high capacity image hiding scheme based on vector quantization a novel grayscale image hiding scheme that is capable of hiding multiple secret images into a host image of the same size is proposed in this paper the secret images to be hidden are first compressed by vector quantization with additional index compression process then the compressed secret images are encrypted and embedded into the least significant bits of the host pixels to provide good image quality of the stego image the modulus function and the image property are employed to hide the secret bits into the host pixels and determine the number of hidden bits in each host pixel respectively according to the results the proposed scheme provides a higher hiding capacity and a higher degree of security than that of the virtual image cryptosystem

image compression and denoising via nonseparable wavelet approximation it was shown in acta math sci 22 1 2002 19 that two dimensional nonseparable scaling function interpolation has better convergence results than scalar wavelet systems in some cases based on several experimental results in this paper we present applications of two dimensional nonseparable wavelet approximation the algorithms are developed by using two dimensional nonseparable scaling function interpolation to perform image compression and image denoising comparing with the separable counterparts our results show that there are some improvements and advantages of two dimensional nonseparable scaling function interpolation

embedded image coding using zerotrees of wavelet coefficients the embedded zerotree wavelet algorithm ezw is a simple yet remarkably effective image compression algorithm having the property that the bits in the bit stream are generated in order of importance yielding a fully embedded code the embedded code represents a sequence of binary decisions that distinguish an image from the null image using an embedded coding algorithm an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly also given a bit stream the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream in addition to producing a fully embedded bit stream the ezw consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images yet this performance is achieved with a technique that requires absolutely no training no pre stored tables or codebooks and requires no prior knowledge of the image source the ezw algorithm is based on four key concepts 1 a discrete wavelet transform or hierarchical subband decomposition 2 prediction of the absence of significant information across scales by exploiting the self similarity inherent in images 3 entropy coded successive approximation quantization and 4 universal lossless data compression which is achieved via adaptive arithmetic coding

stability of mmse state estimators over lossy networks using linear coding this paper studies the state estimation problem for a stochastic discrete time system over a lossy channel where the packet loss is modeled as an independent and identically distributed binary process to counter the effect of random packet loss we propose a linear coding method to preprocess the measured output and prove that the coded output is information preserving when packet loss is void and is information enhancing when packet loss is present an optimal state estimator under the minimum mean square error mmse criterion is derived for the coded output when subject to packet loss the maximum packet loss rate for ensuring a stable estimator is then derived and shown to be very close to a well known lower bound also considered is a compressed linear coding method where the measured output is first compressed onto a lower dimensional space before encoding and it is shown that the similar packet rate condition for stability holds

lsb steganographic method based on reversible histogram transformation function for resisting statistical steganalysis statistical steganalysis schemes detect the existence of secret information embedded by steganography the x 2 detection and regular singular rs attack methods are two well known statistical steganalysis schemes used against lsb least significant bit steganography the embedded message length can be estimated accurately by these two steganalysis schemes for secret communication the resistance of steganography against steganalysis is very important for information security to avoid the enemy s attempts the statistical features between stego images and cover images should be as similar as possible for better resistance to steganalysis in this manuscript a reversible histogram transformation function based lsb steganographic method is proposed to resist statistical steganalysis the experimental results show that the proposed method resists not only rs attack but also x 2 detection methods

an approach for solving a fuzzy multiobjective programming problem abstract in this paper we present a new approach based on the nearest interval approximation operator for dealing with a multiobjective programming problem with fuzzy valued objective functions by the way we have established a karush kuhn tucker k k t kind of pareto optimality conditions for the resulting interval multiobjective program to this end we made use of gh differentiability of involved interval valued functions two algorithms play a pivotal role in the proposed method the first one returns a nearest interval approximation to a given fuzzy number the other one makes use of k k t conditions to deliver a pareto optimal solution of the above mentioned resulting interval program

secure semi blind watermarking based on iteration mapping and image features this paper presents a secure semi blind watermarking technique based on iteration mapping and image features an image gray or color is divided into blocks of fixed size that are analyzed using fractal dimension to determine their properties the feature blocks containing edges and textures are used to form a feature label the watermark is the fusion of image feature label and a binary copyright symbol arnold iteration transform is employed for constructing the watermark to increase the security the secure image adaptive watermark is then embedded in the feature blocks by modifying dct middle frequency coefficients the detection and extraction procedure is a semi blind i e it does not need the original image only those who have the original watermark and the key can detect and extract the right watermark which makes the approach have high security level experimental results demonstrate that this algorithm can achieve good perceptual invisibility adaptability and security it is also robust against spatial attacks including discarding scribbling luminance and contrast changing and other attacks including low or high pass filtering adding noise and jpeg processing

the karush kuhn tucker optimality conditions in multiobjective programming problems with interval valued objective functions the kkt conditions in multiobjective programming problems with interval valued objective functions are derived in this paper many concepts of pareto optimal solutions are proposed by considering two orderings on the class of all closed intervals in order to consider the differentiation of an interval valued function we invoke the hausdorff metric to define the distance between two closed intervals and the hukuhara difference to define the difference of two closed intervals under these settings we are able to consider the continuity and differentiability of an interval valued function the kkt optimality conditions can then be naturally elicited

new efficiency conditions for multiobjective interval valued programming problems abstract in this paper we focus on necessary and sufficient efficiency conditions for optimization problems with multiple objectives and a feasible set defined by interval valued functions a new concept of fritz john and karush kuhn tucker type points is introduced for this mathematical programming problem based on the gh derivative concept the innovation and importance of these concepts are presented from a practical and computational point of view the problem is approached directly without transforming it into a real valued programming problem thereby attaining theoretical results that are more powerful and computationally more efficient under weaker hypotheses we also provide necessary conditions for efficiency which have been inexistent in the relevant literature to date the identification of necessary conditions is important for the development of future computational optimization techniques in an interval valued environment we introduce new generalized convexity notions for gh differentiable interval valued problems which are a generalization of previous concepts and we prove a sufficient efficiency condition based on these concepts finally the efficiency conditions for deterministic programming problems are shown to be particular instances of the results proved in this paper the theoretical developments are illustrated and justified through several numerical examples

image denoising using scale mixtures of gaussians in the wavelet domain we describe a method for removing noise from digital images based on a statistical model of the coefficients of an overcomplete multiscale oriented basis neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables a gaussian vector and a hidden positive scalar multiplier the latter modulates the local variance of the coefficients in the neighborhood and is thus able to account for the empirically observed correlation between the coefficient amplitudes under this model the bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable we demonstrate through simulations with images contaminated by additive white gaussian noise that the performance of this method substantially surpasses that of previously published methods both visually and in terms of mean squared error

