scaling in the aging dynamics of the site diluted ising model we study numerically the phase ordering kinetics of the two dimensional site diluted ising model the data can be interpreted in a framework motivated by renormalization group concepts apart from the usual fixed point of the nondiluted system there exist two disorder fixed points characterized by logarithmic and power law growth of the ordered domains this structure gives rise to a rich scaling behavior with an interesting crossover due to the competition between fixed points and violation of superuniversality

a fuzzy operator for the enhancement of blurred and noisy images rule based fuzzy operators are a novel class of operators specifically designed in order to apply the principles of approximate reasoning to digital image processing this paper shows how a fuzzy operator that is able to perform detail sharpening but is insensitive to noise can be designed the results obtainable by the proposed technique in the enhancement of a real image are presented

a multidimensional nonlinear edge preserving filter for magnetic resonance image restoration the paper presents a multidimensional nonlinear edge preserving filter for restoration and enhancement of magnetic resonance images mri the filter uses both interframe parametric or temporal and intraframe spatial information to filter the additive noise from an mri scene sequence it combines the approximate maximum likelihood equivalently least squares estimate of the interframe pixels using mri signal models with a trimmed spatial smoothing algorithm using a euclidean distance discriminator to preserve partial volume and edge information partial volume information is generated from voxels containing a mixture of different tissues since the filter s structure is parallel its implementation on a parallel processing computer is straightforward details of the filter implementation for a sequence of four multiple spin echo images is explained and the effects of filter parameters neighborhood size and threshold value on the computation time and performance of the filter is discussed the filter is applied to mri simulation and brain studies serving as a preprocessing procedure for the eigenimage filter the eigenimage filter generates a composite image in which a feature of interest is segmented from the surrounding interfering features it outperforms conventional pre and post processing filters including spatial smoothing low pass filtering with a gaussian kernel median filtering and combined vector median with average filtering

fast impulsive noise removal a generic n dimensional filter with the primary purpose of eliminating impulsive like noise is presented this recursive nonlinear filter is composed of two conditional rules which are applied independently in any order one after the other it identifies noisy items by inspection of their surrounding neighborhood and afterwards it replaces their values with the most conservative ones out of their neighbors values in this way no new values are introduced and the histogram distribution range is conserved this n dimensional filter can be decomposed recursively to a lower dimensional space each time generating two sets of n n 1 dimensional filters this study which focuses on the case of two dimensional signals gray scale images explores one possible implementation of this new filter and orients the evaluation of its performance toward the median filter as this filter is the basis of many more sophisticated filters for impulsive noise reduction tests were carried out using both real and artificial images we found this new filter to be much faster than the median filter while performing comparably in terms of both image information conservation and noise reduction which suggests that it could replace the median filter for the preliminary processing included in state of the art noise removal filters this new filter should either eliminate or attenuate most noisy pixels in synthetic and natural images not excessively contaminated it has a slight smoothing effect on nonnoisy image regions in addition it is scalable easily implemented and adaptable to specific applications

alpha trimmed means and their relationship to median filters suppose that x is a finite set of n numbers the trimmed mean of x is obtained by sorting x into ascending order removing trimming a fixed fraction alpha 0 leq alpha leq 0 5 from the high and low ends of the sorted set and computing the average of the remaining values when applied to a sliding window of length lw the trimming process is called trim filtering for 0 5 the trimmed mean of a set is the median of the set and the filtering operation is called median filtering repeated application of a median filter to the output of a previous median filter of the same length lw eventually produces a signal which is invariant to median filtering this final signal is called the root signal this paper explains the relationship between trimmed means and median filters derives a simple straightforward and fast algorithm for applying a median filter and provides a new explanation of the convergence of repeated median filtering to the root signal the latter result incorporates an approach which permits generalization of the associated concepts to a larger class of index map filters

polynomial time verification of diagnosability of partially observed discrete event systems the problem of verifying the properties of diagnosability and i diagnosability is considered we present new polynomial time algorithms for deciding diagnosability and i diagnosability these algorithms are based on the construction of a nondeterministic automaton called a verifier

