variational inference for watson mixture model this paper addresses modelling data using the watson distribution the watson distribution is one of the simplest distributions for analyzing axially symmetric data this distribution has gained some attention in recent years due to its modeling capability however its bayesian inference is fairly understudied due to difficulty in handling the normalization factor recent development of markov chain monte carlo mcmc sampling methods can be applied for this purpose however these methods can be prohibitively slow for practical applications a deterministic alternative is provided by variational methods that convert inference problems into optimization problems in this paper we present a variational inference for watson mixture models first the variational framework is used to side step the intractability arising from the coupling of latent states and parameters second the variational free energy is further lower bounded in order to avoid intractable moment computation the proposed approach provides a lower bound on the log marginal likelihood and retains distributional information over all parameters moreover we show that it can regulate its own complexity by pruning unnecessary mixture components while avoiding over fitting we discuss potential applications of the modeling with watson distributions in the problem of blind source separation and clustering gene expression data sets

asymptotical stability of probabilistic boolean networks with state delays this paper devotes to establishing a bridge between asymptotical stability of a probabilistic boolean network pbn and a solution to its induced equations which are induced from the pbn s transition matrix by utilizing the semitensor product technique routinely the dynamics of a pbn with coincident state delays can be equivalently converted into that of a higher dimensional pbn without delays subsequently several novel stability criteria are derived from the standpoint of equations solution the most significant finding is that a pbn is globally asymptotically stable at a predesignated one point distribution if and only if a vector obtained by adding 1 at the bottom of this distribution is the unique nonnegative solution to pbn s induced equations moreover the influence of coincident state delays on pbn s asymptotical stability is explicitly analyzed without consideration of the convergence rate consequently such bounded state delays are verified to have no impact on pbn s stability albeit delays are time varying based on this worthwhile observation the time computational complexity of the aforementioned approach can be reduced by removing delays directly furthermore this universal procedure is summarized to reduce the time complexity of some previous results in the literature to a certain extent two examples are employed to demonstrate the feasibility and effectiveness of the obtained theoretical results

interface element method treatment of non matching nodes at the ends of interfaces between partitioned domains abstract a methodology for interface element method iem to combine partitioned domains with non matching nodes at the ends of interfaces is presented the iem is introduced to satisfy the continuity and the compatibility conditions on non matching interfaces between partitioned finite element domains interface elements are defined on the finite elements bordering on the interfaces and the moving least square mls approximations are employed to construct the shape functions of the interface elements by modifying the shape functions of the interface elements at the ends of non matching interfaces partitioned domains are glued such that all properties of the iem are satisfied the modifications are made to sub domains and weight functions in the mls approximations the numerical examples show that the present iem is very effective for the analysis of a partitioned system and for global local analysis

single channel blind separation using pseudo stereo mixture and complex 2 d histogram a novel single channel blind source separation scbss algorithm is presented the proposed algorithm yields at least three benefits of the scbss solution 1 resemblance of a stereo signal concept given by one microphone 2 independent of initialization and a priori knowledge of the sources and 3 it does not require iterative optimization the separation process consists of two steps 1 estimation of source characteristics where the source signals are modeled by the autoregressive process and 2 construction of masks using only the single channel mixture a new pseudo stereo mixture is formulated by weighting and time shifting the original single channel mixture this creates an artificial mixing system whose parameters will be estimated through our proposed weighted complex 2 d histogram in this paper we derive the separability of the proposed mixture model conditions required for unique mask construction based on maximum likelihood are also identified finally experimental testing on both synthetic and real audio sources is conducted to verify that the proposed algorithm yields superior performance and is computationally very fast compared with existing methods

on stabilization and set stabilization of multivalued logical systems the stabilization and set stabilization of a multivalued logical system are investigated in this paper by using pinning control first new algorithms are presented to redesign the transition matrix of the multivalued logical system such that the system is stable or set stable then by using reconstruction theory of a k valued logical system the pinning nodes are selected in addition the transition matrix of the pinning control is given by solving some logical matrix equations then a pinning control design algorithm is provided finally an example is employed to illustrate the proposed control design procedure

fast time stability of temporal boolean networks in real systems most of the biological functionalities come from the fact that the connections are not active all the time based on the fact temporal boolean networks tbns are proposed in this paper and the fast time stability is analyzed via semi tensor product stp of matrices and incidence matrices first the algebraic form of a tbn is obtained based on the stp method and one necessary and sufficient condition for global fast time stability is presented moreover incidence matrices are used to obtain several sufficient conditions which reduce the computational complexity from mathcal o n2 n exponential type to mathcal o n 4 polynomial type compared with the stp method in addition the global fast time stabilization of tbns is considered and pinning controllers are designed based on the neighbors of controlled nodes rather than all the nodes finally the local fast time stability of tbns is considered based on the incidence matrices as well several examples are provided to illustrate the effectiveness of the obtained results

multiple emitter location and signal parameter estimation processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions the general problem considers sensors with arbitrary locations and arbitrary directional characteristics gain phase polarization in a noise interference environment of arbitrary covariance matrix this report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution a description is given of the multiple signal classification music algorithm which provides asymptotically unbiased estimates of 1 number of incident wavefronts present 2 directions of arrival doa or emitter locations 3 strengths and cross correlations among the incident waveforms 4 noise interference strength examples and comparisons with methods based on maximum likelihood ml and maximum entropy me as well as conventional beamforming are included an example of its use as a multiple frequency estimator operating on time series is included