prioritized dct for compression and progressive transmission of images an approach is based on the block discrete cosine transform dct the novelty of this approach is that the transform coefficients of all image blocks are coded and transmitted in absolute magnitude order the resulting ordered by magnitude transmission is accomplished without sacrificing coding efficiency by using partition priority coding coding and transmission are adaptive to the characteristics of each individual image and therefore very efficient another advantage of this approach is its high progression effectiveness since the largest transform coefficients that capture the most important characteristics of images are coded and transmitted first this method is well suited for progressive image transmission further compression of the image data is achieved by multiple distribution entropy coding a technique based on arithmetic coding experiments show that the approach compares favorably with previously reported dct and subband image codecs

image compression with variable block size segmentation high quality variable rate image compression is achieved by segmenting an image into regions of different sizes classifying each region into one of several perceptually distinct categories and using a distinct coding procedure for each category segmentation is performed with a quadtree data structure by isolating the perceptually more important areas of the image into small regions and separately identifying larger random texture blocks since the important regions have been isolated the remaining parts of the image can be coded at a lower rate than would be otherwise possible high quality coding results are achieved at rates between 0 35 and 0 7 b p depending on the nature of the original image and satisfactory results have been obtained at 0 25 b p

kalman filtering with intermittent observations motivated by navigation and tracking applications within sensor networks we consider the problem of performing kalman filtering with intermittent observations when data travel along unreliable communication channels in a large wireless multihop sensor network the effect of communication delays and loss of information in the control loop cannot be neglected we address this problem starting from the discrete kalman filtering formulation and modeling the arrival of the observation as a random process we study the statistical convergence properties of the estimation error covariance showing the existence of a critical value for the arrival rate of the observations beyond which a transition to an unbounded state error covariance occurs we also give upper and lower bounds on this expected state error covariance

the karush kuhn tucker optimality conditions in an optimization problem with interval valued objective function the kkt conditions in an optimization problem with interval valued objective function are derived in this paper two solution concepts of this optimization problem are proposed by considering two partial orderings on the set of all closed intervals in order to consider the differentiation of an interval valued function we invoke the hausdorff metric to define the distance between two closed intervals and the hukuhara difference to define the difference of two closed intervals under these settings we derive the kkt optimality conditions

multiscale hybrid linear models for lossy image representation in this paper we introduce a simple and efficient representation for natural images we view an image in either the spatial domain or the wavelet domain as a collection of vectors in a high dimensional space we then fit a piece wise linear model i e a union of affine subspaces to the vectors at each downsampling scale we call this a multiscale hybrid linear model for the image the model can be effectively estimated via a new algebraic method known as generalized principal component analysis gpca the hybrid and hierarchical structure of this model allows us to effectively extract and exploit multimodal correlations among the imagery data at different scales it conceptually and computationally remedies limitations of many existing image representation methods that are based on either a fixed linear transformation e g dct wavelets or an adaptive uni modal linear transformation e g pca or a multimodal model that uses only cluster means e g vq we will justify both quantitatively and experimentally why and how such a simple multiscale hybrid model is able to reduce simultaneously the model complexity and computational cost despite a small overhead of the model our careful and extensive experimental results show that this new model gives more compact representations for a wide variety of natural images under a wide range of signal to noise ratios than many existing methods including wavelets we also briefly address how the same hybrid linear modeling paradigm can be extended to be potentially useful for other applications such as image segmentation

mean square stability for kalman filtering with markovian packet losses this paper studies the stability of kalman filtering over a network subject to random packet losses which are modeled by a time homogeneous ergodic markov process for second order systems necessary and sufficient conditions for stability of the mean estimation error covariance matrices are derived by taking into account the system structure while for certain classes of higher order systems necessary and sufficient conditions are also provided to ensure stability of the mean estimation error covariance matrices all stability criteria are expressed by simple inequalities in terms of the largest eigenvalue of the open loop matrix and transition probabilities of the markov process their implications and relationships with related results in the literature are discussed

stability of kalman filtering with markovian packet losses we consider kalman filtering in a network with packet losses and use a two state markov chain to describe the normal operating condition of packet delivery and transmission failure based on the sojourn time of each visit to the failure or successful packet reception state we analyze the behavior of the estimation error covariance matrix and introduce the notion of peak covariance as an estimate of filtering deterioration caused by packet losses which describes the upper envelope of the sequence of error covariance matrices p t t 1 for the case of an unstable scalar model we give sufficient conditions for the stability of the peak covariance process in the general vector case and obtain a sufficient and necessary condition for the scalar case finally the relationship between two different types of stability notions is discussed

color image steganography scheme using dft spiht codec and modified differential phase shift keying techniques abstract in this research on the basis of the differential phase shift keying dpsk technique which is widely used in digital communication systems to develop a steganography scheme we aim to hide a secret image into a cover image of the same size so that the resultant image has no noticeable degradation in our approach three strategies are used to achieve this goal 1 compress the secret image to reduce the number of secret bits the set partitioning in hierarchical trees spiht codec were used to obtain a high reconstructed image quality and low bit rate image compression 2 a neighbor block signal phase comparison nbspc mechanism is used to offer the location for secret data embedding 3 a fold phase distribution differential phase shift keying fpdpsk mechanism is used to improve the quality of the cover image with our contribution we have developed the fold phase distribution dpsk concept to obtain more than 1 5 db quality improvement and twice the noise margin than the standard dpsk technique on the same test condition

compression unimpaired batch image encryption combining vector quantization and index compression as the network bandwidth has grown rapidly it has become common to share a large number of still images via the internet by means of batch transmission unfortunately most existing methods encrypt only a single image so there is room for improvement in our ability to send batches of images at one time a complete reconstruction of the methods is sometimes necessary especially when considering performance criteria this paper presents a novel batch image encryption algorithm that combines vector quantization vq and additional index compression process to benefit from their computational efficiency and low transmission bandwidth without affecting the original compression rate the experimental results show the performance of this new scheme in terms of compression rate and computational cost

evolving decision tree rule based system for audio stego anomalies detection based on hausdorff distance statistics this work is motivated by the interest in forensics steganalysis which is aimed at detecting the presence of secret messages transmitted through a subliminal channel a critical part of the steganalyser design depends on the choice of stego sensitive features and an efficient machine learning paradigm the goals of this paper are 1 to demonstrate that the higher order statistics of hausdorff distance a dissimilarity metric offers potential discrimination ability for a clean and a stego audio and 2 to achieve promising classification accuracy by realizing the proposed steganalyser with evolving decision tree classifier stego sensitive feature selection process is imparted by the genetic algorithm ga component and the construction of the rule base is facilitated by the decision tree module the objective function is designed to maximize the precision and recall measures of the classifier thereby enhancing the detection accuracy of the system with low dimensional and informative features an extensive experimental evaluation of the proposed system on a database containing 4800 clean and stego audio files generated by using six different embedding schemes with the family of six ga decision trees was conducted the observations reported as 90 detection rate a promising score for a blind steganalyser show that the proposed scheme with the hausdorff distance statistics as features and the evolving decision tree as classifier is a state of the art steganalyser that outperforms many of the previous steganalytic methods

