method of speech recognition using time dependent interpolation and hidden dynamic value classes a speech signal is decoded by determining a production related value for a current state based on an optimal production related value at the end of a preceding state the optimal production related value being selected from a set of continuous values the production related value is used to determine a likelihood of a phone being represented by a set of observation vectors that are aligned with a path between the preceding state and the current state the likelihood of the phone is combined with a score from the preceding state to determine a score for the current state the score from the preceding state being associated with a discrete class of production related values wherein the class matches the class of the optimal production related value

axisymmetric elastic waves in weakly coupled layered media of infinite radial extent abstract the propagation of axisymmetric waves in layered periodic elastic media of infinite radial extent is investigated hankel and laplace transforms are employed to convert radial and time dependence of displacement and stress within a layer to frequency and radial wavenumber continuity of stress and displacement is imposed at the interface between layers yielding transfer matrices the structure of propagation and attenuation zones pz s and az s of the system with infinite number of layers is studied when the ratios of shear and longitudinal mechanical impedances t l of the two layers are large the pz s of the layered system become narrow approaching certain limiting curves in the frequency wavenumber plane for large t l an asymptotic analysis is employed analytically to approximate the width of the pz s numerical computations of pz s are given which are in good agreement with the analytical predictions the spacing of the resonance points of a layered system with finite number of layers is found to depend mainly on the structure of the pz s of the corresponding system with an infinite number of layers

cepstral correction vector quantizer for speech recognition a method for correcting cepstral vectors representative of speech generated in a test environment by use of a vector quantization vq system with a codebook of vectors that was generated using speech and acoustic data from a different training environment the method uses a two step correction to produce test environment cepstral vectors with reduced non speech acoustic content the first correction step subtracts from the test vector a coarse correction vector that is computed from an average of test environment cepstral vectors the second step involves a vq of the coarsely corrected test vector at each node of the vq tree the third step is the addition of a fine correction vector to the coarsely corrected test vector that is generated by subtracting a running moving average of the coarsely corrected test vectors associated with the deepest vq tree node from the vq vector closest to the coarsely corrected test vector the method is independent of the means used to generate the cepstral vectors and the corrected output cepstra vectors may be used in various speech processing and classifying systems the method is adaptable to non stationary environments

recursive parameter estimation using incomplete data abstract stochastic approximation procedures are considered for the estimation of parameters using incomplete data one procedure is stated and illustrated which often leads to asymptotically efficient estimators others are developed which although possibly not optimal in the above sense will be very much easier to apply this will be particularly advantageous when quick recursive estimates are required examples are given and a link is made between one of the sub optimal methods and the em algorithm author

blind equalization of iir channels using hidden markov models and extended least squares in this paper we present a blind equalization algorithm for noisy iir channels when the channel input is a finite state markov chain the algorithm yields estimates of the iir channel coefficients channel noise variance transition probabilities and state of the markov chain unlike the optimal maximum likelihood estimator which is computationally infeasible since the computing cost increases exponentially with data length our algorithm is computationally inexpensive our algorithm is based on combining a recursive hidden markov model hmm estimator with a relaxed spr strictly positive real extended least squares els scheme in simulation studies we show that the algorithm yields satisfactory estimates even in low snr we also compare the performance of our scheme with a truncated fir scheme and the constant modulus algorithm cma which is currently a popular algorithm in blind equalization

diffusion models with blow up a number of physical situations including chemical reactions electrical heating and fluid flow give rise to nonlinear diffusion problems in this paper models are derived and results relating to the blow up of the solutions are given some of the proofs are outlined and the physical significance of the results is noted

spectrum estimation of short time stationary signals in additive noise and channel distortion in this work spectrum estimation of a short time stationary signal that is degraded by both channel distortion and additive noise is addressed a maximum likelihood estimation mle algorithm is developed to jointly identify the degradation system and estimate short time signal spectra the source signal is assumed to be generated by a hidden markov model hmm with state dependent short time spectral distributions described by mixtures of gaussian densities the distortion channel is linear time invariant and the noise is gaussian the algorithm is derived by using the principle of expectation maximization em where the unknown parameters of channel and noise are estimated iteratively and the short time signal power spectra are obtained from the posterior sufficient statistics of the source signal other spectral representation parameters such as autoregressive model parameters or cepstral parameters are obtained by minimum mean squared error mmse estimation from the power spectral estimates the estimation algorithm was evaluated on simulated signals at the signal to noise ratios snrs of 20 db down to 0 db where it produced convergent estimation and significantly reduced spectral distortion

