rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden markov model in this correspondence we have presented a rotation and gray scale transform invariant texture recognition scheme using the combination of quadrature mirror filter qmf bank and hidden markov model hmm in the first stage the qmf bank is used as the wavelet transform to decompose the texture image into subbands the gray scale transform invariant features derived from the statistics based on first order distribution of gray levels are then extracted from each subband image in the second stage the sequence of subbands is modeled as a hidden markov model hmm and one hmm is designed for each class of textures the hmm is used to exploit the dependence among these subbands and is able to capture the trend of changes caused by rotation during recognition the unknown texture is matched against all the models the best matched model identifies the texture class up to 93 33 classification accuracy is reported

generalized chromosome genetic algorithm for generalized traveling salesman problems and its applications for machining traveling salesman problems tsp and generalized traveling salesman problems gtsp are two kinds of well known and challenging combinatorial optimization problems with much diversified application fields between the two application problems the gtsp is more complex than tsp many researchers have studied tsp extensively but relatively fewer studies pay attention to gtsp and also its solution using genetic algorithm ga in this paper the structure of conventional chromosome is generalized to be a chromosome termed as a generalized chromosome gc a genetic scheme named as generalized chromosome based genetic algorithm gcga is also presented the proposed gcga enables gtsp and tsp to be solved under a uniform algorithm mode forty one benchmark test problems have been solved with the known optimal solutions using the proposed algorithm to verify its validity the test results show that gcga can directly solve gtsp without the need of intermediate transformation to tsp

local search for the probabilistic traveling salesman problem correction to the 2 p opt and 1 shift algorithms abstract the probabilistic traveling salesman problem concerns the best way to visit a set of customers located in some metric space where each customer requires a visit only with some known probability a solution to this problem is an a priori tour which visits all customers and the objective is to minimize the expected length of the a priori tour over all customer subsets assuming that customers in any given subset must be visited in the same order as they appear in the a priori tour this problem belongs to the class of stochastic vehicle routing problems a class which has received increasing attention in recent years and which is of major importance in real world applications several heuristics have been proposed and tested for the probabilistic traveling salesman problem many of which are a straightforward adaptation of heuristics for the classical traveling salesman problem in particular two local search algorithms 2 p opt and 1 shift were introduced by bertsimas in a previous report we have shown that the expressions for the cost evaluation of 2 p opt and 1 shift moves as proposed by bertsimas are not correct in this paper we derive the correct versions of these expressions and we show that the local search algorithms based on these expressions perform significantly better than those exploiting the incorrect expressions

2 d shape classification using hidden markov model the authors present a planar shape recognition approach based on the hidden markov model and autoregressive parameters this approach segments closed shapes to make classifications at a finer level the algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion an orientation scheme is described to make the overall classification insensitive to shape orientation excellent recognition results have been reported a distinct advantage of the approach is that the classifier does not have to be trained again when a new class of shapes is added

competitive random sequential adsorption of point and fixed sized particles analytical results we study the kinetics of competitive random sequential adsorption rsa of particles of binary mixture of points and fixed sized particles within the mean field approach the present work is a generalization of the random car parking problem in the sense that it considers the case when either a car of fixed size is parked with probability q or the parking space is partitioned into two smaller spaces with probability 1 q at each time event this allows an interesting interplay between the classical rsa problem at one extreme q 1 and the kinetics of fragmentation processes at the other extreme q 0 we present exact analytical results for coverage for a whole range of q values and physical explanations are given for different aspects of the problem in addition a comprehensive account of the scaling theory emphasizing dimensional analysis is presented and the exact expression for the scaling function and exponents are obtained

rotation invariant texture classification using lbp variance lbpv with global matching local or global rotation invariant feature extraction has been widely used in texture classification local invariant features e g local binary pattern lbp have the drawback of losing global spatial information while global features preserve little local texture information this paper proposes an alternative hybrid scheme globally rotation invariant matching with locally variant lbp texture features using lbp distribution we first estimate the principal orientations of the texture image and then use them to align lbp histograms the aligned histograms are then in turn used to measure the dissimilarity between images a new texture descriptor lbp variance lbpv is proposed to characterize the local contrast information into the one dimensional lbp histogram lbpv does not need any quantization and it is totally training free to further speed up the proposed matching scheme we propose a method to reduce feature dimensions using distance measurement the experimental results on representative databases show that the proposed lbpv operator and global matching scheme can achieve significant improvement sometimes more than 10 in terms of classification accuracy over traditional locally rotation invariant lbp method

modelling gap size distribution of parked cars using random matrix theory abstract we apply the random matrix theory to the car parking problem for this purpose we adopt a coulomb gas model that associates the coordinates of the gas particles with the eigenvalues of a random matrix the nature of interaction between the particles is consistent with the tendency of the drivers to park their cars near to each other and in the same time keep a distance sufficient for manoeuvring we show that the recently measured gap size distribution of parked cars in a number of roads in central london is well represented by the spacing distribution of a gaussian unitary ensemble

further results on the probabilistic traveling salesman problem abstract in 1985 jaillet introduced the probabilistic traveling salesman problem ptsp a variant of the classical tsp in which only a subset of the nodes may be present in any given instance of the problem the goal is to find an a priori tour of minimal expected length with the strategy of visiting the present nodes in a particular instance in the same order as they appear in the a priori tour in this paper we reexamine the ptsp using a variety of theoretical and computational approaches we sharpen the best known bounds for the ptsp derive several asymptotic relations and compare from various veiwpoints the ptsp with the re optimization strategy i e finding an optimal tour in every problem instance when a euclidean metric is used and the nodes are uniformly distributed in the unit square a heuristic for the ptsp is shown to be very close to the re optimization strategy we examine some ptsp heuristics with provable worst case performance and address the question of finding constant guarantee heuristics implementations of various heuristics some based on sorting and some on local optimality permit us to discuss the qualitative and quantitative properties of computational problems with up to 5000 nodes

stochastic discrete optimization in this paper a stochastic search method is proposed for finding a global solution to the stochastic discrete optimization problem in which the objective function must be estimated by monte carlo simulation although there are many practical problems of this type in the fields of manufacturing engineering operations research and management science there have not been any nonheuristic methods proposed for such discrete problems with stochastic infrastructure the proposed method is very simple yet it finds a global optimum solution the method exploits the randomness of monte carlo simulation and generates a sequence of solution estimates this generated sequence turns out to be a nonstationary markov chain and it is shown under mild conditions that the markov chain is strongly ergodic and that the probability that the current solution estimate is global optimum converges to one furthermore the speed of convergence is also analyzed

