a statistical approach to subspace based blind identification blind identification of single input multiple output systems is considered herein the low rank structure of the output signal is exploited to blindly identify the channel using a subspace fitting framework two approaches based on a minimal linear parameterization of a subspace are presented and analyzed the asymptotically best consistent estimate is derived for the class of blind subspace based techniques the asymptotic estimation error covariance of the subspace estimates is derived and the corresponding covariance of the statistically optimal estimates provides a lower bound on the estimation error covariance of subspace methods a two step procedure involving only linear systems of equations is presented that asymptotically achieves the bound simulations and numerical examples are provided to compare the two approaches

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

effective way for determination of multicanonical weights in the last few years generalized ensemble algorithms have become popular as a way to overcome the exponentially slow convergence in numerical simulations of systems with a rough energy landscape prominent examples of such an approach are the multicanonical algorithm 1 2 1 k sampling 3 and simulated tempering 4 5 these algorithms explore larger parts of the phase space than canonical molecular dynamics or monte carlo which at low temperatures are easily trapped in one of the huge number of local minimas this is because in the canonical ensemble the probability to cross an energy barrier of heights de is proportional to e at temperature t on the other hand in generalized ensembles the probability to cross an energy barrier is independent of temperature for instance in the multicanonical algorithm 1 2 configurations with energy e are updated with a weight

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

detection of signals by information theoretic criteria a new approach is presented to the problem of detecting the number of signals in a multichannel time series based on the application of the information theoretic criteria for model selection introduced by akaike aic and by schwartz and rissanen mdl unlike the conventional hypothesis testing based approach the new approach does not requite any subjective threshold settings the number of signals is obtained merely by minimizing the aic or the mdl criteria simulation results that illustrate the performance of the new method for the detection of the number of signals received by a sensor array are presented

blind image deconvolution using a robust gcd approach in this correspondence a new viewpoint is proposed for estimating an image from its distorted versions in presence of noise without the a priori knowledge of the distortion functions in z domain the desired image can be regarded as the greatest common polynomial divisor among the distorted versions with the assumption that the distortion filters are finite impulse response fir and relatively coprime in the absence of noise this becomes a problem of taking the greatest common divisor gcd of two or more two dimensional 2 d polynomials exact gcd is not desirable because even extremely small variations due to quantization error or additive noise can destroy the integrity of the polynomial system and lead to a trivial solution our approach to this blind deconvolution approximation problem introduces a new robust interpolative 2 d gcd method based on a one dimensional 1 d sylvester type gcd algorithm experimental results with both synthetically blurred images and real motion blurred pictures show that it is computationally efficient and moderately noise robust

direct blind mmse channel equalization based on second order statistics a family of new mmse blind channel equalization algorithms based on second order statistics are proposed instead of estimating the channel impulse response we directly estimate the cross correlation function needed in wiener hopf filters we develop several different schemes to estimate the cross correlation vector with which different wiener filters are derived according to minimum mean square error mmse unlike many known sub space methods these equalization algorithms do not rely on signal and noise subspace separation and are consequently more robust to channel order estimation errors their implementation requires no adjustment for either single or multiple user systems they can effectively equalize single input multiple output simo systems and can reduce the multiple input multiple output mimo systems into a memoryless signal mixing system for source separation the implementations of these algorithms on simo system are given and simulation examples are provided to demonstrate their superior performance over some existing algorithms

estimating the number of sinusoids in additive white noise the test proposed uses the eigenvector decomposition of the estimated autocorrelation matrix and is based on matrix perturbation analysis the estimator is shown to be able to resolve closely spaced sinusoids at lower signal to noise ratios than heuristic tests simulation results for two closely spaced sinusoids are detailed several unanswered questions are discussed

on subspace methods for blind identification of single input multiple output fir systems blind identification of single input multiple output simo fir systems based on second order statistics has attracted a great deal of research effort we focus on subspace estimation procedures which exploit the structure of the range space of certain matrix valued statistics constructed by arranging in a prescribed order the covariance coefficients of the observations general subspace identifiability results are obtained based on properties of minimal polynomial bases of rational subspaces several subspace estimation procedures are then derived these estimators are all based on a weighted least square solution of an overdetermined system of linear equations an asymptotic statistical analysis of these estimators is carried out to evaluate the potential of these methods and the impact of the weighting