adjoint optimization of one dimensional hyperbolic equations with constrained periodic boundary conditions abstract this paper presents a continuous adjoint based optimization theory for a general closed loop transport hyperbolic model controlled via a periodic boundary control to minimize a cost functional the periodic boundary control is subject to a nonlinear differential equation constraint thus resulting in a coupling between the hyperbolic equation and the ordinary differential equation variational principles are used to derive the pontryagin s minimum principle for optimality that results in a dual adjoint system a numerical optimization method is implemented using the adjoint based second order gradient method to solve for the optimal trajectory of the control numerical methods for solving the hyperbolic equation using an explicit scheme wave splitting method and for solving the adjoint equation using an implicit scheme and a quasi steady state method are described

method and apparatus for determining articulatory parameters from speech data a system and method for determining from continuous speech the instantaneous values of a set of articulatory parameters the continuous speech data is a sequence of spectral profiles obtained by spectrally sampling continuous speech the spectral samples are presented in sequence to a plurality of class transforms each establishing a respective speech phoneme class which includes plurality of speech phoneme having similar spectral and articulatory characteristics each class transform converts a speech segment included in its class and contained in a spectral sample into a predetermined set of articulatory parameter values a class discriminating transform operates in parallel with the class transforms to produce a set of probability values each indicating the probability that the spectral sample being transformed represents a phoneme in a respective speech phoneme class an array of multipliers adjusts the predetermined values of the sets produced by the class transforms by multiplying the values of each set by the probability value produced for that set by the class discriminating transform the adjusted articulatory parameter value sets are combined by adding corresponding elements to produce a set of adjusted articulatory parameter values indicative of an articulatory tract configuration appropriate for producing the sampled speech

method of noise reduction using correction and scaling vectors with partitioning of the acoustic space in the domain of noisy speech a method and apparatus are provided for reducing noise in a training signal and or test signal the noise reduction technique uses a stereo signal formed of two channel signals each channel containing the same pattern signal one of the channel signals is clean and the other includes additive noise using feature vectors from these channel signals a collection of noise correction and scaling vectors is determined when a feature vector of a noisy pattern signal is later received it is multiplied by the best scaling vector for that feature vector and the best correction vector is added to the product to produce a noise reduced feature vector under one embodiment the best scaling and correction vectors are identified by choosing an optimal mixture component for the noisy feature vector the optimal mixture component being selected based on a distribution of noisy channel feature vectors associated with each mixture component

maximum a posteriori estimation of time varying arma processes from noisy observations the estimation of the parameters of discrete time autoregressive moving average arms processes observed in white noise is considered a class of time varying arma processes in which the parameter process is the output of a known linear system driven by white gaussian noise is examined the maximum a posteriori map estimator is defined for the trajectory of the parameter s random process a two step estimate and maximize em based e step and m step iterative algorithm is derived the posterior probability of the parameters is increased in each iteration and convergence to stationary points of the posterior probability is guaranteed each iteration involves two linear systems and is easily implemented it is shown that similar results can be obtained for a wide class of parameter estimation problems

dynamic quantization and power allocation for multisensor estimation of hidden markov models this paper investigates an optimal quantizer design problem for multisensor estimation of a hidden markov model hmms whose description depends on unknown parameters the sensor measurements are simply binary quantized and transmitted to a remote fusion center over noisy flat fading wireless channels under an average sum transmit power constraint the objective is to determine a set of optimal quantization thresholds and sensor transmit powers called an optimal policy which minimizes the long run average of a weighted combination of the expected state estimation error and sum transmit power we analyze the problem by formulating an adaptive markov decision process mdp problem in this framework adaptive optimal control policies are obtained using a nonstationary value iteration nvi scheme and are termed as nvi adaptive policies these nvi adaptive policies are adapted to the hmm parameter estimates obtained via a strongly consistent maximum likelihood estimator in particular hmm parameter estimation is performed by a recursive expectation maximization em algorithm which computes estimates of the hmm parameters by maximizing a relative entropy information measure using the received quantized observations and the trajectory of the mdp under some regularity assumptions on the observation probability distributions and a geometric ergodicity condition on an extended markov chain the maximum likelihood estimator is shown to be strongly consistent it is shown that the nvi adaptive policy based on this sequence of strongly consistent hmm parameter estimates is asymptotically under appropriate assumptions average optimal essentially it minimizes the long run average cost of the weighted combination of the expected state estimation error and sum transmit power across the sensors for the hmm with true parameters in a time asymptotic sense the advantage of this scheme is that the policies are obtained recursively without the need to solve the bellman equation at each time step which can be computationally prohibitive as is usual with value iteration schemes practical implementation of the nvi adaptive policy requires discretization of the state and action space which results in some loss of optimality nevertheless numerical results illustrate the asymptotic convergence properties of the parameter estimates and the asymptotically close to optimal performance of the adaptive mdp algorithm compared to the performance of an mdp based dynamic quantization and power allocation algorithm designed with perfect knowledge of the true parameters

