adaptive fractionally spaced blind cma equalization excess mse the performance of the constant modulus algorithm cma a reference algorithm for adaptive blind equalization is studied in terms of the excess mean square error emse due to the nonvanishing step size of the gradient descent algorithm an analytical approximation of emse is provided emphasizing the effect of the constellation size and resulting in design guidelines

a fast algorithm for sparse multichannel blind deconvolution abstractwe have addressed blind deconvolution in a multichannel framework recently a robust solution to this problem based on a bayesian approach called sparse multichannel blind deconvolution smbd was proposed in the literature with interesting results however its computational complexity can be high we have proposed a fast algorithm based on the minimum entropy deconvolution which is considerably less expensive we designed the deconvolution filter to minimize a normalized version of the hybrid l1 l2 norm loss function this is in contrast to the smbd in which the hybrid l1 l2 norm function is used as a regularization term to directly determine the deconvolved signal results with synthetic data determined that the performance of the obtained deconvolution filter was similar to the one obtained in a supervised framework similar results were also obtained in a real marine data set for both techniques

a quadratic programming approach to blind equalization and signal separation blind equalization and signal separation are two well established signal processing problems in this paper we present a quadratic programming algorithm for fast blind equalization and signal separation by introducing a special non mean square error mse objective function we reformulate fractionally spaced blind equalization into an equivalent quadratic programming problem based on a clear geometric interpretation and a formal proof we show that a perfect equalization solution is obtained at every local optimum of the quadratic program because blind source separation is by nature and mathematically a closely related problem we also generalize the algorithm for blind signal separation we show that by enforcing source orthogonalization through successive processing the quadratic programming approach can be applied effectively moreover the quadratic program is easily extendible to incorporate additional practical conditions such as jamming suppression constraints we also provide evidence of good performance through computer simulations

a measure of the tracking capability of recursive stochastic algorithms with constant gains a criterion is given for measuring the tracking capability of recursive algorithms when applied to slowly time varying systems the optimal gain for a given disturbance is also calculated this criterion is seen to have some connection with the fisher information matrix and allows us to select a priori the best algorithm for identifying a given unknown parameter which may be subject to smooth unknown disturbances examples of applications are given in the areas of identification theory and data communication theory

criteria for blind deconvolution of multichannel linear time invariant systems blind deconvolution and equalization of linear time invariant systems have widely received attention in various fields such as data communication image processing and geophysical data processing shalvi and weinstein 1990 proposed certain criteria for blind deconvolution of nonminimum phase linear time invariant systems their work is however restricted to the single channel or scalar case this correspondence extends the shalvi weinstein approach to the multichannel case we propose a multistage maximization criterion and a single stage maximization criterion for attaining multichannel blind deconvolution under the normalized condition specified later they are shown to be equivalent

robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size independent component analysis ica aims at decomposing an observed random vector into statistically independent variables deflation based implementations such as the popular one unit fastica algorithm and its variants extract the independent components one after another a novel method for deflationary ica referred to as robustica is put forward in this paper this simple technique consists of performing exact line search optimization of the kurtosis contrast function the step size leading to the global maximum of the contrast along the search direction is found among the roots of a fourth degree polynomial this polynomial rooting can be performed algebraically and thus at low cost at each iteration among other practical benefits robustica can avoid prewhitening and deals with real and complex valued mixtures of possibly noncircular sources alike the absence of prewhitening improves asymptotic performance the algorithm is robust to local extrema and shows a very high convergence speed in terms of the computational cost required to reach a given source extraction quality particularly for short data records these features are demonstrated by a comparative numerical analysis on synthetic data robustica s capabilities in processing real world data involving noncircular complex strongly super gaussian sources are illustrated by the biomedical problem of atrial activity aa extraction in atrial fibrillation af electrocardiograms ecgs where it outperforms an alternative ica based technique

blind identification of fir systems driven by markov like input signals we propose a new algorithm for the blind identification and equalization of finite impulse response fir systems using the second order statistics of the received signal the new algorithm is set in the same context as the algorithms of tong et al 1994 and moulines et al 1995 however unlike those earlier approaches it is designed to allow correlated input signals specifically the algorithm accommodates finite memory sources and sources whose autocorrelation function decays exponentially numerical simulations compare the equalization performance of the new algorithm to those of tong and moulines it is shown that our algorithm yields consistently lower bit error rates at a wide variety of signal to noise ratios and at various equalizer lengths moreover the algorithm maintains this advantage even if it has no a priori information of source correlation or if source symbols are uncorrelated

