blind channel equalization and spl epsiv approximation algorithms we show that a blind equalizer can be obtained without estimating any statistics based on the received data if the constellation alphabet is known the idea is to formulate the blind channel equalization problem into a quadratic optimization with binary constraints then efficient spl epsiv approximation algorithms are presented and applied to find the equalizer coefficients the proposed approach blindly equalizes the channel at symbol baud rates does not rely on estimating any statistics applies to nonminimum phase channels and converges to an spl epsiv neighborhood of a global optimum simulation results support the theoretical analysis

finite alphabet information and multivariate blind deconvolution and identification of linear systems this article addresses the problem of estimating a multivariate linear system from its output when the input is an unobservable sequence of random vectors with finite alphabet distribution by explicitly utilizing the finite alphabet property an estimation method is proposed under the traditional inverse filtering paradigm as a generalization of a univariate method that has been studied previously identifiability of multivariate systems by the proposed method is proved mathematically under very mild conditions that can be satisfied even if the input is nonstationary and has both cross channel and serial statistical dependencies statistical super efficiency in estimating both parametric and nonparametric systems is also established for an alphabet based cost function

two dimensional fourier series based model for nonminimum phase linear shift invariant systems and texture image classification in this paper chi s 1997 1999 real one dimensional 1 d parametric nonminimum phase fourier series based model fsbm is extended to two dimensional 2 d fsbm for a 2 d nonminimum phase linear shift invariant system by using finite 2 d fourier series approximations to its amplitude response and phase response respectively the proposed 2 d fsbm is guaranteed stable and its complex cepstrum can be obtained from its amplitude and phase parameters through a closed form formula without involving complicated 2 d phase unwrapping and polynomial rooting a consistent estimator is proposed for the amplitude estimation of the 2 d fsbm using a 2 d half plane causal minimum phase linear prediction error filter modeled by a 2 d minimum phase fsbm and then two consistent estimators are proposed for the phase estimation of the 2 d fsbm using the chien et al 1997 2 d phase equalizer modeled by a 2 d all pass fsbm the estimated 2 d fsbm can be applied to modeling of 2 d non gaussian random signals and 2 d signal classification using complex cepstra some simulation results are presented to support the efficacy of the three proposed estimators furthermore classification of texture images 2 d non gaussian signals using the estimated fsbm second and higher order statistics is presented together with some experimental results finally we draw some conclusions

robust rotation invariant texture classification using a model based approach in this paper a model based texture classification procedure is presented the texture is modeled as the output of a linear system driven by a binary image this latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function acf we show that features extracted from the acf of the binary excitation suffice to represent the texture for classification purposes specifically we employ a moment invariants based technique to classify the acf the resulting proposed classification procedure is thus inherently rotation invariant moreover it is robust with respect to additive noise experimental results show that this approach allows obtaining high correct rotation invariant classification rates while containing the size of the feature space

on the general ber expression of one and two dimensional amplitude modulations quadrature amplitude modulation qam is an attractive technique to achieve high rate transmission without increasing the bandwidth a great deal of attention has been devoted to the study of bit error rate ber performance of qam and approximate expressions for the bit error probability of qam have been developed in many places in the literature however the exact and general ber expression of qam with an arbitrary constellation size has not been derived yet we provide an exact and general closed form expression of the ber for one dimensional and two dimensional amplitude modulations i e pam and qam under an additive white gaussian noise awgn channel when gray code bit mapping is employed the provided ber expressions offer a convenient way to evaluate the performance of pam and qam systems for various cases of practical interest moreover simple approximations can be found from our expressions which are the same as the well known approximations if only the dominant terms are considered

