power series quantization for noisy channels a recently proposed method for transmission of correlated sources under noise free conditions power series quantization psq uses a separate linear or nonlinear predictor for each quantizer region and has shown to increase performance compared to several common quantization schemes for sources with memory in this paper it is shown how to apply psq for transmission of a source with memory over a noisy channel a channel optimized psq copsq encoder and codebook optimization algorithms are derived the suggested scheme is shown to increase performance compared with previous state of the art methods

tutorial on seismic interferometry part 2 underlying theory and new advances in the 1990s the method of time reversed acoustics was developed this method exploits the fact that the acoustic wave equation for a lossless medium is invariant for time reversal when ultrasonic responses recorded by piezoelectric transducers are reversed in time and fed simultaneously as source signals to the transducers they focus at the position of the original source even when the medium is very complex in seismic interferometry the time reversed responses are not physically sent into the earth but they are convolved with other measured responses the effect is essentially the same the time reversed signals focus and create a virtual source which radiates waves into the medium that are subsequently recorded by receivers a mathematical derivation based on reciprocity theory formalizes this principle the crosscorrelation of responses at two receivers integrated over different sources gives the green s function emitted by a virtual source at the position of one of the receivers and observed by the other receiver this green s function representation for seismic interferometry is based on the assumption that the medium is lossless and nonmoving recent developments circumventing these assumptions include interferometric representations for attenuating and or moving media as well as unified representations for waves and diffusion phenomena bending waves quantum mechanical scattering potential fields elastodynamic electromagnetic poroelastic and electroseismic waves significant improvements in the quality of the retrieved green s functions have been obtained with interferometry by deconvolution a trace by trace deconvolution process compensates for complex source functions and the attenuation of the medium interferometry by multidimensional deconvolution also compensates for the effects of one sided and or irregular illumination

predictor corrector iterative algorithms for solving generalized mixed variational like inequalities the concept of partially relaxed h strong monotonicity of set valued mappings is introduced by applying the auxiliary variational inequality technique some new predictor corrector iterative algorithms for solving generalized mixed variational like inequalities are suggested and analyzed the convergence of the algorithms only need the continuity and the partially relaxed h strongly monotonicity of set valued mappings the algorithm and convergence result are new and generalize some known results in literatures

generalized strongly nonlinear quasivariational inequalities abstract in this paper we prove an existence theorem of solutions and develop the algorithms of approximating solutions for a generalized strongly nonlinear variational inequality and a generalized strongly nonlinear complementarity problem an algorithm for finding the approximate solution of generalized strongly nonlinear quasivariational inequalities and generalized strongly nonlinear quasicomplementarity problems is also given as special cases these results include the corresponding results of fang and peterson noor siddiqi and ansari and ding

an iterative method for generalized set valued nonlinear mixed quasi variational inequalities this paper presents an iterative method for solving the generalized nonlinear set valued mixed quasi variational inequality a problem class that was introduced by huang et al comp math appl 40 2 3 2000 205 215 the method incorporates step size controls that enable application to problems where certain set valued mappings do not always map to nonempty closed bounded sets

robust linear channel estimation methods for per subcarrier transmit antenna selection the linear minimum mean squared error mse channel estimator for systems employing per subcarrier transmit antenna selection is developed frequency domain correlations after the selection process are shown to be approximated well using a simple function which makes near optimal channel estimation practically possible however the resultant estimators are not robust to errors in the assumed model in terms of the antenna to subcarrier assignment used at transmission as we motivate both analytically and via simulations this is an issue when the channels for conveying this information to the receiver are severely constrained we present windowed channel estimations and new robust estimation algorithms either individually or in combination as solutions to counter this sensitivity the robust estimators are developed based on the principles of 1 minimising the worst case mse over the set of possible models and 2 minimising the expected mse over the set of possible models the latter estimator is preferred due to the lower implementation complexity and better mse performance we also prove theoretical asymptotic in signal to noise ratio performances of the two estimators used with the proposed correlation model simulation results illustrate the performance gains and improved robustness offered by the developed estimators

predictor corrector iterative algorithms for solving generalized mixed quasi variational like inclusion by applying the concept of partially relaxed h strong monotonicity of set valued mappings due to author and the auxiliary variational inequality technique some new predictor corrector iterative algorithms for solving generalized mixed quasi variational like inclusions are suggested and analyzed the convergence of the algorithms only need the continuity and the partially relaxed h strongly monotonicity of set valued mappings the algorithm and convergence result are new and generalize some recent known results in literatures