peak quasisymmetric functions and eulerian enumeration abstract via duality of hopf algebras there is a direct association between peak quasisymmetric functions and enumeration of chains in eulerian posets we study this association explicitly showing that the notion of cd index long studied in the context of convex polytopes and eulerian posets arises as the dual basis to a natural basis of peak quasisymmetric functions introduced by stembridge thus eulerian posets having a nonnegative cd index for example face lattices of convex polytopes correspond to peak quasisymmetric functions having a nonnegative representation in terms of this basis we diagonalize the operator that associates the basis of descent sets for all quasisymmetric functions to that of peak sets for the algebra of peak functions and study the g polynomial for eulerian posets as an algebra homomorphism

blind equalization and identification of nonlinear and iir systems a least squares approach a deterministic approach to blind nonlinear channel equalization and identification is presented this approach applies to nonlinear channels that can be approximately linearized by either finite memory finite order volterra filters or by a finite number of finite memory nonpolynomial nonlinearities both the nonlinear equalizers and the linearized channels are identified this method also applies to blind identification of linear iir channels general conditions for existence and uniqueness are discussed and numerical examples are given

nonlinear adaptive blind whitening for mimo channels a nonlinear adaptive whitening method is proposed for blind deconvolution of mimo systems by whitening the received signals in both time and space with a highly nonlinear function of the past output data the whitened signals are isi free and can be viewed as outputs of a memoryless paraunitary mixing system the convergence of the proposed recursive algorithm is proved numerical simulation shows that the whitening method proposed in the paper works well even if the output signal is corrupted by additive noise

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

nonredundant precoding assisted blind channel estimation for single carrier space time block coded transmission with frequency domain equalization relying on nonredundant diagonal precoding and independent and identically distributed i i d source assumption this paper proposes a blind channel estimation scheme for single carrier frequency domain equalization based space time block coded systems the proposed method exploits the precoding induced linear signal structure in the conjugate cross correlation between the two temporal block received signals as well as the circulant channel matrix property and can yield exact solutions whenever the channel noise is circularly gaussian and the receive data statistic is perfectly obtained the channel estimation formulation builds on rearranging the set of linear equations relating the entries of conjugate cross correlation matrix and products of channel impulse responses into one with a distinctive block circulant with circulant block bccb structure this allows a simple identifiability condition depending on precoder parameters alone and also provides a natural yet effective optimal precoder design framework for improving solution accuracy when imperfect data estimation occurs we consider two models of data mismatch from both deterministic and statistical points of view and propose the associated design criteria the optimization problems are formulated to take advantage of the bccb system matrix property and are solved analytically the proposed optimal precoder aims to optimize solution robustness against deterministic error perturbation and also minimize the mean square error when the data mismatch is modeled as a white noise pairwise error probability analysis is conducted for investigating the equalization performance numerical examples are used to illustrate the performance of the proposed method

enriched p partitions and peak algebras abstract we develop a more general view of stembridge s enriched p partitions and use this theory to outline the structure of peak algebras for the symmetric group and the hyperoctahedral group initially we focus on commutative peak algebras spanned by sums of permutations with the same number of peaks where we consider several variations on the definition of peak whereas stembridge s enriched p partitions are related to quasisymmetric functions the dual coalgebra of solomon s type a descent algebra our generalized enriched p partitions are related to type b quasisymmetric functions the dual coalgebra of solomon s type b descent algebra using these functions we move on to explore non commutative peak algebras spanned by sums of permutations with the same set of peaks while some of these algebras have been studied before our approach gives explicit structure constants with a combinatorial description

trees and spatial topology change in causal dynamical triangulations generalized causal dynamical triangulations generalized cdt is a model of two dimensional quantum gravity in which a limited number of spatial topology changes is allowed to occur we solve the model at the discretized level using bijections between quadrangulations and trees in the continuum limit scaling limit the amplitudes are shown to agree with known formulas and explicit expressions are obtained for loop propagators and two point functions it is shown that from a combinatorial point of view generalized cdt can be viewed as the scaling limit of planar maps with a finite number of faces and we determine the distance function on this ensemble of planar maps finally the relation with planar maps is used to illuminate the mysterious identity of certain continuum cylinder amplitudes