development of three dimensional interface elements for coupling of non matching hexahedral meshes gluing of a non matching interface between dissimilar meshes is one of challenging tasks in computational mechanics the interface element method iem has been developed using an appropriate interpolation scheme between partitioned finite element meshes the continuity of displacements across the interface is satisfied exactly and the completeness of interface element shape functions provides a reasonable transfer of strain fields between dissimilar finite element meshes research related to the iem has been mainly accomplished in two dimensions because the construction of interface elements in three dimensions is not as simple as the case of two dimensions in this paper a new scheme is presented to construct the interface elements for connecting dissimilar hexahedral finite element meshes with an arbitrarily curved non matching interface element edges crossing any other edges in an interface region are modified into continuous intersection polygons to eliminate overlays and gaps between dissimilar meshes and these polygons are divided into triangles to create prism shaped interface elements numerical experiments on a linear elasticity problem with non matching meshes illustrate the effectiveness of the method

a new blind method for separating m 1 sources from m mixtures in this paper we consider the problem of underdetermined blind source separation ubss i e there are more sources than mixtures by exploiting the time frequency tf sparsity of the source signals some tf ubss algorithms have recently been proposed in the literature these algorithms require that the number of active sources at any tf point should not exceed one or be strictly less than the number of mixtures in this paper we show that if the number of sources is greater than the number of mixtures by one the sparsity assumption can be further relaxed especially it is allowed to have as many sources as mixtures at any tf point in this case then we propose a new tf ubss method to recover the sources the relaxation on the maximum number of active sources at tf points ensures that the tf based methods can be used in a wider range of applications

cramer rao bounds for complex valued independent component extraction determined and piecewise determined mixing models blind source extraction bse aims at recovering an unknown source signal of interest from the observation of instantaneous linear mixtures of the sources this paper presents cramer rao lower bounds crlb for the complex valued bse problem based on the assumption that the target signal is independent of the other signals the target source is assumed to be non gaussian or non circular gaussian while the other signals background are circular gaussian or non gaussian the results confirm some previous observations known for the real domain and yield new results for the complex domain also the crlb for independent component extraction ice is shown to coincide with that for independent component analysis ica when the non gaussianity of background is taken into account second we extend the crlb analysis to piecewise determined mixing models where the observed signals are assumed to obey the determined mixing model within short blocks where the mixing matrices can be varying from block to block this model has applications for instance when separating dynamic mixtures either the mixing vector or the separating vector corresponding to the target source is assumed to be constant across the blocks the crlbs for the parameters of these models bring new performance limits for the bse problem

characterization of analytic wavelet transforms and a new phaseless reconstruction algorithm we obtain a characterization of all wavelets leading to analytic wavelet transforms wt the characterization is obtained as a byproduct of the theoretical foundations of a new method for wavelet phase reconstruction from magnitude only coefficients the cornerstone of our analysis is an expression of the partial derivatives of the continuous wt which results in phase magnitude relationships similar to the short time fourier transform setting and valid for the generalized family of cauchy wavelets we show that the existence of such relations is equivalent to analyticity of the wt up to a multiplicative weight and a scaling of the mother wavelet the implementation of the new phaseless reconstruction method is considered in detail and compared to previous methods it is shown that the proposed method provides significant performance gains and a great flexibility regarding accuracy versus complexity in addition we discuss the relation between scalogram reassignment operators and the wavelet transform phase gradient and present an observation on the phase around zeros of the wt

anechoic blind source separation using wigner marginals blind source separation problems emerge in many applications where signals can be modeled as superpositions of multiple sources many popular applications of blind source separation are based on linear instantaneous mixture models if specific invariance properties are known about the sources for example translation or rotation invariance the simple linear model can be extended by inclusion of the corresponding transformations when the sources are invariant against translations spatial displacements or time shifts the resulting model is called an anechoic mixing model we present a new algorithmic framework for the solution of anechoic problems in arbitrary dimensions this framework is derived from stochastic time frequency analysis in general and the marginal properties of the wigner ville spectrum in particular the method reduces the general anechoic problem to a set of anechoic problems with non negativity constraints and a phase retrieval problem the first type of subproblem can be solved by existing algorithms for example by an appropriate modification of non negative matrix factorization nmf the second subproblem is solved by established phase retrieval methods we discuss and compare implementations of this new algorithmic framework for several example problems with synthetic and real world data including music streams natural 2d images human motion trajectories and two dimensional shapes

underdetermined blind source separation based on subspace representation this paper considers the problem of blindly separating sub and super gaussian sources from underdetermined mixtures the underlying sources are assumed to be composed of two orthogonal components one lying in the rowspace and the other in the nullspace of a mixing matrix the mapping from the rowspace component to the mixtures by the mixing matrix is invertible using the pseudo inverse of the mixing matrix the mapping from the nullspace component to zero by the mixing matrix is noninvertible and there are infinitely many solutions to the nullspace component the latent nullspace component which is of lower complexity than the underlying sources is estimated based on a mean square error mse criterion this leads to a source estimator that is optimal in the mse sense in order to characterize and model sub and super gaussian source distributions the parametric generalized gaussian distribution is used the distribution parameters are estimated based on the expectation maximization em algorithm when the mixing matrix is unavailable it must be estimated and a novel algorithm based on a single source detection algorithm which detects time frequency regions of single source occupancy is proposed in our simulations the proposed algorithm compared to other conventional algorithms estimated the mixing matrix with higher accuracy and separated various sources with higher signal to interference ratio