applications of cumulants to array processing iii blind beamforming for coherent signals for pt ii see ibid vol 43 no 7 p 1663 7 1995 we provide a cumulant based blind beamforming method for recovery of statistically independent narrowband source signals in the presence of coherent or perfectly correlated multipath propagation our method is based on the fact that for a blind beamformer the presence of coherent multipaths is equivalent to the case of independent sources with a different steering matrix our approach is applicable to any array configuration having unknown response signal sources must have nonzero fourth order cumulants there is no need to estimate the directions of arrival our method maximizes signal to interference plus noise ratio sinr a comparable result does not exist using just second order statistics

on line blind multichannel equalization based on mutually referenced filters this paper presents a novel approach to the blind linear equalization of possibly nonminimum phase and time varying communication channels in the context of channel diversity we introduce the concept of mutually referenced equalizers mres in which several filters are considered the outputs of which act as training signals for each other a corresponding constrained multidimensional mean square error mse cost function is derived the minimization of which is shown to be a necessary and sufficient condition for equalization the links with a standard linear prediction problem are demonstrated the proposed technique exhibits properties of important practical concern 1 the proposed algorithm is globally convergent 2 simple closed form solutions exist for the mres but the mres also lend themselves readily to adaptive implementation in particular the recursive least squares rls algorithm can be used to offer optimal convergence rate 3 the mre method provides a solution for all equalization delays which results in robustness properties with respect to snr and ill defined channel lengths

structural analysis of subjective categorical data a general approach to the analysis of subjective categorical data is considered in which agreement matrices of two or more raters are directly expressed in terms of error and agreement parameters the method provides focused analyses of ratings from several raters for whom ratings have measurement error distributions that may induce bias in the evaluation of substantive questions of interest each rater s judgment process is modeled as a mixture of two components an error variable that is unique for the rater in question as well as an agreement variable that operationalizes the true values of the units of observation the statistical problems of identification estimation and testing of such measurement models are discussed

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

test theory without an answer key a general model is presented for homogeneous dichotomous items when the answer key is not known a priori the model is structurally related to the two class latent structure model with the roles of respondents and items interchanged for very small sets of respondents iterative maximum likelihood estimates of the parameters can be obtained by existing methods for other situations new estimation methods are developed and assessed with monte carlo data the answer key can be accurately reconstructed with relatively small sets of respondents the model is useful when a researcher wants to study objectively the knowledge possessed by members of a culturally coherent group that the researcher is not a member of

convergence analysis of self adaptive equalizers a theoretical analysis of self adaptive equalization for data transmission is carried out starting from known convergence results for the corresponding trained adaptive filter the development relies on a suitable ergodicity model for the sequence of observations at the output of the transmission channel thanks to the boundedness of the decision function used for data recovery it can be proved that the algorithm is bounded strong convergence results can be reached when a perfect noiseless equalizer exists the algorithm will converge to it if the eye pattern is initially open otherwise convergence may take place towards certain other stationary points of the algorithm for which domains of attraction have been defined some of them will result in a poor error rate the case of a noisy channel exhibits limit points for the algorithm that differ from those of the classical trained algorithm the stronger the noise the greater the difference is one of the principal results of this study is the proof of the stability of the usual decision feedback algorithms once the learning period is over

deconvolution and estimation of transfer function phase and coefficients for nongaussian linear processes nongaussian linear processes are considered it is shown that the phase of the transfer function can be estimated under broad conditions this is not true of gaussian linear processes and in this sense gaussian linear processes are atypical the asymptotic behavior of a phase estimate is determined the phase estimates make use of bispectral estimates these ideas are applied to a problem of deconvolution which is effective even when the transfer function is not minimum phase a number of computational illustrations are given