multiresolution feature extraction and selection for texture segmentation an approach is described for unsupervised segmentation of textured images local texture properties are extracted using local linear transforms that have been optimized for maximal texture discrimination local statistics texture energy measures are estimated at the output of an equivalent filter bank by means of a nonlinear transformation absolute value followed by an iterative gaussian smoothing algorithm this procedure generates a multiresolution sequence of feature planes with a half octave scale progression a feature reduction technique is then applied to the data and is determined by simultaneously diagonalizing scatter matrices evaluated at two different spatial resolutions this approach provides a good approximation of r a fisher s 1950 multiple linear discriminants and has the advantage of requiring no a priori knowledge this feature reduction methods appears to be an improvement on the commonly used karhunen loeve transform and allows efficient texture segmentation based on simple thresholding

a method for discrete stochastic optimization this paper addresses the problem of optimizing a function over a finite or countably infinite set of alternatives in situations where this objective function cannot be evaluated exactly but has to be estimated or measured a special focus is on situations where simulation is used to evaluate the objective function we present two versions of a new iterative method for solving such discrete stochastic optimization problems in each iteration of the proposed method a neighbor of the current alternative is selected and estimates of the objective function evaluated at the current and neighboring alternatives are compared the alternative that has a better observed function value becomes the next current alternative we show how one version of the proposed method can be used to solve discrete optimization problems where the objective function is evaluated using transient or steady state simulation and we show how the other version can be applied to solve a special class of discrete stochastic optimizati

log polar wavelet energy signatures for rotation and scale invariant texture classification classification of texture images is important in image analysis and classification this paper proposes an effective scheme for rotation and scale invariant texture classification using log polar wavelet signatures the rotation and scale invariant feature extraction for a given image involves applying a log polar transform to eliminate the rotation and scale effects but at same time produce a row shifted log polar image which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects so the output wavelet coefficients are rotation and scale invariant the adaptive row shift invariant wavelet packet transform is quite efficient with only o n spl middot log n complexity a feature vector of the most dominant log polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification in the experiments we employed a mahalanobis classifier to classify a set of 25 distinct natural textures selected from the brodatz album the experimental results based on different testing data sets for images with different orientations and scales show that the proposed classification scheme using log polar wavelet signatures outperforms two other texture classification methods its overall accuracy rate for joint rotation and scale invariance being 90 8 percent demonstrating that the extracted energy signatures are effective rotation and scale invariant features concerning its robustness to noise the classification scheme also performs better than the other methods

ordinal optimization for a class of deterministic and stochastic discrete resource allocation problems the authors consider a class of discrete resource allocation problems which are hard due to the combinatorial explosion of the feasible allocation search space in addition if no closed form expressions are available for the cost function of interest one needs to evaluate or for stochastic environments estimate the cost function through direct online observation or through simulation for the deterministic version of this class of problems the authors derive necessary and sufficient conditions for a globally optimal solution and present an online algorithm which they show to yield a global optimum for the stochastic version they show that an appropriately modified algorithm analyzed as a markov process converges in probability to the global optimum an important feature of this algorithm is that it is driven by ordinal estimates of a cost function i e simple comparisons of estimates rather than their cardinal values they can therefore exploit the fast convergence properties of ordinal comparisons as well as eliminate the need for step size parameters whose selection is always difficult in optimization schemes an application to a stochastic discrete resource allocation problem is included illustrating the main features of their approach

asymptotic global behavior for stochastic approximation and diffusions with slowly decreasing noise effects global minimization via monte carlo the asymptotic behavior of the systems x n 1 x n a n b x n xi n a n sigma x n psi n and dy bar b y dt sqrt a t sigma y dw is studied where psi n is i i d gaussian xi n is a correlated bounded sequence of random variables and a n approx a 0 log a 1 n without xi n such algorithms are versions of the simulated annealing method for global optimization when the objective function values can only be sampled via monte carlo the discrete algorithm is a combination of stochastic approximation and simulated annealing our forms are appropriate the psi n are the annealing variables and xi n is the sampling noise for large a 0 a full asymptotic analysis is presented via the theory of large deviations mean escape time after arbitrary time n from neighborhoods of stable sets of the algorithm mean transition times after arbitrary time n from a neighborhood of one stable set to another a

geometry of random sequential adsorption by sequentially adding line segments to a line or disks to a surface at random positions without overlaps we obtain configurations of the one and two dimensional random sequential adsorption rsa problem we have simulated the one and two dimensional problem with periodic boundary condition the one dimensional simulations are compared with the exact analytical solutions to give an estimate of the accuracy of the simulation in two dimensions the geometrical properties of the rsa configuration are discussed and in addition known results of the rsa process are reproduced various statistical distributions of the voronoi dirichlet vd network corresponding to the rsa disk configuration are analyzed in order to characterize pores in the rsa configuration we introduce circular holes there is a direct correspondence between vertices of the vd network and these holes and also between direct indirect geometrical neighbors and these holes the hole size distribution is found to be a parabola we also find general relations that connect the asymptotic behavior of the surface coverage the correlation function and the hole size distribution

optimal gabor filters for texture segmentation texture segmentation involves subdividing an image into differently textured regions many texture segmentation schemes are based on a filter bank model where the filters called gabor filters are derived from gabor elementary functions the goal is to transform texture differences into detectable filter output discontinuities at texture boundaries by locating these discontinuities one can segment the image into differently textured regions distinct discontinuities occur however only if the gabor filter parameters are suitably chosen some previous analysis has shown how to design filters for discriminating simple textures designing filters for more general natural textures though has largely been done ad hoc we have devised a more rigorously based method for designing gabor filters it assumes that an image contains two different textures and that prototype samples of the textures are given a priori we argue that gabor filter outputs can be modeled as rician random variables often approximated well as gaussian rv s and develop a decision theoretic algorithm for selecting optimal filter parameters to improve segmentations for difficult texture pairs we also propose a multiple filter segmentation scheme motivated by the rician model experimental results indicate that our method is superior to previous methods in providing useful gabor filters for a wide range of texture pairs

retrospective optimization of mixed integer stochastic systems using dynamic simplex linear interpolation abstract we propose a family of retrospective optimization ro algorithms for optimizing stochastic systems with both integer and continuous decision variables the algorithms are continuous search procedures embedded in a ro framework using dynamic simplex interpolation rodsi by decreasing dimensions corresponding to the continuous variables of simplex the retrospective solutions become closer to an optimizer of the objective function we present convergence results of rodsi algorithms for stochastic convex systems numerical results show that a simple implementation of rodsi algorithms significantly outperforms some random search algorithms such as genetic algorithm ga and particle swarm optimization pso