a novel signaling scheme for blind unique identification of alamouti space time block coded channel in this paper we present a simple signaling scheme to blindly and uniquely identify the alamouti space time block coded channel first under a noise free environment and then under a complex gaussian noise environment in which pth and qth order statistics p and q are coprime of the received signals are available in both cases closed form solutions to determine the channel coefficients are obtained by exploiting specific properties of the alamouti space time block code stbc and the linear diophantine equation theory under gaussian noise when the length of the received data is finite we propose to use the semidefinite relaxation sdr algorithm to approximate maximum likelihood ml detection so that the joint estimation of the channel and symbols can be efficiently implemented simulation results show that while other existing blind methods fail our signaling scheme works well

arma system identification based on second order cyclostationarity previous work has presented novel techniques that exploit cyclostationarity for channel identification in data communication systems the present authors investigate the identifiability of linear time invariant lti arma systems based on second order cyclic statistics they present a parametric and a nonparametric method the parametric method directly identifies the zeros and poles of arma channels with a mixed phase the nonparametric method estimates the channel phase based on the cyclic spectra alone they analyze the phase estimation error of the nonparametric method for finite dimensional arma channels for specific finite dimensional arma channels an improved method is given which combines the parametric method with the nonparametric method computer simulation illustrates the effectiveness of the methods in identifying arma system impulse responses

performance analysis of the matrix pair method for blind channel identification we study the estimation variance performance of the matrix pair mp method for estimating the impulse responses of multiple fir channels driven by an unknown input sequence a first order perturbation analysis of the large data size performance of the mp method is presented and an explicit expression for the estimation variance is derived both the theoretical and simulation results are used to investigate the statistical performance of the mp method and a number of new insights are revealed

relative optimization for blind deconvolution we propose a relative optimization framework for quasi maximum likelihood qml blind deconvolution and the relative newton method as its particular instance special hessian structure allows fast newton system construction and solution resulting in a fast convergent algorithm with iteration complexity comparable to that of gradient methods we also propose the use of rational infinite impulse response iir restoration kernels which constitute a richer family of filters than the traditionally used finite impulse response fir kernels we discuss different choices of nonlinear functions that are suitable for deconvolution of super and sub gaussian sources and formulate the conditions under which the qml estimation is stable simulation results demonstrate the efficiency of the proposed methods

sampling and prefiltering effects on blind equalizer design in the development of equalization algorithms for unknown channels the effects of the sampling rate and the analog receive prefilter prior to discretization of the received signal are often overlooked in this paper these effects are investigated the relationship between the fractionally spaced output samples of a noise limiting prefilter and the symbol spaced output samples of a matched filter is studied for both the time invariant and the time varying channels it is shown that the prefilter and the sampling rate can have significant effects on blind equalization algorithms thus this paper provides a common framework for comparing different blind algorithms that are studied in the literature with different sampling rates a case study of the well known subspace method for blind channel identification is presented the effects of the noise color due to the prefilter on equalizers is investigated and the sensitivity of the truncation of the overall channel impulse response in terms of the mean squared error mse performance criterion is investigated through numerical examples

simulated annealing with an optimal fixed temperature contrary to conventional belief it turns out that in some problem instances of moderate size fixed temperature simulated annealing algorithms based on a heuristic formula for determining the optimal temperature can be superior to algorithms based on cooling such a heuristic formula however often seems elusive in practical cases considered we include instances of traveling salesman quadratic assignment and graph partitioning problems where we obtain results that compare favorably to the ones known in the literature

simulated annealing searching for an optimal temperature schedule a sizable part of the theoretical literature on simulated annealing deals with a property called convergence which asserts that the simulated annealing chain is in the set of global minimum states of the objective function with probability tending to 1 however in practice the convergent algorithms are considered too slow whereas a number of nonconvergent ones are usually preferred we attempt a detailed analysis of various temperature schedules examples will be given of when it is both practically and theoretically justified to use boiling fixed temperature or even fast cooling schedules which have a small probability of reaching global minima applications to traveling salesman problems of various sizes are also given

a characteristic equation associated to an eigenvalue problem on c2 networks abstract a characteristic equation for the spectrum of an eigenvalue problem on c 2 networks is deduced and those special cases are investigated in which the spectrum depends mainly on the structure of the abstract graph belonging to the c 2 network