convexity inequalities for estimating free energy and relative entropy for quantum systems it is shown that the relative entropy s p q tr p log p tr p log q of two positive semi definite operators p and q satisfies gamma 1 tr p p1 gamma q gamma or s p q or gamma 1 tr p1 gamma qgamma p for 0 gamma or 1 and that these bounds become exact in the limit gamma to phi analogous inequalities hold for states in classical statistical mechanics or information theory with trace replaced by integration or summation furthermore the average of these bounds is in general a better approximation to s p q than either bound alone and the average is amenable to further improvement via repeated richardson extrapolation if p and q are gibbs equilibrium states then these inequalities can also be used to obtain bounds on the free energy of a perturbed system in terms of the free energy of the unperturbed system and expectations of the perturbation

fourier series based nonminimum phase model for statistical signal processing in this paper a parametric fourier series based model fsbm for or as an approximation to an arbitrary nonminimum phase linear time invariant lti system is proposed for statistical signal processing applications where a model for lti systems is needed based on the fsbm a minimum phase linear prediction error lpe filter for amplitude estimation of the unknown lti system together with the cramer rao cr bounds is presented then an iterative algorithm for obtaining the optimum lpe filter with finite data is presented that is also an approximate maximum likelihood algorithm when data are gaussian then three iterative algorithms using higher order statistics hos with finite non gaussian data are presented to estimate parameters of the fsbm followed by some simulation results as well as some experimental results with real speech data to support the efficacy of the proposed algorithms using the fsbm finally we draw some conclusions

cumulant based inverse filter criteria for mimo blind deconvolution properties algorithms and application to ds cdma systems in multipath tugnait 1995 and chi and chen proposed multi input multi output inverse filter criteria mimo ifc using higher order statistics for blind deconvolution of mimo linear time invariant systems this paper proposes three properties on the performance of the mimo linear equalizer associated with mimo ifc for any signal to noise ratio including p1 perfect phase equalization property p2 a relation to mimo minimum mean square error mimo mmse equalizer and p3 a connection with the one obtained by mimo super exponential algorithm mimo sea that usually converges fast but does not guarantee convergence for finite data based on p2 a fast algorithm for computing the theoretically optimum mimo equalizer is proposed moreover based on p3 a fast mimo ifc based algorithm with performance similar to that of the mimo sea and with guaranteed convergence is proposed as well as its application to suppression of multiple access interference and intersymbol interference isi for multiuser asynchronous ds cdma systems in multipath finally some simulation results are presented to support the analytic results and the proposed algorithms

improved methods for the blind system identification using higher order statistics it is demonstrated by a detailed analysis of blind system identification that under specific system configurations a recently published least squares algorithm shows a poor convergence behavior especially if the system order is overdetermined to overcome these problems a supplementary condition is introduced that guarantees proper convergence in most cases an alternative approach for the blind identification of mixed phase systems the so called cumulant zero matching method is presented in this approach the solution of a set of nonlinear equations which is necessary in the least squares method is replaced by the calculation of zeros of polynomials the main advantage over the least squares solution is that overdetermination of the system order is rather harmless since it only results in additional zeros in the origin of the z plane the different methods for system identification presented are illustrated by simulation results

on upper bound for the quantum entropy abstract a new upper bound for the von neumann entropy of a state of a compound quantum system is given this leads to a log sobolev inequality on the matrix algebra

blind source separation the location of local minima in the case of finitely many samples cost functions used in blind source separation are often defined in terms of expectations i e an infinite number of samples is assumed an open question is whether the local minima of finite sample approximations to such cost functions are close to the minima in the infinite sample case to answer this question we develop a new methodology of analyzing the finite sample behavior of general blind source separation cost functions in particular we derive a new probabilistic analysis of the rate of convergence as a function of the number of samples and the conditioning of the mixing matrix the method gives a connection between the number of available samples and the probability of obtaining a local minimum of the finite sample approximation within a given sphere around the local minimum of the infinite sample cost function this shows the convergence in probability of the nearest local minima of the finite sample approximation to the local minima of the infinite sample cost function we also answer a long standing problem of the mean squared error mse behavior of the finite sample least squares constant modulus algorithm ls cma namely whether there exist ls cma receivers with good mse performance we demonstrate how the proposed techniques can be used to determine the required number of samples for ls cma to exceed a specified performance the paper concludes with simulations that validate the results

