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

fast identification of state space models via exploitation of displacement structure the computational burden of state space model identification has prevented its real time application although it offers some important advantages over other methods based on input output transfer functions a recently proposed state space identification method uses ideas from sensor array signal processing to somewhat reduce the computational burden the major costs still remain because of the need for the singular value or sometimes qr decomposition which requires o mn sup 2 hops and o n sup 2 storage when the data matrix has size m spl times n n m it turns out that proper exploitation using results from the theory of displacement structure of the toeplitz like nature of several matrices arising in the procedure reduces the computational effort to o mn flops with o m sup 2 n storage further computational gains are made by using the recently developed fast subspace decomposition methods results of the study of an actual system are described

rational invariant subspace approximations with applications subspace methods such as music minimum norm and esprit have gained considerable attention due to their superior performance in sinusoidal and direction of arrival doa estimation but they are also known to be of high computational cost in this paper new fast algorithms for approximating signal and noise subspaces and that do not require exact eigendecomposition are presented these algorithms approximate the required subspace using rational and power like methods applied to the direct data or the sample covariance matrix several esprit as well as music type methods are developed based on these approximations a substantial computational saving can be gained comparing with those associated with the eigendecomposition based methods these methods are demonstrated to have performance comparable to that of music yet will require fewer computations to obtain the signal subspace matrix

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

a new perturbation analysis for signal enumeration in rotational invariance techniques the esprit algorithm is a subspace based high resolution method used in source localization and spectral analysis which provides very accurate estimates of the signal parameters however the underlying theory assumes a known model order which is usually not the case in many applications in particular it is well known that underevaluating the model order biases the estimation in this paper we analyze the perturbation induced by an erroneous model order and we present an error bound for the estimated parameters based on this theoretical framework we propose a new method for selecting an appropriate modeling order which consists in minimizing the error bound this approach is applied to both synthetic and musical signals and its performance is compared to that of existing methods such as the information theoretic criteria

linear concurrent constraint programming in this paper we give a logical semantics for the class cc of concurrent constraint programming languages and for its extension lcc based on linear constraint systems besides the characterization in intuitionistic logic of the stores of cc computations we show that both the stores and the successes of lcc computations can be characterized in intuitionistic linear logic we illustrate the usefulness of these results by showing with examples how the phase semantics of linear logic can be used to give simple semantical proofs of safety properties of lcc programs

esprit estimation of signal parameters via rotational invariance techniques an approach to the general problem of signal parameter estimation is described the algorithm differs from its predecessor in that a total least squares rather than a standard least squares criterion is used although discussed in the context of direction of arrival estimation esprit can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise it exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure the technique when applicable manifests significant performance and computational advantages over previous algorithms such as mem capon s mlm and music

direction of arrival estimation for constant modulus signals in many cases where direction finding is of interest the signals impinging on an antenna array are known to be phase modulated and hence to have a constant modulus cm this is a strong property by itself it is already sufficient for source separation and can be used to construct improved direction finding algorithms we first derive the relevant cramer rao bounds crbs for arbitrary array configurations and specialize to uniform linear arrays we then propose a simple suboptimal direction estimation algorithm in which the signals are separated using the cm property followed by direction finding on the decoupled signals compared with the esprit algorithm and the crb for arbitrary signals the algorithm shows good results

bifurcations of planar hamiltonian systems with impulsive perturbation in this paper by means of the melnikov functions we consider bifurcations of harmonic or subharmonic solutions from a periodic solution of a planar hamiltonian system under impulsive perturbation we give some sufficient conditions under which a harmonic or subharmonic solution exists

analytical performance assessment of multi dimensional matrix and tensor based esprit type algorithms in this paper we present a generic framework for the asymptotic performance analysis of subspace based parameter estimation schemes it is based on earlier results on an explicit first order expansion of the estimation error in the signal subspace obtained via an svd of the noisy observation matrix we extend these results in a number of aspects firstly we demonstrate that an explicit first order expansion of the higher order svd hosvd based subspace estimate can be derived secondly we show how to obtain explicit first order expansions of the estimation error of arbitrary esprit type algorithms and provide the expressions for r d standard esprit r d unitary esprit r d standard tensor esprit as well as r d unitary tensor esprit thirdly we derive closed form expressions for the mean square error mse and show that they only depend on the second order moments of the noise hence to apply this framework we only need the noise to be zero mean and possess finite second order moments additional assumptions such as gaussianity or circular symmetry are not needed