fourth order blind identification of underdetermined mixtures of sources fobium for about two decades numerous methods have been developed to blindly identify overdetermined p spl les n mixtures of p statistically independent narrowband nb sources received by an array of n sensors these methods exploit the information contained in the second order so the fourth order fo or both the so and fo statistics of the data however in practical situations the probability of receiving more sources than sensors increases with the reception bandwidth and the use of blind identification bi methods able to process underdetermined mixtures of sources for which p n may be required although such methods have been developed over the past few years they all present serious limitations in practical situations related to the radiocommunications context for this reason the purpose of this paper is to propose a new attractive bi method exploiting the information contained in the fo data statistics only that is able to process underdetermined mixtures of sources without the main limitations of the existing methods provided that the sources have different trispectrum and nonzero kurtosis with the same sign a new performance criterion that is able to quantify the identification quality of a given source and allowing the quantitative comparison of two bi methods for each source is also proposed in the paper finally an application of the proposed method is presented through the introduction of a powerful direction finding method built from the blindly identified mixture matrix

penalty resolution of the babuska circle paradox abstract if the solution for a simply supported circular plate is approximated by a sequence of finite element solutions on successively refined polygonal domains the finite element approximations converge to the solution of a different problem in the present study we present a finite element formulation with boundary penalty that produces valid approximations to the simply supported plate the penalty term is shown to require the use of reduced integration the dependence of the penalty parameter on mesh size h is also examined numerical experiments confirm the validity of the method and determine rates of convergence a second approach involving a modified corner condition is also considered and error estimates determined this scheme is implemented also using a discrete penalty technique the results and rates are compared with the boundary penalty method and their relative merits discussed

blind separation of speech mixtures via time frequency masking binary time frequency masks are powerful tools for the separation of sources from a single mixture perfect demixing via binary time frequency masks is possible provided the time frequency representations of the sources do not overlap a condition we call w disjoint orthogonality we introduce here the concept of approximate w disjoint orthogonality and present experimental results demonstrating the level of approximate w disjoint orthogonality of speech in mixtures of various orders the results demonstrate that there exist ideal binary time frequency masks that can separate several speech signals from one mixture while determining these masks blindly from just one mixture is an open problem we show that we can approximate the ideal masks in the case where two anechoic mixtures are provided motivated by the maximum likelihood mixing parameter estimators we define a power weighted two dimensional 2 d histogram constructed from the ratio of the time frequency representations of the mixtures that is shown to have one peak for each source with peak location corresponding to the relative attenuation and delay mixing parameters the histogram is used to create time frequency masks that partition one of the mixtures into the original sources experimental results on speech mixtures verify the technique example demixing results can be found online at http alum mit edu www rickard bss html

underdetermined blind separation of nondisjoint sources in the time frequency domain this paper considers the blind separation of nonstationary sources in the underdetermined case when there are more sources than sensors a general framework for this problem is to work on sources that are sparse in some signal representation domain recently two methods have been proposed with respect to the time frequency tf domain the first uses quadratic time frequency distributions tfds and a clustering approach and the second uses a linear tfd both of these methods assume that the sources are disjoint in the tf domain i e there is at most one source present at a point in the tf domain in this paper we relax this assumption by allowing the sources to be tf nondisjoint to a certain extent in particular the number of sources present at a point is strictly less than the number of sensors the separation can still be achieved due to subspace projection that allows us to identify the sources present and to estimate their corresponding tfd values in particular we propose two subspace based algorithms for tf nondisjoint sources one uses quadratic tfds and the other a linear tfd another contribution of this paper is a new estimation procedure for the mixing matrix finally then numerical performance of the proposed methods are provided highlighting their performance gain compared to existing ones

controllability and observability of boolean control networks the controllability and observability of boolean control networks are investigated after a brief review on converting a logic dynamics to a discrete time linear dynamics with a transition matrix some formulas are obtained for retrieving network and its logical dynamic equations from this network transition matrix based on the discrete time dynamics the controllability via two kinds of inputs is revealed by providing the corresponding reachable sets precisely then the problem of observability is also solved by giving necessary and sufficient conditions

the properties of a class of linear fsrs and their applications to the construction of nonlinear fsrs in this paper the cycle structure and adjacency graphs of a class of linear feedback shift registers lfsrs are determined by recursively applying the d morphism to the maximum length lfsrs and representing the cycles by generating functions a new family of maximum length nonlinear feedback shift registers nfsrs are proposed based on the properties of these lfsrs the number of nfsrs in the proposed family is also considered

brief paper controllability of boolean control networks via the perron frobenius theory boolean control networks bcns are recently attracting considerable interest as computational models for genetic and cellular networks addressing control theoretic problems in bcns may lead to a better understanding of the intrinsic control in biological systems as well as to developing suitable protocols for manipulating biological systems using exogenous inputs we introduce two definitions for controllability of a bcn and show that a necessary and sufficient condition for each form of controllability is that a certain nonnegative matrix is irreducible or primitive respectively our analysis is based on a result that may be of independent interest namely a simple algebraic formula for the number of different control sequences that steer a bcn between given initial and final states in a given number of time steps while avoiding a set of forbidden states