understanding blind deconvolution algorithms blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown recent algorithms have afforded dramatic progress yet many aspects of the problem remain challenging and hard to understand the goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally we explain the previously reported failure of the naive map approach by demonstrating that it mostly favors no blur explanations we show that using reasonable image priors a naive simulations map estimation of both latent image and blur kernel is guaranteed to fail even with infinitely large images sampled from the prior on the other hand we show that since the kernel size is often smaller than the image size a map estimation of the kernel alone is well constrained and is guaranteed to succeed to recover the true blur the plethora of recent deconvolution techniques makes an experimental evaluation on ground truth data important as a first step toward this experimental evaluation we have collected blur data with ground truth and compared recent algorithms under equal settings additionally our data demonstrate that the shift invariant blur assumption made by most algorithms is often violated

performance analysis of blind carrier frequency offset estimators for noncircular transmissions through frequency selective channels this paper deals with the problem of blind estimation of the carrier frequency offset of a linearly modulated noncircular transmission through an unknown frequency selective channel a frequency estimator is developed based on the unique conjugate cyclic frequency of the received signal which is equal to twice the frequency offset consistency and asymptotic normality of the frequency estimator together with a closed form expression for its asymptotic variance are also established the closed form expression of the asymptotic variance enables analysis of the performance of the proposed frequency offset estimator as a function of the number of estimated cyclic correlation coefficients used it is shown that optimum is obtained if the number of correlation coefficients taken into account coincides with the degree of the channel numerical simulations are provided and confirm the conclusion of the theoretical asymptotic analysis

hypercontractivity in noncommutative lpspaces we introduce and study a setup for hypercontractivity in the interpolating family of noncommutative lpspaces associated to a locally normal state on an inductive limitc algebra

an efficient subspace method for the blind identification of multichannel fir systems we present a novel method for the blind identification of linear single input multiple output simo finite impulse response fir systems based on second order statistics our approach called the truncated transfer matrix method ttm proceeds in two major steps first the svd analysis of the lagged covariance matrix gives the subspace of the clipped system transfer matrix and second the block toeplitz structure of the transfer matrix gives extra constraints that allow us to reconstruct the matrix through the solution of a linear system of equations the proposed ttm method is analytical no optimization procedure involved and it is robust to noise we find that the method comes with an increased computational cost but it significantly outperforms state of the art second order methods in low signal to noise ratio snr situations

super exponential methods for blind deconvolution a class of iterative methods for solving the blind deconvolution problem i e for recovering the input of an unknown possibly nonminimum phase linear system by observation of its output is presented these methods are universal do not require prior knowledge of the input distribution are computationally efficient and statistically stable and converge to the desired solution regardless of initialization at a very fast rate the effects of finite length of the data finite length of the equalizer and additive noise in the system on the attainable performance intersymbol interference are analyzed it is shown that in many cases of practical interest the performance of the proposed methods is far superior to linear prediction methods even for minimum phase systems recursive and sequential algorithms are also developed which allow real time implementation and adaptive equalization of time varying systems

on channel identification based on second order cyclic spectra gardener 1991 presented an interesting use of cyclostationarity for channel identification in pam data communication systems the present paper presents new results that address the identification of linear rational channels with higher than minimum bandwidth based on the use of second order cyclic statistics unlike the approach taken by gardener the present authors show that channel identification is achievable for a class of linear channels without the need for a pilot tone or training periods moreover channel identification based on cyclic statistics does not preclude gaussian or near gaussian inputs

on blind beamforming for multiple non gaussian signals and the constant modulus algorithm a new method for blindly separating multiple cochannel non gaussian signals received by a sensor array is presented the method is based on a cumulant based least squares criterion that for identically distributed negative kurtosis signals is proven to be identical to the 2 2 constant modulus cm cost function commonly used by cm algorithms a computationally simple algorithm is proposed to minimize the criterion the algorithm performs well even when the number of samples is small thus allowing its application in dynamic environments e g moving emitters for the special case of two signals only the minimization is obtained analytically simulation results are included

