an adaptive order statistic noise filter for gamma corrected image sequences original video signals are often corrupted by a certain amount of noise originating from the camera electronics as a result of the gamma correction in cameras the observed noise is signal dependent we present a spatio temporal order statistic os noise filter that takes into account the gamma correction in the camera the calculation of the filter coefficients requires higher order order statistics hoos of the noise process we make use of a range test rt to determine locally from which neighboring signal values an estimate should be formed the noise filter that we arrive at is adaptive and computationally efficient

application of polynomial chaos in stability and control the polynomial chaos of wiener provides a framework for the statistical analysis of dynamical systems with computational cost far superior to monte carlo simulations it is a useful tool for control systems analysis because it allows probabilistic description of the effects of uncertainty especially in systems having nonlinearities and where other techniques such as lyapunov s method may fail we show that stability of a system can be inferred from the evolution of modal amplitudes covering nearly the full support of the uncertain parameters with a finite series by casting uncertain parameters as unknown gains we show that the separation of stochastic from deterministic elements in the response points to fast iterative design methods for nonlinear control

generalised polynomial chaos expansion approaches to approximate stochastic model predictive control this paper considers the model predictive control of dynamic systems subject to stochastic uncertainties due to parametric uncertainties and exogenous disturbance the effects of uncertainties are quantified using generalised polynomial chaos expansions with an additive gaussian random process as the exogenous disturbance with gaussian approximation of the resulting solution trajectory of a stochastic differential equation using generalised polynomial chaos expansion convex finite horizon model predictive control problems are solved that are amenable to online computation of a stochastically robust control policy over the time horizon using generalised polynomial chaos expansions combined with convex relaxation methods the probabilistic constraints are replaced by convex deterministic constraints that approximate the probabilistic violations this approach to chance constrained model predictive control provides an explicit way to handle a stochastic system model in the presence of both model uncertain

a data driven stochastic collocation approach for uncertainty quantification in mems this work presents a data driven stochastic collocation approach to include the effect of uncertain design parameters during complex multi physics simulation of micro electromechanical systems mems the proposed framework comprises of two key steps first probabilistic characterization of the input uncertain parameters based on available experimental information and second propagation of these uncertainties through the predictive model to relevant quantities of interest the uncertain input parameters are modeled as independent random variables for which the distributions are estimated based on available experimental observations using a nonparametric diffusion mixing based estimator botev nonparametric density estimation via diffusion mixing technical report 2007 the diffusion based estimator derives from the analogy between the kernel density estimation kde procedure and the heat dissipation equation and constructs density estimates that are smooth and asymptotically consistent the diffusion model allows for the incorporation of the prior density and leads to an improved density estimate in comparison with the standard kde approach as demonstrated through several numerical examples following the characterization step the uncertainties are propagated to the output variables using the stochastic collocation approach based on sparse grid interpolation smolyak soviet math dokl 1963 4 240 243 the developed framework is used to study the effect of variations in young s modulus induced as a result of variations in manufacturing process parameters or heterogeneous measurements on the performance of a mems switch copyright 2010 john wiley sons ltd

universality and complexity in cellular automata abstract cellular automata are discrete dynamical systems with simple construction but complex self organizing behaviour evidence is presented that all one dimensional cellular automata fall into four distinct universality classes characterizations of the structures generated in these classes are discussed three classes exhibit behaviour analogous to limit points limit cycles and chaotic attractors the fourth class is probably capable of universal computation so that properties of its infinite time behaviour are undecidable

permutation filters a class of nonlinear filters based on set permutations introduces and analyzes a new class of nonlinear filters that have their roots in permutation theory the authors show that a large body of nonlinear filters proposed to date constitute a proper subset of permutation filters spl pscr filters in particular rank order filters weighted rank order filters and stack filters embody limited permutation transformations of a set indeed by using the full potential of a permutation group transformation one can design very efficient estimation algorithms permutation groups inherently utilize both rank order and temporal order information thus the estimation of nonstationary processes in gaussian nongaussian environments with frequency selection can be effectively addressed an adaptive design algorithm that minimizes the mean absolute error criterion is described as well as a more flexible adaptive algorithm that attains the optimal permutation filter under a deterministic least normed error criterion simulation results are presented to illustrate the performance of permutation filters in comparison with other widely used filters