theoretical aspects of vertically invariant gray level morphological operators and their application on adaptive signal and image filtering we use vertically invariant morphological filters for time varying or adaptive signal processing the morphological filters adopted in this paper are vertically invariant openings and closings vertically invariant openings and closings have intuitive geometric interpretations and can provide different filtering scales with respect to different spatial positions hence they are suitable for adaptive signal filtering to adaptively assign structuring elements of the vertically invariant openings or closings we develop the progressive umbra filling puf procedure experimental results have shown that our approach can successfully eliminate noise without oversmoothing the important features of a signal

on solvability of a decentralized supervisory control problem with communication we study a decentralized control problem of discrete event systems with communication we first give a general automata theoretic formalism that enables us to handle various types of communication including delay the decentralized control problem is shown to be undecidable under this formalism next we propose a semi decision procedure for computing finite state controllers to achieve a given specification in the sense of bisimilarity between the controlled system and a given specification using this procedure we prove decidability of the problem for two special cases one is the case in which the communication behavior is given as k bounded delay communication and the other is the case in which any cycle in the state transition diagram of the system contains an event observable by all controllers we also show a method for optimizing controllers based on a graph problem

supervisory control of a class of discrete event processes this paper studies the control of a class of discrete event processes i e processes that are discrete asynchronous and possibly nondeter ministic the controlled process is described as the generator of a formal language while the controller or supervisor is constructed from the grammar of a specified target language that incorporates the desired closed loop system behavior the existence problem for a supervisor is reduced to finding the largest controllable language contained in a given legal language two examples are provided

on signal recovery with adaptive order statistic filters adaptive order statistic filters are used to estimate a constant amplitude signal embedded in noise with unknown statistics iterative algorithms are proposed which adapt the order statistic filter to minimize the mean square estimation error both with and without an unbiasedness constraint for each case the algorithm used is shown to achieve convergence in the mean to the optimal filter properties of the convergence rates are discussed and conditions for convergence in mean square are noted

design and performance of combination filters for signal restoration a class of nonlinear moving window filters is investigated for restoration and detection of signals embedded in additive white gaussian and non gaussian noise the filters of this class called combination filters c filters use both rank order and temporal order information from the input observation sequence within finite processing windows to produce the outputs a c filter combines the characteristics of linear fir finite impulse response filters and nonlinear filters of the order statistics type the output of a c filter in each processing window is defined to be the rank order dependent weighting of temporal order data both analytical and training procedures for designing the c filters are considered to get further improvement in overall performance the authors extend the concept of c filters and introduce a class of generalized c filters gc filters which are shown to have desirable properties performance characteristics of c and gc filters in signal restoration are considered through computer simulation

adaptive trimmed mean filters for image restoration an adaptive smoothing filter is proposed for reducing noise in digital signals of any dimensionality the adaptive procedure is based on the selection of an appropriate inner or outer trimmed mean filter according to local measurements of the tail behavior impulsivity of the noise process the set of trimmed means used provides robustness against a wide range of noise possibilities ranging from very shallow tailed to very heavy tailed a monte carlo analysis using a family of generalized exponential distributions supports the choice of the trimmed mean selected for measured values of an impulsivity statistic the assumption underlying the definition of the filter is that the signal to be filtered is locally smoothly varying and that the noise process is uncorrelated and derives from an unknown unimodal symmetric distribution for image processing applications a second statistic is used to mark the location of abrupt intensity changes or edges in the vicinity of an edge the trend preserving median filter is used since the impulsivity and edge statistics used in defining the adaptive filter are both functions of order statistics the extra computation required for their calculation is minimal examples are provided of the filter as applied to images corrupted by a variety of noises

a theoretical analysis of the properties of median filters necessary and sufficient conditions for a signal to be invariant under a specific form of median filtering are derived these conditions state that a signal must be locally monotone to pass through a median filter unchanged it is proven that the form of successive median filtering of a signal i e the filtered output is itself again filtered eventually reduces the original signal to an invariant signal called a root signal for a signal of length l samples a maximum of frac 1 2 l 2 repeated filterings produces a root signal