on kalman filtering for detectable systems with intermittent observations we consider the problem of kalman filtering when observations are available according to a bernoulli process it is known that there exists a critical probability pc such that if measurements are available with probability greater than pc then the expected prediction covariance is bounded for all initial conditions otherwise it is unbounded for some initial conditions we show that when the system observation matrix restricted to the observable subspace is invertible the known lower bound on pc is in fact the exact critical probability this result is based on a novel decomposition of positive semidefinite matrices

multiobjective programming in optimization of the interval objective function abstract in this paper the mathematical programming problem whose objective function has interval coefficients is investigated to maximize the interval objective function the order relations which represent the decision maker s preference between interval profits are defined by the right limit the left limit the center and the width of an interval the maximization problem with the interval objective function is converted into a multiobjective problem using the order relations the minimization problem is also converted into a multiobjective problem using the order relations which represent the decision maker s preference between interval costs

optimality conditions for generalized differentiable interval valued functions in this paper we study the optimal solutions set for a generalized differentiable interval valued function necessary and sufficient optimality conditions are established for gh differentiable functions convexity assumptions that are necessary or required to ensure the characterization of the optimal solutions are weaker or less strict than those presented in previous works these convexity assumptions are the weakest to characterize the optimal solutions set known results for classical non interval valued optimization are particular cases of the ones proved here

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

extended karush kuhn tucker condition for constrained interval optimization problems and its application in support vector machines abstract in this paper we correct some errors in the extended first and second gordan s theorems in a recent paper information sciences 504 2019 276 292

a robust watermarking scheme using phase shift keying with the combination of amplitude boost and low amplitude block selection watermarking is a potential method for copyright protection and authentication of multimedia data in the internet the watermarking process can be viewed as a communication task where the watermark acting like information is embedded into a host image acting like noise in a communication channel which is susceptible to all kinds of attacks acting like jamming in a previous paper we proposed a robust watermarking scheme using frequency shift keying fsk in the scheme high variance block selection hvbs is employed to enhance robustness in this paper a novel watermarking scheme using phase shift keying psk modulation with amplitude boost ab and low amplitude block selection labs is proposed ab is hired to increase the robustness while labs is employed to improve the imperceptibility with proper combination of ab and labs the proposed scheme achieves superior performance in terms of robustness and imperceptibility in order to demonstrate the effectiveness of the proposed scheme simulations under various conditions were conducted the empirical results showed that qpsk is the best choice among other psks and the proposed scheme can sustain most common attacks including jpeg compression rotating resizing cropping painting noising and blurring etc the empirical results also showed that the scheme with ab and labs properly combined outperforms the scheme without the gain of the former over the latter is more significant for host images with smooth characteristics than those with high frequency characteristics simulation comparison with two other schemes the hsu s scheme and the chen s scheme showed that the proposed scheme is the most robust among the three

analysis of low bit rate image transform coding calculations based on high resolution quantizations prove that the distortion rate d r of an image transform coding is proportional to 2 sup 2r when r is large enough in wavelet and block cosine bases we show that if r 1 bit pixel then d r varies like r sup 1 2 spl gamma where spl gamma remains of the order of 1 for most natural images the improved performance of embedded codings in wavelet bases is analyzed at low bit rates we show that the compression performance of an orthonormal basis depends mostly on its ability to approximate images with a few nonzero vectors

wirtinger type integral inequalities for interval valued functions abstract this study presents some wirtinger type integral inequalities for interval valued functions by means of the generalized hukuhara differentiability and the pompeiu hausdorff metric these integral inequalities generalize their respective versions for real valued functions numerical examples that illustrate the applicability of the theory developed herein are also provided

entropy based algorithms for best basis selection adapted waveform analysis uses a library of orthonormal bases and an efficiency functional to match a basis to a given signal or family of signals it permits efficient compression of a variety of signals such as sound and images the predefined libraries of modulated waveforms include orthogonal wavelet packets and localized trigonometric functions and have reasonably well controlled time frequency localization properties the idea is to build out of the library functions an orthonormal basis relative to which the given signal or collection of signals has the lowest information cost the method relies heavily on the remarkable orthogonality properties of the new libraries all expansions in a given library conserve energy and are thus comparable several cost functionals are useful one of the most attractive is shannon entropy which has a geometric interpretation in this context

a novel secret image sharing scheme in color images using small shadow images over the past several years secret image sharing techniques have become another branch of the effort to prevent secret images from being eavesdropped on in addition to traditional cryptography because smaller shadows can speed up the transmission of a secret color image in this paper we combine chang and wu s gradual search algorithm for a single bitmap btc gsbtc and shamir s k n threshold concept to propose a novel secret color image sharing scheme that generates smaller shadows experimental results confirm that the proposed scheme successfully reduces shadow size and that each shadow behaves as a random like image that prevents leakage of information about the secret color image furthermore the correlation between two vertically or horizontally adjacent pixels in each shadow is significantly less than those in a color secret image and the presented scheme also achieves on average an npcr 0 414 and auci 32 78 thus with our scheme one pixel difference could cause a significant difference in the corresponding shadows therefore the security of the presented scheme is also confirmed

image hiding scheme with modulus function and dynamic programming strategy on partitioned pixels in this paper we shall propose a novel image hiding scheme our new scheme classifies the host image pixels into two groups of pixels according to the pixel values for each group of pixels the corresponding secret pixel values go through an optimal substitution process and are transformed into other pixel values by following the dynamic programming strategy then we can embed the transformed pixel values in the host pixels by using the modulus functions and obtain the stego image extensive experimental results demonstrate that our new method is capable of offering better stego image quality than a number of well accepted schemes

state estimation over a lossy network in spatially distributed cyber physical systems in this paper we analyze stochastic stability of kalman filter kf based state estimation over a lossy network in spatially distributed cyber physical systems we study a practical scenario in which sensors are arbitrarily deployed over an area to jointly sense the state of underlying physical system the sensors directly communicate observations to a central state estimation unit over a network resulting in random loss in measurements and partial observation updates in kf we analyzed stability of state estimation process in this scenario by establishing conditions under which steady state error covariance matrix is bounded in contrast to previous work on gathered measurement scenario with intermittent loss we considered a dispersed measurement scenario and established bounds on critical probability of receiving measurements over individual sensor communication links our analysis later exploited possible existence of spatial correlation among states in the filtering process and characterized its impact on the bounds we further extended our analysis by considering correlated loss among sensor measurements the overall analysis quantifies the trade off between state estimation accuracy and the quality of underlying communication network in addition our analysis demonstrates that by exploiting spatial correlation among states a higher degree of information loss or lower network quality can be tolerated to achieve a certain estimation accuracy since estimation accuracy directly impacts the stability of control operation this analysis is critical for architecture and network planing design of cyber physical systems