multistage order statistic filters for image sequence processing the application of multistage order statistic filters mos to the task of noise suppression in time varying imagery is studied it is shown that mos filters efficiently preserve image structures under motion without motion compensation preprocessing in particular the families of multistage median and weighted median filters are considered motion preservation and statistical smoothing measures are derived it is shown that spatiotemporal filtering allows for a significant improvement over both spatial and temporal filtering in terms of output image resolution and noise suppression

quantitative image quality analysis of a nonlinear spatio temporal filter digital temporal and spatial filtering of fluoroscopic image sequences can be used to improve the quality of images acquired at low x ray exposure in this study we characterized a nonlinear edge preserving spatio temporal noise reduction filter the bidirectional multistage bms median filter of arce 1991 to assess image quality signal detection and discrimination experiments were performed on stationary targets using a four alternative forced choice paradigm a measure of detectability d was obtained for filtered and unfiltered noisy image sequences at different signal amplitudes filtering gave statistically significant average d improvements of 20 detection and 31 discrimination a nonprewhitening detection model modified to include the human spatio temporal visual system contrast sensitivity underestimated enhancement predicting an improvement of 6 pixel noise standard deviation a commonly applied image quality measure greatly overestimated effectiveness giving 67 improvement in d we conclude that human testing is required to evaluate the filter effectiveness and that human perception models must be improved to account for the spatio temporal filtering of image sequences

multiscale recursive medians scale space and transforms with applications to image processing a cascade of increasing scale 1 d recursive median filters produces a sieve termed an r sieve has a number of properties important to image processing in particular it 1 simplifies signals without introducing new extrema or edges that is it preserves scale space it shares this property with gaussian filters but has the advantage of being significantly more robust 2 the differences between successive stages of the sieve yield a transform to the granularity domain patterns and shapes can be recognized in this domain using idempotent matched sieves and the result transformed back to the spatial domain the r sieve is very fast to compute and has a close relationship to 1 d alternating sequential filters with flat structuring elements they are useful for machine vision applications

physics like models of computation abstract reversible cellular automata are computer models that embody discrete analogues of the classical physics notions of space time locality and microscopic reversibility they are offered as a step towards models of computation that are closer to fundamental physics

fluctuation driven computing on number conserving cellular automata a number conserving cellular automaton ncca is a cellular automaton in which the states of cells are denoted by integers and the sum of all of the numbers in a configuration is conserved throughout its evolution nccas have been widely used to model physical systems that are ruled by conservation laws of mass or energy imai et al 13 showed that the local transition function of ncca can be effectively translated into the sum of a binary flow function over pairs of neighboring cells in this paper we explore the computability of nccas in which the pairwise number flows are performed at fully asynchronous timings despite the randomness that is associated with asynchronous transitions useful computation still can be accomplished efficiently in the cellular automata through the active exploitation of fluctuations 18 specifically certain numbers may flow randomly fluctuating between forward and backward directions in the cellular space as if they were subject to brownian motion because random fluctuations promise a powerful resource for searching through a computational state space the brownian like flow of the numbers allows for efficient embedding of logic circuits into our novel asynchronous ncca