driven stability of nonlinear feedback shift registers with inputs driven stable nonlinear feedback shift registers nfsrs with inputs are not only able to limit error propagations in convolutional decoders but also helpful to analyze the period properties of sequences generated by a cascade connection of nfsrs in stream ciphers an nfsr is driven stable if and only if the reachable set is a subset of the basin due to lack of efficient algebraic tools the driven stability of nfsrs with inputs has been much less studied this paper continues to address this research using a boolean control network approach viewing an nfsr with input as a boolean control network we first give its boolean control network representation which is characterized with a state transition matrix some properties of the state transition matrix are then provided based on these explicit forms are given for the reachable set and the set of basin two algorithms for obtaining both the sets are provided as well compared with the exhaustive search and the existing state operator method the boolean control network approach requires lower computational complexity for those nfsrs with their stages greater than 1

mixing matrix estimation from sparse mixtures with unknown number of sources in blind source separation many methods have been proposed to estimate the mixing matrix by exploiting sparsity however they often need to know the source number a priori which is very inconvenient in practice in this paper a new method namely nonlinear projection and column masking npcm is proposed to estimate the mixing matrix a major advantage of npcm is that it does not need any knowledge of the source number in npcm the objective function is based on a nonlinear projection and its maxima just correspond to the columns of the mixing matrix thus a column can be estimated first by locating a maximum and then deflated by a masking operation this procedure is repeated until the evaluation of the objective function decreases to zero dramatically thus the mixing matrix and the number of sources are estimated simultaneously because the masking procedure may result in some small and useless local maxima particle swarm optimization pso is introduced to optimize the objective function feasibility and efficiency of pso are also discussed comparative experimental results show the efficiency of npcm especially in the cases where the number of sources is unknown and the sources are relatively less sparse

blind source separation based on time frequency signal representations blind source separation consists of recovering a set of signals of which only instantaneous linear mixtures are observed thus far this problem has been solved using statistical information available on the source signals this paper introduces a new blind source separation approach exploiting the difference in the time frequency t f signatures of the sources to be separated the approach is based on the diagonalization of a combined set of spatial t f distributions in contrast to existing techniques the proposed approach allows the separation of gaussian sources with identical spectral shape but with different t f localization properties the effects of spreading the noise power while localizing the source energy in the t f domain amounts to increasing the robustness of the proposed approach with respect to noise and hence improved performance asymptotic performance analysis and numerical simulations are provided

time frequency approach to underdetermined blind source separation this paper presents a new time frequency tf underdetermined blind source separation approach based on wigner ville distribution wvd and khatri rao product to separate n non stationary sources from m m n mixtures first an improved method is proposed for estimating the mixing matrix where the negative value of the auto wvd of the sources is fully considered then after extracting all the auto term tf points the auto wvd value of the sources at every auto term tf point can be found out exactly with the proposed approach no matter how many active sources there are as long as n 2m 1 further discussion about the extraction of auto term tf points is made and finally the numerical simulation results are presented to show the superiority of the proposed algorithm by comparing it with the existing ones

on the statistical distributions of active intensity directions abstract pressure and pressure differences measured using closely spaced microphone pairs enable the calculation of active intensity directions these directions are exploited in various applications such as acoustical analysis and modelling multichannel audio systems source localization and real time blind source separation despite their widespread usage the distribution of the active intensity directions in the presence of interference has not been investigated before except for the diffuse sound field case for which the distribution becomes uniform in this paper the interference problem has been formulated using the pressure and pressure differences algebra of random variables fractional linear transformations and brownian motion have been used for the exposition of the model distributions it is shown that the distribution is wrapped cauchy for a single sound source in the presence of independent interference in an anechoic chamber in a direct plus diffuse sound field the distribution is offset cauchy for a nonstationary target source and von mises for the special case of a stationary target source related to the brownian motion in the presence of multiple independent sound sources the distribution can be modeled as mixtures of circular cauchy distributions and a uniform distribution

finding matching initial states for equivalent nlfsrs in the fibonacci and the galois configurations the fibonacci and the galois configurations of nonlinear feedback shift registers nlfsrs are considered in the former the feedback is applied to the input bit of the shift register only in the latter the feedback can potentially be applied to every bit the sufficient conditions for equivalence of nlfsrs in the fibonacci and the galois configurations have been formulated previously the equivalent nlfsrs in different configurations normally have to be initialized to different states to generate the same output sequences the mapping between the initial states of two equivalent nlfsrs in the fibonacci and the galois configurations is derived in this paper

non conforming mesh gluing in the finite elements method a methodology to connect two or more non conforming meshes generated from a geometric model is presented the problem leads to a variational problem subject to constraints the emphasis of the work is on constraints construction two methods namely discrete connection and continuous connection are developed the relation with hybrid formulations is considered and two application examples are presented