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

anomaly preserving ell _ scriptscriptstyle 2 infty optimal dimensionality reduction over a grassmann manifold in this paper we address the problem of redundancy reduction of high dimensional noisy signals that may contain anomaly rare vectors which we wish to preserve since anomaly data vectors contribute weakly to the l2 norm of the signal as compared to the noise l2 based criteria are unsatisfactory for obtaining a good representation of these vectors as a remedy a new approach named min max svd mx svd was recently proposed for signal subspace estimation by attempting to minimize the maximum of data residual l2 norms denoted as l2 l and designed to represent well both abundant and anomaly measurements however the mx svd algorithm is greedy and only approximately minimizes the proposed l2 l norm of the residuals in this paper we develop an optimal algorithm for the minization of the l2 l norm of data misrepresentation residuals which we call maximum orthogonal complements optimal subspace estimation moose the optimization is performed via a natural conjugate gradient learning approach carried out on the set of n dimensional subspaces in irm m n which is a grassmann manifold the results of applying moose mx svd and l2 based approaches are demonstrated both on simulated and real hyperspectral data

full waveform static corrections using blind channel identification near surface wavefield perturbations can be very complex and completely mask the target reflections despite this complexity conventional methods rely on parameterizations characterized by simple time and amplitude anomalies to compensate for these perturbations determining and compensating for time shifts is generally referred to as residual static corrections whereas surface consistent deconvolution techniques deal with amplitude anomalies we present an approach that uses the full waveform to parameterize near surface perturbations therefore we refer to this method as waveform statics important differences from conventional static corrections are that this approachallowstimeshiftstovarywithfrequencyandtakesamplitudevariationsdirectlyintoaccount furthermore theprocedure is fully automated and does not rely on near surface velocity information the waveform static corrections are obtained using blind channel identification and applied to the recordings using multichannel deconvolution as a result the method implicitly incorporates array forming the developed method is validated onsyntheticdataandappliedtopartofafielddatasetacquiredin an area with significant near surface heterogeneity the source and receiver responses obtained are strongly correlated to the near surfaceconditionsandshowchanges bothinphaseandfrequency content along the spread the application of the waveform statics demonstrates that they not only correct for near surface wavefield perturbations but also strongly reduce coherent noise this results in substantial improvements both in trace totrace coherency and in depth resolution in addition the procedure delineates reflection events that are difficult to detect prior to our proposed correction based on these results we conclude that complex near surface perturbations can be successfully dealt with using the multichannel full waveform static correctionprocedure

persistency of excitation sufficient richness and parameter convergence in discrete time adaptive control abstract the main result of this paper is to give new techniques for deriving explicit conditions on the exogenously specified reference trajectory to guarantee parameter convergence for a class of either single input single output or multi input multi output discrete time adaptive control schemes other interesting results are relations between persistently exciting signals and sufficiently rich signals

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 subspace method for the computation of the gcd of polynomials for the problem of computing the greatest common divisor gcd of a set of polynomials a subspace ss technique is introduced and compared with the matrix pencil mp method proposed by karcanias and mitrouli it is observed that the ss method is easier to implement and more robust to noise than the mp method

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

the conjugate gradient regularization method in computed tomography problems in this work we solve inverse problems coming from the area of computed tomography by means of regularization methods based on conjugate gradient iterations we develop a stopping criterion which is efficient for the computation of a regularized solution for the least squares normal equations the stopping rule can be suitably applied also to the tikhonov regularization method we report computational experiments based on different physical models and with different degrees of noise we compare the results obtained with those computed by other currently used methods such as algebraic reconstruction techniques art and backprojection

blind mimo channel identification from second order statistics using rank deficient channel convolution matrix for multiuser systems several direct blind identification algorithms require that the linear multiple input multiple output mimo system have a full rank convolution matrix this condition requires that the system transfer function be irreducible and column reduced we show that this restrictive identification condition can be relaxed for some direct blind identification methods to accommodate more practical scenarios algorithms such as the outer product decomposition algorithm only require minor length adjustment to its processing window without the column reduced condition this result allows direct blind identification methods to be applicable to mimo without requiring a full rank channel convolution matrix

zero forcing blind equalization based on subspace estimation for multiuser systems input signal recovery from frequency selective fading channels is a problem of great theoretical and practical importance we present several new blind algorithms that utilize second order statistics for direct multichannel equalization the algorithms are based on the subspace extraction of a preselected block column of the channel convolution matrix for a multiuser system user signal separation can be achieved based on partial information of the composite channel response these equalization algorithms do not rely on the precise separation of signal and noise subspaces and therefore tend to be less sensitive to channel order or column rank estimation errors equalization is directly achieved without channel identification furthermore the equalizability conditions of these algorithms are discussed