emergence of universal global behavior from reversible local transitions in asynchronous systems reversible computing usually focuses on how to establish a valid equivalence between the global reversibility and local reversibility in computational systems hitherto the equivalence has been precisely developed in cellular automata combinational circuits and quantum computers but it implicitly assumes that the underlying systems are synchronously timed alternative systems include the delay insensitive di circuits and asynchronous cellular automata acas where the local operations of each component cell may be executed independently at random times despite the randomness associated with asynchronous timings equivalence between the global and local reversibility can be simply achieved in both di circuits morita 2001 and acas lee et al 2003 provided that their local operations transitions are thoroughly serialized the complete exclusion of concurrency in local behavior however will profoundly depress the parallel processing efficiency of di circuits as well as the intrinsic massive parallelism of acas this paper aims at exploring what kind of complex global behavior may arise from the concurrent operations that are invertible at local level to this end we show that di circuits composed of reversible elements can actually exhibit universal input and output behavior with the universality emerging from the concurrency in reversible local operations likewise by further embedding all circuits into the cellular space the emergence of universal global transitions from reversible local transitions can be exactly identified in an aca which due to the bijectivity of its local function has significantly lower complexity as compared to other models

fully asynchronous behavior of double quiescent elementary cellular automata in this paper we propose a probabilistic analysis of the fully asynchronous behavior i e two cells are never simultaneously updated as in a continuous time process of elementary finite cellular automata i e 0 1 states radius 1 and unidimensional for which both states are quiescent i e 0 0 0 0 and 1 1 1 1 it has been experimentally shown in previous works that introducing asynchronism in the global function of a cellular automata was perturbing its behavior but as far as we know only few theoretical work exists on the subject the cellular automata we consider live on a ring of size n and asynchronism is introduced as follows at each time step one cell is selected uniformly at random and the transition is made on this cell while the others stay in the same state among the 64 cellular automata belonging to the class we consider we show that 9 of them diverge on all non trivial configurations while the 55 other converge almost surely to a random fixed point we show that the exact convergence time of these 55 automata can only take the following values either 0 n ln n n2 n3 or n2n furthermore the global behavior of each of these cellular automata is fully determined by reading its code

stack filters the median and other rank order operators possess two properties called the threshold decomposition and the stacking properties the first is a limited superposition property which leads to a new architecture for these filters the second is an ordering property which allows an efficient vlsi implementation of the threshold decomposition architecture motivated by the success of rank order filters in a wide variety of applications and by the ease with which they can now be implemented we consider in this paper a new class of filters called stack filters they share the threshold decomposition and stacking properties of rank order filters but are otherwise unconstrained they are shown to form a very large class of easily implemented nonlinear filters which includes the rank order operators as well as all compositions of morphological operators the convergence properties of these filters are investigated using techniques similar to those used to determine root signal behavior of median filters the results obtained include necessary conditions for a stack filter to preserve monotone regions or edges in signals the output distribution for these filters is also found all the stack filters of window width 3 are determined along with their convergence properties among these filters are found two which we have named asymmetric median filters they share all the properties of median filters except that they remove impulses of one sign only that is one removes only positive going edges the other removes only negative going edges while the median filter removes impulses of both signs this investigation of the properties of stack filters thus produces several new useful and easily implemented filters

interpolation of missing data in image sequences this paper presents a number of model based interpolation schemes tailored to the problem of interpolating missing regions in image sequences these missing regions may be of arbitrary size and of random but known location this problem occurs regularly with archived film material the film is abraded or obscured in patches giving rise to bright and dark flashes known as dirt and sparkle in the motion picture industry both 3 d autoregressive models and 3 d markov random fields are considered in the formulation of the different reconstruction processes the models act along motion directions estimated using a multiresolution block matching scheme it is possible to address this sort of impulsive noise suppression problem with median filters and comparisons with earlier work using multilevel median filters are performed these comparisons demonstrate the higher reconstruction fidelity of the new interpolators