wave equation processing using finite difference propagators part 1 wavefield dissection and imaging of marine multicomponent seismic data abstractmethods for wavefield injection are used in for instance reverse time extrapolation of shot gathers in reverse time migration for correct injection of recorded data without any ambiguity of the propagation direction the wavefield injection methodology requires pressure and particle velocity data such as multicomponent towed marine or seabed seismic recordings we discovered that by carefully considering the models medium parameters and boundary conditions for injection wavefield injection of multicomponent data can also be used to solve several long standing challenges in marine seismic data processing by means of conventional time space domain finite difference propagators we outlined and demonstrated several of these important applications including up down separation of wavefields deghosting direct wave removal source signature estimation multiple removal and imaging using primaries and multiples only acoustic models are considered but the concepts are straightforward to general

algorithm of solutions for mixed implicit quasi variational inequalities with fuzzy mappings abstract in this paper by applying the auxiliary variational principle technique some existence theorems of solutions for a class of mixed implicit quasi variational inequalities with fuzzy mappings are proved in hilbert spaces a novel and innovative iterative algorithm to compute approximate solutions is suggested and analyzed the convergence criteria is also given as special cases of these results the open problem put forward by noor is answered positively our results are new and generalize a number of known results to mixed implicity quasi variational inequalities with fuzzy mappings

a new class of completely generalized quasi variational inclusions in banach spaces in this paper a new notion of j proximal mapping for a nonconvex lower semicontinuous subdifferentiable proper functional on banach space is introduced the existence and lipschitz continuity of j proximal mapping of a lower semicontinuous subdifferentiable proper functional are proved by applying the concept we introduce and study a new class of completely generalized quasi variational inclusions in reflexive banach spaces a novel and innovative iterative algorithm for finding the approximate solutions is suggested and analyzed the convergence criteria of the iterative sequences generated by the new iterative algorithm is also given these algorithm and existence result generalize many known results under hilbert space setting in recent literature to reflexive banach spaces

fuzzy nonlinear set valued variational inclusions the purpose of this paper is to study a new class of fuzzy nonlinear set valued variational inclusions in real banach spaces by using the fuzzy resolvent operator techniques for m accretive mappings we establish the equivalence between fuzzy nonlinear set valued variational inclusions and fuzzy resolvent operator equation problem applying this equivalence and nadler s theorem we suggest some iterative algorithms for solving fuzzy nonlinear set valued variational inclusions in real banach spaces by using the inequality of petryshyn the existence of solutions for these kinds of fuzzy nonlinear set valued variational inclusions without compactness is proved and convergence criteria of iterative sequences generated by the algorithm are also discussed

three step iterative algorithms for solving the system of generalized mixed quasi variational like inclusions in this paper we consider the system of generalized mixed quasi variational like inclusions in hilbert spaces we extend the auxiliary principle technique to develop a three step iterative algorithm for solving the system of generalized mixed quasi variational like inclusions under the assumptions of the continuity and partially relaxed h strong monotonicity of set valued mappings we establish the convergence for our algorithm our algorithm and its convergence results are new and generalize ding s predictor corrector iterative algorithms moreover our results unify some known results in the literature as well

exact wave field simulation for finite volume scattering problems an exact boundary condition is presented for scattering problems involving spatially limited perturbations of arbitrary magnitude to a background model in generally inhomogeneous acoustic media the boundary condition decouples the wave propagation on a perturbed domain while maintaining all interactions with the background model thus eliminating the need to regenerate the wave field response on the full model the method which is explicit relies on a kirchhoff type integral extrapolation to update the boundary condition at every time step of the simulation the green s functions required for extrapolation through the background model are computed efficiently using wave field interferometry

algorithms of solutions for completely generalized mixed implicit quasi variational inclusions in this paper by applying resolvent operator technique an existence theorem of solutions for a class of completely generalized mixed implicit quasi variational inclusions involving relaxed lipschitz generalized pseudo contractive and relaxed monotone set valued mappings is proved in hilbert spaces a novel and innovative iterative algorithm to compute approximate solutions is suggested and analyzed the convergence criteria is also given these results of existence algorithm and convergence are new and unify and generalize some corresponding results in recent literatures