optimization of partition based weighted sum filters and their application to image denoising partition based weighted sum p ws filtering is an effective method for processing nonstationary signals especially those with regularly occurring structures such as images p ws filters were originally formulated as hard partition weighted sum hp ws filters and were successfully applied to image denoising this formulation relied on intuitive arguments to generate the filter class here we present a statistical analysis that justifies the use of weighted sum filters after observation space partitioning unfortunately the hp ws filters are nondifferentiable and an analytical solution for their global optimization is therefore difficult to obtain a two stage suboptimal training procedure has been reported in the literature but prior to this research no evaluation on the optimality of this approach has been reported here a genetic algorithm ga hp ws optimization procedure is developed that in simulations shows that the simpler two stage training procedure yields near optimal results also developed in this paper are soft partition weighted sum sp ws filters the sp ws filters utilize soft or fuzzy partitions that yield a differentiable filtering operation enabling the development of gradient based optimization procedures image denoising simulation results are presented comparing hp ws and sp ws filters their optimization procedures and wavelet based image denoising these results show that p ws filters in general outperform traditional and wavelet based image filters and sp ws filters utilizing soft partitioning not only allow for simple optimization but also yields improved performance

object and texture classification using higher order statistics the problem of the detection and classification of deterministic objects and random textures in a noisy scene is discussed an energy detector is developed in the cumulant domain by exploiting the noise insensitivity of higher order statistics an efficient implementation of this detector is described using matched filtering its performance is analyzed using asymptotic distributions in a binary hypothesis testing framework the object and texture discriminant functions are minimum distance classifiers in the cumulant domain and can be efficiently implemented using a bank of matched filters they are immune to additive gaussian noise and insensitive to object shifts important extensions which can handle object rotation and scaling are also discussed an alternative texture classifier is derived from a ml viewpoint and is statistically efficient at the expense of complexity the application of these algorithms to the texture modeling problem is indicated and consistent parameter estimates are obtained

computation of the exact bit error rate of coherent m ary psk with gray code bit mapping the problem of calculating the average bit error probability bep of coherent m ary phase shift keying psk over a gaussian channel has been studied previously in the literature a solution to the problem for systems using a binary reflected gray code brgc to map bits to symbols was first presented by p j lee see ibid vol com 34 p 488 91 1986 we show that the results obtained by lee are incorrect for m spl ges 16 we show that the reason for this is an invalid assumption that the bit error rate ber is independent of the transmitted symbols an assumption which has also propagated to textbooks we give a new expression for the ber of m psk systems using the brgc and compare this with lee s results

modeling and identification of symmetric noncausal impulse responces spectrally equivalent causal sec models for single input single output noncausal systems are derived with the objective of estimating the noncausal impulse response attention is restricted to symmetric noncausal impulse responses and the measurements of the system output are assumed to be noisy it is shown that by using structured sec models either state space or arma plus noise consistent estimates of the noncausal impulse response can be obtained by exploiting only the second order statistics of the measurements a new version of the correlation technique particularly suited to structured models is presented and analyzed computer simulation results are also presented to illustrate the performance of the proposed approach

an alternative expression for the symbol error probability of mpsk in the presence of i q unbalance this letter provides a new expression for the probability of the phase angle between two vectors perturbed by gaussian noise with in phase quadrature phase i q unbalance in order to simplify the analysis of the error probabilities of m ary phase shift keying mpsk the error probabilities symbol error rates or bit error rates for mpsk with i q balance or unbalance can be presented straightforwardly from the derived expression because the newly derived result is provided in terms of the conventional first order gaussian q function and the joint gaussian q function with a correlation coefficient dependent on m it readily allows rapid evaluation for various cases of practical interest

reduced complexity modeling and reproduction of colored textures an unsupervised color texture synthesis by analysis method is described the texture is reproduced to appear perceptually similar to a given prototype by copying its statistical properties up to the second order the synthesized texture is obtained at the output of a single input three output nonlinear system driven by a realization of a white gaussian random field significant complexity reduction is gained by exploiting the rank deficiency of the cross power spectral density matrix of the color texture samples

texture synthesis by analysis with hard limited gaussian processes a twin stage texture synthesis by analysis method is presented it aims to approximate first and second order distributions of the texture accordingly to the julesz conjecture in the first stage the binary textural behavior of a given prototype is represented by means of a hard limited gaussian process in the second stage the texture is synthesized by passing the binary hard limited gaussian process through a linear filter followed by a zero memory histogram equalizer