modelling the gap size distribution of parked cars abstract we have measured the distribution of distances between parked cars in a number of roads in central london we compare the results with models of random sequential adsorption or random car parking models as they are often called our empirical results do not agree with these models and hence we introduce alternative models for the parking process the first model we propose is one where cars can only park if the space is greater than or equal to a chosen minimum size where the extra space can be used for manoeuvering the results of this model suggest that in addition to a deposition process a re positioning process is required to explain our empirical results hence we introduce a further model where at each time step with probability p a car is randomly placed on a line if the space is empty and with probabilty 1 p is placed in a space but then rolls to the nearest parked car so that it is y distance from the nearest car with probability f y we concentrate on two specific probabilities f y and find that for one case the gap size distribution agrees with the empirical results

a simulated annealing algorithm with constant temperature for discrete stochastic optimization we present a modification of the simulated annealing algorithm designed for solving discrete stochastic optimization problems like the original simulated annealing algorithm our method has the hill climbing feature so it can find global optimal solutions to discrete stochastic optimization problems with many local solutions however our method differs from the original simulated annealing algorithm in that it uses a constant rather than decreasing temperature we consider two approaches for estimating the optimal solution the first approach uses the number of visits the algorithm makes to the different states divided by a normalizer to estimate the optimal solution the second approach uses the state that has the best average estimated objective function value as estimate of the optimal solution we show that both variants of our method are guaranteed to converge almost surely to the set of global optimal solutions and discuss how our work applies in the discrete deterministic optimization setting we also show how both variants can be applied for solving discrete optimization problems when the objective function values are estimated using either transient or steady state simulation finally we include some encouraging numerical results documenting the behavior of the two variants of our algorithm when applied for solving two versions of a particular discrete stochastic optimization problem and compare their performance with that of other variants of the simulated annealing algorithm designed for solving discrete stochastic optimization problems

extraction of shift invariant wavelet features for classification of images with different sizes an effective shift invariant wavelet feature extraction method for classification of images with different sizes is proposed the feature extraction process involves a normalization followed by an adaptive shift invariant wavelet packet transform an energy signature is computed for each subband of these invariant wavelet coefficients a reduced subset of energy signatures is selected as the feature vector for classification of images with different sizes experimental results show that the proposed method can achieve high classification accuracy of 98 5 percent and outperforms the other two image classification methods

an adaptive hybrid pattern for noise robust texture analysis local binary patterns lbp achieve great success in texture analysis however they are not robust to noise the two reasons for such disadvantage of lbp schemes are 1 they encode the texture spatial structure based only on local information which is sensitive to noise and 2 they use exact values as the quantization thresholds which make the extracted features sensitive to small changes in the input image in this paper we propose a noise robust adaptive hybrid pattern ahp for noised texture analysis in our scheme two solutions from the perspective of texture description model and quantization algorithm have been developed to reduce the feature s noise sensitiveness first a hybrid texture description model is proposed in this model the global texture spatial structure which is depicted by a global description model is encoded with the primitive microfeature for texture description second we develop an adaptive quantization algorithm in which equal probability quantization is utilized to achieve the maximum partition entropy higher noise tolerance can be obtained with the minimum lost information in the quantization process the experimental results of texture classification on two texture databases with three different types of noise show that our approach leads significant improvement in noised texture analysis furthermore our scheme achieves state of the art performance in noisy face recognition highlightsa hybrid texture description model is proposed for noise robust texture modeling an adaptive quantization algorithm is designed for robust angular space quantization based on the new description model and quantization algorithm we develop the ahp experimental results demonstrate the significant improvement achieved by our scheme

comment on random sequential packing in square cellular structures based on a paper by nakamura j phys a vol 19 p 2345 1986 the authors correlate two methods for packing squares on a square lattice and find agreement with published computer simulation results

a completed modeling of local binary pattern operator for texture classification in this correspondence a completed modeling of the local binary pattern lbp operator is proposed and an associated completed lbp clbp scheme is developed for texture classification a local region is represented by its center pixel and a local difference sign magnitude transform ldsmt the center pixels represent the image gray level and they are converted into a binary code namely clbp center clbp c by global thresholding ldsmt decomposes the image local differences into two complementary components the signs and the magnitudes and two operators namely clbp sign clbp s and clbp magnitude clbp m are proposed to code them the traditional lbp is equivalent to the clbp s part of clbp and we show that clbp s preserves more information of the local structure than clbp m which explains why the simple lbp operator can extract the texture features reasonably well by combining clbp s clbp m and clbp c features into joint or hybrid distributions significant improvement can be made for rotation invariant texture classification

a global search method for discrete stochastic optimization this paper is concerned with the problem of optimizing the performance of a stochastic system over a finite set of alternatives in situations where the performance of the system cannot be evaluated analytically but must be estimated or measured for instance through simulation we present two variants of a new method for solving such discrete stochastic optimization problems this new method uses global search to look for the optimal solution it generates a sequence taking values in the set of feasible alternatives where each new element of the sequence is generated by comparing the current element with another candidate alternative and letting the next element of the sequence be the one of the current and candidate alternatives that appears to yield better performance for both versions of the proposed method the element of the set of feasible alternatives that the generated sequence visits most often is shown to converge almost surely to a globally optimal solution of the underlying optimization pr

a modification of the stochastic ruler method for discrete stochastic optimization abstract we propose a modified stochastic ruler method for finding a global optimal solution to a discrete optimization problem in which the objective function cannot be evaluated analytically but has to be estimated or measured our method generates a markov chain sequence taking values in the feasible set of the underlying discrete optimization problem it uses the number of visits this sequence makes to the different states to estimate the optimal solution we show that our method is guaranteed to converge almost surely a s to the set of global optimal solutions then we show how our method can be used for solving discrete optimization problems where the objective function values are estimated using either transient or steady state simulation finally we provide some numerical results to check the validity of our method and compare its performance with that of the original stochastic ruler method

classification of textures using gaussian markov random fields the problem of texture classification arises in several disciplines such as remote sensing computer vision and image analysis in this paper we present two feature extraction methods for the classification of textures using two dimensional 2 d markov random field mrf models it is assumed that the given m m texture is generated by a gaussian mrf model in the first method the least square ls estimates of model parameters are used as features in the second method using the notion of sufficient statistics it is shown that the sample correlations over a symmetric window including the origin are optimal features for classification simple minimum distance classifiers using these two feature sets yield good classification accuracies for a seven class problem

relative frequencies of generalized simulated annealing we consider a class of nonhomogeneous markov chains arising in simulated annealing and related stochastic search algorithms using only elementary first principles we analyze the convergence and rate of convergence of the relative frequencies of visits to states in the markov chain we describe in detail three examples including the standard simulated annealing algorithm to show how our framework applies to specific stochastic search algorithmsthese examples have not previously been recognized to be sufficiently similar to share common analytical grounds our analysis though elementary provides the strongest sample path convergence results to date for simulated annealing type markov chains our results serve to illustrate that by taking a purely sample path view surprisingly strong statements can be made using only relatively elementary tools