a model independent finite difference method for removal of free surface generated multiples abstractwe have developed a new data driven method to solve the free surface related multiple problem using forward space time domain modeling we build a finite difference fd modeling engine in which the interaction of any desired wavefield with an unknown subsurface but without the reflecting sea surface on top can be modeled this can either be used to predict the desired primary reflections of the subsurface or to simultaneously model all orders of free surface related multiples in this method we use conventional forward modeling techniques in combination with a novel type of boundary condition the wave propagation in the known part of the medium i e the homogeneous water layer is modeled on an fd grid whereas the wave propagation in the unknown part of the medium i e the subsurface is predicted using the recorded multicomponent data the methodology dynamically links these two distinct domains we demonstrate the exact method and an adapted version that works under realistic conditi

auxiliary principle for generalized mixed variational like inequalities abstract in this paper we use the auxiliary principle technique to prove the existence of a solution of some new classes of variational inequalities which are called the generalized mixed variational like inequalities this technique is used to analyze an iterative algorithm several special cases which can be obtained from our main result are also discussed

strongly nonlinear quasivariational inequalities abstract in this paper we develop the algorithms for finding the approximate solution of a strongly nonlinear quasivariational inequality and a strongly nonlinear quasicomplementarity problem and we include as special cases known results in this area we also observe that these two problems are equivalent if the convex set involved in the formulation of these problem is a convex cone

the design of joint source and channel trellis waveform coders the generalized lloyd algorithm is applied to the design of joint source and channel trellis waveform coders to encode discrete time continuous amplitude stationary and ergodic sources operating over discrete memoryless noisy channels experimental results are provided for independent and autoregressive gaussian sources binary symmetric channels and absolute error and squared error distortion measures performance of the joint codes is compared with the tandem combination of a trellis source code and a trellis channel code on the independent gaussian source using the squared error distortion measure operating over an additive white gaussian noise channel it is observed that the jointly optimized codes achieve performance close to or better than that of separately optimized tandem codes of the same constraint length performance improvement via a predictive joint source and channel trellis code is demonstrated for the autoregressive gaussian source using the squared error distortion measure

on random variational inclusions with random fuzzy mappings and random relaxed cocoercive mappings in this paper we introduce and study the random variational inclusions with random fuzzy and random relaxed cocoercive mappings we define an iterative algorithm for finding the approximate solutions of this class of variational inclusions and establish the convergence of iterative sequences generated by proposed algorithm our results improve and generalize many known corresponding results

on finite state vector quantization for noisy channels finite state vector quantization fsvq over a noisy channel is studied a major drawback of a finite state decoder is its inability to track the encoder in the presence of channel noise in order to overcome this problem we propose a nontracking decoder which directly estimates the code vectors used by a finite state encoder the design of channel matched finite state vector quantizers for noisy channels using an iterative scheme resembling the generalized lloyd algorithm is also investigated simulation results based on encoding a gauss markov source over a memoryless gaussian channel show that the proposed decoder exhibits graceful degradation of performance with increasing channel noise as compared with a finite state decoder also the channel matched finite state vector quantizers are shown to outperform channel optimized vector quantizers having the same vector dimension and rate however the nontracking decoder used in the channel matched finite state quantizer has a higher computational complexity compared with a channel optimized vector quantizer decoder thus if they are allowed to have the same overall complexity encoding and decoding the channel optimized vector quantizer can use a longer encoding delay and achieve similar or better performance finally an example of using the channel matched finite state quantizer as a backward adaptive quantizer for nonstationary signals is also presented

the hadamard transform a tool for index assignment we show that the channel distortion for maximum entropy encoders due to noise on a binary symmetric channel is minimized if the vector quantizer can be expressed as a linear transform of a hypercube the index assignment problem is regarded as a problem of linearizing the vector quantizer we define classes of index assignments with related properties within which the best index assignment is found by sorting not searching two powerful algorithms for assigning indices to the codevectors of nonredundant coding systems are presented one algorithm finds the optimal solution in terms of linearity whereas the other finds a very good but suboptimal solution in a very short time