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

adaptive alpha trimmed mean filters under deviations from assumed noise model alpha trimmed mean filters are widely used for the restoration of signals and images corrupted by additive non gaussian noise they are especially preferred if the underlying noise deviates from gaussian with the impulsive noise components the key design issue of these filters is to select its only parameter spl alpha optimally for a given noise type in image restoration adaptive filters utilize the flexibility of selecting spl alpha according to some local noise statistics in the present paper we first review the existing adaptive alpha trimmed mean filter schemes we then analyze the performance of these filters when the underlying noise distribution deviates from the gaussian and does not satisfy the assumptions such as symmetry specifically the clipping effect and the mixed noise cases are analyzed we also present a new adaptive alpha trimmed filter implementation that detects the nonsymmetry points locally and applies alpha trimmed mean filter that trims out the outlier pixels such as edges or impulsive noise according to this local decision comparisons of the speed and filtering performances under deviations from symmetry and gaussian assumptions show that the proposed filter is a very good alternative to the existing schemes

deciding co observability is pspace complete in this note we reduce the deterministic finite state automata intersection problem to the problem of deciding co observability for regular languages using a polynomial time many one mapping this demonstrates that the problem of deciding co observability for languages marked by deterministic finite state automata is pspace complete we use a similar reduction to reduce the deterministic finite state automata intersection problem to deciding other versions of co observability introduced in a previous paper these results imply that the co observability of regular languages most likely cannot be decided in polynomial time unless we make further restrictions on the languages these results also show that deciding decentralized supervisor existence is pspace complete and therefore probably intractable

characterization of co observable languages and formulas for their super sublanguages we present fixed point based characterization of several classes of co observable languages that are of interest in the context of decentralized supervisory control of discrete event systems including c p spl or d a co observable languages c p co observable languages and d a co observable languages we also provide formulas for computing super sublanguages for each of these classes in cases where the class of co observable languages is not closed under intersection union we provide upper lower bound of the super sublanguage formula we present the computation of super sublanguages and also computation of their upper lower bounds has lead to the introduction of other classes of co observable languages namely strongly c p co observable languages strongly d a co observable languages locally observable languages and strongly locally observable languages fixed point based characterization of all the above language classes is also given and their closure under intersection union is investigated we also study whether the fixed point operator preserves prefix closure relative closure also called l sub m g closure and controllability

characterizing intransitive noninterference for 3 domain security policies with observability this note introduces a new algorithmic approach to the problem of checking the property of intransitive noninterference ini using discrete event systems dess tools and concepts ini property is widely used in formal verification of security problems in computer systems and protocols the approach consists of two phases first a new property called ip observability observability based on a purge function is introduced to capture ini we prove that a system satisfies ini if and only if it is ip observable second a relation between ip observability and p observability observability as used in des is established by transforming the automaton modeling a system protocol into an automaton where p observability and hence ip observability can be determined this allows us to check ini by checking p observability which can be done efficiently our approach can be used for all systems protocols with three domains or levels which is sufficient for most noninterference problems for cryptographic protocols and systems

brief paper formulae relating controllability observability and co observability we present formulae relating controllability observability and co observability arising in the context of supervisory control of discrete event systems given a discrete event plant g with event set e uncontrollable event set e u i and observation mask m i of the ith supervisor we first show that the infimal prefix closed l g i e u i controllable and l g e u i m i co observable superlanguage equals the intersection taken over all i s of the infimal prefix closed l g e u i controllable and l g m i observable superlanguage next we show that the infimal prefix closed and e m observable superlanguage computation preserves e e u controllability these results can be used to compute individual supervisors in the decentralized control setting