unsupervised texture segmentation using gabor filters abstract this paper presents a texture segmentation algorithm inspired by the multi channel filtering theory for visual information processing in the early stages of human visual system the channels are characterized by a bank of gabor filters that nearly uniformly covers the spatial frequency domain and a systematic filter selection scheme is proposed which is based on reconstruction of the input image from the filtered images texture features are obtained by subjecting each selected filtered image to a nonlinear transformation and computing a measure of energy in a window around each pixel a square error clustering algorithm is then used to integrate the feature images and produce a segmentation a simple procedure to incorporate spatial information in the clustering process is proposed a relative index is used to estimate the true number of texture categories

regime switching stochastic approximation algorithms with application to adaptive discrete stochastic optimization this work is devoted to a class of stochastic approximation problems with regime switching modulated by a discrete time markov chain our motivation stems from using stochastic recursive algorithms for tracking markovian parameters such as those in spreading code optimization in cdma code division multiple access wireless communication the algorithm uses constant step size to update the increments of a sequence of occupation measures it is proved that least squares estimates of the tracking errors can be developed assume that the adaptation rate is of the same order of magnitude as that of the time varying parameter which is more difficult to deal with than that of slower parameter variations due to the time varying characteristics and markovian jumps the usual stochastic approximation sa techniques cannot be carried over in the analysis by a combined use of the sa method and two time scale markov chains asymptotic properties of the algorithm are obtained which are distinct from the usual sa

a priori optimization consider a complete graph g v e in which each node is present with probability p we are interested in solving combinatorial optimization problems on subsets of nodes which are present with a certain probability we introduce the idea of a priori optimization as a strategy competitive to the strategy of reoptimization under which the combinatorial optimization problem is solved optimally for every instance we consider four problems the traveling salesman problem tsp the minimum spanning tree vehicle routing and traveling salesman facility location we discuss the applicability of a priori optimization strategies in several areas and show that if the nodes are randomly distributed in the plane the a priori and reoptimization strategies are very close in terms of performance we characterize the complexity of a priori optimization and address the question of approximating the optimal a priori solutions with polynomial time heuristics with provable worst case guarantees finally we use the tsp as an example to find practical solutions based on ideas of local optimality

continuous optimization via simulation using golden region search simulation optimization so is a class of mathematical optimization techniques in which the objective function can only be numerically evaluated through simulation in this paper a new so approach called golden region gr search is developed for continuous problems gr divides the feasible region into a number of sub regions and selects one region in each iteration for further search based on the quality and distribution of simulated points in the feasible region and the result of scanning the response surface through a metamodel monte carlo experiments show that the gr method is efficient compared to three well established approaches in the literature we also prove the asymptotic convergence in probability to a global optimum for a large class of random search methods in general and gr in particular

classification of rotated and scaled textured images using gaussian markov random field models consideration is given to the problem of classifying a test textured image that is obtained from one of c possible parent texture classes after possibly applying unknown rotation and scale changes to the parent texture the training texture images parent classes are modeled by gaussian markov random fields gmrfs to classify a rotated and scaled test texture the rotation and scale changes are incorporated in the texture model through an appropriate transformation of the power spectral density of the gmrf for the rotated and scaled image a bona fide likelihood function that shows the explicit dependence of the likelihood function on the gmrf parameters as well as on the rotation and scale parameters is derived although in general the scaled and or rotated texture does not correspond to a finite order gmrf it is possible nonetheless to write down a likelihood function for the image data the likelihood function of the discrete fourier transform of the image data corresponds to that of a white nonstationary gaussian random field with the variance at each pixel i j being a known function of the rotation the scale the gmrf model parameters and i j the variance is an explicit function of the appropriately sampled power spectral density of the gmrf the estimation of the rotation and scale parameters is performed in the frequency domain by maximizing the likelihood function associated with the discrete fourier transform of the image data cramer rao error bounds on the scale and rotation estimates are easily computed a modified bayes decision rule is used to classify a given test image into one of c possible texture classes the classification power of the method is demonstrated through experimental results on natural textures from the brodatz album

adaptive mimo antenna selection via discrete stochastic optimization recently it has been shown that it is possible to improve the performance of multiple input multiple output mimo systems by employing a larger number of antennas than actually used and selecting the optimal subset based on the channel state information existing antenna selection algorithms assume perfect channel knowledge and optimize criteria such as shannon capacity or various bounds on error rate this paper examines mimo antenna selection algorithms where the set of possible solutions is large and only a noisy estimate of the channel is available in the same spirit as traditional adaptive filtering algorithms we propose simulation based discrete stochastic optimization algorithms to adaptively select a better antenna subset using criteria such as maximum mutual information bounds on error rate etc these discrete stochastic approximation algorithms are ideally suited to minimize the error rate since computing a closed form expression for the error rate is intractable we also consider scenarios of time varying channels for which the antenna selection algorithms can track the time varying optimal antenna configuration we present several numerical examples to show the fast convergence of these algorithms under various performance criteria and also demonstrate their tracking capabilities

criteria for an optimum simulated annealing schedule for problems of the travelling salesman type the simulated annealing algorithm for optimisation problems such as the travelling salesman problem is reviewed the concept of the autocorrelation function for cost functions is introduced and it is shown how numerical experiments to measure this quantity can provide criteria as to how rapidly a system can be annealed close to equilibrium from this the authors obtain an optimum annealing schedule of general applicability

radon transform orientation estimation for rotation invariant texture analysis this paper presents a new approach to rotation invariant texture classification the proposed approach benefits from the fact that most of the texture patterns either have directionality anisotropic textures or are not with a specific direction isotropic textures the wavelet energy features of the directional textures change significantly when the image is rotated however for the isotropic images the wavelet features are not sensitive to rotation therefore for the directional textures it is essential to calculate the wavelet features along a specific direction in the proposed approach the radon transform is first employed to detect the principal direction of the texture then the texture is rotated to place its principal direction at 0 degrees a wavelet transform is applied to the rotated image to extract texture features this approach provides a features space with small intraclass variability and therefore good separation between different classes the performance of the method is evaluated using three texture sets experimental results show the superiority of the proposed approach compared with some existing methods

texture classification using dominant neighborhood structure this paper proposes a new approach to extract global image features for the purpose of texture classification the proposed texture features are obtained by generating an estimated global map representing the measured intensity similarity between any given image pixel and its surrounding neighbors within a certain window the intensity similarity map is an average representation of the texture image dominant neighborhood similarity the estimated dominant neighborhood similarity is robust to noise and referred to as image dominant neighborhood structure the global rotation invariant features are then extracted from the generated image dominant neighborhood structure features obtained from the local binary patterns lbps are then extracted in order to supply additional local texture features to the generated features from the dominant neighborhood structure both features complement each other the experimental results on representative texture databases show that the proposed method is robust to noise and can achieve significant improvement in terms of the obtained classification accuracy in comparison to the lbp method in addition the method classification accuracy is comparable to the two recent lbp extensions dominant lbp and completed lbp