neumann boundary control of hyperbolic equations with pointwise state constraints we consider optimal control problems for hyperbolic equations with controls in neumann boundary conditions with pointwise constraints on the control and state functions focusing on the multidimensional wave equation with a nonlinear term we derive new necessary optimality conditions in the form of a pointwise pontryagin maximum principle for the state constrained problem under consideration our approach is based on modern methods of variational analysis that allow us to obtain refined necessary optimality conditions with no convexity assumptions on integrands in the minimizing cost functional

sequential algorithms for parameter estimation based on the kullback leibler information measure methods of stochastic approximation are used to convert iterative algorithms for maximizing the kullback leibler information measure into sequential algorithms special attention is given to the case of incomplete data and several algorithms are presented to deal with situations of this kind the application of these algorithms to the identification of finite impulse response systems is considered

method of speech recognition using multimodal variational inference with switching state space models a method of efficiently setting posterior probability parameters for a switching state space model begins by defining a window containing at least two but fewer than all of the frames a separate posterior probability parameter is determined for each frame in the window the window is then shifted sequentially from left to right in time so that it includes one or more subsequent frames in the sequence of frames a separate posterior probability parameter is then determined for each frame in the shifted window this method closely approximates a more rigorous solution but saves computational cost by two to three orders of magnitude further a method of determining the optimal discrete state sequence in the switching state space model is invented that directly exploits the observation vector on a frame by frame basis and operates from left to right in time

em based recursive estimation of channel parameters recursive online expectation maximization em algorithm along with stochastic approximation is employed in this paper to estimate unknown time invariant variant parameters the impulse response of a linear system channel is modeled as an unknown deterministic vector process and as a gaussian vector process with unknown stochastic characteristics using these models which are embedded in white or colored gaussian noise different types of recursive least squares rls kalman filtering and smoothing and combined rls and kalman type algorithms are derived directly from the recursive em algorithm the estimation of unknown parameters also generates new recursive algorithms for situations such as additive colored noise modeled by an autoregressive process the recursive em algorithm is shown as a powerful tool which unifies the derivations of many adaptive estimation methods

dispersion of stress waves in one dimensional semi infinite weakly coupled layared systems an analytical study of the dispersion of transient stress waves in the first layer of a weakly coupled semi infinite bi layered system is carried out the analysis is based on approximate fourier transform inversions and makes use of the fact that the weakly coupled system possesses small propagation zones pzs in the frequency domain low and high frequency asymptotic approximations to the transient waves are computed taking into account frequency components of the transfer function in the first and second pzs respectively the derived analytic expressions are superpositions of non oscillating terms and convolution integrals with decaying oscillatory kernels it is shown that depending on the frequency and the amplitude of the convolution kernels the dispersed waves overshoot or undershoot the applied impulsive excitation this result is of significant practical importance in the design of layered systems as stress attenuators

active classification for pomdps a kalman like state estimator author s zois ds levorato m mitra u abstract 1991 2012 ieee the problem of state tracking with active observation control is considered for a system modeled by a discrete time finite state markov chain observed through conditionally gaussian measurement vectors the measurement model statistics are shaped by the underlying state and an exogenous control input which influence the observations quality exploiting an innovations approach an approximate minimum mean squared error mmse filter is derived to estimate the markov chain system state to optimize the control strategy the associated mean squared error is used as an optimization criterion in a partially observable markov decision process formulation a stochastic dynamic programming algorithm is proposed to solve for the optimal solution to enhance the quality of system state estimates approximate mmse smoothing estimators are also derived finally the performance of the proposed framework is illustrated on the problem of physical activity detection in wireless body sensing networks the power of the proposed framework lies within its ability to accommodate a broad spectrum of active classification applications including sensor management for object classification and tracking estimation of sparse signals and radar scheduling

an online sequential algorithm for the estimation of transition probabilities for jump markov linear systems this paper describes a new method to estimate the transition probabilities associated with a jump markov linear system the new algorithm uses stochastic approximation type recursions to minimize the kullback leibler divergence between the likelihood function of the transition probabilities and the true likelihood function since the calculation of the likelihood function of the transition probabilities is impossible an incomplete data paradigm which has been previously applied to a similar problem for hidden markov models is used the algorithm differs from the existing algorithms in that it assumes that the transition probabilities are deterministic quantities whereas the existing approaches consider them to be random variables with prior distributions

self inversive hilbert space operator polynomials with spectrum on the unit circle abstract hilbert space operator polynomials with self inversive structure are studied if the inner numerical radius of an associated polynomial is greater than or equal to one then the spectrum lies on the unit circle and consists of normal approximate characteristic values

a soft output hybrid algorithm for ml map sequence estimation the classical viterbi algorithm ml sequence estimation can be computed using a forward backward structure similar to that of the classical hidden markov model forward backward algorithm map state estimation this similarity is exploited to develop a hybrid algorithm which provides a mathematical connection between ml sequence estimation and map state estimation