immersive boundary conditions theory implementation and examples abstractmany applications in computational geophysics involve the modeling of seismic wave propagation on a set of closely related subsurface models in such scenarios it is of interest to recompute the seismic wavefields locally only in the regions of change instead of in the full subsurface model we have developed a method for local acoustic wavefield recomputation that makes it possible to fully immerse a local modeling domain within a larger domain of arbitrary extent and complexity such that the wave propagation in the full domain is completely accounted for the method enables wavefield modeling on much smaller local domains while relying on the up front generation of a large number of green s functions and a wavefield extrapolation step at each time step of the simulation a kirchhoff helmholtz extrapolation integral is used to predict the interaction of the wavefield leaving the local domain with the exterior domain the outward propagating wavefield and the wavefield reentering the local d

trellis coded quantization designed for noisy channels trellis coded quantization tcq of memoryless sources is developed for transmission over a binary symmetric channel the optimized tcq coder can achieve essentially the same performance as ayanoglu and gray s 1987 unconstrained trellis coding optimized for the binary symmetric channel but with a much lower implementation complexity for transmission rates above 1 b sample in most cases the optimized tcq coder also provides larger signal to noise ratio than farvardin and vaishampayan s 1991 channel optimized vector quantization algorithms are developed for the joint design of trellis coded quantization modulation tcq tcm the jointly designed tcq tcm system outperforms the straightforward cascade of separately designed tcq and tcm systems the improvement is most significant at low channel signal to noise ratio for a first order gauss markov source the predictive tcq tcm performance can exceed that of optimum pulse amplitude modulation

rapid 4d fwi using a local wave solver much of the computational cost involved in full waveform inversion comes from the solution of the wave equation in a large domain these computations must be done for the entire domain through which we expect waves to pass for a particular survey despite the fact that our region of interest is often significantly smaller in addition to the wasted time spent propagating waves through less important parts of the model computing updates on the entire domain may result in slower convergence of the inversion algorithm due to the larger model space this can be especially important in 4d seismic monitoring where we often see the majority of changes within a small subregion of the total domain such as the reservoir we present a local wave solver that accurately computes the solution of the wave equation within only a subdomain of the region covered by the survey representing a significant cost saving in the computation of full waveform inversion we also show how this solver can improve the resulting velocity estimates in full waveform inversion for time lapse applications and observe that the local solver requires fewer iterations to converge than does the full domain solver

an iterative method for generalized nonlinear set valued mixed quasi variational inequalities with h monotone mappings generalized nonlinear set valued mixed quasi variational inequalities with maximal monotone mappings have been studied in the literature since the maximal monotone operators are known as special cases of the h monotone operators we generalize the study for these inequality problems with h monotone mappings in this paper precisely we establish the existence of solutions for the generalized nonlinear set valued mixed quasi variational inequality problem with h monotone mappings an iterative method for finding their approximate solutions are proposed furthermore a new convergence criteria for this iterative method is imposed

on the performance and complexity of channel optimized vector quantizers the performance and complexity of channel optimized vector quantizers are studied for the gauss markov source observations on the geometric structure of these quantizers are made which have an important implication on the encoding complexity for the squared error distortion measure it is shown that an operation equivalent to a euclidean distance measurement with respect to an appropriately defined set of points used to identify the encoding regions can be used to perform the encoding this implies that the encoding complexity is proportional to the number of encoding regions it is then demonstrated that for very noisy channels and a heavily correlated source when the codebook size is large the number of encoding regions is considerably smaller than the codebook size implying a reduction in encoding complexity

perturbed ishikawa type iterative algorithm for generalized quasivariational inclusions in this paper a perturbed ishikawa type iterative algorithm of solving a new class of generalized nonlinear implicit quasivariational inclusions are suggested and analysed first an existence theorem of solutions for generalized nonlinear implicit quasivariational inclusion is proved under suitable assumptions then the convergence of the perturbed ishikawa type iterative sequences generated by the new algorithm is proved as special cases some known results are also discussed

a simple and exact acoustic wavefield modeling code for data processing imaging and interferometry applications improvements in industrial seismic seismological acoustic or interferometric theory and applications often result in quite subtle changes in sound quality seismic images or information which are nevertheless crucial for improved interpretation or experience when evaluating new theories and algorithms using synthetic data an important aspect of related researchisthereforethat numerical errors due towavefield modeling are reduced to a minimum we present a new matlab code based on the foldy method that models theoretically exact direct and scattered parts of a wavefield its main advantage lies in the fact that while all multiple scattering interactions aretakenintoaccount unlikefinite difference or finite element methods numerical dispersion errors are avoided the method is therefore ideal for testing new theory in industrial seismics seismology acoustics and in wavefield interferometry in particular because the latter is particularly sensitive to the dynamics of scattering interactions we present the theory behind the foldy acoustic modeling method and provide examples of its implementation we also benchmark the code against a good finite difference code because our foldy code was written and optimized to test new theory in seismic interferometry examples of its application to seismicinterferometryarealsopresented showing its validity and importance when exact modeling results are needed