exploring space frequency co occurrences via local quantized patterns for texture representation local binary pattern lbp has shown its power in texture classification and face recognition however the lbp operator is performed in the original image space and it lacks deeper pixel interactions to capture a richer description in this paper we propose to explore space frequency co occurrences via local quantized patterns for texture representation the proposed method proceeds in two channels in each channel the multi resolution spatial maps are first obtained by specific spatial filtering and local frequency features spectral maps are subsequently extracted by applying the local fourier transform to the spatial map two types of quantization via global thresholding are employed to quantize the spatial and spectral maps into three and two levels respectively the quantized patterns are then jointly encoded to construct a space frequency co occurrence histogram finally the two channel histograms are combined to characterize the texture without resort to the texton based representation our method directly encodes the joint information in the space and frequency domains while preserving the robustness to image rotation illumination scale and viewpoint changes extensive experiments have been conducted on three well known texture databases and our method achieves the best classification results compared with state of the art approaches investigated highlightstwo channel space frequency feature spaces are designed to obtain invariant features two types of quantization via global thresholding are developed the joint space frequency coding is explored to capture a richer description our method outperforms the state of the art on three well known texture datasets

markov random field texture models we consider a texture to be a stochastic possibly periodic two dimensional image field a texture model is a mathematical procedure capable of producing and describing a textured image we explore the use of markov random fields as texture models the binomial model where each point in the texture has a binomial distribution with parameter controlled by its neighbors and number of tries equal to the number of gray levels was taken to be the basic model for the analysis a method of generating samples from the binomial model is given followed by a theoretical and practical analysis of the method s convergence examples show how the parameters of the markov random field control the strength and direction of the clustering in the image the power of the binomial model to produce blurry sharp line like and blob like textures is demonstrated natural texture samples were digitized and their parameters were estimated under the markov random field model a hypothesis test was used for an objective assessment of goodness of fit under the markov random field model overall microtextures fit the model well the estimated parameters of the natural textures were used as input to the generation procedure the synthetic microtextures closely resembled their real counterparts while the regular and inhomogeneous textures did not

subband coding of images subband coding has become quite popular for the source encoding of speech this paper presents a simple yet efficient extension of this concept to the source coding of images we specify the constraints for a set of two dimensional quadrature mirror filters qmf s for a particular frequency domain partition and show that these constraints are satisfied by a separable combination of one dimensional qmf s bits are then optimally allocated among the subbands to minimize the mean squared error for dpcm coding of the subbands also an adaptive technique is developed to allocate the bits within each subband by means of a local variance mask optimum quantization is employed with quantizers matched to the laplacian distribution subband coded images are presented along with their signal to noise ratios snr s the snr performance of the subband coder is compared to that of the adaptive discrete cosine transform dct vector quantization and differential vector quantization for bit rates of 0 67 1 0 and 2 0 bits per pixel for 256 256 monochrome images the adaptive subband coder has the best snr performance

generalized random sequential adsorption in one dimension the process of random sequential adsorption in one dimension is generalized so that the rate at which a particle is placed in an empty space is dependent on the size of the space being destroyed and on the sizes of the two spaces created on either side of the particle we write down the equation for this process involving p z the probability of choosing to deposit a particle of size z and f x y mod z the rate at which spaces of length x and y are created from a space of length x y z given that a particle of size z is deposited a scaling theory is given for this process and solutions are presented for two special cases the results of a numerical simulation of a third case are presented

rotation invariant multiresolution texture analysis using radon and wavelet transforms a new rotation invariant texture analysis technique using radon and wavelet transforms is proposed this technique utilizes the radon transform to convert the rotation to translation and then applies a translation invariant wavelet transform to the result to extract texture features a k nearest neighbors classifier is employed to classify texture patterns a method to find the optimal number of projections for the radon transform is proposed it is shown that the extracted features generate an efficient orthogonal feature space it is also shown that the proposed features extract both of the local and directional information of the texture patterns the proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the radon transform step to test and evaluate the method we employed several sets of textures along with different wavelet bases experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture analysis methods

different initial solution generators in genetic algorithms for solving the probabilistic traveling salesman problem the probabilistic traveling salesman problem ptsp is a topic of theoretical and practical importance in the study of stochastic network problems it provides researchers with a modeling framework for exploring the stochastic effects in routing problems this paper proposed three initial solution generators nn1 nn2 ran under a genetic algorithm ga framework for solving the ptsp a set of numerical experiments based on heterogeneous and homogeneous ptsp instances were conducted to test the effectiveness and efficiency of the proposed algorithms the results from the heterogeneous ptsp show that the average e t values obtained by the three generators under a ga framework are similar to those obtained by the previous best but with an average computation time saving of 50 2 as for the homogeneous ptsp instances nn1 is a relatively better generator among the three examined while ran consistently performs worse than the other two generators in terms of average e t values additionally as compared to previously reported studies no one single algorithm consistently outperformed the others across all homogeneous ptsp instances in terms of the best e t values the fact that no one initial solution generator consistently performs best in terms of the e t value obtained across all instances in heterogeneous cases and that the performance of each examined algorithm is dependent on the number of nodes n and probability p for homogeneous cases suggest the possibility of context dependent phenomenon finally to obtain valid results researchers are advised to include at least a certain amount of test instances with the same combination of n and p when conducting ptsp experiments

simulated annealing for discrete optimization with estimation we extend the basic convergence results for the simulated annealing sa algorithm to a stochastic optimization problem where the objective function is stochastic and can be evaluated only through monte carlo simulation hence disturbed with random error this extension is important when either the objective function cannot be evaluated exactly or such an evaluation is computationally expensive we present a modified sa algorithm and show that under suitable conditions on the random error the modified sa algorithm converges in probability to a global optimizer computational results and comparisons with other approaches are given to demonstrate the performance of the proposed modified sa algorithm