z transform methods for the optimal design of one dimensional layered elastic media in this work we develop a finite trigonometric series representation for the stress in a multilayered goupillaud type elastic strip subjected to transient heaviside loading on one end while the other end is held fixed this representation is achieved by means of the z transform method and involves the so called base angles generally different layered designs could share the same set of base angles and the more layers the design has the more base angles are expected necessary conditions for the base angles and design parameters for any given design are described as a result of the stress representation we are able to identify optimal layered designs which provide the smallest stress amplitude for two and three layered designs for which the coefficients of the stress representation are easy to find the optimization results are achieved using a custom made discrete optimization technique applied in a p velo and g a gazonas int j solids structures 40 2003 pp 6417 6428 for other laye

singularities of the hypergeometric system associated with a monomial curve we compute using d module restrictions the slopes of the irregular hypergeometric system associated with a monomial curve we also study rational solutions and reducibility of such systems

the poles of symmetric linear prediction models lie on the unit circle symmetric linear prediction models have their poles on the unit circle a simple proof of this result is presented

on line estimation of dynamic shock error models based on the kullback leibler information measure develops two sequential or on line estimation schemes in the time domain for dynamic shock error models which are special cases of errors in variables models the author s approach utilizes a state space representation of the model kalman filtering techniques and on line algorithms the first on line algorithm is based on the expectation maximization algorithm and uses a recursive gauss newton scheme to maximize the kullback leibler information measure the second on line algorithm the author proposes is a gradient based scheme and uses stochastic approximations to maximize the log likelihood in comparison to the off line maximum likelihood estimation scheme used in ghosh 1989 the author s on line algorithms have significantly reduced computational costs and negligible memory requirements simulations illustrate the satisfactory performance of the algorithms in estimating errors in variables systems with parameters that vary slowly with time or undergo infrequent jump changes

hidden markov processes an overview of statistical and information theoretic aspects of hidden markov processes hmps is presented an hmp is a discrete time finite state homogeneous markov chain observed through a discrete time memoryless invariant channel in recent years the work of baum and petrie 1966 on finite state finite alphabet hmps was expanded to hmps with finite as well as continuous state spaces and a general alphabet in particular statistical properties and ergodic theorems for relative entropy densities of hmps were developed consistency and asymptotic normality of the maximum likelihood ml parameter estimator were proved under some mild conditions similar results were established for switching autoregressive processes these processes generalize hmps new algorithms were developed for estimating the state parameter and order of an hmp for universal coding and classification of hmps and for universal decoding of hidden markov channels these and other related topics are reviewed

sufficient optimality conditions for dirichlet boundary control of wave equations we study optimal control problems for wave equations focusing on the multidimensional wave equation with control functions in the dirichlet boundary conditions under pointwise control and we admit state by assuming weak hypotheses constraints

irregular modified a hypergeometric systems a modified a hypergeometric system is a system of differential equations for the function f tw x where f y is a solution of an a hypergeometric system in n variables and w is an n dimensional integer vector which is called the weight vector we study the irregularity of modified systems by adapting to this case the notion of umbrella introduced by m schulze and u walther especially we study slopes and gevrey series solutions we develop some applications of this study under some conditions we give laplace integral representations of divergent series solutions of the modified system and we show that certain gevrey series solutions of the original a hypergeometric system along coordinate varieties are gevrey asymptotic expansions of holomorphic solutions of the a hypergeometric system

irregular hypergeometric systems associated with a singular monomial curve in this paper we study irregular hypergeometric systems defined by one row specifically we calculate slopes of such systems in the case of reduced semigroups we generalize the case studied by castro and takayama in all the cases we find that there always exists a slope with respect to a hyperplane of this system only in the case of an irregular system defined by a 1 x 2 integer matrix we might need a change of coordinates to study slopes at infinity in the other cases slopes are always at the origin defined with respect to a hyperplane we also compute all the l characteristic varieties of the system so we have a section of the grobner fan of the module defined by the hypergeometric system

method of pattern recognition using noise reduction uncertainty a method and apparatus are provided for using the uncertainty of a noise removal process during pattern recognition in particular noise is removed from a representation of a portion of a noisy signal to produce a representation of a cleaned signal in the meantime an uncertainty associated with the noise removal is computed and is used with the representation of the cleaned signal to modify a probability for a phonetic state in the recognition system in particular embodiments the uncertainty is used to modify a probability distribution by increasing the variance in each gaussian distribution by the amount equal to the estimated variance of the cleaned signal which is used in decoding the phonetic state sequence in a pattern recognition task