iterative algorithms for solving generalized nonlinear mixed variational inequalities a new concept of g partially relaxed strong monotonicity of mappings is introduced by applying the auxiliary variational inequality technique some new predictor corrector iterative algorithms for solving generalized nonlinear mixed variational inequalities are suggested and analyzed the convergence of the algorithms only need the continuity and the g partially relaxed strongly monotonicity of mappings these algorithms and convergence result are new and generalize some known results in literature

generalized complementarity problems for fuzzy mappings in this paper we introduce a new class of generalized complementarity problems for the fuzzy mappings and construct a new iterative algorithm we also discuss the existence of solutions for the generalized complementarity problems and the convergence of iterative sequence

vector predictive coding of speech at 16 kbits s vector quantization in its simplest form may be regarded as a generalization of pcm independent quantization of each sample of a waveform to what might be called vector pcm where a block of consecutive samples a vector is simultaneously quantized as one unit in theory a performance arbitrarily close to the ultimate rate distortion limit is achievable with waveform vector quantization if the dimension of the vector k is large enough the main obstacle in effectively using vector quantization is complexity a vector quantizer of dimension k operating at a rate of r bits sample requires a number of computations on the order of k2 kr and a memory of the same order however a low dimensional vector quantizer dimensions 4 8 achieves a remarkable improvement over scalar quantization pcm consequently using the vector quantizer as a building block and imbedding it with other waveform data compression techniques may lead to the development of a new and powerful class of waveform coding systems this paper proposes and analyzes a waveform coding system adaptive vector predictive coding avpc in which a low dimensionality vector quantizer is used in an adaptive predictive coding scheme in the encoding process a locally generated prediction of the current input vector is subtracted from the current vector and the resulting error vector is coded by a vector quantizer each frame consisting of many vectors is classified into one of m statistical types this classification determines which one of m fixed predictors and of m vector quantizers will be used for encoding the current frame

variational inequalities for fuzzy mappings ii in this paper we establish the equivalence between the variational inequalities for fuzzy mappings and the wiener hopf equations for fuzzy mappings we use this equivalence to suggest a number of new iterative algorithms for solving the variational inequalities for fuzzy mappings we also study the convergence criteria of these iterative algorithms the auxiliary principle technique is used to study the existence of a solution of the variational inequality for fuzzy mappings and to suggest a novel and innovative iterative algorithm for computing the approximate solution the results proved in this paper represent an improvement of previously known results

parametric completely generalized mixed implicit quasi variational inclusions involving h maximal monotone mappings a new class of parametric completely generalized mixed implicit quasi variational inclusions involving h maximal monotone mappings is introduced by applying resolvent operator technique of h maximal monotone mapping and the property of fixed point set of set valued contractive mappings the behavior and sensitivity analysis of the solution set of the parametric completely generalized mixed implicit quasi variational inclusions involving h maximal monotone mappings are studied the continuity and lipschitz continuity of the solution set with respect to the parameter are proved under suitable assumptions our approach and results are new and improve unify and extend previous many known results in this field

modeling of wave propagation in inhomogeneous media we present a methodology providing a new perspective on modeling and inversion of wave propagation satisfying time reversal invariance and reciprocity in generally inhomogeneous media the approach relies on a representation theorem of the wave equation to express the green function between points in the interior as an integral over the response in those points due to sources on a surface surrounding the medium following a predictable initial computational effort green s functions between arbitrary points in the medium can be computed as needed using a simple cross correlation algorithm

a new class of generalized nonlinear implicit quasivariational inclusions with fuzzy mappings in this paper we introduce and study a new class of generalized nonlinear implicit quasivariational inclusions with fuzzy mappings an existence theorem of solutions is proved without compactness assumptions a new iterative algorithm is suggested and analysed the convergence of iterative sequences generated by the new algorithm are also given as special cases some known results are also discussed