robustness analysis of spatial time frequency distributions based on the influence function standard spatial time frequency distribution stfd estimators derived based on the gaussian noise assumption are known to have poor performance in the case of impulsive noise recently different stfd estimators have been proposed which based on simulations are claimed to be robust in this paper we provide an influence function robustness analysis of stfd estimators we derive the influence functions for the asymptotic and for the finite sample case and study robustness of the standard as well as for some recently proposed robust stfd estimators the empirical influence function gives practitioners a simple way to pre select stfd estimators for their scenario our analysis confirms that unlike for the standard estimator the proposed robust estimators yield a bounded influence function and are robust over a broad class of distributions future research on stfd estimation will allow for the design of robust and efficient estimators based on the influence function

built in test for circuits with scan based on reseeding of multiple polynomial linear feedback shift registers we propose a new scheme for built in test bit that uses multiple polynomial linear feedback shift registers mp lfsr s the same mp lfsr that generates random patterns to cover easy to test faults is loaded with seeds to generate deterministic vectors for difficult to test faults the seeds are obtained by solving systems of linear equations involving the seed variables for the positions where the test cubes have specified values we demonstrate that mp lfsr s produce sequences with significantly reduced probability of linear dependence compared to single polynomial lfsr s we present a general method to determine the probability of encoding as a function of the number of specified bits in the test cube the length of the lfsr and the number of polynomials theoretical analysis and experiments show that the probability of encoding a test cube with s specified bits in an s stage lfsr with 16 polynomials is 1 10 sup 6 we then present the new bit scheme that allows for an efficient encoding of the entire test set here the seeds are grouped according to the polynomial they use and an implicit polynomial identification reduces the number of extra bits per seed to one bit the paper also shows methods of processing the entire test set consisting of test cubes with varied number of specified bits experimental results show the tradeoffs between test data storage and test application time while maintaining complete fault coverage

on robust control invariance of boolean control networks this paper investigates the robust control invariance of boolean control networks bcns via the semi tensor product of matrices firstly based on an algebraic state space representation of bcns two necessary and sufficient conditions are presented to check whether or not a given set is a robust control invariant set under a given state feedback controller secondly by defining a series of suitable sets all possible state feedback gain matrices under which a given set is a robust control invariant set are characterized an illustrative example is presented to demonstrate the obtained new results

on penalty methods for interelement constraints the problem of enforcing constraints across interelement boundaries can be treated directly in the construction of the basis or by using multiplier or penalty methods we demonstrate that the multiplier approach can induce linear dependence in the constraints and lead to a singular system of equations if appropriate precautions are not exercised the penalty method on the other hand is shown to be directly applicable to the overconstrained1 problem there are however some subtle points regarding loss of accuracy in the penalty approach we present some new results and corroborating numerical experiments to support the arguments in particular we show that there is progressive erosion of accuracy in the approximate solution using finite precision arithmetic as the penalty factor is increased beyond an optimal value this implies that a control must be applied on the size of the penalty factor these ideas are quite broadly pertinent to computation of solutions to other constrained optimization problems in which multiplier and penalty methods are used as well as the specific class of problems we consider here

underdetermined blind source separation based on sparse representation this paper discusses underdetermined i e with more sources than sensors blind source separation bss using a two stage sparse representation approach the first challenging task of this approach is to estimate precisely the unknown mixing matrix in this paper an algorithm for estimating the mixing matrix that can be viewed as an extension of the duet and the tifrom methods is first developed standard clustering algorithms e g k means method also can be used for estimating the mixing matrix if the sources are sufficiently sparse compared with the duet the tifrom methods and standard clustering algorithms with the authors proposed method a broader class of problems can be solved because the required key condition on sparsity of the sources can be considerably relaxed the second task of the two stage approach is to estimate the source matrix using a standard linear programming algorithm another main contribution of the work described in this paper is the development of a recoverability analysis after extending the results in a necessary and sufficient condition for recoverability of a source vector is obtained based on this condition and various types of source sparsity several probability inequalities and probability estimates for the recoverability issue are established finally simulation results that illustrate the effectiveness of the theoretical results are presented

fourth order cumulant based blind identification of underdetermined mixtures in this paper we study two fourth order cumulant based techniques for the estimation of the mixing matrix in underdetermined independent component analysis the first method is based on a simultaneous matrix diagonalization the second is based on a simultaneous off diagonalization the number of sources that can be allowed is roughly quadratic in the number of observations for both methods explicit expressions for the maximum number of sources are given simulations illustrate the performance of the techniques

laplace beltrami enhancement for unstructured two dimensional meshes having dendritic elements and boundary node movement the laplace beltrami mesh enhancement algorithm of hansen et al 1 3 2 has been implemented and broadened to include meshes containing dendritic elements and allowing for boundary node movement this implementation operates on an unstructured two dimensional mesh by forming an equivalent weak statement using finite element interpolation assembly and solution ideas to iteratively place those nodes allowed to move moving boundary nodes are constrained to follow the boundary geometry described as a wilson fowler spline e g 3 section 2 1 3 1 implementation details concerning the element basis set modifications the metric tensor for dendritic element treatment and boundary node movement are presented laplacian e g 6 enhancement is included as a special case results are presented which illustrate the algorithm for three test problems

a deflation procedure for subspace decomposition a general deflation framework is described for the separation of a desired signal subspace of arbitrary dimensions from noisy multichannel observations the method simultaneously uses single and multichannel priors to split the desired and undesired subspaces even for coplanar intersecting subspaces by appropriate use of signal priors it can even extract signals from degenerate mixtures of signals and noise recorded from a few number of channels in low snr scenarios without the reduction of the data dimensions as a case study the performance of the proposed method is studied for the problem of extracting fetal cardiac signals from maternal abdominal recordings over simulated and real data a second case study deals with the degenerate problem of extracting diaphragmatic electromyogram from electrocardiograph artifacts a provisional patent application based on this method has been filed

