α stable interference modeling and cauchy receiver for an ir uwb ad hoc network in this paper we investigate ad hoc networks based on impulse radio ultra wideband due to multiple access the interference distribution is not gaussian one important reason for errors is the presence of close interferers generating pulse collision however such events are rare and we propose an stable model compatible with this fact due to its heavy tailed distribution we derive the analytical expression of the two significant parameters they depend on the attenuation coefficient the users density the pulse collision probability and the pulse shape we finally propose receiver strategies cauchy receiver and p norm that outperforms the classical gaussian receiver

average rate of downlink heterogeneous cellular networks over generalized fading channels a stochastic geometry approach in this paper we introduce an analytical framework to compute the average rate of downlink heterogeneous cellular networks the framework leverages recent application of stochastic geometry to other cell interference modeling and analysis the heterogeneous cellular network is modeled as the superposition of many tiers of base stations bss having different transmit power density path loss exponent fading parameters and distribution and unequal biasing for flexible tier association a long term averaged maximum biased received power tier association is considered the positions of the bss in each tier are modeled as points of an independent poisson point process ppp under these assumptions we introduce a new analytical methodology to evaluate the average rate which avoids the computation of the coverage probability pcov and needs only the moment generating function mgf of the aggregate interference at the probe mobile terminal the distinguishable characteristic of our analytical methodology consists in providing a tractable and numerically efficient framework that is applicable to general fading distributions including composite fading channels with small and mid scale fluctuations in addition our method can efficiently handle correlated log normal shadowing with little increase of the computational complexity the proposed mgf based approach needs the computation of either a single or a two fold numerical integral thus reducing the complexity of pcov based frameworks which require for general fading distributions the computation of a four fold integral

uwb receiver designs based on a gaussian laplacian noise plus mai model a more appropriate statistical model for the multiple access interference than the generally used gaussian approximation is proposed to reflect the heavy tailed nature of the multiple access interference in ultra wide bandwidth systems novel receiver structures which surpass the performance of the conventional matched filter receiver are studied for ultrawide bandwidth multiple access communications in both awgn and fading multipath channels a proposed rake receiver is advantageous over the conventional rake receiver when multiuser interference dominates ambient gaussian noise and originates from a small to moderate number of interferers explanation is provided for the inaccuracy of a gaussian approximation with regard to the multiple access interference and the heavy tailed nature of the probability density function of the multiple access interference is also discussed

on a generalization of mittag leffler function and its properties abstract let s and z be complex variables s the gamma function and s s s for any complex the generalized pochhammer symbol the principal aim of the paper is to investigate the function e q z n 0 q n n z n n where c re 0 re 0 re 0 and q 0 1 n this is a generalization of the exponential function exp z the confluent hypergeometric function z the mittag leffler function e z the wiman s function e z and the function e z defined by prabhakar for the function e q z its various properties including usual differentiation and integration laplace transforms euler beta transforms mellin transforms whittaker transforms generalised hypergeometric series form mellin barnes integral representation with their several special cases are obtained and its relationship with laguerre polynomials fox h function and wright hypergeometric function is also established

robust control of nonlinear jump parameter systems governed by uncertain chains we consider an infinite horizon minimax optimal control problem for stochastic uncertain systems governed by a discrete state uncertain continuous time chain using existing risk sensitive control results a robust suboptimal absolutely stabilizing guaranteed cost controller is constructed conditions are presented under which this suboptimal controller is minimax optimal we then present a numeric algorithm for calculating a robust sub optimal controller using a markov chain approximation technique

linearization of f 1 curves by adaptation we show that negative feedback to highly nonlinear frequency current f i curves results in an effective linearization by highly nonlinear we mean that the slope at threshold is infinite or very steep we then apply this to a specific model for spiking neurons and show that the details of the adaptation mechanism do not affect the results the crucial points are that the adaptation is slow compared to other processes and the unadapted f i curve is highly nonlinear