identification of linear stochastic systems via second and fourth order cumulant matching the identification problem for time invariant single input single output linear stochastic systems driven by non gaussian white noise is considered the system is not restricted to be minimum phase and it is allowed to contain all pass components a least squares criterion that involves matching the second and the fourth order cumulant functions of the noisy observations is proposed knowledge of the probability distribution of the driving noise is not required an order determination criterion that is a modification of the akaike information criterion is also proposed strong consistency of the proposed estimator is proved under certain sufficient conditions simulation results are presented to illustrate the method

on time reversibility and the uniqueness of moving average representations for non gaussian stationary time series a recent result by findley 1986 on the uniqueness of moving average representations for non gaussian time series is shown to establish a conjecture by weiss 1975 on the time reversibility of general linear processes 1988 biometrika trust

dynamic green s functions in anisotropic piezoelectric thermoelastic and poroelastic solids a procedure is described to generate fundamental solutions or green s functions for time harmonic point forces and sources the linearity of the field equations permits the green s function to be represented as an integral over the surface of a unit sphere where the integrand is the solution of a one dimensional impulse response problem the method is demonstrated for the theories of piezoelectricity thermoelasticity and poroelasticity time domain analogues are discussed and compared with known expressions for anisotropic elasticity

a perceptually lossless model based texture compression technique in natural scenes still images as well as sequences backgrounds and objects surfaces usually have a textural structure therefore in order to efficiently code images it is crucial to investigate the texture compression problem in this paper a perceptually lossless synthesis by analysis texture coding method is presented the proposed approach is model based the parameters of the model consist of a binary excitation signal and the parsimonious representation of the reconstruction filter the estimated parameters which allow to one synthesize at the decoder site a texture that is perceptually indistinguishable from the original one are then compressed using a lossless strategy which is based on a fast binary wavelet transformation specifically tailored to binary images the proposed method leads to very good perceptual results superior to those of existing techniques

miscellanea on time reversibility of linear processes summary a necessary and sufficient condition for time reversibility of stationary linear processes is given which contrary to previous results does not require existence of moments of order higher than two

double blind deconvolution the analysis of post synaptic currents in nerve cells summary this paper is concerned with the analysis of observations made on a system that is being stimulated at fixed time intervals but where the precise nature and effect of any individual stimulus is unknown the realized values are modelled as a stochastic process consisting of a random signal embedded in noise the aim of the analysis is to use the data to unravel the unknown structure of the system and to ascertain the probabilistic behaviour of the stimuli a method of parameter estimation based on quasi profile likelihood is presented and the statistical properties of the estimates are established while recognizing that there will be a discrepancy between the model and the true data generating mechanism a method of model validation and determination is also advanced and kernel smoothing techniques are proposed as a basis for identifying the amplitude distribution of the stimuli the data processing techniques described have a direct application to the investigation of excitatory post synaptic currents recorded from nerve cells in the central nervous system and their use in quantal analysis of such data is illustrated

a unifying maximum likelihood view of cumulant and polyspectral measures for non gaussian signal classification and estimation classification and estimation of non gaussian signals observed in additive gaussian noise of unknown covariance are addressed using cumulants or polyspectra by integrating ideas from pattern recognition and model identification asymptotically optimum maximum likelihood classifiers and arma autoregressive moving average parameter estimators are derived without knowledge of the data distribution identifiability of noncausal and nonminimum phase arma models is established using a finite number of cumulant or polyspectral lags of any order greater than two a unifying view of cumulant and polyspectral discriminant measures utilizes these lags and provides a common framework for development and performance analysis of novel and existing estimation and classification algorithms tentative order determination and model validation tests for non gaussian arma processes are described briefly illustrative simulations are also presented