crossover in growth law and violation of superuniversality in the random field ising model we study the nonconserved phase ordering dynamics of the d 2 3 random field ising model quenched to below the critical temperature motivated by the puzzling results of previous work in two and three dimensions reporting a crossover from power law to logarithmic growth together with superuniversal behavior of the correlation function we have undertaken a careful investigation of both the domain growth law and the autocorrelation function our main results are as follows we confirm the crossover to asymptotic logarithmic behavior in the growth law but at variance with previous findings we find the exponent in the preasymptotic power law to be disorder dependent rather than being that of the pure system furthermore we find that the autocorrelation function does not display superuniversal behavior this restores consistency with previous results for the d 1 system and fits nicely into the unifying scaling scheme we have recently proposed in the study of the random bond ising model

on the statistical optimality of locally monotonic regression locally monotonic regression is a recently proposed technique for the deterministic smoothing of finite length discrete signals under the smoothing criterion of local monotonicity locally monotonic regression falls within a general framework for the processing of signals that may be characterized in three ways regressions are given by projections that are determined by semimetrics the processed signals meet shape constraints that are defined at the local level and the projections are optimal statistical estimates in the maximum likelihood sense the authors explore the relationship between the geometric and deterministic concept of projection onto generally nonconvex sets and the statistical concept of likelihood with the object of characterizing projections under the family of the p semi metrics as maximum likelihood estimates of signals contaminated with noise from a well known family of exponential densities

edge detectors based on nonlinear filters a class of edge detectors is proposed which is obtained from the difference of the outputs of two nonlinear filters the nonlinear filters used are based on nonlinear means which contain homomorphic filters or order statistic which contain median filters the performance of these edge detectors in the presence of noise is evaluated and is compared to the performance of well established edge detectors their computational requirements are described and some examples are presented

undecidability in decentralized supervision abstract it is well known that supervisor synthesis problems involving partial observations generally have high computational complexity but was only recently shown in the preliminary version of this article that the solvability of decentralized supervisor synthesis problems may be undecidable even in cases where all pertinent languages are represented as finite automata the result is established by reduction from the halting problem for turing machines alternative proofs discovered independently by other authors employ a reduction from post s correspondence problem

decentralized control of discrete event systems with bounded or unbounded delay communication we introduce problems of decentralized control with delayed communication where delays are either unbounded or bounded by a given constant k in the k bounded delay model between the transmission of a message and its reception the plant can execute at most k events in the unbounded delay model the plant can execute any number of events between transmission and reception we show that our framework yields an infinite hierarchy of control problems where the containments are strict cc is the set of control problems solvable with a single controller centralized case and dcc sub k respectively dcuc dc is the set of problems solvable with two controllers in a k bounded delay network respectively in an unbounded delay network without communication the hierarchy is a result of the following property controllers which work in a given network will also work in a less nondeterministic network this property does not hold when nonblockingness is introduced checking the existence of controllers in the unbounded delay case or in the case without communication are undecidable problems however a related decentralized observation problem with bounded delay communication is decidable

iterative truncated arithmetic mean filter and its properties the arithmetic mean and the order statistical median are two fundamental operations in signal and image processing they have their own merits and limitations in noise attenuation and image structure preservation this paper proposes an iterative algorithm that truncates the extreme values of samples in the filter window to a dynamic threshold the resulting nonlinear filter shows some merits of both the fundamental operations some dynamic truncation thresholds are proposed that guarantee the filter output starting from the mean to approach the median of the input samples as a by product this paper unveils some statistics of a finite data set as the upper bounds of the deviation of the median from the mean some stopping criteria are suggested to facilitate edge preservation and noise attenuation for both the long and short tailed distributions although the proposed iterative truncated mean itm algorithm is not aimed at the median it offers a way to estimate the median by simple arithmetic computing some properties of the itm filters are analyzed and experimentally verified on synthetic data and real images