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

on the breakage problem with a homogeneous erosion type kernel the breakage equation with a homogeneous erosion type kernel is studied herein this type of kernel renders the handling of the breakage equation by conventional techniques very difficult necessitating alternative problem solving approaches exploiting the structure of the erosion breakage kernel a new particle erosion equation is derived as the first order term of a formal perturbation expansion with respect to kernel parameters however even this new equation is very difficult to treat because of the multimodality of its solution associated with the developing generations of fragments in order to overcome this difficulty the problem is decomposed into a system of equations for the size distribution of the generations of fragments which admits unimodal solutions the properties and the methods of solution analytical method of moments etc are studied extensively using solution techniques developed in this paper results are reported for some simple cases revealing a very interesting and rather unusual structure of the solutions of the erosion breakage equation pacs number 0590

a priori solution of a travelling salesman problem in which a random subset of the customers are visited consider a problem of routing through a set of n points on any given instance of the problem only a subset consisting of k out of n points 0 k n has to be visited with the number k random with known probability distribution we wish to find a priori a tour through all n points on any given instance the k points present will then be visited in the same order as they appear in the a priori tour the problem of finding such a tour of minimum length in the expected value sense is defined as a probabilistic traveling salesman problem ptsp what distinguishes one ptsp from another is the probability distribution or more generally the probability law that specifies the number k and the identity of the points that need to be visited on any given instance of the problem after motivating the problem by applications we first derive closed form expressions for computing efficiently the expected length of any given tour under very general probabilistic assumptions we then provide in a unified way

multiresolution statistical analysis of high resolution digital mammograms a multiresolution statistical method for identifying clinically normal tissue in digitized mammograms is used to construct an algorithm for separating normal regions from potentially abnormal regions that is small regions that may contain isolated calcifications this is the initial phase of the development of a general method for the automatic recognition of normal mammograms the first step is to decompose the image with a wavelet expansion that yields a sum of independent images each containing different levels of image detail when calcifications are present there is strong empirical evidence that only some of the image components are necessary for the purpose of detecting a deviation from normal the underlying statistic for each of the selected expansion components can be modeled with a simple parametric probability distribution function this function serves as an instrument for the development of a statistical test that allows for the recognition of normal tissue regions the distribution function depends on only one parameter and this parameter itself has an underlying statistical distribution the values of this parameter define a summary statistic that can be used to set detection error rates once the summary statistic is determined spatial filters that are matched to resolution are applied independently to each selected expansion image regions of the image that correlate with the normal statistical model are discarded and regions in disagreement suspicious areas are flagged these results are combined to produce a detection output image consisting only of suspicious areas this type of detection output is amenable to further processing that may ultimately lead to a fully automated algorithm for the identification of normal mammograms

diversified local search strategy under scatter search framework for the probabilistic traveling salesman problem this paper focuses on introducing a concept of diversified local search strategy under the scatter search framework for the probabilistic traveling salesman problem ptsp different combinations of three commonly used local search methods in the ptsp i e 1 shift 2 opt and 3 opt were used to investigate its effects a set of numerical experiments were conducted to test the validity of the proposed strategy based on randomly generated test instances the numerical results and the permutation test showed that the diversified local search strategy especially by combining 1 shift and 2 opt algorithms can most effectively solve the homogeneous and heterogeneous ptsp in most of the tested instances in comparison with the single local search strategy under scatter search framework

learning completed discriminative local features for texture classification we propose a novel feature representation method namely completed discriminative local features cdlf for texture classification the cdlf learn transformation matrices for texture images that maximize the mutual information between the local features and their category labels we propose an adaptive histogram accumulation aha algorithm which leverages the local contrast characteristic in the process of histogram accumulation the cdlf achieves higher accuracy than state of the art methods on three databases local binary patterns lbp and its variants have shown great potentials in texture classification tasks lbp like texture classification methods usually follow a two step feature extraction process in the first pattern encoding step the local structure information around each pixel is encoded into a binary string in the second histogram accumulation step the binary strings are accumulated into a histogram as the feature vector of a texture image the performances of these classification methods are closely related to the distinctiveness of the feature vectors in this paper we propose a novel feature representation method namely completed discriminative local features cdlf for texture classification the proposed cdlf improves the distinctiveness of lbp like feature vectors in two aspects in the pattern encoding stage we learn a transformation matrix using labeled data which significantly increases the discrimination power of the encoded binary strings in the histogram accumulation step we use an adaptive weight strategy to consider the contributions of pixels in different regions the experimental results on three challenging texture databases demonstrate that the proposed cdlf achieves significantly better results than previous lbp like feature representation methods for texture classification tasks

balancing exploitation and exploration in discrete optimization via simulation through a gaussian process based search random search algorithms are often used to solve discrete optimization via simulation dovs problems the most critical component of a random search algorithm is the sampling distribution that is used to guide the allocation of the search effort a good sampling distribution can balance the trade off between the effort used in searching around the current best solution which is called exploitation and the effort used in searching largely unknown regions which is called exploration however most of the random search algorithms for dovs problems have difficulties in balancing this trade off in a seamless way in this paper we propose a new scheme that derives a sampling distribution from a fast fitted gaussian process based on previously evaluated solutions we show that the sampling distribution has the desired properties and can automatically balance the exploitation and exploration trade off furthermore we integrate this sampling distribution into a random research algorithm called a gaussian process based search gps and show that the gps algorithm has the desired global convergence as the simulation effort goes to infinity we illustrate the properties of the algorithm through a number of numerical experiments

optimization by multicanonical annealing and the traveling salesman problem we demonstrate a powerful and general simulated annealing method to study combinatorial optimization problems it combines the multicanonical method which samples directly the microcanonical entropy of the system with an elaborate but straightforward annealing scheme the idea is to fully utilize the information about the local entropy obtained during short monte carlo simulations for optimization in an iterative fashion we present results of an extensive investigation of the traveling salesman problem in a unit square

pattern formation in size polydisperse adsorption fractal properties and transition to order abstract random sequential adsorption of particles with size polydispersity prsa is investigated for the prsa with a power law small size distribution p r r 1 we reveal the fractal nature of the arising patterns the fractal dimension d is found to decrease from 2 to d a 1 305 the fractal dimension of the apollonian packing as increases from 0 to we examine prsa by a combination of exact scaling mean field and numerical approaches we find that the scaling theory works fairly well for the whole range of while the mean field theory mft is applicable only for small we attribute this failure of the mft to the increasing regularity of the prsa patterns with increasing we confirm this conclusion by the direct measurement of the entropy production rate of the pattern formation process which demonstrates the transition from an irregular regime of the pattern formation to the regular one

wavelet based level set evolution for classification of textured images we present a supervised classification model based on a variational approach this model is specifically devoted to textured images we want to get a partition of an image composed of texture regions separated by regular interfaces each kind of texture defines a class we use a wavelet packet transform to analyze the textures characterized by their energy distribution in each sub band in order to have an image segmentation according to the classes we model the regions and their interfaces by level set functions we define a functional on these level sets whose minimizers define the optimal classification according to texture a system of coupled pdes is deduced from the functional by solving this system each region evolves according to its wavelet coefficients and interacts with the neighbor regions in order to obtain a partition with regular contours experiments are shown on synthetic and real images