on conservative and monotone one dimensional cellular automata and their particle representation number conserving or conservative cellular automata ca have been used in several contexts in particular traffic models where it is natural to think about them as systems of interacting particles in this article we consider several issues concerning one dimensional cellular automata which are conservative monotone specially non increasing or that allow a weaker kind of conservative dynamics we introduce a formalism of particle automata and discuss several properties that they may exhibit some of which like anticipation and momentum preservation happen to be intrinsic to the conservative ca they represent for monotone ca we give a characterization and then show that they too are equivalent to the corresponding class of particle automata finally we show how to determine for a given ca and a given integer b whether its states admit a b neighborhood dependent relabeling whose sum is conserved by the ca iteration this can be used to uncover conservative principles and particle like behavior underlying the dynamics of some ca compliments at http www dim uchile cl anmoreir ncca

probabilistic collocation for period 1 limit cycle oscillations in this paper probabilistic collocation for limit cycle oscillations pclco is proposed probabilistic collocation pc is a non intrusive approach to compute the polynomial chaos description of uncertainty numerically polynomial chaos can require impractical high orders to approximate long term time integration problems due to the fast increase of required polynomial chaos order with time pclco is a pc formulation for modeling the long term stochastic behavior of dynamical systems exhibiting a periodic response i e a limit cycle oscillation lco in the pc method deterministic time series are computed at collocation points in probability space in pclco pc is applied to a time independent parametrization of the periodic response of the deterministic solves instead of to the time dependent functions themselves due to the time independent parametrization the accuracy of pclco is independent of time the approach is applied to period 1 oscillations with one main frequency subject to a random parameter numerical results are presented for the harmonic oscillator a two dof airfoil flutter model and the fluid structure interaction of an elastically mounted cylinder

brief paper gradient dynamic optimization with legendre chaos the polynomial chaos approach for stochastic simulation is applied to trajectory optimization by conceptually replacing random variables with free variables using the gradient method we generate with low computational cost an accurate parametrization of optimal trajectories

state description for the root signal set of median filters median filtering is a simple digital technique for smoothing signals one main characteristic of the filter is that it maps the input signal space into a root signal space where signals invariant to median filters are called roots of the signal in this paper we develop the theory for the root signal set of median filters a tree structure for the root signal set is obtained for binary signals the number of roots r n for a signal of length n and window size filter 2s 1 is exactly represented by the difference equation r n r n 1 r n s a general solution is obtained in a z domain approach finally a method for faster one dimensional median filter operation is introduced

characterization of random fluctuation based computation in cellular automata a brownian cellular automaton bca is an asynchronous cellular automaton aca in which the timing of the local cell state transitions approaches a poisson point process and the global transitions between cell configurations can be formulated as an irreducible and reversible markov chain bcas exploit fluctuations to find solutions of a computation via a stochastic search process this characteristic has been shown to contribute to a decreased complexity of bcas lee and peper 2008 as compared to non brownian acas with equivalent functionality this paper proposes a computationally universal bca that is based on a conventional aca rather than the previously employed block cellular automata which have transition rules that update each cell simultaneously with its neighboring cells the proposed allows for a more natural update model in which no need arises to locally synchronize the cells in a neighborhood the resulting bca is significantly less complex than equivalent non block non brownian acas which demonstrates that random fluctuations can serve as a powerful resource for efficient computation on cellular automata

ingredients for a general purpose stochastic finite elements implementation abstract the stochastic finite element method has seen a number of incarnations over the past few years ranging from perturbation based methods and neumann expansion based methods to methods for evaluating bounds on the response variability and more recent functional expansions combined with error minimization techniques of these only the last class of methods provides an explicit expression to the solution of the problem as opposed to evaluating a global norm of this solution and can thus be viewed as a rational extension of concepts developed for the deterministic finite element method the main obstacle in developing a substantial user community of various stochastic finite element methods has been the lack of a general purpose formalism for addressing issues of general engineering interest this paper presents the main ingredients for developing a general purpose version of the spectral stochastic finite element method