accurate evaluation of multiple access performance in th ppm and th bpsk uwb systems a characteristic function method is proposed for precisely calculating the bit error probability of time hopping th ultra wideband uwb systems with multiple access interference in an additive white gaussian noise environment the analytical expressions are validated by simulation and used to assess the accuracy of the gaussian approximation the gaussian approximation is shown to be inaccurate for predicting bit error rates bers for medium and large signal to noise ratio snr values the performances of th pulse position modulation ppm and binary phase shift keying bpsk modulation schemes are accurately compared in terms of the ber it is shown that the bpsk system outperforms the binary ppm system for all values of snr the sensitivity of the performance of the modulation schemes to the system parameters is also addressed through numerical examples

unified framework for the effective rate analysis of wireless communication systems over miso fading channels this paper proposes a unified framework for the effective rate analysis over arbitrary correlated and not necessarily identical multiple input single output miso fading channels which uses the moment generating function mgf based approach and h transform representation the proposed framework has the potential to simplify the cumbersome analysis procedure compared with the probability density function based approach moreover the effective rates over two specific fading scenarios are investigated namely independent but not necessarily identical distributed i n i d miso hyper fox s h fading channels and arbitrary correlated generalized k fading channels the exact analytical representations for these two scenarios are also presented by substituting corresponding parameters the effective rates in various practical fading scenarios such as rayleigh nakagami m weibull gamma and generalized k fading channels are readily available in addition asymptotic approximations are provided for the proposed h transform and mgf based approach as well as for the effective rate over i n i d miso hyper fox s h fading channels simulations under various fading scenarios are also presented which support the validity of the proposed method

on the efficient simulation of the distribution of the sum of gamma gamma variates with application to the outage probability evaluation over fading channels the gamma gamma distribution has recently emerged in a number of applications ranging from modeling scattering and reverberation in sonar and radar systems to modeling atmospheric turbulence in wireless optical channels in this respect assessing the outage probability achieved by some diversity techniques over this kind of channels is of major practical importance in many circumstances this is related to the difficult question of analyzing the statistics of a sum of gamma gamma random variables answering this question is not a simple matter this is essentially because outage probabilities encountered in practice are often very small and hence the use of classical monte carlo methods is not a reasonable choice this lies behind the main motivation of this paper in particular this paper proposes a new approach to estimate the left tail of the sum of gamma gamma variates more specifically we propose robust importance sampling schemes that efficiently evaluates the outage probability of diversity receivers over gamma gamma fading channels the proposed estimators satisfy the well known bounded relative error criterion for both maximum ratio combining and equal gain combining cases we show the accuracy and the efficiency of our approach compared with naive monte carlo via some selected numerical simulations

spatially structured activity in synaptically coupled neuronal networks ii lateral inhibition and standing pulses we consider the existence and stability of standing pulse solutions to a system of integro differential equations used to describe the activity of synaptically coupled networks of excitatory and inhibitory neurons in a single spatial domain assuming an arrangement of synaptic connections described by lateral inhibition previous formal arguments have demonstrated the existence of both stable and unstable standing pulses s amari biol cybern 27 1977 pp 77 87 these results have formed the basis for several recent hypotheses regarding the generation of sustained activity patterns in prefrontal cortex and other brain regions implicit in the lateral inhibition arrangement however is the assumption that the dynamics of inhibition are instantaneous here we present two arguments demonstrating the loss of stability of standing pulse solutions through a hopf bifurcation when more realistic inhibitory dynamics are considered the first argument parallels amari s formal presentation while the seco

capacity of mrc on correlated rician fading channels a new exact explicit expression is derived for the ergodic capacity of maximal ratio combining mrc schemes over arbitrarily correlated rician fading channels this is used to study the effects of channel correlation on the ergodic capacity numerical results reveal that both the phase and the magnitude of correlation have an impact on the ergodic capacity of rician fading channels this is in contrast to correlated rayleigh fading where the phase of the correlation has no effect on the ergodic capacity it is also observed that negatively correlated branches in rician fading may lead to an increase in ergodic capacity beyond that obtained by uncorrelated branches