rank estimation and redundancy reduction of high dimensional noisy signals with preservation of rare vectors in this paper we address the problem of redundancy reduction of high dimensional noisy signals that may contain anomaly rare vectors which we wish to preserve for example when applying redundancy reduction techniques to hyperspectral images it is essential to preserve anomaly pixels for target detection purposes since rare vectors contribute weakly to the norm of the signal as compared to the noise based criteria are unsatisfactory for obtaining a good representation of these vectors the proposed approach combines and norms for both signal subspace and rank determination and considers two aspects one aspect deals with signal subspace estimation aiming to minimize the maximum of data residual norms denoted as for a given rank conjecture the other determines whether the rank conjecture is valid for the obtained signal subspace by applying extreme value theory results to model the distribution of the noise norm these two operations are performed alternately using a suboptimal greedy algorithm which makes the proposed approach practically plausible the algorithm was applied on both synthetically simulated data and on a real hyperspectral image producing better results than common based methods

the peak algebra of the symmetric group revisited abstract the linear span p n of the sums of all permutations in the symmetric group sn with a given set of peaks is a sub algebra of the symmetric group algebra due to nyman this peak algebra is a left ideal of the descent algebra d n and the direct sum p of all p n is a hopf sub algebra of the direct sum d of all d n dual to the stembridge algebra of peak functions in our self contained approach peak counterparts of several results on the descent algebra are established including a simple combinatorial characterization of the algebra p n an algebraic characterization of p n based on the action on the poincare birkhoff witt basis of the free associative algebra the display of peak variants of the classical lie idempotents an eulerian type sub algebra of p n a description of the jacobson radical of p n and its nil potency index of the principal indecomposable and irreducible p n modules and of the cartan matrix of p n furthermore it is shown that the primitive lie algebra of p is free and that p is its enveloping algebra

deterministic blind identification of iir systems with output switching operations in this paper a deterministic blind identification approach is proposed for linear output switching systems which are modeled by multiple infinite impulse response iir dynamic functions by adopting a new over sampling strategy the concerned single input single output siso output switching system is equivalently transformed into a time invariant multi input multi output mimo system further by exploring the mutual relations among the multiple inputs the time invariant mimo system model and subsequently the output switching system model are identified uniquely up to a scalar constant using the proposed identification approach sufficient identifiability conditions are provided for output switching systems and numerical simulations are carried out to validate the proposed approach

periodic modulation based blind channel identification for single carrier block transmission with frequency domain equalization this paper proposes a periodic modulation based blind channel identification scheme for single carrier sc block transmission with frequency domain equalization fde the proposed approach relies on the block system model and exploits the circulant channel matrix structure after the cyclic prefix is removed it is shown that the set of linear equations relating the autocorrelation matrix of the block received signal and the product channel coefficients can be rearranged into one with a distinctive block circulant structure the identification equations thus obtained lead to a very simple identifiability condition as well as a natural formulation of the optimal modulating sequence design problem which based on the block circulant signal structure can be cast as a constrained quadratic problem that allows for a simple closed form solution the impact of the optimal modulating sequence on the peak to average power ratio papr is investigated also it is shown that the optimal sequence results in a consistent channel estimate irrespective of white noise perturbation pairwise error probability analysis is used to examine the equalization performance and based on which several design tradeoffs are discussed simulation results are used for illustrating the performance of the proposed method

fast subspace decomposition many recent signal processing techniques in various areas e g array signal processing system identification and spectrum estimation require a step of extracting the low dimensional subspace from a large space this step is usually called subspace decomposition which is conventionally accomplished by an eigendecomposition ed since an ed requires o m sup 3 flops for an m spl times m matrix it may represent a barrier to the real time implementation of these algorithms the authors present a fast algorithm for signal subspace decomposition which is termed fsd that exploits the special matrix structure low rank plus identity associated with signal subspace algorithms and requires only o m sup 2 d flops where d often spl lt m denotes the signal subspace dimension unlike most of alternatives the dimension of the signal subspace is not assumed known apriori and is estimated as part of the procedure moreover theoretical analysis has been conducted and its results show the strong consistency of the new detection scheme and the asymptotic equivalence between fsd and ed in estimating the signal subspace their new approach can also exploit other matrix structure common in signal processing areas e g toeplitz or hankel and thus achieve another order of computational reduction moreover it can be easily implemented in parallel to reduce further the computation time to as little as o md using o m simple processors