movie denoising by average of warped lines here we present an efficient method for movie denoising that does not require any motion estimation the method is based on the well known fact that averaging several realizations of a random variable reduces the variance for each pixel to be denoised we look for close similar samples along the level surface passing through it with these similar samples we estimate the denoised pixel the method to find close similar samples is done via warping lines in spatiotemporal neighborhoods for that end we present an algorithm based on a method for epipolar line matching in stereo pairs which has per line complexity o n where is the number of columns in the image in this way when applied to the image sequence our algorithm is computationally efficient having a complexity of the order of the total number of pixels furthermore we show that the presented method is unsupervised and is adapted to denoise image sequences with an additive white noise while respecting the visual details on the movie frames we have also experimented with other types of noise with satisfactory results

modeling uncertainty in flow simulations via generalized polynomial chaos we present a new algorithm to model the input uncertainty and its propagation in incompressible flow simulations the stochastic input is represented spectrally by employing orthogonal polynomial functionals from the askey scheme as trial basis to represent the random space a standard galerkin projection is applied in the random dimension to obtain the equations in the weak form the resulting system of deterministic equations is then solved with standard methods to obtain the solution for each random mode this approach can be considered as a generalization of the original polynomial chaos expansion first introduced by wiener am j math 60 1938 897 the original method employs the hermite polynomials one of the 13 members of the askey scheme as the basis in random space the algorithm is applied to micro channel flows with random wall boundary conditions and to external flows with random freestream efficiency and convergence are studied by comparing with exact solutions as well as numerical solutions obtained by monte carlo simulations it is shown that the generalized polynomial chaos method promises a substantial speed up compared with the monte carlo method the utilization of different type orthogonal polynomials from the askey scheme also provides a more efficient way to represent general non gaussian processes compared with the original wiener hermite expansions

a generalization of median filtering using linear combinations of order statistics we consider a class of nonlinear filters whose output is given by a linear combination of the order statistics of the input sequence assuming a constant signal in white noise the coefficients in the linear combination are chosen to minimize the output mse for several noise distributions it is shown that the optimal order statistic filter osf tends toward the median filter as the noise becomes more impulsive the optimal osf is applied to an actual noisy image and is shown to perform well combining properties of both the averaging and median filters a more general design scheme for applications involving nonconstant signals is also given

the side information median filter for sequence reconstruction the side information median filter simf is introduced as an extension of the standard median filter mf for sequence reconstruction purposes the simf uses the filter input sequence index information for determining the filter output sequence index of an equivalent mf output the set of simf roots is shown to be larger than that of mf roots including many bounded variations which would be distorted by the mf several deterministic and statistical properties are derived for the simf and the simf is shown to preserve details in many more sequences than the mf while still maintaining noise impulse rejection properties similar to those of the mf experimental results are presented from sequence reconstruction tasks involving the simf the mf and other sequence reconstruction filters results for both 1 d and 2 d sequences are reported

a new class of detail preserving filters for image processing a new class of median type filters for image processing is proposed in the filters linear fir substructures are used in conjunction with the median operation the root signals and noise attenuation properties of the fir median hybrid filters are analyzed and compared to representative edge preserving filtering operations the concept of multilevel median operation is introduced to improve the detail preserving property of conventional median and the fir median hybrid filters in the multilevel filters there exists a tradeoff between noise attenuation and detail preservation the analysis and examples indicate that fir median hybrid filters preserve details better and are computationally much more efficient than the conventional median and the k nearest neighbor averaging filters

decreasing the sensitivity of open loop optimal solutions in decision making under uncertainty abstract we consider a framework for approximating the effects of uncertainty within the mean variance optimization model for nonlinear systems we present formulations for controlling the effects of parameter uncertainty characterized by continuous probability density functions pdfs robust strategies capable of capturing up to second order effects of the uncertainty are derived and tested on a dynamic optimisation problem arising from a chemical engineering application an associated sensitivity robustness concept is discussed and shown to be an instance of the general approximation framework developed the accuracy of the approximations or the local nature of the robust solutions is studied using monte carlo mc simulations