estimating the parameters of exponentially damped sinusoids and pole zero modeling in noise we have presented techniques 1 6 based on linear prediction lp and singular value decomposition svd for accurate estimation of closely spaced frequencies of sinusoidal signals in noise in this note we extend these techniques to estimate the parameters of exponentially damped sinusoidal signals in noise the estimation procedure presented here makes use of backward prediction in addition to svd first the method is applied to data consisting of one and two exponentially damped sinusoids the choice of one and two signal components facilitates the comparison of estimation error in pole damping factors and pole frequencies to the appropriate cramer rao cr bounds and to traditional methods of linear prediction second our method is applied to an example due to steiglitz 8 in which the data consists of noisy values of the impulse response samples composed of many exponentially damped sinusoids of a linear system having both poles and zeros the poles of the system are accurately determined by our method and the zeros are obtained subsequently using shanks method

esprit a subspace rotation approach to estimation of parameters of cisoids in noise the application of a subspace invariance approach esprit to the estimation of parameters frequencies and powers of cisoids in noise is described esprit exploits an underlying rotational invariance of signal subspaces spanned by two temporally displaced data sets the new approach has several advantages including improved resolution over pisarenko s technique for harmonic retrieval

frequency domain prony method for autoregressive model identification and sinusoidal parameter estimation in this study the weighted integral method for identifying differential equation models is extended to a discrete time system with a difference equation de model and a finite length sampled data sequence and obtain a frequency domain algorithm for short time signal analysis and frequency estimation the derivation consists of three steps 1 provide the de autoregressive model with unknown coefficients which is satisfied in a finite observation interval 2 discrete fourier transform dft the de to obtain algebraic equations aes among the fourier coefficients two mathematical techniques are introduced to maintain the circulant nature of time shifts 3 simultaneously solve a sufficient number of aes with least squares criterion to obtain unknowns exactly when the driving term is absent or to obtain unknowns that minimize the driving power when it is present the methods developed enable a decomposed processing of identification and estimation in the frequency domain thus they will be suitable for maximizing statistical efficiency smallness of estimation error variance reducing the computational cost and use in a resolution enhanced time frequency analysis of real world signals the performance of the proposed methods are compared with those of several dft based methods and cramer rao lower bound also the interference effect and its reduction in frequency decomposed processing are examined

bearing estimation in the bispectrum domain a new array processing method is presented for bearing estimation based on the cross bispectrum of the array output data the method is based on the asymptotic distribution of cross bispectrum estimates and uses maximum likelihood theory it is demonstrated that when the noise additive sources are spatially correlated gaussian with unknown cross spectral matrix csm the cross bispectrum method provides better bearing estimates than the stochastic maximum likelihood method with known csm analytical studies and simulations are given to document the performance of the new method

subspace algorithms for error localization with quantized dft codes recently a class of real number bose chaudhuri hocquengem codes known as discrete fourier transform dft codes have been considered as joint source and channel codes for providing robustness to erasures and errors over wireless networks we propose three subspace algorithms for error localization with quantized dft codes the algorithms are similar to the music the minimum norm and the esprit algorithms used in array signal processing for direction of arrival estimation they provide different but related formulations of the error localizations by first partitioning a vector space into the channel error subspace and its orthogonal complement the noise subspace the locations of the errors are determined from either the error subspace eigenvectors or the noise subspace eigenvectors we also present a brief performance analysis of the localization error in terms of the perturbation of the error subspace due to quantization simulation results show that their localization performances are similar and they perform better than the coding theoretic approach over a broad range of channel error to quantization noise ratios

sensor array signal processing via a procrustes rotations based eigenanalysis of the esprit data pencil the authors propose the procrustes rotation based esprit algorithm pro esprit for estimating characteristic parameters associated with a scene of radiating sources given data derived from two identical sensor arrays the x and y arrays which are displaced relative to one another pro esprit uses the inherent redundancy built into the esprit array system to compensate for deviations from the ideal array model this redundancy is manifested by two fundamental properties of the noiseless x and y data matrices comprising the esprit data pencil a they have the same row space the signal subspace and 2 they have the same column space the source subspace these properties are utilized by invoking two primary tools 1 the solution to the procrustes problem for optimally approximating an invariant subspace rotation and 2 the total least squares method for perturbing each of two estimates of a common subspace in a minimal fashion until the two perturbed spaces are the same