image modeling using inverse filtering criteria with application to textures statistical approaches to image modeling have largely relied upon random models that characterize the 2 d process in terms of its first and second order statistics and therefore cannot completely capture phase properties of random fields that are non gaussian this constrains the parameters of noncausal image models to be symmetric and therefore the underlying random field to be spatially reversible research indicates that this assumption may not be always valid for texture images in this paper higher than second order statistics are used to derive and implement two classes of inverse filtering criteria for parameter estimation of asymmetric noncausal autoregressive moving average arma image models with known orders contrary to existing approaches fir inverse filters are employed and image models with zeros on the unit bicircle can be handled one of the criteria defines the smallest set of cumulant lags necessary for identifiability of these models to date consistency of these estimators is established and their performance is evaluated with monte carlo simulations as well as texture classification and synthesis experiments

texture decomposition by harmonics extraction from higher order statistics in this paper a method of harmonics extraction from higher order statistics hos is developed for texture decomposition we show that the diagonal slice of the fourth order cumulants is proportional to the autocorrelation of a related noiseless sinusoidal signal with identical frequencies we propose to use this fourth order cumulants slice to estimate a power spectrum from which the harmonic frequencies can be easily extracted hence a texture can be decomposed into deterministic components and indeterministic components as in a unified texture model through a wold like decomposition procedure the simulation and experimental results demonstrated that this method is effective for texture decomposition and it performs better than traditional lower order statistics based decomposition methods

bit error probability for coherent m ary psk systems a simple technique for evaluating the bit error probability of coherent m ary phase shift keying psk with any bit mapping is proposed closed form expressions are given in terms of error functions for half and quadrant plane probabilities in the decision space avoiding the numerical evaluation of complicated integrals that occurs in the direct method bit error probability expressions for m ary psk with gray natural binary and folded binary bit mappings are derived

characterization and estimation of two dimensional arma models a class of finite order two dimensional autoregressive moving average arma is introduced that can represent any process with rational spectral density in this model the driving noise is correlated and need not be gaussian currently known classes of arma models or ar models are shown to be subsets of the above class the three definitions of markov property are discussed and the class of arma models are precisely stated which have the noncausal and semicausal markov property without imposing any specific boundary conditions next two approaches are considered to estimate the parameters of a model to fit a given image the first method uses only the empirical correlations and involves the solution of linear equations the second method is the likelihood approach since the exact likelihood function is difficult to compute we resort to approximations suggested by the toroidal models numerical experiments compare the quality of the two estimation schemes finally the problem of synthesizing a texture obeying an arma model is considered

time domain tests for gaussianity and time reversibility statistical signal processing algorithms often rely upon gaussianity and time reversibility two important notions related to the probability structure of stationary random signals and their symmetry parametric models obtained via second order statistics sos are appropriate when the available data is gaussian and time reversible on the other hand evidence of nonlinearity non gaussianity or time irreversibility favors the use of higher order statistics hos in order to validate gaussianity and time reversibility and quantify the tradeoffs between sos and hos consistent time domain chi squared statistical tests are developed exact asymptotic distributions are derived to estimate the power of the tests including a covariance expression for fourth order sample cumulants a modification of existing linearity tests in the presence of additive gaussian noise is discussed briefly the novel gaussianity statistic is computationally attractive leads to a constant false alarm rate test and is well suited for parametric modeling because it employs the minimal hos lags which uniquely characterize arma processes simulations include comparisons with an existing frequency domain approach and an application to real seismic data time reversibility tests are also derived and their performance is analyzed both theoretically and experimentally

a multiresolution approach for texture synthesis using the circular harmonic functions in this paper an unsupervised model based texture reproduction technique is described in accordance with the julesz s 1962 conjecture the statistical properties of the prototype up to the second order are copied in order to generate a synthetic texture perceptually indistinguishable from the given sample however this task is accomplished using a hybrid approach which operates partially in the spatial domain and partially in a multiresolution domain the latter employed is the circular harmonic function chf domain since it has been proven to be well suited for mimicking the behavior of the human visual system hvs this approach allows for a wide range of textures typologies obtaining synthetic textures that better match the prototype with respect to the ones obtained using techniques based on the julesz s conjecture operating only in the spatial domain and to dramatically reduce the computational complexity of similar methods operating only in the multiresolution domain