fundamentals of statistical signal processing estimation theory minimum variance unbiased estimation cramer rao lower bound linear models general minimum variance unbiased estimation best linear unbiased estimators maximum likelihood estimation least squares method of moments the bayesian philosophy general bayesian estimators linear bayesian estimators kalman filters summary of estimators extension for complex data and parameters

iterative algorithm for generalized quasi variational like inclusions with fuzzy mappings in banach spaces in this paper we introduce and study a class of generalized quasi variational like inclusions with fuzzy mappings in real banach spaces and show its equivalence with a class of relations using this equivalence we suggest and analyze an iterative algorithm for finding the approximate solution of this class of inclusions further we prove the existence of solution of this class of inclusions and discuss the convergence criteria of iterative sequences generated by the iterative algorithm

gradient algorithms for designing predictive vector quantizers a predictive vector quantizer pvq is a vector extension of a predictive quantizer it consists of two parts a conventional memoryless vector quantizer vq and a vector predictor two gradient algorithms for designing a pvq are developed in this paper the steepest descent sd algorithm and the stochastic gradient sg algorithm both have the property of improving the quantizer and the predictor in the sense of minimizing the distortion as measured by the average mean squared error the differences between the two design approaches are the period and the step size used in each iteration to update the codebook and predictor the sg algorithm updates once for each input training vector and uses a small step size while the sd updates only once for a long period possibly one pass over the entire training sequence and uses a relatively large step size code designs and tests are simulated for both gauss markov sources and for sampled speech waveforms and the results are compared to codes designed using techniques that attempt to optimize only the quantizer for the predictor and not vice versa

some aspects of variational inequalities abstract in this paper we provide an account of some of the fundamental aspects of variational inequalities with major emphasis on the theory of existence uniqueness computational properties various generalizations sensitivity analysis and their applications we also propose some open problems with sufficient information and references so that someone may attempt solution s in his her area of special interest we also include some new results which we have recently obtained

a study of vector quantization for noisy channels several issues related to vector quantization for noisy channels are discussed an algorithm based on simulated annealing is developed for assigning binary codewords to the vector quantizer code vectors it is shown that this algorithm could result in dramatic performance improvements as compared to randomly selected codewords a modification of the simulated annealing algorithm for binary codeword assignment is developed for the case where the bits in the codeword are subjected to unequal error probabilities resulting from unequal levels of error protection an algorithm for the design of an optimal vector quantizer for a noisy channel is briefly discussed and its robustness under channel mismatch conditions is studied numerical results for a stationary first order gauss markov source and a binary symmetric channel are provided it is concluded that the channel optimized vector quantizer design algorithm if used carefully can result in a fairly robust system with no additional delay the case in which the communication channel is nonstationary as in mobile radio channels is studied and some preliminary ideas for quantizer design are presented

optimal nonlinear interpolative vector quantization a process by which a reduced dimensionality feature vector can be extracted from a high dimensionality signal vector and then vector quantized with lower complexity than direct quantization of the signal vector is discussed in this procedure a receiver must estimate or interpolate the signal vector from the quantized features the task of recovering a high dimensional signal vector from a reduced dimensionality feature vector can be viewed as a generalized form of interpolation or prediction a way in which optimal nonlinear interpolation can be achieved with negligible complexity eliminating the need for ad hoc linear or nonlinear interpolation techniques is presented the range of applicability of nonlinear interpolative vector quantization is illustrated with examples in which optimal nonlinear estimation from quantized data is needed for efficient signal compression

a hybrid bie born series modeling scheme generalized born series a new hybrid modeling method which incorporates boundary integral equations bie and a born series in solving multibody scattering problems is presented this method designated the gbs generalized born series is based on perturbing the surface boundary integral equation sbie matrix into a part to be easily inverted and another part that often need not be inverted at all under appropriate conditions the perturbed sbies can be efficiently solved by a born series hence gbs avoids the inversion of the relatively much larger sbie matrix of a pure bie method and can require several orders of magnitude fewer computations even greater efficiency can be achieved if the analytic green s function of a scatterer is known in this case the bies are used to compute the green s operators of the irregular scatterers and gbs efficiently couples them to the analytic green s operator the key advantage of gbs lies in interactive modeling once the green s operators of individual scatters are computed then the resp