quality of trial functions in quadratic isoparametric representation of an arc an examination is made of displacement trial functions found in the quadratic isoparametric representation of an arc special attention is given to the quality of curvature change description and to effective manipulation of the trial functions various interpolation formulae are derived which have relevance to curved shell finite element design under love kirchhoff assumptions

analytical and numerical results for vibration analysis of multi stepped and multi damaged circular arches this paper investigates the in plane linear dynamic behaviour of multi stepped and multi damaged circular arches under different boundary conditions cracked cross sections are modelled as massless elastic rotational hinges in damaged configuration cracks can be located both at the interface between two adjacent portions as well as inside the portion itself for each arch portion bounded by two cracks the differential equations of motion have been established considering axial extension transverse shear effects and rotatory inertia the equilibrium equations of arch portions are combined in the coupled fundamental system in terms of radial displacement tangential displacement and rotation analytical and numerical solutions for multi stepped arches in undamaged as well as in damaged configurations are proposed the analytical solution is based on the euler characteristic exponent procedure involving the roots of characteristic polynomials while the numerical method is focused on the generalized differential quadrature gdq method and the generalized differential quadrature element gdqe technique numerical results are shown in terms of the first 10 analytical and numerical frequencies of multi stepped and multi damaged arches with different boundary conditions finally convergence and stability characteristics of the gdqe procedure are investigated the convergence rate of the natural frequencies is shown to be very fast and the stability of the numerical procedure is very good

time varying filtering and signal estimation using wigner distribution synthesis techniques the short time fourier transform stft the ambiguity function af and the wigner distribution wd are mixed time frequency signal representations that use fourier transform techniques to map a one dimensional function of time into a two dimensional function of time and frequency these mixed time frequency mappings have been used to analyze the local frequency characteristics of a variety of signals and systems although much work has also been done to develop stft and af synthesis algorithms that can be used to implement a variety of time varying signal processing operations no such synthesis techniques have thus far been developed for the wd in this paper a signal synthesis algorithm that works directly with the real valued high resolution wd will be derived examples of how this wd synthesis procedure can be used to perform time varying filtering operations or signal separation will be given

pseudonoise sequences based on algebraic feedback shift registers over the past half century various statistical properties of pseudorandom sequences have played important roles in a variety of applications among these properties are golomb s randomness conditions r1 balance r2 run property and r3 ideal autocorrelations as well as the closely related properties r4 shift and add and r5 de bruin uniform distribution of subblocks the purpose of this paper is to describe the relationships among these conditions and to introduce a new method for generating sequences with all these properties using algebraic feedback shift registers

a transformation from the fibonacci to the galois nlfsrs conventional nonlinear feedback shift registers nlfsrs use the fibonacci configuration in which the feedback is applied to the last bit only in this paper we show how to transform a fibonacci nlfsr into an equivalent nlfsr in the galois configuration in which the feedback can be applied to every bit such a transformation can potentially reduce the depth of the circuits implementing feedback functions thus decreasing the propagation time and increasing the throughput

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

multichannel arma processes parametric modeling of multichannel time series is accomplished by using higher than second order statistics hos of the observed nongaussian data cumulants of vector processes are defined using a kronecker product formulation and consistency of their sample estimators is addressed identifiability results in connection with the hos based parameter estimation of causal and noncausal multivariate arma processes are established estimates of the parameters of causal arma models are obtained as the solution to a set of linear equations whereas those of noncausal arma models are obtained as the solution to a cumulant matching algorithm conventional approaches based on second order statistics can identify a multichannel system only to within post multiplication by a unimodular matrix hos based methods yield solutions that are unique to within post multiplication by an extended permutation matrix additionally the multiminimum phase assumption can be relaxed and the observations may be contaminated with colored gaussian noise frequency domain methods for nonparametric system identification are discussed briefly simulations results validating the multichannel parameter estimation algorithms are provided

parameter extension for combined hybrid finite element methods and application to plate bending problems abstract based on a weighted average of the modified hellinger reissner principle and its dual the combined hybrid finite element chfe method was originally proposed with a combination parameter limited in the interval 0 1 in actual computation this parameter plays an important role in adjusting the energy error of discretization models in this paper a novel expression of the combined hybrid variational form is used to show the relationship between the resultant method and some galerkin least squares stabilized finite scheme for plate bending problems the choice of combination parameter is then extended to 0 0 1 existence uniqueness and convergence of the solution of discrete schemes are proved and the advantage of the parameter extension in computation is discussed as an application improvement of adini s rectangular element by the chfe approach is performed

the uniqueness characteristics of mixed finite element methods in linear elasticity having established the non uniqueness in general of quadratic linear functionals in elasticity the investigative work on this topic is reviewed whence it is noted that there is no existing formal approach for the identification of either the causes of singular solutions or suitable remedial measures that might ensure their elimination a comparison of the weighted averages obtained from the single field and reissner s principles is then used to identify a higher order uniqueness criterion for the latter a detailed consideration of the application of the new condition to the arch equations demonstrates how and why singular solutions can be obtained and thence how suitable forms of remedial action can often be identified finally several other classes of problem are briefly investigated in order to show how the criterion can be used to explain the full range of known behaviour of the mixed methods