a moving bump in a continuous manifold a comprehensive study of the tracking dynamics of continuous attractor neural networks understanding how the dynamics of a neural network is shaped by the network structure and consequently how the network structure facilitates the functions implemented by the neural system is at the core of using mathematical models to elucidate brain functions this study investigates the tracking dynamics of continuous attractor neural networks canns due to the translational invariance of neuronal recurrent interactions canns can hold a continuous family of stationary states they form a continuous manifold in which the neural system is neutrally stable we systematically explore how this property facilitates the tracking performance of a cann which is believed to have clear correspondence with brain functions by using the wave functions of the quantum harmonic oscillator as the basis we demonstrate how the dynamics of a cann is decomposed into different motion modes corresponding to distortions in the amplitude position width or skewness of the network state we then develop a perturbation approach that utilizes the dominating movement of the network s stationary states in the state space this method allows us to approximate the network dynamics up to an arbitrary accuracy depending on the order of perturbation used we quantify the distortions of a gaussian bump during tracking and study their effects on tracking performance results are obtained on the maximum speed for a moving stimulus to be trackable and the reaction time for the network to catch up with an abrupt change in the stimulus

on the calculation of the incomplete mgf with applications to wireless communications the incomplete moment generating function imgf has paramount relevance in communication theory since it appears in a plethora of scenarios when analyzing the performance of communication systems we here present a general method for calculating the imgf of any arbitrary fading distribution then we provide exact closed form expressions for the imgf of the very general kappa mu shadowed fading model which includes the popular kappa mu eta mu rician shadowed and other classical models as particular cases we illustrate the practical applicability of this result by analyzing several scenarios of interest in wireless communications 1 physical layer security in the presence of an eavesdropper 2 outage probability analysis with interference and background noise 3 channel capacity with side information at the transmitter and the receiver and 4 average bit error rate with adaptive modulation when the fading on the desired link can be modeled by any of the aforementioned distributions

stimulus locked traveling waves and breathers in an excitatory neural network we analyze the existence and stability of stimulus locked traveling waves in a one dimensional synaptically coupled excitatory neural network the network is modeled in terms of a nonlocal integro differential equation in which the integral kernel represents the spatial distribution of synaptic weights and the output firing rate of a neuron is taken to be a heaviside function of activity given an inhomogeneous moving input of amplitude i0 and velocity v we derive conditions for the existence of stimulus locked waves by working in the moving frame of the input we use this to construct existence tongues in v i0 parameter space whose tips at i0 0 correspond to the intrinsic waves of the homogeneous network we then determine the linear stability of stimulus locked waves within the tongues by constructing the associated evans function and numerically calculating its zeros as a function of network parameters we show that as the input amplitude is reduced a stimulus locked wave within the tongue of

persistent neural states stationary localized activity patterns in nonlinear continuous n population q dimensional neural networks neural continuum networks are an important aspect of the modeling of macroscopic parts of the cortex two classes of such networks are considered voltage and activity based in both cases our networks contain an arbitrary number n of interacting neuron populations spatial nonsymmetric connectivity functions represent cortico cortical local connections and external inputs represent nonlocal connections sigmoidal nonlinearities model the relationship between average membrane potential and activity departing from most of the previous work in this area we do not assume the nonlinearity to be singular that is represented by the discontinuous heaviside function another important difference from previous work is that we relax the assumption that the domain of definition where we study these networks is infinite that is equal to or we explicitly consider the biologically more relevant case of a bounded subset of a better model of a piece of cortex the time behavior of these networks is described by systems of integro differential equations using methods of functional analysis we study the existence and uniqueness of a stationary i e time independent solution of these equations in the case of a stationary input these solutions can be seen as persistent they are also sometimes called bumps we show that under very mild assumptions on the connectivity functions and because we do not use the heaviside function for the nonlinearities such solutions always exist we also give sufficient conditions on the connectivity functions for the solution to be absolutely stable that is independent of the initial state of the network we then study the sensitivity of the solutions to variations of such parameters as the connectivity functions the sigmoids the external inputs and last but not least the shape of the domain of existence of the neural continuum networks these theoretical results are illustrated and corroborated by a large number of numerical experiments in most of the cases 2 n 3 2 q 3

