificial intelligence 101 1998 251 265 arc consistency was generalised to valued constraints by sacrificing the uniqueness of the arc consistency closure m c cooper t schiex artificial intelligence in press the notion of 3 cyclic consistency introduced in this paper again sacrifices the unique closure property in order to obtain a generalisation of path consistency to valued constraints which is checkable in polynomial time in max csp 3 cyclic consistency can be established in polynomial time and even guarantees a local form of optimality the space complexity of 3 cyclic consistency is optimal since it creates no new constraints

parallel and adaptive high resolution direction finding recently the new class of so called subspace methods for high resolution direction finding has received a great deal of attention in the literature when a real time implementation is aimed at the computational complexity involved is known to represent a serious impediment in this paper an esprit type algorithm is developed which is fully adaptive and therefore particularly suited for real time processing furthermore a systolic array is described which allows the processing of incoming data at a rate which is independent of the problem size the algorithm is based on orthogonal transformations only estimates are computed for the angles of arrival as well as for the source signals our aim is not so much to develop yet another esprit type algorithm but rather to show that it is indeed possible to develop an algorithm that is fully parallel and adaptive this is something that has not been done before

linear estimation in krein spaces ii applications we have shown that several interesting problems in h sup spl infin filtering quadratic game theory and risk sensitive control and estimation follow as special cases of the krein space linear estimation theory developed in part i we show that all these problems can be cast into the problem of calculating the stationary point of certain second order forms and that by considering the appropriate state space models and error gramians we can use the krein space estimation theory to calculate the stationary points and study their properties the approach discussed here allows for interesting generalizations such as finite memory adaptive filtering with varying sliding patterns

sensor array processing based on subspace fitting algorithms for estimating unknown signal parameters from the measured output of a sensor array are considered in connection with the subspace fitting problem the methods considered are the deterministic maximum likelihood method ml esprit and a recently proposed multidimensional signal subspace method these methods are formulated in a subspace fitting based framework which provides insight into their algebraic and asymptotic relations it is shown that by introducing a specific weighting matrix the multidimensional signal subspace method can achieve the same asymptotic properties as the ml method the asymptotic distribution of the estimation error is derived for a general subspace weighting and the weighting that provides minimum variance estimates is identified the resulting optimal technique is termed the weighted subspace fitting wsf method numerical examples indicate that the asymptotic variance of the wsf estimates coincides with the cramer rao bound the performance improvement compared to the other techniques is found to be most prominent for highly correlated signals

comments on mohr and henderson s path consistency algorithm abstract mohr and henderson have presented new algorithms for arc and path consistency in 1 though the underlying ideas of their algorithms are correct the path consistency algorithm pc 3 is in error in this paper we point out the errors in this algorithm and give a correct one the time complexity and space complexity of the revised algorithm are also analyzed

estimation of directions of arrival of multiple scattered sources we consider the problem of estimating the directions of arrival doa of multiple sources in the presence of local scattering this problem is encountered in wireless communications due to the presence of scatterers in the vicinity of the mobile or when the signals propagate through a random inhomogeneous medium assuming a uniform linear array ula we develop doa estimation algorithms based on covariance matching applied to a reduced size statistic obtained from the sample covariance matrix after redundancy averaging next a computationally efficient estimator based on ar modelling of the coherence loss function is derived a theoretical expression for the asymptotic covariance matrix of this estimator is derived finally the corresponding cramer rao bounds crbs are derived despite its simplicity the ar based estimator is shown to possess performance that is nearly as good as that of the covariance matching method

semiring based constraint satisfaction and optimization we introduce a general framework for constraint satisfaction and optimization where classical csps fuzzy csps weighted csps partial constraint satisfaction and others can be easily cast the framework is based on a semiring structure where the set of the semiring specifies the values to be associated with each tuple of values of the variable domain and the two semiring operations and x model constraint projection and combination respectively local consistency algorithms as usually used for classical csps can be exploited in this general framework as well provided that certain conditions on the semiring operations are satisfied we then show how this framework can be used to model both old and new constraint solving and optimization schemes thus allowing one to both formally justify many informally taken choices in existing schemes and to prove that local consistency techniques can be used also in newly defined schemes

soft arc consistency revisited the valued constraint satisfaction problem vcsp is a generic optimization problem defined by a network of local cost functions defined over discrete variables it has applications in artificial intelligence operations research bioinformatics and has been used to tackle optimization problems in other graphical models including discrete markov random fields and bayesian networks the incremental lower bounds produced by local consistency filtering are used for pruning inside branch and bound search in this paper we extend the notion of arc consistency by allowing fractional weights and by allowing several arc consistency operations to be applied simultaneously over the rationals and allowing simultaneous operations we show that an optimal arc consistency closure can theoretically be determined in polynomial time by reduction to linear programming this defines optimal soft arc consistency osac to reach a more practical algorithm we show that the existence of a sequence of arc consistency operations which increases the lower bound can be detected by establishing arc consistency in a classical constraint satisfaction problem csp derived from the original cost function network this leads to a new soft arc consistency method called virtual arc consistency which produces improved lower bounds compared with previous techniques and which can solve submodular cost functions these algorithms have been implemented and evaluated on a variety of problems including two difficult frequency assignment problems which are solved to optimality for the first time our implementation is available in the open source toulbar2 platform