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

nonlinear scale space filtering and multiresolution system we derive and demonstrate a nonlinear scale space filter and its application in generating a nonlinear multiresolution system for each datum in a signal a neighborhood of weighted data is used for clustering the cluster center becomes the filter output the filter is governed by a single scale parameter that dictates the spatial extent of nearby data used for clustering this together with the local characteristic of the signal determines the scale parameter in the output space which dictates the influences of these data on the output this filter is thus adaptive and data driven it provides a mechanism for a removing impulsive noise b improved smoothing of nonimpulsive noise and c preserving edges comparisons with gaussian scale space filtering and median filters are made using real images using the architecture of the laplacian pyramid and this nonlinear filter for interpolation we construct a nonlinear multiresolution system that has two features 1 edges are well preserved at low resolutions and 2 difference signals are small and spatially localized this filter implicitly presents a new mechanism for detecting discontinuities differing from techniques based on local gradients and line processes this work shows that scale space filtering nonlinear filtering and scale space clustering are closely related and provides a framework within which further image processing image coding and computer vision problems can be investigated

correction to scaling in the response function of the two dimensional kinetic ising model the aging part r ag t s of the impulsive response function of the two dimensional ferromagnetic ising model quenched below the critical point is studied numerically employing an algorithm without the imposition of the external field we find that the simple scaling form r ag t s s ensuremath 1 a f t s which is usually believed to hold in the aging regime is not obeyed we analyze the data assuming the existence of a correction to scaling we find a 0 273 ifmmode pm else textpm fi 0 006 in agreement with previous numerical results obtained from the zero field cooled magnetization we investigate in detail also the scaling function f t s and we compare the results with the predictions of analytical theories we make an ansatz for the correction to scaling deriving an analytical expression for r ag t s this gives a satisfactory qualitative agreement with the numerical data for r ag t s and for the integrated response functions with the analytical model we explore the overall behavior extrapolating beyond the time regime accessible with the simulations we explain why the data for the zero field cooled susceptibility are not too sensitive to the existence of the correction to scaling in r ag t s making this quantity the most convenient for the study of the asymptotic scaling properties

a structure for adaptive order statistics filtering in applications such as smoothing and enhancement of images adaptive filtering techniques offer the flexibility needed for good performance with non stationary observations many adaptive schemes can be based on the idea of determining the local statistics of the signal through appropriate tests on the data to aid in the selection of a filtering procedure that is suited to the data in the paper the authors consider decision directed or data dependent adaptive filtering schemes that are based on order statistics a general formulation for such a class of adaptive order statistics filters is presented approximate statistical performance analysis especially in the presence of edges may be carried out for this entire class of filters the authors give examples of some existing filters that fit into this framework the formulation also accommodates filters that employ multiple windows in their operation to illustrate the potential of this class of multiple window mw filters they construct and analyze simple filters like the triple window median tw med and the triple window median of means tw mom filters that are shown to yield useful performance the class of mean median hybrid mmh filters is also presented as a simple example which may be extended to give interesting performance

modular control of discrete event systems with coalgebra modular supervisory control of discrete event systems where the overall system is a synchronous parallel product of subsystems is considered the main results of this paper are formulations of sufficient conditions for the compatibility between the synchronous product and various operations stemming from supervisory control as supervised product and supremal controllable sublanguages these results are generalized to the case of modules with partial observations e g modular computation of supremal normal sublanguages is studied coalgebraic techniques based on the coinduction proof principle are used in our main results sufficient conditions are derived for modular to equal global control synthesis an algorithmic procedure for checking the new conditions is proposed and the computational benefit of the modular approach is discussed and illustrated by comparing the time complexity of modular and monolithic computation