the wiener askey polynomial chaos for stochastic differential equations we present a new method for solving stochastic differential equations based on galerkin projections and extensions of wiener s polynomial chaos specifically we represent the stochastic processes with an optimum trial basis from the askey family of orthogonal polynomials that reduces the dimensionality of the system and leads to exponential convergence of the error several continuous and discrete processes are treated and numerical examples show substantial speed up compared to monte carlo simulations for low dimensional stochastic inputs

relationship between backward stochastic differential equations and stochastic controls a linear quadratic approach it is well known that backward stochastic differential equations bsdes stem from the study on the pontryagin type maximum principle for optimal stochastic controls a solution of a bsde hits a given terminal value which is a random variable by virtue of an it additional martingale term and an indefinite initial state this paper attempts to explore the relationship between bsdes and stochastic controls by interpreting bsdes as some stochastic optimal control problems more specifically associated with a bsde a new stochastic control problem is introduced with the same dynamics but a definite given initial state the martingale term in the original bsde is regarded as the control and the objective is to minimize the second moment of the difference between the terminal state and the terminal value given in the bsde this problem is solved in a closed form by the stochastic linear quadratic lq theory developed recently the general result is then applied to the black scholes model where an optimal mean variance hedging portfolio is obtained explicitly in terms of the option price finally a modified model is investigated where the difference between the state and the expectation of the given terminal value at any time is taken into account

median filtering in constant time the median filter is one of the basic building blocks in many image processing situations however its use has long been hampered by its algorithmic complexity o tau of in the kernel radius with the trend toward larger images and proportionally larger filter kernels the need for a more efficient median filtering algorithm becomes pressing in this correspondence a new simple yet much faster algorithm exhibiting o 1 runtime complexity is described and analyzed it is compared and benchmarked against previous algorithms extensions to higher dimensional or higher precision data and an approximation to a circular kernel are presented as well

detail preserving ranked order based filters for image processing the theoretical analysis of multistage median filters is developed it is shown that multistage median filters are a combination of max median and min median filters since multistage median filters belong to the class of two dimensional stack filters they have threshold decomposition attributes making their theoretical analysis simple statistical threshold decomposition is applied to derive the statistical characteristics of these filters and the results are used to evaluate the performance of these two types of multistage filters finally a quantitative and qualitative comparison of the multistage filters and of other efficient detail preserving filters is presented the comparisons are made using the mean squared error and the mean absolute error criteria

optimal pivot selection in fast weighted median search weighted median filters are increasingly being used in signal processing applications and thus fast implementations are of importance this paper introduces a fast algorithm to compute the weighted median wm of n samples which has linear time and space complexity as opposed to o n log n which is the time complexity of traditional sorting algorithms a popular selection algorithm often used to find the wm in large data sets is quickselect whose performance is highly dependent on how the pivots are chosen we introduce an optimization based pivot selection strategy which results in significantly improved performance as well as a more consistent runtime compared to traditional approaches the selected pivots are order statistics of subsets in order to find the optimal order statistics as well as the optimal subset sizes a set of cost functions are derived which when minimized lead to optimal design parameters we compare the complexity to floyd and rivest s algorithm select which to date has been the fastest median finding algorithm and we show that the proposed algorithm compared with select requires close to 30 fewer comparisons it is also shown that the proposed selection algorithm is asymptotically optimal for large n

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

analysis of the properties of median and weighted median filters using threshold logic and stack filter representation the deterministic properties of weighted median wm filters are analyzed threshold decomposition and the stacking property together establish a unique relationship between integer and binary domain filtering the authors present a method to find the weighted median filter which is equivalent to a stack filter defined by a positive boolean function because the cascade of wm filters can always be expressed as a single stack filter this allows expression of the cascade of wm filters as a single wm filter a direct application is the computation of the output distribution of a cascade of wm filters the same method is used to find a nonrecursive expansion of a recursive wm filter as applications of theoretical results several interesting deterministic and statistical properties of wm filters are derived