underdetermined blind source separation based on relaxed sparsity condition of sources recently aissa el bey et al have proposed two subspace based methods for underdetermined blind source separation ubss in time frequency tf domain these methods allow multiple active sources at tf points so long as the number of active sources at any tf point is strictly less than the number of sensors and the column vectors of the mixing matrix are pairwise linearly independent in this correspondence we first show that the subspace based methods must also satisfy the condition that any m times m submatrix of the mixing matrix is of full rank then we present a new ubss approach which only requires that the number of active sources at any tf point does not exceed that of sensors an algorithm is proposed to perform the ubss

underdetermined sparse blind source separation of nonnegative and partially overlapped data we study the solvability of sparse blind separation of n nonnegative sources from m linear mixtures in the underdetermined regime m n the geometric properties of the mixture matrix and the sparseness structure of the source matrix are closely related to the identification of the mixing matrix we first illustrate and establish necessary and sufficient conditions for the unique separation for the case of m mixtures and m 1 sources and develop a novel algorithm based on data geometry source sparseness and ell 1 minimization then we extend the results to any order m times n 3 leq m n based on the degree of degeneracy of the columns of the mixing matrix numerical results substantiate the proposed solvability conditions and show satisfactory performance of our approach

cumulant based identification of multichannel moving average models given cumulants of a stationary perhaps noisy non gaussian r variate moving average ma q process identifiability conditions are studied under which the ma coefficient matrices the input statistics and the order q can be uniquely determined the selection of a unique representative from the equivalence class corresponding to a given cumulant structure involves fewer restrictions than that corresponding to a given covariance structure two algorithms are derived for estimating the possibly nonminimum phase ma coefficient matrices

differential fast fixed point algorithms for underdetermined instantaneous and convolutive partial blind source separation this paper concerns underdetermined linear instantaneous and convolutive blind source separation bss i e the case when the number of observed mixed signals is lower than the number of sources we propose partial bss methods which separate supposedly nonstationary sources of interest while keeping residual components for the other supposedly stationary ldquonoiserdquo sources these methods are based on the general differential bss concept that we introduced before in the instantaneous case the approach proposed in this paper consists of a differential extension of the fastica method which does not apply to underdetermined mixtures in the convolutive case we extend our recent time domain fast fixed point c fica algorithm to underdetermined mixtures both proposed approaches thus keep the attractive features of the fastica and c fica methods our approaches are based on differential sphering processes followed by the optimization of the differential nonnormalized kurtosis that we introduce in this paper experimental tests show that these differential algorithms are much more robust to noise sources than the standard fastica and c fica algorithms

blind source separation by sparse decomposition in a signal dictionary the blind source separation problem is to extract the underlying source signals from a set of linear mixtures where the mixing matrix is unknown this situation is common in acoustics radio medical signal and image processing hyperspectral imaging and other areas we suggest a two stage separation process a priori selection of a possibly overcomplete signal dictionary for instance a wavelet frame or a learned dictionary in which the sources are assumed to be sparsely representable followed by unmixing the sources by exploiting the their sparse representability we consider the general case of more sources than mixtures but also derive a more efficient algorithm in the case of a nonovercomplete dictionary and an equal numbers of sources and mixtures experiments with artificial signals and musical sounds demonstrate significantly better separation than other known techniques

the transformation between the galois nlfsrs and the fibonacci nlfsrs via semi tensor product of matrices in this paper a novel way is proposed to investigate the transformation between galois nonlinear feedback shift registers nlfsrs and fibonacci nlfsrs first the galois nlfsrs and fibonacci nlfsrs are regarded as two boolean networks bns and the corresponding algebraic equations are obtained based on semi tensor product stp of matrices then the definition of absolute equivalence is given to investigate the transformation between these two kinds of nlfsrs furthermore some interesting results are presented to achieve the transformation finally an example is provided to illustrate the effectiveness of obtained results

comparing measures of sparsity sparsity of representations of signals has been shown to be a key concept of fundamental importance in fields such as blind source separation compression sampling and signal analysis the aim of this paper is to compare several commonly used sparsity measures based on intuitive attributes intuitively a sparse representation is one in which a small number of coefficients contain a large proportion of the energy in this paper six properties are discussed robin hood scaling rising tide cloning bill gates and babies each of which a sparsity measure should have the main contributions of this paper are the proofs and the associated summary table which classify commonly used sparsity measures based on whether or not they satisfy these six propositions only two of these measures satisfy all six the pq mean with p les 1 q 1 and the gini index