exact sinr statistics in the presence of heterogeneous interferers we derive new results for the higher order moments of signal to interference plus noise ratio sinr in the presence of an arbitrary poisson point process ppp based heterogeneous interference field the analysis leverages on a moment generating function mgf methodology which only requires the statistics of intended signal and aggregate interference thus eliminating the need for the exact distribution of sinr we extend the existing results on interference statistics by deriving a generalized closed form expression of the interference mgf considering nakagami m fading channels with exclusion region in certain special cases explicit expressions for the averages of different functions of sinr are found which also lead to closed form solutions for the probability distributions of aggregate interference reciprocal and signal to interference ratio we prove that in such cases the effect of total ppp based interference power on useful transmission is mathematically equivalent to the severe fluctuations from a one sided gaussian fading channel as an application example the proposed methodology is used together with stochastic geometry theory to characterize the average sinr and rate in heterogeneous cellular networks the validity of our analytical derivations is confirmed via monte carlo simulations for various system settings we show that with cellular network densification there exists a tradeoff between the average sinr and rate performance

equivalence of state representations for hidden markov models in this paper we consider the following problem for hidden markov models given a minimal hidden markov model derive conditions for another hidden markov model to be equivalent and give a description of the complete set of equivalent models a distinction is made between quasi and positive hidden markov models and between mealy and moore hidden markov models we derive a condition for two positive mealy models to be equivalent and give a description of the complete set of mealy models that are equivalent to a given mealy model we show that under certain conditions minimal quasi moore models are unique up to a permutation of the states we derive a condition for two positive moore models to be equivalent and give a description of the complete set of moore models equivalent to a given moore model finally we compare the results for hidden markov models and linear gaussian systems

dynamics and computation of continuous attractors continuous attractor is a promising model for describing the encoding of continuous stimuli in neural systems in a continuous attractor the stationary states of the neural system form a continuous parameter space on which the system is neutrally stable this property enables the neutral system to track time varying stimuli smoothly but it also degrades the accuracy of information retrieval since these stationary states are easily disturbed by external noise in this work based on a simple model we systematically investigate the dynamics and the computational properties of continuous attractors in order to analyze the dynamics of a large size network which is otherwise extremely complicated we develop a strategy to reduce its dimensionality by utilizing the fact that a continuous attractor can eliminate the noise components perpendicular to the attractor space very quickly we therefore project the network dynamics onto the tangent of the attractor space and simplify it successfully as a one dimensional ornstein uhlenbeck process based on this simplified model we investigate 1 the decoding error of a continuous attractor under the driving of external noisy inputs 2 the tracking speed of a continuous attractor when external stimulus experiences abrupt changes 3 the neural correlation structure associated with the specific dynamics of a continuous attractor and 4 the consequence of asymmetric neural correlation on statistical population decoding the potential implications of these results on our understanding of neural information processing are also discussed

symbol error rate of mpsk over egk channels perturbed by a dominant additive laplacian noise the laplacian noise has received much attention during the recent years since it affects many communication systems we consider in this paper the probability of error of an m ary phase shift keying psk constellation operating over a generalized fading channel in presence of a dominant additive laplacian noise in this context the decision regions of the receiver are determined using the maximum likelihood and the minimum distance detectors once the decision regions are extracted the resulting symbol error rate expressions are computed and averaged over an extended generalized k fading distribution generic closed form expressions of the conditional and the average probability of error are obtained in terms of the fox s h function simplifications for some special cases of fading are presented and the resulting formulas end up being often expressed in terms of well known elementary functions finally the mathematical formalism is validated using some selected analytical based numerical results as well as monte carlo simulation based results