elastodynamics of an axisymmetric problem in an anisotropic liquid saturated porous medium the laplace and hankel transforms have been employed to find the general solution to the field equations in an anisotropic liquid saturated porous medium for plain axisymmetric problem in the transformed form an application of an infinite space with impulsive force at the origin has been considered to show the utility of the solution obtained the results of the corresponding problem in isotropic liquid saturated porous medium can be derived as a special case to get the solutions in the physical form a numerical inversion technique has been applied the results in the form of displacement and stress components have been obtained numerically and discussed graphically for a particular model

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

a comparison of optimized higher order spectral detection techniques for non gaussian signals using the gaussian noise rejection property of higher order spectra hos hos based detectors have been proposed that outperform conventional second order techniques in certain scenarios based on statistical tests proposed by suhha rao and gabr 1980 as well as hinich 1982 kletter and messer 1990 and hinich and wilson 1990 have developed similar bifrequency domain detectors that are dependent on bispectral estimates of the observation process formalizing the estimate consistency requirements and the asymptotics for these detectors we derive a new f test statistic we consider the detrimental effects of using spectral estimates in the denominator of hinich s test we determine refined conditional distributions for third and fourth order versions of his detector we also modify his test for colored scenarios extending the bispectral detectors to their kth order counterparts we calculate the optimal smoothing bandwidth to use in constructing the hos estimates producing the best detection performances for both our f test and hinich s test with our refined distributions these new bandwidths yield significant improvements in detector performance over previous results for the finite sample case our calculations characterize the tradeoff between the two detectors and demonstrate that a larger smoothing bandwidth than the one suggested by previous researchers should be used our calculations are verified using simulations for both white and colored cases

tests of gaussianity and linearity for random fields using estimated higher order spectra we present frequency domain tests of gaussianity and linearity for random fields and study their power both analytically and by simulation our tests conform to the likelihood ratio principle and the test statistics chi squared in both cases are simple and intuitively plausible using these tests we found certain texture images to be both non gaussian and nonlinear

deconvolution bandwidth and the trispectrum abstract in the largest application area of time series analysis geophysical exploration the underlying innovations sequence is of primary interest and must be estimated this sequence is estimated by deconvolving the non gaussian noninvertible time series this involves estimation of a phase shift correction from the time series which can be carried out by maximizing the kurtosis of the series unfortunately the method is hampered by the fact that the time series is typically deficient in power in certain bands of frequencies band limited the consequences of this can be analyzed by studying the trispectrum the third of the polyspectra of the series this reveals two important results first we are able to easily appreciate why for certain types of band limitation kurtosis cannot be used to determine a phase correction second by looking at the inner and outer subvolumes of the support volume for the discrete parameter trispectrum we see that for the standard linear model the trispectrum is no

estimation of linear parametric models of nongaussian discrete random fields with application to texture synthesis a general possibly asymmetric noncausal and or nonminimum phase 2d autoregressive moving average random field model driven by an independent and identically distributed 2d nongaussian sequence is considered the model is restricted to be invertible i e system zeros are not allowed to lie on the unit bicircle three performance criteria are investigated for parameter estimation of the system parameters given only the output measurements image pixels the proposed criteria are functions of the higher order cumulant statistics of an inverse filter output one of these criteria is novel and the others have been considered in past only for moving average inverses and without any analysis of their consistency in the paper strong consistency of the proposed methods under the assumption that the system order is known is proved the convergence of the proposed parameter estimators under overparametrization is also analyzed experimental results involving synthesized as well as real life textures are presented to illustrate the performance of two of the considered approaches experimental results of synthesis of 128 spl times 128 textures visually resembling several real life textures in the brodatz album and other sources are presented