irregularity of hypergeometric systems via slopes along coordinate subspaces we study the irregularity sheaves attached to the a hypergeometric d module ma introduced by gel fand et al where a z d n is pointed of full rank and c more precisely we investigate the slopes of this module along coordinate subspaces in the process we describe the associated graded ring to a positive semigroup ring for a filtration defined by an arbitrary weight vector l on torus equivariant generators to this end we introduce the a l umbrella a simplicial complex determined by a and l and identify its facets with the components of the associated graded ring we then establish a correspondence between the full a l umbrella and the components of the l characteristic variety of ma we compute in combinatorial terms the multiplicities of these components in the l characteristic cycle of the associated euler koszul complex identifying them with certain intersection multiplicities we deduce from this that slopes of ma are combinatorial independent of and in one to one correspondence with jumps of the a l umbrella this confirms a conjecture of sturmfels and gives a converse of a theorem of hotta ma is regular if and only if a defines a projective variety 1991 mathematics subject classification 13n10 14m25 16w70

on irregular binomial d modules we prove that a holonomic binomial d module m a i is regular if and only if certain associated primes of i determined by the parameter vector beta in mathbb c d are homogeneous we further describe the slopes of m a i along a coordinate subspace in terms of the known slopes of some related hypergeometric d modules that also depend on when the parameter is generic we also compute the dimension of the generic stalk of the irregularity of m a i along a coordinate hyperplane and provide some remarks about the construction of its gevrey solutions

a bayesian estimation approach for speech enhancement using hidden markov models a bayesian estimation approach for enhancing speech signals which have been degraded by statistically independent additive noise is motivated and developed in particular minimum mean square error mmse and maximum a posteriori map signal estimators are developed using hidden markov models hmms for the clean signal and the noise process it is shown that the mmse estimator comprises a weighted sum of conditional mean estimators for the composite states of the noisy signal where the weights equal the posterior probabilities of the composite states given the noisy signal the estimation of several spectral functionals of the clean signal such as the sample spectrum and the complex exponential of the phase is also considered a gain adapted map estimator is developed using the expectation maximization algorithm the theoretical performance of the mmse estimator is discussed and convergence of the map estimator is proved both the mmse and map estimators are tested in enhancing speech signals degraded by white gaussian noise at input signal to noise ratios of from 5 to 20 db

exact analytical solutions for elastodynamic impact abstract we consider the one dimensional impact problem in which a semi infinite flyer collides with and adheres to the front face of a stationary target plate of finite thickness with the back face of the target bonded to another semi infinite medium all three bodies are assumed to be linear elastic and homogeneous our interest is in explicit expressions for the stress and velocity in the target at all times after impact the analysis of this problem is simplified by reducing it to an initial boundary value problem for the target only which is solved by combining the d alembert solution of the wave equation with the laplace transform method an appropriate impact boundary condition is required on the front face of the target in the literature this is usually taken as a prescribed step in stress or velocity but the correct boundary condition involves a linear combination of the unknown stress and velocity at the impact face our solutions are expressed in an apparently new compact form involving the floor or greatest integer function the results are amenable to asymptotic analysis in particular solutions for stress free or rigid back faces follow easily as limiting cases of the backing impedance and the long time asymptotes of stress and velocity in the target are seen to be independent of the target s elastic properties all of our results are corroborated by derivation of exact discrete solutions from recursive equations for the impact problems

noise resistant auditory model for parametrization of speech a method and system are provided for alleviating the harmful effects of convolutional and additive noise in speech such as due to the environmental noise and linear spectral modification based on the filtering of time trajectories of an auditory like spectrum in a particular spectral domain

on line estimation of hidden markov model parameters based on the kullback leibler information measure sequential or online hidden markov model hmm signal processing schemes are derived and their performance is illustrated by simulation the online algorithms are sequential expectation maximization em schemes and are derived by using stochastic approximations to maximize the kullback leibler information measure the schemes can be implemented either as filters or fixed lag or sawtooth lag smoothers they yield estimates of the hmm parameters including transition probabilities markov state levels and noise variance in contrast to the offline em algorithm baum welch scheme which uses the fixed interval forward backward scheme the online schemes have significantly reduced memory requirements and improved convergence and they can estimate hmm parameters that vary slowly with time or undergo infrequent jump changes similar techniques are used to derive online schemes for extracting finite state markov chains imbedded in a mixture of white gaussian noise wgn and deterministic signals of known functional form with unknown parameters

optimal control of the blowup time the problem of optimal control of the blowup time of a system of nonlinear controlled ordinary differential equations is considered in this paper the blowup time is defined to be the first time that the norm of the trajectory becomes infinite when one seeks to maximize the blowup time the pair v x omega comes under consideration where x in r n mapsto v x in 0 infty is the value function and omega subset r n is the blowup set this is the set of initial points from which finite time blowup will occur for any control we prove that v omega is the unique viscosity solution of the equation 1 max z d xv x f x z 0 x in omega and conditions lim x to infty v x 0 lim x to partial omega v x infty finally we derive the pontryagin maximum principle for an optimal control some generalizations are also discussed