bayesian methods for multiaspect target tracking in image sequences in this paper we introduce new algorithms for automatic tracking of multiaspect targets in cluttered image sequences we depart from the conventional correlation filter kalman filter association approach to target tracking and propose instead a nonlinear bayesian methodology that enables direct tracking from the image sequence incorporating the statistical models for the background clutter target motion and target aspect change proposed algorithms include 1 a batch hidden markov model hmm smoother and a sequential hmm filter for joint multiframe target detection and tracking and 2 two mixed state sequential importance sampling trackers based on the sampling importance resampling sir and the auxiliary particle filtering apf techniques performance studies show that the proposed algorithms outperform the association of a bank of template correlators and a kalman filter in adverse scenarios of low target to clutter ratio and uncertainty in the true target aspect

practical stability of approximating discrete time filters with respect to model mismatch this paper establishes practical stability results for an important range of approximate discrete time filtering problems involving mismatch between the true system and the approximating filter model practical stability is established in the sense of an asymptotic bound on the amount of bias introduced by the model approximation our analysis applies to a wide range of estimation problems and justifies the common practice of approximating intractable infinite dimensional nonlinear filters by simpler computationally tractable filters

error probabilities for binary direct sequence spread spectrum communications with random signature sequences binary direct sequence spread spectrum multiple access communications an additive white gaussian noise channel and a coherent correlation receiver are considered an expression for the output of the receiver is obtained for the case of random signature sequences and the corresponding characteristic function is determined the expression is used to study the density function of the multiple access interference and to determine arbitrarily tight upper and lower bounds on the average probability of error the bounds which are obtained without making a gaussian approximation are compared to results obtained using a gaussian approximation the effects of transmitter power the length of the signature sequences and the number of interfering transmitters are illustrated each transmitter is assumed to have the same power although the general approach can accommodate the case of transmitters with unequal powers

probabilistic distances between finite state finite alphabet hidden markov models this note considers the problem of evaluating a probabilistic distance between discrete time homogeneous first order finite state finite alphabet hidden markov models hmms our approach is based on a correspondence between probability measures and hmms established in this note using a probability measure transformation technique we obtain recursive expressions for the relative entropy between the marginal probability distributions of two hmms under consideration also the relative entropy rate as the limit of the time averaged value of the above relative entropy is obtained these expressions are given in terms of the parameters of the given hmms furthermore we show that the probabilistic distance between hmms used in the existing literature admits a representation in terms of a conditional expectation given the observation sequence this representation allows us to evaluate this distance using an information state approach

effective capacity of l_p norm diversity receivers over generalized fading channels under adaptive transmission schemes this paper presents novel moment generating function mgf and characteristic function chf based frameworks for the ec performance analysis of a generic l p norm diversity combining scheme under adaptive transmission policies the considered system operates over generalized fading channels a maximum delay constraint and under various channel state information csi conditions depending upon the operational csi four policies are studied namely i constant power with optimal rate adaptation ora ii optimal power and rate adaptation opra iii channel inversion with fixed rate cifr and iv truncated cifr tifr the l p norm diversity is a generic diversity structure which includes as special cases various well known diversity schemes such as equal gain combining egc maximal ratio combining mrc and amplify and forward af relaying under the ora and opra policies we derive single integral expressions for evaluating the ec of l p norm diversity reception directly from the mgf or the incomplete mgf of the signal to noise ratio snr at the receiver respectively for the ec performance evaluation of the egc and af relaying systems operating under the opra policy a chf based approach which is computationally more efficient as compared to the mgf based approach is further presented it is shown that the ec for the cifr and tifr policies can be directly evaluated from the mgf or the chf of the snr at the receiver respectively for the ora policy a novel analytical approach for the asymptotic ec performance analysis is also developed and evaluated revealing how important system operation parameters affect the overall system performance the mathematical formalism is validated with selected numerical and equivalent simulation performance evaluation results thus confirming the correctness of the proposed analytical methodology

analytical performance of m ary time hopping orthogonal ppm uwb systems under multiple access interference in this paper we extend the characteristic function approach to derive an exact symbol error rate expression for the m ary time hopping orthogonal pulse position modulation thppm ultra wideband uwb system under the interference from multiple users monte carlo simulations are then used to validate the analytical solutions the results are found to be valid even when that the chip width is greater than the pulse duration moreover the analytical results are compared to those found by gaussian approximation and the gaussian quadrature rule method we further show that compared to a binary system m ary systems can produce better error rate performance