bispectral analysis and model validation of texture images statistical approaches to texture analysis and synthesis have largely relied upon random models that characterize the 2 d process in terms of its first and second order statistics and therefore cannot completely capture phase properties of random fields that are non gaussian and or asymmetric in this paper higher than second order statistics are used to derive and implement 2 d gaussianity linearity and spatial reversibility tests that validate the respective modeling assumptions the nonredundant region of the 2 d bispectrum is correctly defined and proven a consistent parameter estimator for nonminimum phase asymmetric noncausal 2 d arma models is derived by minimizing a quadratic error polyspectrum matching criterion simulations on synthetic data are performed and the results of the bispectral analysis on real textures are reported

dynamic analysis of a poroelastic layered half space using continued fraction absorbing boundary conditions abstract a methodology for dynamic analysis of a poroelastic layered half space is proposed a poroelastic layered half space is discretized with regular thin layers to a certain depth below the layers continued fraction absorbing boundary conditions cfabcs which are accurate and effective in modeling wave propagation in various unbounded domains are applied in order to represent the infinite extent of the half space with the representation a spectral decomposition which is an effective approach in the solution of wave propagation problems is utilized green functions of a poroelastic layered half space are obtained accurately with the proposed numerical model the green functions can be applied to various dynamic problems in the half space including foundation dynamics and soil structure interaction problems

texture synthesis using 2 d noncausal autoregressive models the purpose of this paper is to illustrate the usefulness of two dimensional noncausal autoregressive ncar models for the synthesis of textures these models characterize the gray level at a pixel as a linear combination of gray levels at nearby locations in all directions and an additive white noise variable we first show that the class of ncar models is capable of generating a wide variety of image patterns posessing the local replication attribute an essential ingredient of many natural textures it is also shown that the theoretical variograms of many ncar models possess an oscillatory behavior a characteristic of the variograms of many natural textures next we give experimental results of synthesis of 64 64 textures resembling several real textures in the brodatz album the synthetic textures generated by 16 parameter ncar models retain most the visual characteristics of the original textures the variograms of the original and synthetic textures are also similar

cumulant based blind identification of linear multi input multi output systems driven by colored inputs the blind identification problem of a linear multi input multi output mimo system is widely noticed by many researchers in diverse fields due to its relevance to blind signal separation however such a problem is ill posed and has no unique solution therefore we can only find a solution of the problem within an equivalence class we address the blind identification problem of the linear mimo system driven by unobservable colored inputs using higher order statistics hos particularly the fourth order cumulants of the outputs where the unobservable inputs are mutually independent but temporally colored linear processes we first define the set which is denoted by s of stable scalar transfer functions and then define the notion of a generalized permutation matrix which is abbreviated by a g matrix over s then it is shown that the transfer function matrix of an unknown system is identified only up to post multiplication by a g matrix this result is applied to identifying fir systems for blind signal separation

fitting ma models to linear non gaussian random fields using higher order cumulants a general possibly nonminimum phase and or asymmetric noncausal two dimensional 2 d moving average ma model driven by a zero mean i i d 2 d sequence is considered the input sequence is not observed the signal observations may be noisy we consider the problems of model order determination and model parameter estimation using the higher order third or fourth order for example cumulants of the 2 d signal second order statistics of the data can consistently identify only a smaller class of ma models the proposed approaches are illustrated via computer simulations

maximum standardized cumulant deconvolution of non gaussian linear processes a linear process is represented as a driving white noise convolved with a system response sequence the concept of natural peakedness of a system response sequence is defined and its properties are investigated utilizing natural peakedness the convergence theory of maximum standardized cumulant deconvolution is established and the uniqueness theorem of non gaussian linear process representations is proved in addition autoregressive models on a countable abelian group are defined and the relation between cumulant deconvolution and autoregressive models is given