optimal sensor selection for discrete event systems with partial observation for discrete event systems under partial observation we study the problem of selection of an optimal set of sensors that can provide sufficient yet minimal events observation information the sufficiency of the observed information is captured as the fulfillment of a desired formal property selection of sensors can be viewed as a selection of an observation mask and also of an equivalence class of events a sensor set is called optimal if any coarser selection of the corresponding equivalence class of events results in some significant loss of the events observation information we study an optimal selection of sensors over the set of general nonprojection observation masks we show that this problem is np hard in general for mask monotonic properties we present a top down and a bottom up algorithm each of polynomial complexity we show that observerness is not mask monotonic we show that the computational complexity can be further improved if the property is preserved under the projection via an intermediary observation mask that is an observer our results are obtained in a general setting so that they can be adapted for an optimal selection of sensors for a variety of applications

control of networked discrete event systems dealing with communication delays and losses in this paper we investigate supervisory control of networked discrete event systems such systems are now widely used in practice the key feature of a networked discrete event system is communication delays and losses in observation and control without addressing communication delays and losses in networked systems no control will be effective we propose to use some special observation mappings to deal with observation delays and losses based on these mappings we define network observability we also consider control delays and losses in networked discrete event systems and define network controllability we show that these definitions capture precisely the requirements on effective supervisory control in networked discrete event systems in the sense that a networked supervisor exists if and only if both network controllability and network observability are satisfied

decentralized control of discrete event systems using prioritized composition with exclusion a necessary and sufficient condition for decentralized control of discrete event systems via prioritized composition with exclusion pcx is provided pcx was extended from prioritized synchronous composition psc in order to model various boolean modes of decision fusion by allowing the exclusivity of participation in system interactions a notion of pcx coobservability is introduced that captures the nature of pcx based decision fusion and a testing algorithm with polynomial complexity in the states of plant and specification is presented the result is extended for multiple supervisors with multiple exclusivity sets based on an extended pcx definition

a new algorithm for image noise reduction using mathematical morphology morphological openings and closings are useful for the smoothing of gray scale images however their use for image noise reduction is limited by their tendency to remove important thin features from an image along with the noise the paper presents a description and analysis of a new morphological image cleaning algorithm mic that preserves thin features while removing noise mic is useful for gray scale images corrupted by dense low amplitude random or patterned noise such noise is typical of scanned or still video images mic differs from previous morphological noise filters in that it manipulates residual images the differences between the original image and morphologically smoothed versions it calculates residuals on a number of different scales via a morphological size distribution it discards regions in the various residuals that it judges to contain noise mic creates a cleaned image by recombining the processed residual images with a smoothed version the paper describes the mic algorithm in detail discusses the effects of parametric variations presents the results of a noise analysis and shows a number of examples of its use including the removal of scanner noise it also demonstrates that mic significantly improves the jpeg compression of a gray scale image

lum filters a class of rank order based filters for smoothing and sharpening a new class of rank order based filters called lower upper middle lum filters is introduced the output of these filters is determined by comparing a lower and an upper order statistic to the middle sample in the filter window these filters can be designed for smoothing and sharpening or outlier rejection the level of smoothing done by the filter can range from no smoothing to that of the median filter this flexibility allows the lum filter to be designed to best balance the tradeoffs between noise smoothing and signal detail preservation lum filters for enhancing edge gradients can be designed to be insensitive to low levels of additive noise and to remove impulsive noise furthermore lum filters do not cause overshoot or undershoot some statistical and deterministic properties of the lum filters are developed and a number of experimental results are presented to illustrate the performance these experiments include applications to 1d signals and to images

think globally act locally decentralized supervisory control decentralized supervisory control is investigated by considering problem formulations that model systems whose specifications are given as global constraints but whose solution is described by local controllers a necessary and sufficient condition is given for the existence of a solution to the problem of finding decentralized supervisors that ensure that the behavior of the closed loop system lies in a given range where the range of behavior can be described by regular languages it can be effectively tested whether the decentralized control problem is solvable in this case a procedure is given to compute the associated supervisors

theory of order statistic filters and their relationship to linear fir filters necessary and or sufficient conditions on both the filter coefficients and the signal process are derived in order that nonrecursive order statistic os and linear filtering are equivalent operations the results indicate that an understanding of os filters hinges on a better understanding of the properties of signals containing logically monotonic components the results extend a number of previous theories characterizing the well known median and ranked order filters to a broader class of filters and input signals