a generalized ldpc decoder for blind turbo equalization the equations for iteratively decoding low density parity check ldpc codes are generalized to compute joint probabilities of arbitrary sets of codeword bits and parity checks the standard iterative ldpc decoder which computes single variable probabilities is realized as a special case another specialization allows pairwise joint posterior probabilities of pairs of codeword bits to be computed these pairwise joint probabilities are used in an expectation maximization em based blind channel estimator that is ignorant of the code constraints channel estimates are input to a turbo equalizer that exploits the structure of the ldpc code feeding pairwise posterior probabilities back to the channel estimator eliminates the need to average across time for channel estimation therefore this scheme can be used to equalize very long codewords even when the channel is time varying this blind turbo equalizer is evaluated through computer simulations and found to perform as well as a channel informed turbo equalizer but with approximately twice the number of turbo iterations

a reduced complexity online state sequence and parameter estimator for superimposed convolutional coded signals this paper develops a reduced complexity online state sequence and parameter estimator for superimposed convolutional coded signals joint state sequence and parameter estimation is achieved by iteratively estimating the state sequence via a variable reduced complexity viterbi algorithm vrcva and the model parameters via a recursive expectation maximization em approach the vrcva is developed from a fixed reduced complexity viterbi algorithm frcva the frcva is a special case of the delayed decision feedback sequence estimation ddfse algorithm the performance of online versions of the frcva vrcva and the standard viterbi algorithm va are compared when they are used to estimate the state sequence as part of the reduced complexity online state sequence and parameter estimator

a blind decision feedback equalizer incorporating fixed lag smoothing a new type of blind decision feedback equalizer dfe incorporating fixed lag smoothing is developed in this paper the structure is motivated by the fact that if we make full use of the dependence of the observed data on a given transmitted symbol delayed decisions may produce better estimates of that symbol to this end we use a hidden markov model hmm suboptimal formulation that offers a good tradeoff between computational complexity and bit error rate ber performance the proposed equalizer also provides estimates of the channel coefficients and operates adaptively so that it can adapt to a fading channel for instance by means of an online version of the expectation maximization em algorithm the resulting equalizer structure takes the form of a linear feedback system including a quantizer and hence it is easily implemented in fact because of its feedback structure the proposed equalizer shows some similarities with the well known dfe a full theoretical analysis of the initial version of the algorithm is not available but a characterization of a simplified version is provided we demonstrate that compared to the zero forcing dfe zf dfe the algorithm yields many improvements a large range of simulations on finite impulse response fir channels and on typical fading gsm channel models illustrate the potential of the proposed equalizer

log spectral compensation of pmc gaussian mean vectors for noisy speech recognition using log max assumption reducing mismatch between hmms trained with clean speech and speech signals recorded under background noise can be approached by distribution adaptation using parallel model combination pmc accurate pmc has no closed form expression therefore simplification assumptions must be made in implementation under a new log max assumption adaptation formula for log spectral parameters are presented both for static and dynamic parameters the system takes the mean vector 41 made up of the static part 41 a and the dynamic part 41 b and the noise vector 43 made up of the static part 43 a and dynamic part 43 b and applies to a decision circuit 45 to determine if the quiet vector plus the gain of speech produced in noise with respect to clean speech is greater than the noisy mean vector and if so the static part is equal to the gain plus the clean speech vector and the dynamic part is the change in the the quiet speech vector and if not greater than the noise vector then the static part equals the noise vector and the dynamic part is zero

exponential stability of filters and smoothers for hidden markov models we address the problem of filtering and fixed lag smoothing for discrete time and discrete state hidden markov models hmms with the intention of extending some important results in kalman filtering notably the property of exponential stability by appealing to a generalized perron frobenius result for non negative matrices we are able to demonstrate exponential forgetting for both the recursive filters and smoothers furthermore methods for deriving overbounds on the convergence rate are indicated simulation studies for a two state and two output hmm verify qualitatively some of the theoretical predictions and the observed convergence rate is shown to be bounded in accordance with the theoretical predictions

blowup time optimal control for ordinary differential equations this paper studies a time optimal control problem governed by a classical controlled system of nonlinear ordinary differential equations corresponding to certain controls the solutions of the system have the behavior of blowup namely they blow up at a finite time the goal for controlling the system is to minimize the blowup time therefore the time optimal control problem studied in this paper holds its target set outside of its state space from this perspective it essentially differentiates from the most time optimal control problems in studies from past publications the purpose of this study is to develop a new method for proving the existence of time optimal controls for such kinds of time optimal control problems through analyzing a typical problem we also aim to create a new method for establishing the pontryagin maximum principle of optimal controls the main results derived from this paper can be extended to controlled systems where nonlinear terms have a more general form by the two methods mentioned above

optimal blowup time of diffusion equations with control we study the blowup time for semilinear parabolic differential equations with control function by constructing new dynamic programming methodology this new approach allows us to formulate verifica