stochastic approximation methods for systems over an infinitehorizon the paper develops efficient and general stochastic approximation sa methods for improving the operation of parametrized systems of either the continuous or discrete event dynamical systems types and which are of interest over a long time period for example one might wish to optimize or improve the stationary or average cost per unit time performance by adjusting the systems parameters the number of applications and the associated literature are increasing at a rapid rate this is partly due to the increasing activity in computing pathwise derivatives and adapting them to the average cost problem although the original motivation and the examples come from an interest in the infinite horizon problem the techniques and results are of general applicability in sa we present an updating and review of powerful ordinary differential equation type methods in a fairly general context and based on weak convergence ideas the results and proof techniques are applicable to a wide variety of applications exploiting the full potential of these ideas can greatly simplify and extend much current work their breadth as well as the relative ease of using the basic ideas are illustrated in detail via typical examples drawn from discrete event dynamical systems piecewise deterministic dynamical systems and a stochastic differential equations model in these particular illustrations we use either infinitesimal perturbation analysis type estimators mean square derivative type estimators or finite difference type estimators markov and non markov models are discussed the algorithms for distributed asynchronous updating as well as the fully synchronous schemes are developed

on optimal allocation of indivisibles under uncertainty the optimal allocation of indivisible resources is formalized as a stochastic optimization problem involving discrete decision variables a general stochastic search procedure is proposed which develops the concept of the branch and bound method the main idea is to process large collections of possible solutions and to devote more attention to the most promising groups by gathering more information to reduce the uncertainty and by narrowing the search area the optimal solution can be found with probability one special techniques for calculating stochastic lower and upper bounds are discussed the results are illustrated by a computational experiment

a priori optimization of the probabilistic traveling salesman problem the probabilistic traveling salesman problem ptsp is defined on a graph g v e where v is the vertex set and e is the edge set each vertex vi has a probability pi of being present with each edge vi vj is associated a distance or cost cij in a first stage an a priori hamiltonian tour on g is designed the list of present vertices is then revealed in a second stage the a priori tour is followed by skipping the absent vertices the ptsp consists of determining a first stage solution that minimizes the expected cost of the second stage tour the problem is formulated as an integer linear stochastic program and solved by means of a branch and cut approach which relaxes some of the constraints and uses lower bounding functionals on the objective function problems involving up to 50 vertices are solved to optimality

shape based invariant texture indexing this paper introduces a new texture analysis scheme which is invariant to local geometric and radiometric changes the proposed methodology relies on the topographic map of images obtained from the connected components of level sets this morphological tool providing a multi scale and contrast invariant representation of images is shown to be well suited to texture analysis we first make use of invariant moments to extract geometrical information from the topographic map this yields features that are invariant to local similarities or local affine transformations these features are invariant to any local contrast change we then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases the obtained experimental results outperform the current state of the art in locally invariant texture analysis

brief review of invariant texture analysis methods this paper considers invariant texture analysis texture analysis approaches whose performances are not affected by translation rotation affine and perspective transform are addressed existing invariant texture analysis algorithms are carefully studied and classified into three categories statistical methods model based methods and structural methods the importance of invariant texture analysis is presented first each approach is reviewed according to its classification and its merits and drawbacks are outlined the focus of possible future work is also suggested

general solutions and scaling violation for fragmentation with mass loss explicit solutions of a linear rate equation lead to improved understanding of fragmenlatian with discrete and continuous mass loss discrete mass loss provides a general expression for the overall mass 10 rate which accounts for mass loss in the shattering regime where runaway fragmentation for small panicles producer a phase of zero mass panicles an explicit solution far the recession regime where small panicles lose large fractionsoftheir mass to surface recession shows that the total number ofpaniclesincreases with time until the typical panicles become small enough to lose all of their mass and disappear rather than to break a general series solution is presented for fragmentation with continuous and discrete mass loss a proof that continuous mass loss precludes dynamic scaling is presented

exact self similar solutions to the fragmentation equation with homogeneous discrete kernel abstract new large time asymptotic self similar solutions of the fragmentation breakage equation are derived in closed form for the case of certain homogeneous discrete fragmentation kernel a fragmentation kernel is called homogeneous and discrete if it allows only certain ratios between the size of the fragments and that of parent particle in this case the fragmentation kernel is not a continuous function the present solutions are the first explicit self similarity distributions for discontinuous breakage functions

frame representations for texture segmentation we introduce a novel method of feature extraction for texture segmentation that relies on multichannel wavelet frames and 2 d envelope detection we describe and compare two algorithms for envelope detection based on 1 the hilbert transform and 2 zero crossings we present criteria for filter selection and discuss quantitatively their effect on feature extraction the performance of our method is demonstrated experimentally on samples of both natural and synthetic textures

theoretical analysis of the steady state particle size distribution in limited breakage processes the steady state particle size distribution is examined resulting from a breakage process with a maximum stable size and a homogeneous continuous kernel the dynamic breakage problem is transformed into one that allows direct solutions for the steady state distribution the latter depends on the breakage kernel and on the ratio of critical to initial size as this ratio goes to zero the steady state distribution approaches its limiting form obtained by the authors previously 7 a general theoretical analysis concerning the steady state distribution is presented herein the asymptotic behaviour is determined with regard to various limits perturbation analysis for nearly uniform kernels reveals several interesting features of the problem for the general continuous kernel the problem can be cast in a matrix form amenable to a conventional theoretical treatment finally comparisons of the new results with existing solutions of the dynamic problem for large times confirm their validity

a multitype random sequential process a random sequential process rsp can be formulated in two different ways i a linear array of n identical compartments is ceaselessly bombarded by particles which occupy 2 contiguous lattice sites these so called bell particles are assumed to make contact in a spatially random manner and to stick only if striking adjacent vacant compartments ii a linear array of n identical compartments is sequentially filled by bell particles the occupation process being selective i e always being directed to vacant sequences of compartments of lengths at least dynamics of rsp s has always been formulated in either way and in the case of lattice spaces all the efforts have been addressed so far to allow in the space filling process just one kind of particle the aim of this paper is to remove that restriction and to consider a lattice space filling problem with various kinds of particles involved the situations of the two above described models making no difference in the one type case

multiaspect target identification with wave based matched pursuits and continuous hidden markov models multiaspect target identification is effected by fusing the features extracted from multiple scattered waveforms these waveforms are characteristic of viewing the target from a sequence of distinct orientations classification is performed in the maximum likelihood sense which we show under reasonable assumptions can be implemented via a hidden markov model hmm we utilize a continuous hmm paradigm and compare its performance to its discrete counterpart the feature parsing is performed via wave based matched pursuits algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets

a decentralized approach to discrete optimization via simulation application to network flow we study a new class of decentralized algorithms for discrete optimization via simulation which is inspired by the fictitious play algorithm applied to games with identical interests in this approach each component of the solution vector of the optimization model is artificially assumed to have a corresponding player and the interaction of these players in simulation allows for exploration of the solution space and for some problems ultimately results in the identification of the optimal solution our algorithms also allow for correlation in players decision making a key feature when simulation output is shared by multiple decision makers we first establish convergence under finite sampling to equilibrium solutions in addition in the context of discrete network flow models we prove that if the underlying link cost functions are convex then our algorithms converge almost surely to an optimal solution