generalized hukuhara gâteaux and frechet derivatives of interval valued functions and their application in optimization with interval valued functions abstract in this article the notions of gh directional derivative gh g teaux derivative and gh frechet derivative for interval valued functions are proposed the existence of gh frechet derivative is shown to imply the existence of gh g teaux derivative and the existence of gh g teaux derivative is observed to indicate the presence of gh directional derivative for an interval valued gh lipschitz function it is proved that the existence of gh g teaux derivative implies the existence of gh frechet derivative it is observed that for an interval valued convex function on a linear space the gh directional derivative exists at any point for every direction concepts of linear and monotonic interval valued functions are studied in the sequel further it is shown that the proposed derivatives are useful to check the convexity of an interval valued function and to characterize efficient points of an optimization problem with interval valued objective function it is observed that at an efficient point of an interval valued function none of its gh directional derivatives dominates zero and the gh g teaux derivative must contain zero the entire study is supported by suitable illustrative examples

on fuzzy generalized convex mappings and optimality conditions for fuzzy weakly univex mappings in this paper we first introduce the weakly invex fuzzy mappings based on weakly differentiable functions and discuss the relationships between several kinds of fuzzy generalized convex mappings then a kind of fuzzy weakly univex functions are introduced and some properties of them are investigated furthermore optimality and duality results are derived for a class of generalized convex optimization problems with fuzzy weakly univex functions

extended kalman filtering with stochastic nonlinearities and multiple missing measurements in this paper the extended kalman filtering problem is investigated for a class of nonlinear systems with multiple missing measurements over a finite horizon both deterministic and stochastic nonlinearities are included in the system model where the stochastic nonlinearities are described by statistical means that could reflect the multiplicative stochastic disturbances the phenomenon of measurement missing occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over the interval 0 1 such a probability distribution is allowed to be any commonly used distribution over the interval 0 1 with known conditional probability the aim of the addressed filtering problem is to design a filter such that in the presence of both the stochastic nonlinearities and multiple missing measurements there exists an upper bound for the filtering error covariance subsequently such an upper bound is minimized by properly designing the filter gain at each sampling instant it is shown that the desired filter can be obtained in terms of the solutions to two riccati like difference equations that are of a form suitable for recursive computation in online applications an illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme

the laplacian pyramid as a compact image code we describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions the representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space pixel to pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself the result is a net data compression since the difference or error image has low variance and entropy and the low pass filtered image may represented at reduced sample density further data compression is achieved by quantizing the difference image these steps are then repeated to compress the low pass image iteration of the process at appropriately expanded scales generates a pyramid data structure the encoding process is equivalent to sampling the image with laplacian operators of many scales thus the code tends to enhance salient image features a further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression fast algorithms are described for coding and decoding

secret image sharing based on cellular automata and steganography recently lin and tsai secret image sharing with steganography and authentication the journal of systems and software 73 2004 405 414 and yang et al improvements of image sharing with steganography and authentication the journal of systems and software 80 2007 1070 1076 proposed secret image sharing schemes combining steganography and authentication based on shamir s polynomials the schemes divide a secret image into some shadows which are then embedded in cover images in order to produce stego images for distributing among participants to achieve better authentication ability chang et al sharing secrets in stego images with authentication pattern recognition 41 2008 3130 3137 proposed in 2008 an improved scheme which enhances the visual quality of the stego images as well and the probability of successful verification for a fake stego block is 1 16 in this paper we employ linear cellular automata digital signatures and hash functions to propose a novel t n threshold image sharing scheme with steganographic properties in which a double authentication mechanism is introduced which can detect tampering with probability 255 256 employing cellular automata instead of shamir s polynomials not only improves computational complexity from o nlog 2n to o n but obviates the need to modify pixels of cover images unnecessarily compared to previous methods c lin w tsai secret image sharing with steganography and authentication the journal of systems and software 73 2004 405 414 c yang t chen k yu c wang improvements of image sharing with steganography and authentication the journal of systems and software 80 2007 1070 1076 c chang y hsieh c lin sharing secrets in stego images with authentication pattern recognition 41 2008 3130 3137 we use fewer number of bits in each pixel of cover images for embedding data so that a better visual quality is guaranteed we further present some experimental results

wavelets and dilation equations a brief introduction wavelets are new families of basis functions that yield the representation f x sum b jk w 2 j x k their construction begins with the solution phi x to a dilation equation with coefficients c k then w comes from phi and the basis comes by translation and dilation of w it is shown in part 1 how conditions on the c k lead to approximation properties and orthogonality properties of the wavelets part 2 describes the recursive algorithms also based on the c k that decompose and reconstruct f the object of wavelets is to localize as far as possible in both time and frequency with efficient algorithms

generalized interval vector spaces and interval optimization this paper presents a method of endowing the generalized interval space with some different structures such as vector space topological space order relations and algebraic calculus we formulate interval optimization problems and relate them to classic multi objective optimization problems we also present a version of the mini max s theorem in the interval space context this paper presents a method for endowing the generalized interval space with some different structures such as vector spaces order relations and an algebraic calculus with these concepts we formulate interval optimization problems and relate them to classic multi objective optimization problems we also present a version of the von neumann s mini max theorem in the interval context

symmetry based scalable lossless compression of 3d medical image data we propose a novel symmetry based technique for scalable lossless compression of 3d medical image data the proposed method employs the 2d integer wavelet transform to decorrelate the data and an intraband prediction method to reduce the energy of the sub bands by exploiting the anatomical symmetries typically present in structural medical images a modified version of the embedded block coder with optimized truncation ebcot tailored according to the characteristics of the data encodes the residual data generated after prediction to provide resolution and quality scalability performance evaluations on a wide range of real 3d medical images show an average improvement of 15 in lossless compression ratios when compared to other state of the art lossless compression methods that also provide resolution and quality scalability including 3d jpeg2000 jpeg2000 and h 264 avc intra coding