method of reducing index sizes used to represent spectral content vectors a method identifies a codeword to represent a vector derived from an audio signal by applying the vector to first and second decision trees the first decision tree is associated with a first type of audio sound and produces a first codeword the second decision tree is associated with a second type of audio sound and produces a second codeword one of the first and second codewords is then selected as the codeword for the vector in further embodiments the vector describes the spectral content of the audio signal and a linear prediction value is generated for the vector the difference between the linear prediction value and the vector is used to identify the codeword

adaptive estimation of hmm transition probabilities this paper presents new schemes for recursive estimation of the state transition probabilities for hidden markov models hmms via extended least squares els and recursive state prediction error rspe methods local convergence analysis for the proposed rspe algorithm is shown using the ordinary differential equation ode approach developed for the more familiar recursive output prediction error rpe methods the presented scheme converges and is relatively well conditioned compared with the previously proposed rpe scheme for estimating the transition probabilities that perform poorly in low noise the els algorithm presented is computationally of order n sup 2 which is less than the computational effort of order n sup 4 required to implement the rspe and previous rpe scheme where n is the number of markov states building on earlier work an algorithm for simultaneous estimation of the state output mappings and the state transition probabilities that requires less computational effort than earlier schemes is also presented and discussed implementation aspects of the proposed algorithms are discussed and simulation studies are presented to illustrate the convergence and convergence rates

blowup in diffusion equations a survey this paper deals with quasilinear reaction diffusion equations for which a solution local in time exists if the solution ceases to exist for some finite time we say that it blows up in contrast to linear equations blowup can occur even if the data are smooth and well defined for all times depending on the equation either the solution or some of its derivatives become singular we shall concentrate on those cases where the solution becomes unbounded in finite time this can occur in quasilinear equations if the heat source is strong enough there exist many theoretical studies on the question on the occurrence of blowup in this paper we shall recount some of the most interesting criteria and most important methods for analyzing blowup the asymptotic behavior of solutions near their singularities is only completely understood in the special case where the source is a power a better knowledge would be useful also for their numerical treatment thus not surprisingly the numerical analysis of this type of problems is still at a rather early stage the goal of this paper is to collect some of the known results and algorithms and to direct the attention to some open problems

hidden markov model state estimation with randomly delayed observations this paper considers state estimation for a discrete time hidden markov model hmm when the observations are delayed by a random time the delay process is itself modeled as a finite state markov chain that allows an augmented state hmm to model the overall system state estimation algorithms for the resulting hmm are then presented and their performance is studied in simulations the motivation for the model stems from the situation when distributed sensors transmit measurements over a connectionless packet switched communications network

hidden markov model signal processing in presence of unknown deterministic interferences expectation maximization algorithms are used to extract discrete time finite state markov signals imbedded in a mixture of gaussian white noise and deterministic signals of known functional form with unknown parameters maximum likelihood estimates of the markov state levels state estimates transition possibilities and the parameters of the deterministic signals are obtained two types of deterministic signals are considered periodic or almost periodic signals with unknown frequency components amplitudes and phases and polynomial drift in the states of the markov process with the coefficients of the polynomial unknown the techniques and supporting theory appear more elegant and powerful than ad hoc heuristic alternatives an illustrative application to extracting ionic channel currents in cell membranes in the presence of white gaussian noise and ac hum is included

method of determining uncertainty associated with acoustic distortion based noise reduction a method and apparatus are provided for determining uncertainty in noise reduction based on a parametric model of speech distortion the method is first used to reduce noise in a noisy signal in particular noise is reduced from a representation of a portion of a noisy signal to produce a representation of a cleaned signal by utilizing an acoustic environment model the uncertainty associated with the noise reduction process is then computed in one embodiment the uncertainty of the noise reduction process is used in conjunction with the noise reduced signal to decode a pattern state

method and apparatus for denoising and deverberation using variational inference and strong speech models a probability distribution for speech model parameters such as auto regression parameters is used to identify a distribution of denoised values from a noisy signal under one embodiment the probability distributions of the speech model parameters and the denoised values are adjusted to improve a variational inference so that the variational inference better approximates the joint probability of the speech model parameters and the denoised values given a noisy signal in some embodiments this improvement is performed during an expectation step in an expectation maximization algorithm the statistical model can also be used to identify an average spectrum for the clean signal and this average spectrum may be provided to a speech recognizer instead of the estimate of the clean signal

irregular hypergeometric d modules abstract we study the irregularity of hypergeometric d modules m a via the explicit construction of gevrey series solutions along coordinate subspaces in x c n as a consequence we prove that along coordinate hyperplanes the combinatorial characterization of the slopes of m a given by m schulze and u walther 2008 in 23 still holds for any full rank integer matrix a we also provide a lower bound for the dimensions of the spaces of gevrey solutions along coordinate subspaces in terms of volumes of polytopes and prove the equality for very generic parameters holomorphic solutions of m a at nonsingular points can be understood as gevrey solutions of order one along x at generic points and so they are included as a particular case