robust direction of arrival estimation in non gaussian noise a nonlinearly weighted least squares method is developed for robust modeling of sensor array data weighting functions for various observation noise scenarios are determined using maximum likelihood estimation theory the computational complexity of the new method is comparable with the standard least squares estimation procedures simulation examples of direction of arrival estimation are presented

filtering and smoothing in an h sup infinity setting the problems of filtering and smoothing are considered for linear systems in an h sup infinity setting i e the plant and measurement noises have bounded energies are in l sub 2 but are otherwise arbitrary two distinct situations for the initial condition of the system are considered the initial condition is assumed known in one case while in the other the initial condition is not known but the initial condition the plant and measurement noise are in some weighted ball of r sup n xl sub 2 finite horizon and infinite horizon cases are considered necessary and sufficient conditions are presented for the existence of estimators both filters and smoothers that achieve a prescribed performance bound and algorithms that result in performance within the bounds are developed in case of smoothers the optimal smoother is also presented the approach uses basic quadratic optimization theory in a time domain setting as a consequence of which both linear time varying and time invariant systems can be considered with equal ease in the smoothing problem for linear time varying systems one considers only the finite horizon case

a transfer function approach to the problems of discrete time systems h sub infinity optimal linear control and filtering a solution is derived to the problems of h sub infinity optimal linear state regulation and filtering the solution method for both problems is based on a transfer function approach which applies standard spectral factorization return difference relations are given which are extensions of the relations associated with the linear quadratic problem

brief robust h filtering with error variance constraints for discrete time varying systems with uncertainty the robust h filtering problem with error variance constraints is considered for discrete time varying systems subject to norm bounded parameter uncertainties in both the state and the output matrices of the state space model sufficient conditions for a finite horizon filter to satisfy state estimation error variance constraints as well as prescribed h performance for all admissible perturbations are given in terms of two discrete riccati difference equations which are of a form suitable for recursive computation the results are extended to cover the case of stationary filtering over an infinite horizon for uncertain time invariant systems

arc consistency for soft constraints the notion of arc consistency plays a central role in constraint satisfaction r dechter constraint processing morgan kaufmann san mateo ca 2003 it is known since the introduction of valued and semi ring constraint networks in 1995 that the notion of local consistency can be extended to constraint optimisation problems defined by soft constraint frameworks based on an idempotent cost combination operator this excludes non idempotent operators such as which define problems which are very important in practical applications such as max csp where the aim is to minimise the number of violated constraints in this paper we show that using a weak additional axiom satisfied by most existing soft constraints proposals it is possible to define a notion of soft arc consistency that extends the classical notion of arc consistency and this even in the case of non idempotent cost combination operators a polynomial time algorithm for enforcing this soft arc consistency exists and its space and time complexities are identical to that of enforcing arc consistency in csps when the cost combination operator is strictly monotonic for example max csp a directional version of arc consistency first introduced by m c cooper reduction operations in fuzzy or valued constraint satisfaction fuzzy sets and systems 134 3 2003 311 342 is potentially even stronger than the non directional version since it allows non local propagation of penalties we demonstrate the utility of directional arc consistency by showing that it not only solves soft constraint problems on trees but that it also implies a form of local optimality which we call arc irreducibility

robust h filtering for discrete time varying uncertain systems with a known deterministic input in this paper a new discrete time dynamic game where two players seek to reinforce and suppress the output of a system under the influence of a known deterministic input is considered a necessary and sufficient condition for the existence of a saddle point solution to this game is derived in terms of a discrete time riccati difference equation the result of the game is used to solve the robust h x filtering problem for discrete time varying systems subject to a known deterministic input and norm bounded parameter uncertainties occurring in both the state and the output matrices of the state space model there is no restriction on the form of the filter this allows the effect of the known input on the estimation error due to system uncertainties to be optimally reduced without any prior assumption on the filter structure

partial constraint satisfaction abstract a constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed in some cases it may be impossible or impractical to solve these problems completely we may seek to partially solve the problem in particular by satisfying a maximal number of constraints standard backtracking and local consistency techniques for solving constraint satisfaction problems can be adapted to cope with and take advantage of the differences between partial and complete constraint satisfaction extensive experimentation on maximal satisfaction problems illuminates the relative and absolute effectiveness of these methods a general model of partial constraint satisfaction is proposed

brief nonlinear h filtering of sampled data systems this paper considers the problem of h filtering for nonlinear sampled data systems sufficient conditions are obtained for the existence of filters that guarantee the l 2 gain from an exogenous input to an estimation error is less than or equal to a prescribed value these conditions are expressed in terms of both continuous and discrete hamilton jacobi equations