an invisible hybrid color image system using spread vector quantization neural networks with penalized fcm in this paper an invisible hybrid color image hiding scheme based on spread vector quantization vq neural network with penalized fuzzy c means pfcm clustering technology named spfnn is proposed the goal is to offer safe exchange of a color stego image in the internet in the proposed scheme the secret color image is first compressed by a spread unsupervised neural network with pfcm based on interpolative vq ivq then the block cipher data encryption standard des and the rivest shamir and adleman rsa algorithms are hired to provide the mechanism of a hybrid cryptosystem for secure communication and convenient environment in the internet in the spfnn the penalized fuzzy clustering technology is embedded in a two dimensional hopfield neural network in order to generate optimal solutions for ivq then we encrypted color ivq indices and sorted the codebooks of secret color image information and embedded them into the frequency domain of the cover color image by the hadamard transform ht our proposed method has two benefits comparing with other data hiding techniques one is the high security and convenience offered by the hybrid des and rsa cryptosystems to exchange color image data in the internet the other benefit is that excellent results can be obtained using our proposed color image compression scheme spfnn method

calculus for interval valued functions using generalized hukuhara derivative and applications this paper is devoted to studying differential calculus for interval valued functions by using the generalized hukuhara differentiability which is the most general concept of differentiability for interval valued functions conditions examples and counterexamples for limit continuity integrability and differentiability are given special emphasis is set to the class f t c g t where c is an interval and g is a real function of a real variable here the emphasis is placed on the fact that f and g do not necessarily share their properties underlying the extra care that must be taken into account when dealing with interval valued functions two applications of the obtained results are presented the first one determines a delta method for interval valued random elements in the second application a new procedure to obtain solutions to an interval differential equation is introduced our results are relevant to fuzzy set theory because the usual fuzzy arithmetic extension functions and mathematical analysis are done on a cuts which are intervals

kalman filtering with intermittent observations on the boundedness of the expected error covariance this paper addresses the stability of a kalman filter when measurements are intermittently available due to constraints in the communication channel between the sensor and the estimator we give a necessary condition and a sufficient condition with a trivial gap between them for the boundedness of the expected value of the estimation error covariance these conditions are more general than the existing ones in the sense that they only require the state matrix of the system to be diagonalizable and the sequence of packet losses to be a stationary finite order markov process hence we extend the class of systems for which these conditions are known in two directions namely by including degenerate systems and by considering network models more general than i i d and gilbert elliott we show that these conditions recover known results from the literature when evaluated for non degenerate systems under the assumption of i i d or gilbert elliott packet loss models

adaptive entropy coded subband coding of images the authors describe a design approach called 2 d entropy constrained subband coding ecsbc based upon recently developed 2 d entropy constrained vector quantization ecvq schemes the output indexes of the embedded quantizers are further compressed by use of noiseless entropy coding schemes such as huffman or arithmetic codes resulting in variable rate outputs depending upon the specific configurations of the ecvq and the ecpvq over the subbands many different types of sbc schemes can be derived within the generic 2 d ecsbc framework among these the authors concentrate on three representative types of 2 d ecsbc schemes and provide relative performance evaluations they also describe an adaptive buffer instrumented version of 2 d ecsbc called 2 d ecsbc aec for use with fixed rate channels which completely eliminates buffer overflow underflow problems this adaptive scheme achieves performance quite close to the corresponding ideal 2 d ecsbc system

vcpss a two in one two decoding options image sharing method combining visual cryptography vc and polynomial style sharing pss approaches this paper presents a novel method to combine two major branches of image sharing vc and pss n transparencies are created for a given gray valued secret image if the decoding computer is temporarily not available at or not connected to the decoding scene we can still physically stack any t received transparencies t n is a threshold value to get a vague black and white view of the secret image immediately on the other hand when the decoding computer is finally available then we can get a much finer gray valued view of the secret image using the information hidden in the transparencies in summary each transparency is a two in one carrier of the information and the decoding has two options

a karhunen loeve like expansion for 1 f processes via wavelets while so called 1 f or scaling processes emerge regularly in modeling a wide range of natural phenomena as yet no entirely satisfactory framework has been described for the analysis of such processes orthonormal wavelet bases are used to provide a new construction for nearly 1 f processes from a set of uncorrelated random variables

circularly symmetric watermark embedding in 2 d dft domain in this paper a method for digital image watermarking is described that is resistant to geometric transformations a private key which allows a very large number of watermarks determines the watermark which is embedded on a ring in the dft domain the watermark possesses circular symmetry correlation is used for watermark detection the original image is not required in detection the proposed method is resistant to jpeg compression filtering noise addition scaling translation cropping rotation printing and rescanning experimental results prove the robustness of this method against the aforementioned attacks

multispectral code excited linear prediction coding and its application in magnetic resonance images this paper reports a multispectral code excited linear prediction mcelp method for the compression of multispectral images different linear prediction models and adaptation schemes have been compared the method that uses a forward adaptive autoregressive ar model has been proven to achieve a good compromise between performance complexity and robustness this approach is referred to as the mfcelp method given a set of multispectral images the linear predictive coefficients are updated over nonoverlapping three dimensional 3 d macroblocks each macroblock is further divided into several 3 d micro blocks and the best excitation signal for each microblock is determined through an analysis by synthesis procedure the mfcelp method has been applied to multispectral magnetic resonance mr images to satisfy the high quality requirement for medical images the error between the original image set and the synthesized one is further specified using a vector quantizer this method has been applied to images from 26 clinical mr neuro studies 20 slices study three spectral bands slice 256 spl times 256 pixels band 12 b pixel the mfcelp method provides a significant visual improvement over the discrete cosine transform dct based joint photographers expert group jpeg method the wavelet transform based embedded zero tree wavelet ezw coding method and the vector tree vt coding method as well as the multispectral segmented autoregressive moving average msarma method we developed previously

a two codebook combination and three phase block matching based image hiding scheme with high embedding capacity image hiding is a technique that embeds the important images into a cover image such that the important images are imperceptible and can be securely transmitted to the receiver in such research the common goals are to enlarge the embedding capacity as much as possible since the visual quality of the cover image is degraded slightly and to keep high visual quality of the important images when they are extracted from the stego image in this paper we propose an image hiding method based on the two codebook combination the three phase block matching procedure and the modulus substitution the proposed method can achieve these benefits 1 multiple relatively large important images can be embedded into a relatively small cover image 2 the quality of the stego image after embedding the secret data is not distorted significantly 3 the important images have an acceptable visual quality after they are extracted the experimental results also show that the proposed method is more flexible than previous methods

on sufficiency and duality for a class of interval valued programming problems abstract in this paper we are concerned with an interval valued programming problem sufficient optimality conditions are established under generalized convex functions for a feasible solution to be an efficient solution appropriate duality theorems for mond weir and wolfe type duals are discussed in order to relate the efficient solutions of primal and dual programs