on the stability of periodic orbits of high dimensional autonomous hamiltonian systems we study the stability of periodic orbits of autonomous hamiltonian systems with n 1 degrees of freedom or equivalently of 2n dimensional symplectic maps with n 1 we classify the different stability types introducing a new terminology which is perfectly suited for systems with many degrees of freedom since it clearly reflects the configuration of the eigenvalues of the corresponding monodromy matrix on the complex plane the different stability types correspond to different regions of the n dimensional parameter space s defined by the coefficients of the characteristic polynomial of the monodromy matrix all the possible direct transitions between different stability types are classified and the corresponding transition hypersurface in s is determined the dimension of the transition hypersurface is an indicator of how probable to happen is the corresponding transition as an application of the general results we consider the well known cases of hamiltonian systems with two and three degrees of freedom we also describe in detail the different stability regions in the three dimensional parameter space s of a hamiltonian system with four degrees of freedom or equivalently of a six dimensional symplectic map 2001 elsevier science b v all rights reserved

learning statistically characterized resonance targets in a hidden trajectory model a statistical trajectory speech model is constructed where the targets for vocal tract resonances are represented as random vectors and where the mean vectors of the target distributions are estimated using a likelihood function for joint acoustic observation vectors the target mean vectors can be estimated without formant data to form the model time dependent filter parameter vectors based on time dependent coarticulation parameters are constructed that are a function of the ordering and identity of the phones in the phone sequence in each speech utterance the filter parameter vectors are also a function of the temporal extent of coarticulation and of the speaker s speaking effort

method of noise estimation using incremental bayesian learning a method and apparatus estimate additive noise in a noisy signal using incremental bayes learning where a time varying noise prior distribution is assumed and hyperparameters mean and variance are updated recursively using an approximation for posterior computed at the preceding time step the additive noise in time domain is represented in the log spectrum or cepstrum domain before applying incremental bayes learning the results of both the mean and variance estimates for the noise for each of separate frames are used to perform speech feature enhancement in the same log spectrum or cepstrum domain

noise compensation in speech recognition apparatus a method and apparatus for noise suppression for speech recognition systems which employs the principle of a least means square estimation which is implemented with conditional expected values essentially according to this method one computes a series of optimal estimators which estimators and their variances are then employed to implement a noise immune metric this noise immune metric enables the system to substitute a noisy distance with an expected value which value is calculated according to combined speech and noise data which occurs in the bandpass filter domain thus the system can be used with any set of speech parameters and is relatively independent of a specific speech recognition apparatus structure

reduced state sequence estimation with set partitioning and decision feedback a reduced state sequence estimator for linear intersymbol interference channels is described the estimator uses a conventional viterbi algorithm with decision feedback to search a reduced state subset trellis that is constructed using set partitioning principles the complexity of maximum likelihood sequence estimation mlse due to the length of the channel memory and the size of the signal set is systematically reduced an error probability analysis shows that a good performance complexity tradeoff can be obtained in particular the results indicate that the required complexity to achieve the performance of mlse is independent of the size of the signal set for large enough signal sets simulation results are provided for two partial response systems a simple technique for quadrature partial response signaling qprs is described that eliminates the quasicatastrophic nature of the ml trellis

a unified approach for encoding clean and noisy sources by means of waveform and autoregressive model vector quantization data compression by vector quantization is considered for sources which have been degraded by noise it is shown that by appropriately modifying the given distortion measure the problem becomes a standard quantization problem for the noisy source and the modified distortion measure for the special case of sources corrupted by statistically independent additive noise the authors provide sufficient conditions on the original distortion measure and probability distributions of the source and the noise for convergence of the generalized lloyd algorithm in designing the quantizers the results are specialized to waveform and autoregressive model vector quantization using the weighted quadratic and the itakura saito distortion measures respectively

asymptotic impact behavior of goupillaud type layered elastic media abstract in this paper we use the method of riemann invariants to develop a system of recurrence relations for the one dimensional elastic impact problem in which a semi infinite flyer collides with and adheres to a stationary goupillaud type layered elastic target plate of finite thickness that is bonded to a semi infinite half space we derive explicit expressions for the long time asymptotes of stress and particle velocity that are independent of the layered target s elastic properties this result is verified by numerical simulations of impact into layered targets with periodic or randomly distributed elastic properties and holds when the half space backing the target is replaced by either free or fixed boundary conditions our discrete recurrence relation solutions for impact into layered media generalize our recent analytical results concerning impact into homogeneous single layer targets as well as prior discrete recurrence relation solutions for stress wave propagation in goupillaud type layered elastic media