a study of the nonlinear breakage equation analytical and asymptotic solutions new solutions of the nonlinear collisional breakage equation are given using analytical and asymptotic methods the dynamic nonlinear breakage equation is transformed to a linear one for some simple forms of the collision kernel methods for treating the linear equation are employed to obtain solutions for the nonlinear case furthermore it is shown that under particular conditions the particle size distribution can take asymptotically a self similar form i e the shape of the appropriately normalized distribution is independent of time the self similar distribution is obtained from the solution of a double nonlinear integral equation the latter is solved in closed form and numerically after transformation to a boundary value problem for simple forms of the collision and breakage kernels results for the self similar distribution are presented and discussed

definition of the feasible solution set in multicriteria optimization problems with continuous discrete and mixed design variables abstract engineering optimization problems are multicriteria with continuous discrete and mixed design variables correct definition of the feasible solution set is of fundamental importance in these problems it is quite difficult for the expert to define this set for this reason the results of searching for optimal solutions frequently have no practical meaning furthermore correct definition of this set makes it possible to significantly reduce the time of searching for optimal solutions this paper describes construction of the feasible solution set with continuous discrete and mixed design variables on the basis of parameter space investigation psi method

budget dependent convergence rate of stochastic approximation convergence rate results are derived for a stochastic optimization problem where a performance measure is minimized with respect to a vector parameter t assuming that a gradient estimator is available and that both the bias and the variance of the estimator are known functions of the budget devoted to its computation the gradient estimator is employed in conjunction with a stochastic approximation sa algorithm our interest is to figure out how to allocate the total available computational budget to the successive sa iterations the effort is devoted to solving the asymptotic version of this problem by finding the convergence rate of sa toward the optimizer first as a function of the number of iterations and then as a function of the total computational effort as a result the optimal rate of increase of the computational budget per iteration can be found explicit expressions for the case where the computational budget devoted to an iteration is a polynomial in the iteration number and where the bias and variance of the gradient estimator are polynomials of the computational budget are derived applications include the optimization of steady state simulation models with likelihood ratio perturbation analysis or finite difference gradient estimators optimization of infinite horizon models with discounting optimization of functions of several expectations and so on several examples are discussed our results readily generalize to general root finding problems

correction to rotation and gray scale transform invariant texture classification using spiral resampling subband decomposition and hidden markov model this paper proposes a new texture classification algorithm that is invariant to rotation and gray scale transformation first we convert two dimensional 2 d texture images to one dimensional 1 d signals by spiral resampling then we use a quadrature mirror filter qmf bank to decompose sampled signals into subbands in each band we take high order autocorrelation functions as features features in different bands which form a vector sequence are then modeled as a hidden markov model bmm during classification the unknown texture is matched against all the models and the best match is taken as the classification result simulations showed that the highest correct classification rate for 16 kinds of texture was 95 14

characterization of the probabilistic traveling salesman problem we show that stochastic annealing can be successfully applied to gain new results on the probabilistic traveling salesman problem the probabilistic traveling salesman must decide on an a priori order in which to visit n cities randomly distributed over a unit square before learning that some cities can be omitted we find the optimized average length of the pruned tour follows e l over bar pruned rootnp 0 872 0 105p f np where p is the probability of a city needing to be visited and f np 1 as np infinity the average length of the a priori tour before omitting any cities is found to follow e l a priori rootn pbeta p where beta p 1 1 25 0 82 ln p is measured for 0 05less than or equal topless than or equal to0 6 scaling arguments and indirect measurements suggest that beta p tends towards a constant for p 0 03 our stochastic annealing algorithm is based on limited sampling of the pruned tour lengths exploiting the sampling error to provide the analog of thermal fluctuations in simulated thermal annealing the method has general application to the optimization of functions whose cost to evaluate rises with the precision required

kinetics of random sequential parking on a line we study the kinetics of irreversible random sequential parking of intervals of different sizes on an infinite line for the simplest fixed length parking distribution the model reduces to the known car parking problem and we present an alternate solution to this problem we also consider the general homogeneous case when the parking distribution varies asx 1 atx 1 with the lengthx of the filling interval we develop a scaling theory describing such mixture deposition processes and show that the scaled hole size distribution with xtz a scaling variable decays with the scaled mass as exp const 1 as we determine scaling exponentsz and and find that at large times the coverage t has a power law form 1 t t v with nonuniversal exponent 2 1 depending on the homogeneity index

adaptive partitioned random search to global optimization this paper considers global optimization maximization problems for a generic function it is inherently difficult to find the global optimum within a finite number of function evaluations it is more realistic to talk about maximizing the expectation of the largest function value that can be obtained for a given number of function evaluations based on decision theoretic argument we propose that the search region of the objective function be partitioned into certain number of subregions using the sampled function values from each subregion estimators are derived to determine how promising each subregion is the most promising subregion is further partitioned the proposed adaptive partitioned random search aprs is a tree search type of algorithms like branch and bound algorithms the aprs however abandons the idea of finding the subregion where the global maximum is likely located in the first place instead it seeks the subregion where the largest improvement of the performance is most likely to be obtained if more function evaluations are taken the aprs in general can provide a much better than average solution within a modest number of function evaluations in fact our various numerical experiments have shown that in comparison with the crude random search crs in terms of number of function evaluations the aprs can be at least hundreds of times more efficient the simplicity and robustness of the aprs in terms of easy implementation and minimum assumptions are also demonstrated

non trivial exponents in the zero temperature dynamics of the 1d ising and potts models we consider the glauber dynamics of the q state potts model in one dimension at zero temperature starting with a random initial configuration we measure the density r t of spins which have never flipped from the beginning of the simulation until time t we find that for large t the density r t has a power law decay left r t sim t theta right where the exponent theta varies with q our simulations lead to theta simeq 37 for q 2 theta simeq 53 for q 3 and theta longrightarrow 1 as q longrightarrow infty

spreading code optimization and adaptation in cdma via discrete stochastic approximation the aim of this paper is to develop discrete stochastic approximation algorithms that adaptively optimize the spreading codes of users in a code division multiple access cdma system employing linear minimum mean square error mmse receivers the proposed algorithms are able to adapt to slowly time varying channel conditions one of the most important properties of the algorithms is their self learning capability they spend most of the computational effort at the global optimizer of the objective function tracking analysis of the adaptive algorithms is presented together with mean square convergence an adaptive step size algorithm is also presented for optimally adjusting the step size based on the observations numerical examples illustrating the performance of the algorithms in multipath fading channels show substantial improvement over heuristic algorithms

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