color image steganography scheme using set partitioning in hierarchical trees coding digital fourier transform and adaptive phase modulation in this research of color image hidden scheme we aim to conceal a secret image into the cover image of the same size without noticeable degradation in our approach discrete wavelet transform dwt and set partitioning in hierarchical trees spiht codec are used to obtain a low bit rate and high reconstructed quality image compression in the embedding process an adaptive phase modulation apm mechanism and discrete fourier transform dft were adopted for secret data embedding simultaneously nearest phase modulations npm was used to improve the imperceptibility and decrease degradation of cover image both the chaotic mechanism cm and frequency hopping fh structure were hired to enhance the security of the scheme comparing with other data hiding techniques our proposed method has three advantages a the extracted secret image has high quality reconstruction by using spiht coder b the stego image possesses excellent imperceptibility without noticeable degradation because secret data was concealed in the phase by using apm and npm c have very large embedding capacity it allows the size of secret image to be the same as the cover image

an image hiding method with high hiding capacity based on best block matching and k means clustering this paper presents a novel image hiding method that exhibits a high hiding capacity that allows the embedded important image to be larger than the cover image a facility that is seldom described in the literature in the proposed method the entire important image is divided into many nonoverlapping blocks for each block of the important image a block matching procedure is used to search for the best similar block from a series of numbered candidate blocks the obtained indices of the best matching blocks are encoded using huffman coding scheme and then recorded in the least significant bit planes of the cover image through a monoalphabetic transposition cipher the proposed method exhibits the following advantages over existing methods 1 a high hiding capacity such that the embedded important image can be larger than the cover image 2 a stego image with a high quality which improves the secrecy of the hidden image and 3 a small error between the extracted important image and the original important image indicating that the extracted important image is of acceptable quality

the wavelet transform time frequency localization and signal analysis two different procedures for effecting a frequency analysis of a time dependent signal locally in time are studied the first procedure is the short time or windowed fourier transform the second is the wavelet transform in which high frequency components are studied with sharper time resolution than low frequency components the similarities and the differences between these two methods are discussed for both schemes a detailed study is made of the reconstruction method and its stability as a function of the chosen time frequency density finally the notion of time frequency localization is made precise within this framework by two localization theorems

image subband coding using context based classification and adaptive quantization adaptive compression methods have been a key component of many proposed subband or wavelet image coding techniques this paper deals with a particular type of adaptive subband image coding where we focus on the image coder s ability to adjust itself on the fly to the spatially varying statistical nature of image contents this backward adaptation is distinguished from more frequently used forward adaptation in that forward adaptation selects the best operating parameters from a predesigned set and thus uses considerable amount of side information in order for the encoder and the decoder to operate with the same parameters specifically we present backward adaptive quantization using a new context based classification technique which classifies each subband coefficient based on the surrounding quantized coefficients we couple this classification with online parametric adaptation of the quantizer applied to each class a simple uniform threshold quantizer is employed as the baseline quantizer for which adaptation is achieved our subband image coder based on the proposed adaptive classification quantization idea exhibits excellent rate distortion performance in particular at very low rates for popular test images it is comparable or superior to most of the state of the art coders in the literature

a new nonlinear interval programming method for uncertain problems with dependent interval variables this paper proposes a new nonlinear interval programming method that can be used to handle uncertain optimization problems when there are dependencies among the interval variables the uncertain domain is modeled using a multidimensional parallelepiped interval model the model depicts single variable uncertainty using a marginal interval and depicts the degree of dependencies among the interval variables using correlation angles and correlation coefficients based on the order relation of interval and the possibility degree of interval the uncertain optimization problem is converted to a deterministic two layer nesting optimization problem the affine coordinate is then introduced to convert the uncertain domain of a multidimensional parallelepiped interval model to a standard interval uncertain domain a highly efficient iterative algorithm is formulated to generate an efficient solution for the multi layer nesting optimization problem after the conversion three computational examples are given to verify the effectiveness of the proposed method

steganography of capacity required using modulo operator for embedding secret image in this paper a novel mechanism using modulus operations to incorporate secret data with image form into a host image is proposed the used modulus is a threshold value that indicates how many bits of the secret image are being incorporated into the host image in our scheme there are two kinds of secret images processed the capacities of these two types are half the size and a quarter the size of the chosen host image respectively as can be seen in the result of our experiment not only can the secret images be totally embedded but the extra information such as the auxiliary table assisting the secret extraction is released as well even then there is still space remaining after the hiding procedure is completed the result is a higher capacity exploration for embedding the secret into a host image upon inspection of the quality of the stego image using the measurement of psnr it is noted that not only does the image display make the grade of the human vision system i e it is imperceptible by human eyes as far as the embedded secret is concerned but also the valued results of psnr remain sufficiently stable for a variety of host image selections therefore our proposal scheme is notably superior to those in the previous literature iee proc vision image signal process 147 1 2000 29 the advantages of this proposed scheme are the higher capacity exploration on secret embedding and better stability on stego image displays

sharing secrets in stego images with authentication recently lin and tsai and yang et al proposed secret image sharing schemes with steganography and authentication which divide a secret image into the shadows and embed the produced shadows in the cover images to form the stego images so as to be transmitted to authorized recipients securely in addition these schemes also involve their authentication mechanisms to verify the integrity of the stego images such that the secret image can be restored correctly unfortunately these schemes still have two shortcomings one is that the weak authentication cannot well protect the integrity of the stego images so the secret image cannot be recovered completely the other shortcoming is that the visual quality of the stego images is not good enough to overcome such drawbacks in this paper we propose a novel secret image sharing scheme combining steganography and authentication based on chinese remainder theorem crt the proposed scheme not only improves the authentication ability but also enhances the visual quality of the stego images the experimental results show that the proposed scheme is superior to the previously existing methods

karush kuhn tucker conditions for interval and fuzzy optimization in several variables under total and directional generalized differentiability abstract we extend the interval and fuzzy gh differentiability to consider interval and fuzzy valued functions of several variables and to include directional gh differentiability the proposed setting is more general than the existing definitions in the literature and allows a unified view of total and direction gh differentiability and for the computation of partial gh derivatives directional gh derivative and level wise gh differentiability in the fuzzy valued case a concept of gh differential is then deduced and its values are used to define an abstract local tangency property for a gh differentiable function similar to the well known tangency between a differentiable function and its tangent plane the proposed new setting allows an analysis of conditions for local optimality dominance with respect to interval and level wise partial orders well known in the literature in terms of directional gh derivatives including concepts of local convexity and to formulate kkt like conditions for non dominated solutions in constrained optimization problems

fast planar oriented ripple search algorithm for hyperspace vq codebook this paper presents a fast codebook search method for improving the quantization complexity of full search vector quantization vq the proposed method is built on the planar voronoi diagram to label a ripple search domain then the appropriate codeword can easily be found just by searching the local region instead of global exploration in order to take a step further and obtain the close result full search vq would we equip the proposed method with a duplication mechanism that helps to bring down the possible quantizing distortion to its lowest level according to the experimental results the proposed method is indeed capable of providing better outcome at a faster quantization speed than the existing partial search methods moreover the proposed method only requires a little extra storage for duplication