hybrid parallel tempering and simulated annealing method in this paper we propose a new hybrid scheme of parallel tempering and simulated annealing hybrid pt sa within the hybrid pt sa scheme a composite system with multiple conformations is evolving in parallel on a temperature ladder with various transition step sizes the simulated annealing sa process uses a cooling scheme to decrease the temperature values in the temperature ladder to the target temperature the parallel tempering pt scheme is employed to reduce the equilibration relaxation time of the composite system at a particular temperature ladder configuration in the sa process the hybrid pt sa method reduces the waiting time in deep local minima and thus leads to a more efficient sampling capability on high dimensional complicated objective function landscapes compared to the approaches pt and parallel sa with the same temperature ladder transition step sizes and cooling scheme parallel sa configurations our preliminary results obtained with the hybrid pt sa method confirm the expected improvements in simulations of several test objective functions including the rosenbrock s function and the rugged funnel like function and several instantiations of the traveling salesman problem the hybrid pt sa may have slower convergence than genetic algorithms ga with good crossover heuristics but it has the advantage of tolerating bad initial values and displaying robust sampling capability even in the absence of additional information moreover the hybrid pt sa has natural parallelization potential

investigation of temperature parallel simulated annealing for optimizing continuous functions with application to hyperspectral tomography abstract the simulated annealing sa algorithm is a well established optimization technique which has found applications in many research areas however the sa algorithm is limited in its application due to the high computational cost and the difficulties in determining the annealing schedule this paper demonstrates that the temperature parallel simulated annealing tpsa algorithm a parallel implementation of the sa algorithm shows great promise to overcome these limitations when applied to continuous functions the tpsa algorithm greatly reduces the computational time due to its parallel nature and avoids the determination of the annealing schedule by fixing the temperatures during the annealing process the main contributions of this paper are threefold first this paper explains a simple and effective way to determine the temperatures by applying the concept of critical temperature t c second this paper presents systematic tests of the tpsa algorithm on various continuous functions demonstrating comparable performance as well established sequential sa algorithms third this paper demonstrates the application of the tpsa algorithm on a difficult practical inverse problem namely the hyperspectral tomography problem the results and conclusions presented in this work provide are expected to be useful for the further development and expanded applications of the tpsa algorithm

ill convergence of godard blind equalizers in data communication systems the existence of stable undesirable equilibria for the godard algorithm is demonstrated through a simple autoregressive ar channel model these undesirable equilibria correspond to local but nonglobal minima of the underlying mean cost function and thus permit the ill convergence of the godard algorithms which are stochastic gradient descent in nature simulation results confirm predicted misbehavior for channel input of constant modulus it is shown that attaining the global minimum of the mean cost necessarily implies correct equalization a criterion is also presented for allowing a decision at the equalizer as to whether a global or nonglobal minimum has been reached

evam an eigenvector based algorithm for multichannel blind deconvolution of input colored signals a new algorithm is proposed for the deconvolution of an unknown possibly colored gaussian or nonstationary signal that is observed through two or more unknown channels described by rational system transfer functions more specifically not only the root pole and zero locations but also the orders of the channel transfer functions are unknown it is assumed that the channel orders may be overestimated the proposed algorithm estimates the orders and root locations of the channel transfer functions therefore it can also be used in multichannel system identification problems the input signal is allowed to be nonstationary and the channel transfer functions may be a nonminimum phase as well as noncausal hence the proposed algorithm is particularly suitable for applications such as dereverberation of speech signals recorded through multiple microphones several experimental results indicate improvement compared to the existing methods in the literature

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

application of fast subspace decomposition to signal processing and communication problems the authors previously described a class of fast subspace decomposition fsd algorithms though these algorithms can be applied to solve a variety of signal processing and communication problems with significant computational reduction they focus their discussion on two typical applications i e sensor array processing and time series analysis in many cases replacing the usual eigenvalue decomposition evd or singular value decomposition svd by the fsd is quite straightforward however the fsd approach can exploit more structure of some special problems to further simplify the implementation they first discuss the implementation details of the fsd such as how to choose an optimal starting vector how to handle correlated noise and how to exploit additional matrix structure for further computational reduction then they describe an fsd approach targeted at data matrices rectangular n spl times m n spl ges m which requires only o nmd flops where d denotes the signal subspace dimension versus a regular o nm sup 2 m sup 3 svd the computational reduction is substantial in typical scenarios i e d spl lt m spl les n in the spectrum estimation problems the data matrix has additional structure such as toeplitz or hankel they finally show how the fsd can exploit such structure for further computational reduction