blind adaptation of stable discrete time iir filters in state space form blind deconvolution consists of extracting a source sequence and impulse response of a linear system from their convolution in the presence of system zeros close to the unit circle which give rise to very long impulse responses infinite impulse response iir adaptive structures are of use whose adaptation should be carefully designed in order to guarantee stability in this paper we propose a blind type discrete time iir adaptive filter structure realized in state space form that with a suitable parameterization of its coefficients remains stable the theory is first developed for a two pole filter whose numerical behavior is investigated via numerical experiments the proposed structure adaptation theory is then extended to a multipole structure realized as a cascade of two pole filters computer based experiments are proposed and discussed which aim at illustrating the behavior of the filter cascade on several cases of study the numerical results obtained show the proposed filters remain stable during adaptation and provide satisfactory deconvolution results

the golden thompson trace inequality is complemented we prove a class of trace inequalities which complements the golden thompson inequality for example tr epa epb 2 p tr ea b holds for all p 0 when a and b are hermitian matrices and denotes the geometric mean we also prove related trace inequalities involving the logarithmic function namely p 1tr x log yp 2xpyp 2 tr x log x log y p 1tr x log xp 2ypxp 2 for all p 0 when x and y are nonnegative matrices these inequalities supply lower and upper bounds on the relative entropy

prediction error method for second order blind identification blind channel identification methods based on the oversampled channel output are a problem of current theoretical and practical interest in this paper we introduce a second order blind identification technique based on a linear prediction approach in contrast to eigenstructure based methods it will be shown that the linear prediction error method is robust to order overdetermination an asymptotic performance analysis of the proposed estimation method is carried out consistency and asymptotic normality of the estimates is established a closed form expression for the asymptotic covariance of the estimates is given numerical simulations and investigations are finally presented to demonstrate the potential and the robustness of the proposed method

multichannel sparse blind deconvolution on the sphere multichannel blind deconvolution is the problem of recovering an unknown signal f and multiple unknown channels x i from their circular convolution y i x i circledast f i 1 2 dots n we consider the case where the x i s are sparse and convolution with f is invertible our nonconvex optimization formulation solves for a filter h on the unit sphere that produces sparse output y i circledast h under some technical assumptions we show that all local minima of the objective function correspond to the inverse filter of f up to an inherent sign and shift ambiguity and all saddle points have strictly negative curvatures this geometric structure allows successful recovery of f and x i using a simple manifold gradient descent mgd algorithm the same approach is also applicable to blind gain and phase calibration with a fourier sensing matrix our algorithm and analysis require fewer assumptions than previous algorithms for the same problem our theoretical findings are complemented by numerical experiments which demonstrate superior performance of the proposed approach over the previous methods empirically our algorithm has low computation cost converging in a small number of iterations and low memory footprint solving only for the inverse filter of f

a least squares approach to blind channel identification conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input in some practical applications however the input statistical model may not be known or there may not be sufficient data to obtain accurate enough estimates of certain statistics in this paper we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel fir systems without requiring the knowledge of the input statistical model a new blind identification algorithm based solely on the system outputs is proposed necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented

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

monotonically convergent algorithms for symmetric tensor approximation abstract reduced rank approximations to symmetric tensors find use in data compaction and in multi user blind source separation we derive iterative algorithms which feature monotonic convergence to a minimum of a frobenius norm approximation criterion for a certain rank r tucker product version of the approximation problem the approach exploits the gradient inequality for convex functions to establish monotonic convergence while sparing the cumbersome step size analysis required from a manifold gradient approach it likewise overcomes some limitations of symmetric versions of alternating least squares the computational load per iteration amounts to computing an unfolded matrix and a qr decomposition

the least mean fourth lmf adaptive algorithm and its family new steepest descent algorithms for adaptive filtering and have been devised which allow error minimization in the mean fourth and mean sixth etc sense during adaptation the weights undergo exponential relaxation toward their optimal solutions time constants have been derived and surprisingly they turn out to be proportional to the time constants that would have been obtained if the steepest descent least mean square lms algorithm of widrow and hoff had been used the new gradient algorithms are insignificantly more complicated to program and to compute than the lms algorithm their general form is w j 1 w j 2 mu k epsilon j 2k 1 x j where w j is the present weight vector w j 1 is the next weight vector epsilon j is the present error x j is the present input vector mu is a constant controlling stability and rate of convergence and 2k is the exponent of the error being minimized conditions have been derived for weight vector convergence of the mean and of the variance for the new gradient algorithms the behavior of the least mean fourth lmf algorithm is of special interest in comparing this algorithm to the lms algorithm when both are set to have exactly the same time constants for the weight relaxation process the lmf algorithm under some circumstances will have a substantially lower weight noise than the lms algorithm it is possible therefore that a minimum mean fourth error algorithm can do a better job of least squares estimation than a mean square error algorithm this intriguing concept has implications for all forms of adaptive algorithms whether they are based on steepest descent or otherwise