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

linear quadratic blind source separation using nmf to unmix urban hyperspectral images in this work we propose algorithms to perform blind source separation bss for the linear quadratic mixing model the linear quadratic model is less studied in the literature than the linear one in this paper we propose original methods that are based on non negative matrix factorization nmf this class of methods is well suited to many applications where the data are non negative we are here particularly interested in spectral unmixing extracting reflectance spectra of materials present in pixels and associated abundance fractions for urban hyperspectral images the originality of our work is that we developed extensions of nmf which is initially suited to the linear model for the linear quadratic model the proposed algorithms are tested with simulated hyperspectral images using real reflectance spectra and the obtained results are very satisfactory

the synchronized short time fourier transform properties and definitions for multichannel source separation this paper proposes the use of a synchronized linear transform the synchronized short time fourier transform sstft for time frequency analysis of anechoic mixtures we address the short comings of the commonly used time frequency linear transform in multichannel settings namely the classical short time fourier transform cstft we propose a series of desirable properties for the linear transform used in a multichannel source separation scenario stationary invertibility relative delay relative attenuation and finally delay invariant relative windowed disjoint orthogonality dirwdo multisensor source separation techniques which operate in the time frequency domain have an inherent error unless consideration is given to the multichannel properties proposed in this paper the sstft preserves these relationships for multichannel data the crucial innovation of the sstft is to locally synchronize the analysis to the observations as opposed to a global clock improvement in separation performance can be achieved because assumed properties of the time frequency transform are satisfied when it is appropriately synchronized numerical experiments show the sstft improves instantaneous subsample relative parameter estimation in low noise conditions and achieves good synthesis

sequential blind identification of underdetermined mixtures using a novel deflation scheme in this brief we consider the problem of blind identification in underdetermined instantaneous mixture cases where there are more sources than sensors a new blind identification algorithm which estimates the mixing matrix in a sequential fashion is proposed by using the rank 1 detecting device blind identification is reformulated as a constrained optimization problem the identification of one column of the mixing matrix hence reduces to an optimization task for which an efficient iterative algorithm is proposed the identification of the other columns of the mixing matrix is then carried out by a generalized eigenvalue decomposition based deflation method the key merit of the proposed deflation method is that it does not suffer from error accumulation the proposed sequential blind identification algorithm provides more flexibility and better robustness than its simultaneous counterpart comparative simulation results demonstrate the superior performance of the proposed algorithm over the simultaneous blind identification algorithm

a frequency bin wise nonlinear masking algorithm in convolutive mixtures for speech segregation a frequency bin wise nonlinear masking algorithm is proposed in the spectrogram domain for speech segregation in convolutive mixtures the contributive weight from each speech source to a time frequency unit of the mixture spectrogram is estimated by a nonlinear function based on location cues for each sound source a non binary mask is formed from the estimated weights and is multiplied to the mixture spectrogram to extract the sound head related transfer functions hrtfs are used to simulate convolutive sound mixtures perceived by listeners simulation results show our proposed method outperforms convolutive independent component analysis and degenerate unmixing and estimation technique methods in almost all test conditions

two microphone separation of speech mixtures based on interclass variance maximization sparse methods for speech separation have become a discussed issue in acoustic signal processing these sparse methods provide a powerful approach to the separation of several signals in the underdetermined case i e when there are more sources than sensors in this paper a two microphone separation method is presented the proposed algorithm is based on grouping time frequency points with similar direction of arrival doa using a multi level thresholding approach the thresholds are calculated via the maximization of the interclass variance between doa estimates and allow to identify angular sections wherein the speakers are located with a strong likelihood these sections define a set of time frequency masks that are able to separate several sound sources in realistic scenarios and with little computational cost several experiments carried out under different mixing situations are discussed showing the validity of the proposed approach

blind separation of quasi stationary sources exploiting convex geometry in covariance domain this paper revisits blind source separation of instantaneously mixed quasi stationary sources bss qss motivated by the observation that in certain applications e g speech there exist time frames during which only one source is active or locally dominant combined with nonnegativity of source powers this endows the problem with a nice convex geometry that enables elegant and efficient bss solutions local dominance is tantamount to the so called pure pixel separability assumption in hyperspectral unmixing nonnegative matrix factorization respectively building on this link a very simple algorithm called successive projection algorithm spa is considered for estimating the mixing system in closed form to complement spa in the specific bss qss context an algebraic preprocessing procedure is proposed to suppress short term source cross correlation interference the proposed procedure is simple effective and supported by theoretical analysis solutions based on volume minimization volmin are also considered by theoretical analysis it is shown that volmin guarantees perfect mixing system identifiability under an assumption more relaxed than exact local dominance which means wider applicability in practice exploiting the specific structure of bss qss a fast volmin algorithm is proposed for the overdetermined case careful simulations using real speech sources showcase the simplicity efficiency and accuracy of the proposed algorithms

blind identification of overcomplete mixtures of sources biome the problem of blind identification of linear mixtures of independent random processes is known to be related to the diagonalization of some tensors this problem is posed here in terms of a non conventional joint approximate diagonalization of several matrices in fact a congruent transform is applied to each of these matrices the left transform being rectangular full rank and the right one being unitary the application in antenna signal processing is described and suboptimal numerical algorithms are proposed

an adaptive method for subband decomposition ica subband decomposition ica sdica an extension of ica assumes that each source is represented as the sum of some independent subcomponents and dependent subcomponents which have different frequency bands in this article we first investigate the feasibility of separating the sdica mixture in an adaptive manner second we develop an adaptive method for sdica namely band selective ica bs ica which finds the mixing matrix and the estimate of the source independent subcomponents this method is based on the minimization of the mutual information between outputs some practical issues are discussed for better applicability a scheme to avoid the high dimensional score function difference is given third we investigate one form of the overcomplete ica problems with sources having specific frequency characteristics which bs ica can also be used to solve experimental results illustrate the success of the proposed method for solving both sdica and the overcomplete ica problems