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

fuzzy rank lum filters the rank information of samples is widely utilized in nonlinear signal processing algorithms recently developed fuzzy transformation theory introduces the concept of fuzzy ranks which incorporates sample spread or sample diversity information into the sample ranking framework thus the fuzzy rank reflects a sample s rank as well as its similarity to the other sample namely joint rank order and spread and can be utilized to improve the performance of the conventional rank order based filters in this paper the well known lower upper middle lum filters are generalized utilizing the fuzzy ranks yielding the class of fuzzy rank lum f lum filters statistical and deterministic properties of the f lum filters are derived showing that the f lum smoothers have similar impulsive noise removal capability to the lum smoothers while preserving the image details better the f lum sharpeners are capable of enhancing strong edges while simultaneously preserving small variations the performance of the f lum filters are evaluated for the problems of image impulsive noise removal sharpening and edge detection preprocessing the experimental results show that the f lum smoothers can achieve a better tradeoff between noise removal and detail preservation than the lum smoothers the f lum sharpeners are capable of sharpening the image edges without amplifying the noise or distorting the fine details the joint smoothing and sharpening operation of the general f lum filters also showed superiority in edge detection preprocessing application in conclusion the simplicity and versatility of the f lum filters and their advantages over the conventional lum filters are desirable in many practical applications this also shows that utilizing fuzzy ranks in filter generalization is a promising methodology

asynchronous game of life abstract this paper proposes the 8 state asynchronous equivalence of the well known game of life gl our cellular automaton called asynchronous game of life simulates exactly the behavior of the gl such as universal computation and self organization no matter whether the update of cells is simultaneous or independent according to some updating scheme like a step driven or time driven method see biosystems 51 1999 123 we employ the updating scheme of blok and bergersen phys rev e 59 1999 3876 such that at every time step each cell has a certain probability to be updated and investigate the statistical properties of our model through power spectral analyses

detection of missing data in image sequences bright and dark flashes are typical artifacts in degraded motion picture material the distortion is referred to as dirt and sparkle in the motion picture industry this is caused either by dirt becoming attached to the frames of the film or by the film material being abraded the visual result is random patches of the frames having grey level values totally unrelated to the initial information at those sites to restore the film without causing distortion to areas of the frames that are not affected the locations of the blotches must be identified heuristic and model based methods for the detection of these missing data regions are presented in this paper and their action on simulated and real sequences is compared

noise reduction of image sequences using motion compensation and signal decomposition in this paper a new spatio temporal filtering method for removing noise from image sequences is proposed this method combines the use of motion compensation and signal decomposition to account for the effects of object motion because of object motion image sequences are temporally nonstationary which requires the use of adaptive filters by motion compensating the sequence prior to filtering nonstationarities i e parts of the signal that are momentarily not stationary can be reduced significantly however since not all nonstationarities can be accounted for by motion a motion compensated signal still contains nonstationarities an adaptive algorithm based on order statistics is described that decomposes the motion compensated signal into a noise free nonstationary part and a noisy stationary part an rls filter is then used to filter the noise from the stationary signal our new method is experimentally compared with various noise filtering approaches from literature

simulation of stochastic systems via polynomial chaos expansions and convex optimization polynomial chaos expansions represent a powerful tool to simulate stochastic models of dynamical systems yet deriving the expansion s coefficients for complex systems might require a significant and nontrivial manipulation of the model or the computation of large numbers of simulation runs rendering the approach too time consuming and impracticable for applications with more than a handful of random variables we introduce a computationally tractable technique for computing the coefficients of polynomial chaos expansions the approach exploits a regularization technique with a particular choice of weighting matrices which allows to take into account the specific features of polynomial chaos expansions the method completely based on convex optimization can be applied to problems with a large number of random variables and uses a modest number of monte carlo simulations while avoiding model manipulations additional information on the stochastic process when available can be also incorporated in the approach by means of convex constraints we show the effectiveness of the proposed technique in three applications in diverse fields including the analysis of a nonlinear electric circuit a chaotic model of organizational behavior and finally a chemical oscillator