discontinuous galerkin methods for wave propagation in poroelastic media we have developed a new numerical method to solve the heterogeneous poroelastic wave equations in bounded three dimensional domains this method is a discontinuous galerkin method that achieves arbitrary high order accuracy on unstructured tetrahedral meshes for the low frequency range and the inviscid case by using biot s equations and darcy s dynamic laws we have built a scheme that can successfully model wave propagation in fluid saturated porous media when anisotropy of the pore structure is allowed zero inflow fluxes are used as absorbing boundary conditions a continuous arbitrary high order derivatives time integration is used for the high frequency inviscid case whereas a space time discontinuous scheme is applied for the low frequency case we conducted a numerical convergence test of the proposed methods we used a series of examples to quantify the quality of our numerical results comparing them to analytic solutions as well as numerical solutions obtained by other methodologies in partic

some new twists to problems involving the gaussian probability integral using an alternate form of the gaussian probability integral discovered a number of years ago it is shown that the solution to a number of previously considered communication problems can be simplified and in some cases made more accurate i e exact rather than bounded these problems include the evaluation of 1 the bit error probability of uncoded phase shift keying psk with costas loop tracking 2 word error probability of antipodal modulation in the presence of fading 3 bit error probability of coded m ary psk mpsk over the memoryless fading channel with given channel state information 4 conditional symbol error probability of mpsk in the presence of carrier synchronization error and 5 the average error probability for the binary additive white gaussian noise awgn intersymbol interference channel also obtained is a generalization of this new alternate form to the case of a two dimensional gaussian probability integral with arbitrary correlation which can be used to evaluate the symbol error probability of mpsk with i q unbalance

a closed form expression for the exact ber of generalized pam and qam constellations we derive an explicit closed form expression for the exact bit error rate ber computation of generalized hierarchical m ary pulse amplitude modulations over additive white gaussian noise awgn and fading channels this expression can also be used to obtain the exact ber of generalized hierarchical quadrature amplitude modulations square and rectangular for the awgn case these expressions are in the form of a weighted sum of complementary error functions and are solely dependent on the constellation size m the carrier to noise ratio and a constellation parameter which controls the relative message importance

estimation and choice of neighbors in spatial interaction models of images some aspects of statistical inference for a class of spatial interaction models for finite images are presented primarily the simultaneous autoregressive sar models and conditional markov cm models each of these models is characterized by a set of neighbors a set of coefficients and a noise sequence of specified characteristics we are concerned with two problems the estimation of the unknown parameters in both sar and cm models and the choice of an appropriate model from a class of such competing models assuming gaussian distributed variables we discuss maximum likelihood ml estimation methods in general the ml scheme leads to nonlinear optimization problems to avoid excessive computation an iterative scheme is given for sar models which gives approximate ml estimates in the gaussian case and reasonably good estimates in some non gaussian situations as well likewise for cm models an easily computable consistent estimate is given the asymptotic mean squared error mse of this estimate for a four neighbor cm model is shown tn be substantially less than the mse of the popular coding estimate asymptotically consistent decision rules are given for choosing an appropriate sar or cm model the usefulness of the estimation scheme and the decision rule for the choice of neighbors is illustrated by using synthetic patterns synthetic patterns obeying known sar and cm models are generated and the models corresponding to true and several competing neighbor sets are fitted the estimation scheme yields estimates close to the parameters of the true models and the decision rule for the choice of neighbors picks up the true model from the class of competing models

blind deconvolution via sequential imputations abstract the sequential imputation procedure is applied to adaptively and sequentially reconstruct discrete input signals that are blurred by an unknown linear moving average channel and contaminated by additive gaussian noises a problem known as blind deconvolution in digital communication a rejuvenation procedure for improving the efficiency of sequential imputation is introduced and theoretically justified the proposed method does not require the channel to be nonminimum phase and can be used in real time signal restoration two simulated systems are studied to illustrate the proposed method our result shows that the ideas of multiple imputations and flexible simulation techniques are as powerful in engineering as in survey sampling