a novel image hiding scheme based on block difference to transmit a secret image securely by embedding it into a cover image without drawing the unintended observer s attention a novel image hiding scheme is proposed by exploring the block correlation between the cover image and the secret image the stego image quality can be greatly improved experimental results demonstrate that the stego image has a high peak signal to noise ratio and the stego image is almost the same as the cover image

nonlinear wavelet transforms for image coding via lifting we investigate central issues such as invertibility stability synchronization and frequency characteristics for nonlinear wavelet transforms built using the lifting framework the nonlinearity comes from adaptively choosing between a class of linear predictors within the lifting framework we also describe how earlier families of nonlinear filter banks can be extended through the use of prediction functions operating on a causal neighborhood of pixels preliminary compression results for model and real world images demonstrate the promise of our techniques

adaptive lms l filters for noise suppression in images several adaptive least mean squares lms l filters both constrained and unconstrained ones are developed for noise suppression in images and compared in this paper first the location invariant lms l filter for a nonconstant signal corrupted by zero mean additive white noise is derived it is demonstrated that the location invariant lms l filter can be described in terms of the generalized linearly constrained adaptive processing structure proposed by griffiths and jim 1982 subsequently the normalized and the signed error lms l filters are studied a modified lms l filter with nonhomogeneous step sizes is also proposed in order to accelerate the rate of convergence of the adaptive l filter finally a signal dependent adaptive filter structure is developed to allow a separate treatment of the pixels that are close to the edges from the pixels that belong to homogeneous image regions

hiding data in images by simple lsb substitution in this paper a data hiding scheme by simple lsb substitution is proposed by applying an optimal pixel adjustment process to the stego image obtained by the simple lsb substitution method the image quality of the stego image can be greatly improved with low extra computational complexity the worst case mean square error between the stego image and the cover image is derived experimental results show that the stego image is visually indistinguishable from the original cover image the obtained results also show a significant improvement with respect to a previous work

nonseparable multidimensional perfect reconstruction filter banks and wavelet bases for r sup n new results on multidimensional filter banks and their connection to multidimensional nonseparable wavelets are presented among the topics discussed are sampling in multiple dimensions multidimensional perfect reconstruction filter banks the two channel case in multiple dimensions the synthesis of multidimensional filter banks and the design of compactly supported wavelets

brief paper robust filtering with stochastic nonlinearities and multiple missing measurements this paper is concerned with the filtering problem for a class of discrete time uncertain stochastic nonlinear time delay systems with both the probabilistic missing measurements and external stochastic disturbances the measurement missing phenomenon is assumed to occur in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution over the interval 01 such a probabilistic distribution could be any commonly used discrete distribution over the interval 01 the multiplicative stochastic disturbances are in the form of a scalar gaussian white noise with unit variance the purpose of the addressed filtering problem is to design a filter such that for the admissible random measurement missing stochastic disturbances norm bounded uncertainties as well as stochastic nonlinearities the error dynamics of the filtering process is exponentially mean square stable by using the linear matrix inequality lmi method sufficient conditions are established that ensure the exponential mean square stability of the filtering error and then the filter parameters are characterized by the solution to a set of lmis illustrative examples are exploited to show the effectiveness of the proposed design procedures

kkt optimality conditions in interval valued multiobjective programming with generalized differentiable functions we devote this paper to study a class of interval valued multiobjective programming problems for this we consider two order relations lu and ls on the set of all closed intervals and propose many concepts of pareto optimal solutions based on convexity concepts viz lu and ls convexity and generalized differentiability viz gh differentiability of interval valued functions the kkt optimality conditions for aforesaid problems are obtained in addition we compare our results with the results given in wu 2009 and we show some advantages of our results the theoretical development is illustrated by suitable examples

a virtual image cryptosystem based upon vector quantization we propose a new image cryptosystem to protect image data it encrypts the original image into another virtual image since both original and virtual images are significant our new cryptosystem can confuse illegal users besides the camouflage this new cryptosystem has three other benefits first our cryptosystem is secure even if the illegal users know that our virtual image is a camouflage second this cryptosystem can compress image data finally our method is more efficient than a method that encrypts the entire image directly

multiple description wavelet based image coding we consider the problem of coding images for transmission over error prone channels the impairments we target are transient channel shutdowns as would occur in a packet network when a packet is lost or in a wireless system during a deep fade when data is delivered it is assumed to be error free but some of the data may never reach the receiver the proposed algorithms are based on a combination of multiple description scalar quantizers with techniques successfully applied to the construction of some of the most efficient subband coders a given image is encoded into multiple independent packets of roughly equal length when packets are lost the quality of the approximation computed at the receiver depends only on the number of packets received but does not depend on exactly which packets are actually received when compared with previously reported results on the performance of robust image coders based on multiple descriptions on standard test images our coders attain similar psnr values using typically about 50 60 of the bit rate required by these other state of the art coders while at the same time providing significantly more freedom in the mechanism for allocation of redundancy among descriptions

on multirate optimality of jpeg2000 code stream arguably the most important and defining feature of the jpeg2000 image compression standard is its r d optimized code stream of multiple progressive layers this code stream is an interleaving of many scalable code streams of different sample blocks in this paper we reexamine the r d optimality of jpeg2000 scalable code streams under an expected multirate distortion measure emrd which is defined to be the average distortion weighted by a probability distribution of operational rates in a given range rather than for one or few fixed rates we prove that the jpeg2000 code stream constructed by embedded block coding of optimal truncation is almost optimal in the emrd sense for uniform rate distribution function even if the individual scalable code streams have nonconvex operational r d curves we also develop algorithms to optimize the jpeg2000 code stream for exponential and laplacian rate distribution functions while maintaining compatibility with the jpeg2000 standard both of our analytical and experimental results lend strong support to jpeg2000 as a near optimal scalable image codec in a fairly general setting

on invex fuzzy mappings and fuzzy variational like inequalities in this paper we first show the need for introducing invex fuzzy mappings after that we show that the concept of invex fuzzy mapping previously given by wu and xu are very restrictive and the examples presented there are not correct then we present more general concepts of invex and incave fuzzy mappings involving strongly generalized differentiable fuzzy mapping finally we show that the results obtained by wu and xu on the relationship between fuzzy variational like inequalities and fuzzy optimization problems are still valid using these new concepts

successive refinement of information the successive refinement of information consists of first approximating data using a few bits of information then iteratively improving the approximation as more and more information is supplied the goal is to achieve an optimal description at each stage in general an ongoing description which is rate distortion optimal whenever it is interrupted is sought it is shown that in order to achieve optimal successive refinement the necessary and sufficient conditions are that the solutions of the rate distortion problem can be written as a markov chain in particular all finite alphabet signals with hamming distortion satisfy these requirements it is also shown that the same is true for gaussian signals with squared error distortion and for laplacian signals with absolute error distortion a simple counterexample with absolute error distortion and a symmetric source distribution which shows that successive refinement is not always achievable is presented