bounded component analysis of linear mixtures a criterion of minimum convex perimeter this study presents a blind and geometric technique which pursues the linear decomposition of the observations in bounded component signals the bounded component analysis of the observations relies on the hypotheses of compactness and cartesian decomposition of the convex support of the vector of component signals and in the invertibility of the mixture assumptions which in absence of noise are able to guarantee the identifiability of the mixture and separability of the components up to permutation scaling and phase ambiguities under these conditions the convex perimeter of the normalized linear combination of the observations is shown to be a global contrast function whose minima correspond with the extraction of bounded components of the observations practical extraction and separation algorithms based on the minimization of this criterion are given the experimental results with communications signals serve to illustrate the good performance of the proposed method in high snr scenarios even for a small number of samples

on the nonvanishing stability of undesirable equilibria for fir godard blind equalizers the existence of undesirable equilibria of godard 1980 or constant modulus algorithms cma is demonstrated for all finite dimensional fir equalizers sufficient stability conditions are presented for these equilibria it is shown that for the godard 2 algorithm these undesirable equilibria are locally stable as long as the equalizer length remains finite results indicate that merely increasing the equalizer length does not necessarily nullify the potential for local convergence by godard algorithms

using the conditional grade of membership model to assess judgment accuracy consider the case wherej instruments are used to classify each ofi objects relative tok nominal categories the conditional grade of membership gom model provides a method of estimating the classification probabilities of each instrument or judge when the objects being classified consist of both pure types that lie exclusively in one ofk nominal categories and mixtures that lie in more than one category classification probabilities are identifiable whenever the sample of gom vectors includes pure types from each category when additional relatively mild assumptions are made about judgment accuracy the identifiable correct classification probabilities are the greatest lower bounds among all solutions that might correspond to the observed multinomial process even when the unobserved gom vectors do not include pure types from each category estimation using the conditional gom model is illustrated on a simulated data set further simulations show that the estimates of the classification probabilities are relatively accurate even when the sample contains only a small percentage of approximately pure objects

identification and deconvolution of multichannel linear non gaussian processes using higher order statistics and inverse filter criteria this paper is concerned with the problem of estimation and deconvolution of the matrix impulse response function of a multiple input multiple output mimo system given only the measurements of the vector output of the system the system is assumed to be driven by a temporally i i d and spatially independent non gaussian vector sequence which is not observed an iterative inverse filter criteria based approach is developed using the third order or the fourth order normalized cumulants of the inverse filtered data at zero lag stationary points of the proposed cost functions are investigated the approach is input iterative i e the input sequences are extracted and removed one by one the matrix impulse response is then obtained by cross correlating the extracted inputs with the observed outputs identifiability conditions are analyzed the strong consistency of the proposed approach is also briefly discussed computer simulation examples are presented to illustrate the proposed approaches

new cumulant based inverse filter criteria for deconvolution of nonminimum phase systems this work proposes a new family of cumulant based inverse filter criteria j sub m m which require a single slice of mth order m spl ges 3 cumulants a 2m th order cumulant and a 2m 2m th order cumulant of the inverse filter output where 1 spl les m spl les m 1 for deconvolution of linear time invariant lti nonminimum phase systems with only non gaussian output measurements contaminated by gaussian noise some simulation results are then presented for a performance comparison of the proposed criteria tugnait s 1993 criteria and chi and kung s 1992 criteria finally conclusions are presented

new criteria for blind deconvolution of nonminimum phase systems channels a necessary and sufficient condition for blind deconvolution without observing the input of nonminimum phase linear time invariant systems channels is derived based on this condition several optimization criteria are proposed and their solution is shown to correspond to the desired response these criteria involve the computation only of second and fourth order moments implying a simple tap update procedure the proposed methods are universal in the sense that they do not impose any restrictions on the probability distribution of the unobserved input sequence it is shown that in several important cases e g when the additive noise is gaussian the proposed criteria are essentially unaffected