blind deconvolution of linear systems with multilevel nonstationary inputs a method is proposed to deal with the problem of blind deconvolution of a special non gaussian linear process in which the input to the linear system is a real or complex valued multilevel random sequence that satisfies certain regularity conditions the gist of the method is to apply a linear filter to the observed process and adjust the filter until a multilevel output is obtained it is shown that the deconvolution problem can be solved with only scale rotation and shift ambiguities if the output sequence of the filter contains a subsequence that converges weakly to a multilevel random variable a cost function is proposed so that any minimizer of the cost function provides a solution to the deconvolution problem moreover when the process is parametric it is shown that a consistent estimator of the parameter can be obtained by minimizing an empirical criterion the estimation accuracy is shown to depend on the tail behavior of the inverse system which in many cases decays exponentially as the sample size grows special consideration is given to applications in the equalization of digital communications systems

probability density function of reliability metrics in bicm with arbitrary modulation closed form through algorithmic approach in the popular bit interleaved coded modulation bicm the output of the channel encoder and the input of the modulator are separated by a bit level interleaver from the decoder s point of view the modulator the transmission channel and the demodulator calculating bits reliability metrics become a memoryless bicm channel with binary inputs and real outputs in unfaded channels the bicm channel s outputs reliability metrics are known to be gaussian for binary or quaternary phase shift keying but their probability density function pdf is not known for higher order modulation we fill this gap by presenting an algorithmic method to calculate closed form expressions for the pdf of reliability metrics in bicm with arbitrary modulation and bits to symbol mapping when the so called max log approximation is applied such probabilistic description of bicm channel is useful to analyze from an information theoretic point of view any bicm constellation mapping design

dynamic behaviour of an anisotropic liquid saturated porous medium in frequency domain a general solution to the field equations of an anisotropic liquid saturated porous medium has been obtained in the transformed form using the fourier transform assuming the disturbances to be harmonically time dependent the transformed solution is obtained in the frequency domain an application of a time harmonic concentrated force acting at some interior point of an infinite medium has been considered to show the utility of the solution obtained the transformed solutions are inverted numerically using a numerical inversion technique to invert the fourier transform the results in the form of stress components have been obtained numerically and discussed graphically for a particular model the results of the corresponding problem in isotropic liquid saturated porous medium can be derived as a special case

upper bounds on the symbol error probability of mpsk with phase error using pawula f function iv this letter presents an alternative derivation of the case iv of pawula f function and an approximation for the corresponding probability density function pdf in the presence of the tikhonov distributed phase error the pdf of case iv is used to obtain new upper bounds for the conditional and average symbol error probabilities of m ary phase shift keying with phase error over additive white gaussian noise channels numerical results demonstrate the validity of the upper bounds

two dimensional frequency domain blind system identification using higher order statistics with application to texture synthesis in this paper shalvi and weinstein s 1993 super exponential se algorithm using higher order statistics for blind deconvolution of one dimensional 1 d linear time invariant systems is extended to a two dimensional 2 d se algorithm then a 2 d frequency domain blind system identification bsi algorithm for 2 d linear shift invariant lsi systems using the computationally efficient 2 d se algorithm and the 2 d linear prediction error filter is proposed in addition to the lsi system estimate the proposed bsi algorithm also provides a minimum mean square error mmse equalizer estimate and an mmse signal enhancement filter estimate then a texture synthesis method tsm using the proposed bsi algorithm is presented some simulation results to support the efficacy of the proposed bsi algorithm and some experimental results to support the efficacy of the proposed tsm are presented finally some conclusions are drawn

green s functions in an infinite transversely isotropic saturated poroelastic medium the purpose of this article is to study green s functions for an infinite transversely isotropic saturated poroelastic medium in the first part equations of motions of such a medium are recalled according to biot s theory and completed by homogenization techniques for harmonic displacements the second part gives green s functions for such an infinite medium in the 3 d fourier transformed geometrical space in the third part a 1 d inverse analytical fourier transformation followed by an inverse hankel transformation leads to an analytical expression of green s functions for harmonic motions some numerical methods to compute final green s functions in time space are listed