on concurrent behaviors and focusing in linear logic abstract concurrent constraint programming ccp is a simple and powerful model of concurrency where processes interact by telling and asking constraints into a global store of partial information since its inception ccp has been endowed with declarative semantics where processes are interpreted as formulas in a given logic this allows for the use of logical machinery to reason about the behavior of programs and to prove properties of them nevertheless the logical characterization of ccp programs exhibits normally a weak level of adequacy since proofs in the logical system may not correspond directly to traces of the program in this paper we study different encodings from ccp into intuitionistic linear logic ill and we compare the level of adequacy attained in each by relying on a focusing discipline we show that it is possible to give a logical characterization to ccp with the highest level of adequacy moreover we show how to characterize maximal parallelism semantics for ccp by relying on a multi focusing discipline for ill these results besides giving proof techniques for ccp entail safe optimizations for the execution of ccp programs finally we show how to interpret ccp procedure calls as fixed points in ill thus opening the possibility of reasoning by induction about properties of ccp programs

performance comparison of subspace rotation and music methods for direction estimation the statistical performance of subspace rotation sr methods such as the toeplitz approximation method and a variant of esprit for direction estimation using arrays composed of matched sensor doublets is studied the distributional properties of these methods are established and a compact explicit formula for the covariance matrix of their estimation error is provided next using this formula and a similar formula for music covariance matrix it is shown that the sr methods are statistically less efficient than music at least for a sufficiently large number of snapshots the difference in statistical performance between the commonly used sr method and music may be substantial if the number of sensors in the array is large an optimally weighted sr method which may approach the music level of statistical performance for one direction parameter specified by the user is introduced

adaptive spatial domain forward backward predictors for bearing estimation adaptive algorithms are proposed for computing the coefficients of spatial domain predictors the method presented uses the least mean squares approach to compute the coefficients of the predictors realized by using the tapped delay line and the escalator structures the predictors presented differ from the conventional ones in that the relevant weights are updated such that the sum of the mean squared values of the forward and the backward prediction errors is minimized using the coefficients of such spatial domain predictors yields improved linear predictive spatial spectrums the algorithms are applied to the problem of estimating incident angles of multiple narrowband signals received by a linear array of sensors simulation results demonstrating the performances of the proposed methods are presented

a spatial logic for concurrency ii we present a modal logic for describing the spatial organization and the behavior of distributed systems in addition to standard logical and temporal operators our logic includes spatial operations corresponding to process composition and name hiding and a fresh name quantifier in part i of this work we study the fundamental semantic properties of our logic the focus of the present part ii is on proof theory the main contributions are a sequent based proof system for our logic and a proof of cut elimination for its first order fragment

p systems with minimal parallelism a current research topic in membrane computing is to find more realistic p systems from a biological point of view and one target in this respect is to relax the condition of using the rules in a maximally parallel way we contribute in this paper to this issue by considering the minimal parallelism of using the rules if at least a rule from a set of rules associated with a membrane or a region can be used then at least one rule from that membrane or region must be used without any other restriction e g more rules can be used but we do not care how many weak as it might look this minimal parallelism still leads to universality we first prove this for the case of symport antiport rules the result is obtained both for generating and accepting p systems in the latter case also for systems working deterministically then we consider p systems with active membranes and again the usual results are obtained universality and the possibility to solve np complete problems in polynomial time by trading space for time

matrix pencil method for estimating parameters of exponentially damped undamped sinusoids in noise a study of a matrix pencil method for estimating frequencies and damping factors of exponentially damped and or undamped sinusoids in noise is presented comparison of this method to a polynomial method svd prony method shows that the matrix pencil method and the polynomial method are two special cases of a matrix prediction approach and that the pencil method is more efficient in computation and less restrictive about signal probes it is found through perturbation analysis and simulation that for signals with unknown damping factors the pencil method is less sensitive to noise than the polynomial method an expression of the cramer rao bound for the exponential signals is presented