an adaptive beamforming technique based on cyclostationary signal properties a new approach to adaptive beamforming is presented the method is based on the property of cyclostationary signals to generate spectral lines when they pass through certain nonlinear transformations the beamformer coefficients are selected according to a new optimization objective which consists on minimizing the mean square error between the array output after the nonlinearity and a complex exponential this approach optimally extracts any signal that generates a spectral line at the same frequency as the reference complex exponential a gradient based algorithm is derived to compute the optimum weights since the proposed cost function is a nonconvex function of the array coefficients minima are analyzed for the three most common types of perturbations found in communications gaussian noise multiple interferences and multipath propagation it is demonstrated via analysis and simulations that minima correspond to points where output noise power is minimized interferences are canceled and intersymbol interference is removed i e the beamformer eliminates the distortion introduced by the radiocommunication channel

a fuzzy k partitions model for categorical data and its comparison to the gom model the grade of membership gom model uses fuzzy sets as memberships of each individual to extreme profiles or classes on the likelihood function of multivariate multinomial distributions the gom clustering algorithm derived from the gom model is used in cluster analysis for categorical data but it is iterated with complicated calculations in this paper we create another approach termed a fuzzy k partitions fkp model which is also based on the likelihood function of multivariate multinomial distributions however the calculations of the fkp algorithm for clustering categorical data derived from the proposed fkp model are simpler the proposed fkp clustering algorithm is not only easier in calculation than the gom but also has more accuracy and computation efficiency to verify it we employ real empirical data and also some simulation data we find that fkp has superior results to gom we then apply these two algorithms to classification of pathology the results show the superiority of the fkp clustering algorithm moreover the proposed fkp algorithm can be used as a fuzzy clustering algorithm for categorical data some comparisons between fkp and two popular algorithms fuzzy k modes and fuzzy centroids are made these results show that the fkp clustering algorithm can be another useful tool in analyzing categorical data

globally convergent blind source separation based on a multiuser kurtosis maximization criterion we consider the problem of recovering blindly i e without the use of training sequences a number of independent and identically distributed source user signals that are transmitted simultaneously through a linear instantaneous mixing channel the received signals are hence corrupted by interuser interference iui and we can model them as the outputs of a linear multiple input multiple output mimo memoryless system assuming the transmitted signals to be mutually independent i i d and to share the same non gaussian distribution a set of necessary and sufficient conditions for the perfect blind recovery up to scalar phase ambiguities of all the signals exists and involves the kurtosis as well as the covariance of the output signals we focus on a straightforward blind constrained criterion stemming from these conditions from this criterion we derive an adaptive algorithm for blind source separation which we call the multiuser kurtosis muk algorithm at each iteration the algorithm combines a stochastic gradient update and a gram schmidt orthogonalization procedure in order to satisfy the criterion s whiteness constraints a performance analysis of its stationary points reveals that the muk algorithm is free of any stable undesired local stationary points for any number of sources hence it is globally convergent to a setting that recovers them all

general estimators for the reliability of qualitative data we study a proportional reduction in loss prl measure for the reliability of categorical data and consider the general case in which each ofn judges assigns a subject to one ofk categories this measure has been shown to be equivalent to a measure proposed by perreault and leigh for a special case when there are two equally competent judges and the correct category has a uniform prior distribution we consider a general framework where the correct category is assumed to have an arbitrary prior distribution and where classification probabilities vary by correct category judge and category of classification in this setting we consider prl reliability measures based on two estimators of the correct category the empirical bayes estimator and an estimator based on the judges consensus choice we also discuss four important special cases of the general model and study several types of lower bounds for prl reliability

blind intensity estimation from shot noise data the estimation of the intensity function of an inhomogeneous poisson process is considered when the observable data consists of sampled shot noise that results from passing the poisson process through an unknown linear time invariant system the proposed method consists of first estimating a histogram of the underlying point process the estimated histogram is used to construct a kernel estimate of the intensity function an estimate of the unknown impulse response of the linear time invariant system is constructed via a regularized backsubstitution of a discrete time convolution with the estimated histogram