fundamental limits of linear equalizers diversity capacity and complexity linear equalizers les have been widely adopted for practical systems due to their low computational complexity however it is also well known that les provide inferior performance relative to a maximum likelihood equalizer mle or other near mles because les usually cannot collect the diversity order enabled by the transmitter and at the same time they lose mutual information more importantly unlike mle or near mles the performance of les has not been well quantified this hinders more general applications of les in wireless systems in this paper we reveal a fundamental link between a channel parameter orthogonality deficiency od of the channel matrix and the diversity and capacity of les we identify that when the od of channel matrix has an upper bound strictly less than 1 the same diversity order as that of mles is collected by les and the outage capacity loss relative to mles is also a constant over signal to noise ratio snr these results can be applied to designing a framework for hybrid equalizers furthermore by studying the statistical properties of the od and comparing the complexity of different equalizers we show that hybrid equalizers can trade off between performance and complexity by tuning the channel matrix od the theoretical analysis is corroborated by computer simulations

general algorithm of solutions for nonlinear variational inequalities in banach space abstract in this paper we shall study a class of general nonlinear variational inequalities in reflexive banach spaces by applying a minimax inequality obtained by the author some existence uniqueness theorems of solutions for the general nonlinear variational inequalities are proved next by applying the auxiliary problem technique we suggest an innovative iterative algorithm to compute the approximate solutions of the general nonlinear variational inequality finally the convergence criteria is also discussed

on posterior distributions for signals in gaussian noise with unknown covariance matrix a bayesian approach to estimate parameters of signals embedded in complex gaussian noise with unknown color is presented the study specifically focuses on a bayesian treatment of the unknown noise covariance matrix making up a nuisance parameter in such problems by integrating out uncertainties regarding the noise color an enhanced ability to estimate both the signal parameters as well as properties of the error is exploited several noninformative priors for the covariance matrix such as the reference prior the jeffreys prior and modifications to this are considered some of the priors result in analytical solutions whereas others demand numerical approximations in the linear signal model connections are made between the standard adaptive maximum likelihood aml estimate and a bayesian solution using the jeffreys prior with adjustments to the jeffreys prior correspondence to the regularized solution is also established this in turn enables a formal treatment of the regularization parameter simulations indicate that significant improvements compared to the aml estimator can be obtained by considering both the derived regularized solutions as well as the one obtained using the reference prior the simulations also indicate the possibility of enhancing the predictions of properties of the error as uncertainties in the noise color are acknowledged

source receiver wave field interferometry correlation or convolution of recordings of diffuse fields at a pair of locations have been shown to result in estimates of the green s function between the two locations variously referred to as wave field or seismic interferometry in different fields of research green s functions can thus be constructed between either pairs of receivers or pairs of energy sources proofs of these results rely on representation theorems we show how to derive three acoustic and elastic representation theorems that unify existing correlational and convolutional approaches we thus derive three forms of interferometry that provide green s functions on source to receiver paths using only energy that has propagated from surrounding sources or to surrounding receivers the three forms correspond to three possible canonical geometries we thus allow interferometric theory and methods to be applied to commonly used source receiver configurations

hybrid fm polynomial phase signal modeling parameter estimation and cramer rao bounds parameter estimation for a class of nonstationary signal models is addressed the class contains combination of a polynomial phase signal pps and a frequency modulated fm component of the sinusoidal or hyperbolic type such signals appear in radar and sonar applications involving moving targets with vibrating or rotating components a novel approach is proposed that allows us to decouple estimation of the fm parameters from those of the pps relying on properties of the multilag high order ambiguity function ml haf the accuracy achievable by any unbiased estimator of the hybrid fm pps parameters is investigated by means of the cramer rao lower bounds crlbs both exact and large sample approximate expressions of the bounds are derived and compared with the performance of the proposed methods based on monte carlo simulations

existence results of a perturbed variational inequality with a fuzzy mapping abstract this paper is devoted to the existence of a perturbed variational inequality with a fuzzy mapping in a finite dimensional space we introduce two perturbation methods for a fuzzy mapping in one method it is perturbed by a nonlinear mapping and in the other it is perturbed by a vector under suitable coercivity conditions we show that the perturbed variational inequality with a fuzzy mapping has a solution when perturbed by both of the methods mentioned above the main results presented in this paper generalize and improve some known results