direction finding algorithms based on high order statistics two direction finding algorithms are presented for nongaussian signals which are based on the fourth order cumulants of the data received by the array the first algorithm is similar to music while the second is asymptotically minimum variance in a certain sense the first algorithm requires singular value decomposition of the cumulant matrix while the second is based on nonlinear minimization of a certain cost function the performance of the minimum variance algorithm can be assessed by analytical means at least for the case of discrete probability distributions of the source signals and spatially uncorrelated gaussian noise the numerical experiments performed seem to confirm the insensitivity of these algorithms to the gaussian noise parameters

an esprit like method for quadratic phase coupling estimation two algorithms are proposed for estimating the quadratically coupled frequency pairs qc pairs in a signal consisting of complex sinusoids in white noise three matrices are constructed from the complex third order cumulants of the noisy signal the second and third being time shifted versions of the first the list of coupled frequencies is obtained from the rank reducing numbers of the matrix pencil formed from the first matrix and either of the latter two the first algorithm then pairs these components by relating quadratic coupling to the intersection of generalized eigenspaces corresponding to two of these frequencies the coupling strengths are also obtained in terms of generalized eigenvectors in this intersection space the second algorithm constructs a two parameter matrix pencil using all the three matrices the rank reducing pairs of this pencil on the unit circle yield the qc pairs and the associated generalized eigenvectors the coupling strengths

markov based eigenanalysis method for frequency estimation this paper proposes an eigenanalysis based method for estimating the frequencies of complex valued sine waves the basic idea behind this method consists of using a set of linearly independent vectors that are orthogonal to the signal subspace spanned by the principal eigenvectors of the data covariance matrix exploiting that orthogonality condition gives an overdetermined system of linear equations the unknown parameters of which are uniquely related to the frequencies analytical expressions are derived for the covariances of the equation errors in the sample version of the aforementioned linear system of equations based on these expressions a markov like estimate of the unknown parameters is introduced which asymptotically with respect to either the number of data samples or the signal to noise ratio provides the minimum variance frequency estimates in a fairly large class of consistent estimators the paper includes monte carlo simulations that support the theoretical analysis results and show that those results may apply to scenarios with rather low values of the number of data samples and the signal to noise ratio

the performance of bearing estimation using spatial domain forward backward predictor with higher order statistics the modified forward backward linear predictor mfblp methods with higher order statistics viz the second order statistics and the fourth order cumulants together with the broadband array structure are developed in this paper for bearing estimation here the desired source signals of interest are narrow band and the additive gaussian noise sources in the related sensors are assumed to be spatially correlated or uncorrelated with each other and white colored processes in the temporal domain in this paper the mfblp method with second order statistics for bearing estimation will be emphasized moreover to extract the principal eigenvalues of presented mfblp methods a new search algorithm is also proposed an analytical study of the mfblp method with second order statistics is first developed under the assumption that the additive noise is white gaussian process from the observation of analytic results a modification of the mfblp method with second order statistics is suggested from simulation re

a proof theoretic view of spatial and temporal dependencies in biochemical systems the behavior of biochemical systems such as metabolic and signaling pathways may depend on either the location of the reactants or on the time needed for a reaction to occur in this paper we propose a formalism for specifying and verifying properties of biochemical systems that combines coherently temporal and spatial modalities to this aim we consider a fragment of intuitionistic linear logic with subexponentials sell the subexponential signature allows us to capture the spatial relations among the different components of the system and the timed constraints we illustrate our approach by specifying some well known biological systems and verifying properties of them moreover we show that our framework is general enough to give a logic based semantics to p systems we show that the proposed logical characterizations have a strong level of adequacy hence derivations in sell follow exactly the behavior of the modeled system

perturbation analysis of subspace based methods in estimating a damped complex exponential we present a study of mode variance statistics for three svd based estimation methods in the case of a single mode damped exponential the methods considered are namely kumaresan tufts matrix pencil and kung s direct data approximation through first order perturbation analysis we derive closed form expressions of the variance of the complex mode frequency and damping factor estimates these expressions are used to compare the different methods and to determine the optimal prediction order for matrix pencil and direct data approximation methods application to the undamped case shows the coherence of the results with those already stated in the literature it is also found that the variances converge linearly towards the cramer rao bound finally the theoretical results are verified using monte carlo simulations