estimation from quantized gaussian measurements when and how to use dither subtractive dither is a powerful method for removing the signal dependence of quantization noise for coarsely quantized signals however estimation from dithered measurements often naively applies the sample mean or midrange even when the total noise is not well described with a gaussian or uniform distribution we show that the generalized gaussian distribution approximately describes subtractively dithered quantized samples of a gaussian signal furthermore a generalized gaussian fit leads to simple estimators based on order statistics that match the performance of more complicated maximum likelihood estimators requiring iterative solvers the order statistics based estimators outperform both the sample mean and midrange for nontrivial sums of gaussian and uniform noise additional analysis of the generalized gaussian approximation yields rules of thumb for determining when and how to apply dither to quantized measurements specifically we find subtractive dither to be beneficial when the ratio between the gaussian standard deviation and quantization interval length is roughly less than one third when that ratio is also greater than 0 822 k text 0 930 for the number of measurements k text 20 estimators we present are more efficient than the midrange

robust edge detection edge detection is an important issue in computer vision and image understanding systems most conventional techniques have assumed gaussian noise and their performance could decrease with the departure of noise distribution from normality in this paper we present an edge detection approach using robust statistics the edge structure is first detected by a robust one way design model and then localized by a robust contrast test finally hysteresis thresholding is applied to yield the output edge map to evaluate its performance experiments were carried out on synthetic and real images corrupted with both gaussian noise and a mixture of gaussian and impulsive noise the results show that the performance of the proposed edge detector is stable and reliable under severe impulsive noise conditions

on the supermal controllable sublanguage of a given language the concept of controllable language has been shown to play a basic role in the existence theory of supervisory controls for discrete event processes in this paper the supremal controllable sublanguage s of a given language l is characterized as the largest fixpoint of a monotone operator omega in the case where the languages involved are regular it is shown that the fixpoint s can be computed as the limit of the finite sequence k j given by k j 1 omega k j k 0 l an effective computational algorithm is developed and three examples are provided for illustration

nonlinear response and fluctuation dissipation relations a unified derivation of the off equilibrium fluctuation dissipation relations fdrs is given for ising and continuous spins to arbitrary order within the framework of markovian stochastic dynamics knowledge of the fdrs allows one to develop zero field algorithms for the efficient numerical computation of the response functions two applications are presented in the first one the problem of probing for the existence of a growing cooperative length scale is considered in those cases like in glassy systems where the linear fdr is of no use the effectiveness of an appropriate second order fdr is illustrated in the test case of the edwards anderson spin glass in one and two dimensions in the second application the important problem of the definition of an off equilibrium effective temperature through the nonlinear fdr is considered it is shown that in the case of coarsening systems the effective temperature derived from the second order fdr is consistent with the one obtained from the linear fdr

streaking in median filtered images this paper presents a probabilistic analysis of the streaking or blotching effect commonly observed in median filtered signals in both one and two dimensions the effcts are identified as runs of equal or nearly equal values which create visual impressions that have no visual correlate for one dimensional discrete iid random signals with continuous input probability densities the probability of a streak of length l occurring is computed and shown to be independent of the input probability distribution expressions for the first and second moments of the streak length are also derived and certain asymptotic results are given as the analysis and definition of the analogous effect in two dimensions is less tractable the probability that medians taken from distinct overlapping windows will take the same value is derived for various filter geometries the analytic results are supported by examples using both one and two dimensional signals

a class of order statistic lms algorithms conventional gradient based adaptive filters as typified by the well known lms algorithm use an instantaneous estimate of the error surface gradient to update the filter coefficients such a strategy leaves the algorithm extremely vulnerable to impulsive interference a class of adaptive algorithms employing order statistic filtering of the sampled gradient estimates is presented these algorithms dubbed order statistic least mean squares oslms are designed to facilitate adaptive filter performance close to the least squares optimum across a wide range of input environments from gaussian to highly impulsive three specific oslms filters are defined the median lms the average lms and the trimmed mean lms the properties of these algorithms are investigated and the potential for improvement demonstrated finally a general adaptive oslms scheme in which the nature of the order statistic operator is also adapted in response to the statistics of the input signal is presented it is shown that this can facilitate performance gains over a wide range of input data types