possibilistic linear programming a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem in this paper we review some fuzzy linear programming methods and techniques from a practical point of view in the rst part the general history and the approach of fuzzy mathematical programming are introduced using a numerical example some models of fuzzy linear programming are described in the second part of the paper fuzzy mathematical programming approaches are compared to stochastic programming ones the advantages and disadvantages of fuzzy mathematical programming approaches are exemplied in the setting of an optimal portfolio selection problem finally some newly developed ideas and techniques in fuzzy mathematical programming are briey reviewed c 2000 elsevier science b v all rights reserved

significance linked connected component analysis for wavelet image coding the success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation there have been several very competitive wavelet coders developed namely shapiro s 1993 embedded zerotree wavelets ezw servetto et al s 1995 morphological representation of wavelet data mrwd and said and pearlman s see ieee trans circuits syst video technol vol 6 p 245 50 1996 set partitioning in hierarchical trees spiht we develop a novel wavelet image coder called significance linked connected component analysis slcca of wavelet coefficients that extends mrwd by exploiting both within subband clustering of significant coefficients and cross subband dependency in significant fields extensive computer experiments on both natural and texture images show convincingly that the proposed slcca outperforms ezw mrwd and spiht for example for the barbara image at 0 25 b pixel slcca outperforms ezw mrwd and spiht by 1 41 db 0 32 db and 0 60 db in psnr respectively it is also observed that slcca works extremely well for images with a large portion of texture for eight typical 256 spl times 256 grayscale texture images compressed at 0 40 b pixel slcca outperforms spiht by 0 16 db 0 63 db in psnr this performance is achieved without using any optimal bit allocation procedure thus both the encoding and decoding procedures are fast

stability conditions for multi sensor state estimation over a lossy network this paper studies a networked state estimation problem for a spatially large linear system with a distributed array of sensors each of which offers partial state measurements a lossy communication network is used to transmit the sensor measurements to a central estimator where the minimum mean square error mmse state estimate is computed under a markovian packet loss model we provide necessary and sufficient conditions for the stability of the estimator for any diagonalizable system in the sense that the mean of the state estimation error covariance matrix is uniformly bounded in particular the stability conditions for the second order systems with an i i d packet loss model are explicitly expressed as simple inequalities in terms of the largest open loop pole and the packet loss rate

orthonormal bases of compactly supported wavelets we construct orthonormal bases of compactly supported wavelets with arbitrarily high regularity the order of regularity increases linearly with the support width we start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction the construction then follows from a synthesis of these different approaches

recursive filtering with random parameter matrices multiple fading measurements and correlated noises this paper is concerned with the recursive filtering problem for a class of discrete time nonlinear stochastic systems with random parameter matrices multiple fading measurements and correlated noises the phenomenon of measurement fading occurs in a random way and the fading probability for each sensor is governed by an individual random variable obeying a certain probability distribution over the known interval b k c k such a probability distribution could be any commonly used discrete distribution over the interval b k c k that covers the bernoulli distribution as a special case the process noise and the measurement noise are one step autocorrelated respectively the process noise and the measurement noise are two step cross correlated the purpose of the addressed filtering problem is to design an unbiased and recursive filter for the random parameter matrices stochastic nonlinearity and multiple fading measurements as well as correlated noises intensive stochastic analysis is carried out to obtain the filter gain characterized by the solution to a recursive matrix equation the proposed scheme is of a form suitable for recursive computation in online applications a simulation example is given to illustrate the effectiveness of the proposed filter design scheme

multiple watermarking scheme of the european article number barcode using similar code division multiple access technique abstract the code division multiple access technique is widely used in digital communication systems in this paper a mix codec hamming codec and spread spectrum technique construct a similar code division multiple access scdma structure it is used to configure a multiple watermarking scheme for still images meanwhile another type of multiple watermarking scheme a non code division multiple access ncdma watermarking structure is used to demonstrate that the scdma structure is superior in the embedding process three watermark bit streams are modulated to the bit stream of the scdma code successively the scdma code stream is concealed into three coefficient pairs of each dft block by an adaptive phase shift keying apsk modulation the barcode has several advantages it is not as easily worn down as the magnetic card it has a very low rate of error and it is very cheap and easy to be fabricate therefore we use the barcode pattern to generate the watermarks to enhance the effect of the digital rights management compared to other multiple watermarking schemes our proposed method has two advantages 1 the secret image has very high security by using scdma and apsk as in digital communication and 2 the extracted watermarks have the same quality even under all kinds of attacks which is better than other schemes with different extracted watermark quality

tiling and adaptive image compression we investigate the task of compressing an image by using different probability models for compressing different regions of the image in this task using a larger number of regions would result in better compression but would also require more bits for describing the regions and the probability models used in the regions we discuss using quadtree methods for performing the compression we introduce a class of probability models for images the k rectangular tilings of an image that is formed by partitioning the image into k rectangular regions and generating the coefficients within each region by using a probability model selected from a finite class of n probability models for an image of size n spl times n we give a sequential probability assignment algorithm that codes the image with a code length which is within o k log nn k of the code length produced by the best probability model in the class the algorithm has a computational complexity of o nn sup 3 an interesting subclass of the class of k rectangular tilings is the class of tilings using rectangles whose widths are powers of two this class is far more flexible than quadtrees and yet has a sequential probability assignment algorithm that produces a code length that is within o k log nn k of the best model in the class with a computational complexity of o nn sup 2 logn similar to the computational complexity of sequential probability assignment using quadtrees we also consider progressive transmission of the coefficients of the image

wavelet footprints theory algorithms and applications wavelet based algorithms have been successful in different signal processing tasks the wavelet transform is a powerful tool because it manages to represent both transient and stationary behaviors of a signal with few transform coefficients discontinuities often carry relevant signal information and therefore they represent a critical part to analyze we study the dependency across scales of the wavelet coefficients generated by discontinuities we start by showing that any piecewise smooth signal can be expressed as a sum of a piecewise polynomial signal and a uniformly smooth residual theorem 1 we then introduce the notion of footprints which are scale space vectors that model discontinuities in piecewise polynomial signals exactly we show that footprints form an overcomplete dictionary and develop efficient and robust algorithms to find the exact representation of a piecewise polynomial function in terms of footprints this also leads to efficient approximation of piecewise smooth functions finally we focus on applications and show that algorithms based on footprints outperform standard wavelet methods in different applications such as denoising compression and nonblind deconvolution in the case of compression we also prove that at high rates footprint based algorithms attain optimal performance theorem 3