general approach to blind source separation this paper identifies and studies two major issues in the blind source separation problem separability and separation principles we show that separability is an intrinsic property of the measured signals and can be described by the concept of m row decomposability introduced in this paper we also show that separation principles can be developed by using the structure characterization theory of random variables in particular we show that these principles can be derived concisely and intuitively by applying the darmois skitovich theorem which is well known in statistical inference theory and psychology some new insights are gained for designing blind source separation filters

blind convolution using signal reconstruction from partial higher order cepstral information a blind deconvolution scheme for the reconstruction of a signal that propagates in the presence of reverberation and additive noise is presented the reconstruction employs data collected by two receivers placed well apart from each other each received sequence consists of the convolution of the transmitted signal with a channel applying the bicepstrum iterative reconstruction algorithm on the differences of the cepstra coefficients of the two received sequences the cepstra coefficients of the transmission channels can be computed and used for the reconstruction of the transmitted signal the deconvolution can be performed even if the data sequences are corrupted by additive noise the computation of the cepstra coefficients is based on the cross bispectrum if the noise processes present in the observation sequences are zero mean and uncorrelated while the bicepstrum of each observation is used if the noise processes are zero mean correlated with a symmetric probability density function

time domain signal detection based on second order statistics for mimo ofdm systems in this paper a time domain signal detection algorithm based on second order statistics sos is proposed for multiple input multiple output mimo orthogonal frequency division multiplexing ofdm systems over frequency selective fading channels a new system model in which the ith received ofdm block is left shifted by j samples is introduced based on some structural properties of the new system model an equalizer is designed using sos of the received signals to cancel most of the intersymbol interference the transmitted signals are then detected from the equalizer output in the proposed algorithm only 2p p is the number of transmit antennas users in mimo ofdm systems columns of the channel matrix need to be estimated and channel length estimation is unnecessary which is an advantage over existing algorithms in addition the proposed algorithm is applicable irrespective of whether the channel length is shorter than equal to or longer than the cyclic prefix cp length simulation results verify the effectiveness of the proposed algorithm and show that it outperforms the existing ones in all cases

eigenanalysis based blind methods for identification equalization and inversion of linear time invariant channels some blind algorithms are presented for estimation of equalizers for linear time invariant lti channels from the eigenvectors of certain rank one matrices constructed from the second order statistics of the oversampled received signal it is shown that the channel can also be identified from the same matrices it can be shown that in multipath dominated environments equalizers with symbol spread of only one referred to as inverters can be used when sufficient diversity is available because of the manner in which structure in the channel distortion is exploited the proposed identification and equalization algorithms are also applicable to this case for the same reason the proposed algorithms do not require estimation of the channel memory only an upper bound is required equalizers of desired delay are estimated directly independent of others

annealing markov chain monte carlo with applications to ancestral inference abstract markov chain monte carlo mcmc the metropolis hastings algorithm has been used for many statistical problems including bayesian inference likelihood inference and tests of significance though the method generally works well doubts about convergence often remain here we propose mcmc methods distantly related to simulated annealing our samplers mix rapidly enough to be usable for problems in which other methods would require eons of computing time they simulate realizations from a sequence of distributions allowing the distribution being simulated to vary randomly over time if the sequence of distributions is well chosen then the sampler will mix well and produce accurate answers for all the distributions even when there is only one distribution of interest these annealing like samplers may be the only known way to get a rapidly mixing sampler these methods are essential for attacking very hard problems which arise in areas such as statistical genetics we illustrate the methods wi

linear multichannel blind equalizers of nonlinear fir volterra channels truncated volterra expansions model nonlinear systems encountered with satellite communications magnetic recording channels and physiological processes a general approach for blind deconvolution of single input multiple output volterra finite impulse response fir systems is presented it is shown that such nonlinear systems can be blindly equalized using only linear fir filters the approach requires that the volterra kernels satisfy a certain coprimeness condition and that the input possesses a minimal persistence of excitation order no other special conditions are imposed on the kernel transfer functions or on the input signal which may be deterministic or random with unknown statistics the proposed algorithms are corroborated with simulation examples