combination median filter a detail and structure preserving smoothing filter is introduced it is called the combination median filter as it uses directional median multilevel median and median filters to smooth different regions of the image the decision about the region is made by robust dixon s r test which is well known in statistics for outlier detection the threshold value of dixon s test can be kept constant as a result the filtering algorithm operates like a quasi nonadaptive filter and no computation of local statistics is involved some properties of the filter as well as detailed experimental results that demonstrate its superior performance are presented

a class of adaptive directional image smoothing filters the gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions the idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates a line segment in that direction hence the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance in this paper we describe a class of adaptive directional image smoothing filters based on generalized gaussian distributions we propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized gaussian distribution several experimental results indicate a significant improvement compared to some standard filters

the computational complexity of decentralized discrete event control problems computational complexity results are obtained for decentralized discrete event control problems these results generalize the earlier work of tsitsiklis 1989 who showed that for a special class of centralized supervisory control problems under partial observation there is an algorithm for determining in polynomial time whether or not a solution exists the negative complexity results associated with tsitsiklis work also carry over to the decentralized case so that solution existence for the more general class is not decidable in polynomial time nor does there exist a polynomial time algorithm for producing supervisor solutions when such solutions exist

a robust approach to image enhancement based on fuzzy logic in this paper we propose a robust approach to image enhancement based on fuzzy logic that addresses the seemingly conflicting goals of image enhancement i removing impulse noise ii smoothing out nonimpulse noise and iii enhancing or preserving edges and certain other salient structures we derive three different filters for each of the above three tasks using the weighted or fuzzy least squares ls method and define the criteria for selecting each of the three filters the criteria are based on the local context and they constitute the antecedent clauses of the fuzzy rules the overall result of the fuzzy rule based system is the combination of the results of the individual filters where each result contributes to the degree that the corresponding antecedent clause is satisfied this approach gives us a powerful and flexible image enhancement paradigm results of the proposed method on several types of images are compared with those of other standard techniques

decentralized supervisory control with intersection based architecture we investigate a new decentralized control architecture called intersection based architecture we propose a decentralized control protocol under this architecture called state estimator intersection based protocol sei protocol where each local supervisor sends its state estimate to the fusion sites and these sites take the intersection of these estimates in order to make a control decision the necessary and sufficient conditions for the achievability of the specification under this protocol is provided this condition is termed as state estimator intersection based coobservability sei coobservability a polynomial time algorithm for the verification of sei coobservability is provided we show that the languages that can be achieved under the sei protocol are incomparable with languages that can be achieved under existing architectures

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

on codiagnosability and coobservability with dynamic observations codiagnosability and coobservability in discrete event systems where observations are dynamic are considered instead of having a fixed set of observable events the observation of an event is dynamic trace dependent in this paper a procedure is developed to transform the problem of coobservability to the problem of codiagnosability in the context of dynamic observations this proves that problems of coobservability are transformable to problems of codiagnosability and enables us to leverage the large literature available for codiagnosability to solve problems of coobservability furthermore in the case of dynamic observations the known polynomial complexity tests for the property of codiagnosability based on verifier automata with fixed observable event set s are no longer directly applicable a new testing procedure is developed that can handle transition based dynamic observations and remains of polynomial complexity in the state space of the system this new testing procedure employs a covering of the state space of the system based on cluster automata which enhances its computational efficiency based on cluster automata a new type of verifier automaton is built called the c verifier for verification of codiagnosability as an application of the above mentioned transformation the c verifier becomes a unified method for verifying both codiagnosability and coobservability