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

relative entropy rate based multiple hidden markov model approximation this paper proposes a novel relative entropy rate rer based approach for multiple hmm mhmm approximation of a class of discrete time uncertain processes under different uncertainty assumptions the model design problem is posed either as a min max optimisation problem or stochastic minimization problem on the rer between joint laws describing the state and output processes rather than the more usual rer between output processes a suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the rer is reduced these filter consistency and convergence bounds are the first results characterizing multiple hmm approximation performance and suggest that joint rer concepts provide a useful model selection criteria the proposed model design process and mhmm filter are demonstrated on an important image processing dim target detection problem

multiple multiindex mittag leffler functions and relations to generalized fractional calculus the classical mittag leer m l functions have already proved their eciency as solutions of fractional order differential and integral equations and thus have become important elements of the fractional calculus theory and applications in this paper we introduce analogues of these functions depending on two sets of multiple m tuple m 2i s an integer indices the hint for this comes from a paper by dzrbashjan izv an arm ssr 13 3 1960 21 63 related to the case m 2 we study the basic properties and the relations of the multiindex m l functions with the operators of the generalized fractional calculus corresponding generalized operators of integration and dierention of the so called gelfond leontiev type as well as borel laplace type integral transforms are also introduced and studied c 2000 elsevier science b v all rights reserved msc 26a33 33c60 33e30 44a15 44a30

effects of synaptic depression and adaptation on spatiotemporal dynamics of an excitatory neuronal network we analyze the spatiotemporal dynamics of a system of integro differential equations that describes a one dimensional excitatory neuronal network with synaptic depression and spike frequency adaptation physiologically suggestive forms are used for both types of negative feedback we also consider the effects of employing two different types of firing rate function a heaviside step function and a piecewise linear function we first derive conditions for the existence of traveling fronts and pulses in the case of a heaviside step firing rate and show that adaptation plays a relatively minor role in determining the characteristics of traveling waves we then derive conditions for the existence and stability of stationary pulses or bumps and show that a purely excitatory network with synaptic depression cannot support stable bumps however bumps do not exist in the presence of adaptation finally in the case of a piecewise linear firing rate function we show numerically that the network also supports self sustained oscillations between an up state and a down state in which a spatially localized oscillating core periodically emits pulses at each cycle

misspecified and asymptotically minimax robust quickest change detection we investigate the quickest detection of an unknown change in the distribution of a stochastic process generating independent and identically distributed observations we develop new bounds on the performance of misspecified cumulative sum cusum rules and pose minimax robust versions of the popular lorden and pollak criteria with polynomial or higher order moment detection delay penalties by exploiting our results for misspecified cusum rules we identify solutions to our robust quickest change detection problems in the asymptotic regime of few false alarms in contrast to previous robust quickest change detection treatments our asymptotic results hold under relaxed conditions on the uncertainty sets of possible prechange and postchange distributions we illustrate our results in simulations and apply them to the problem of detecting target manoeuvres in low signal to noise ratio settings i e dim target manoeuvre detection

a unified mgf based capacity analysis of diversity combiners over generalized fading channels unified exact ergodic capacity results for l branch coherent diversity combiners including equal gain combining egc and maximal ratio combining mrc are not known this paper develops a novel generic framework for the capacity analysis of l branch egc mrc over generalized fading channels the framework is used to derive new results for the gamma shadowed generalized nakagami m fading model which can be a suitable model for the fading environments encountered by high frequency 60 ghz and above communications the mathematical formalism is illustrated with some selected numerical and simulation results confirming the correctness of our newly proposed framework

spatially structured activity in synaptically coupled neuronal networks i traveling fronts and pulses we consider traveling front and pulse solutions to a system of integro differential equations used to describe the activity of synaptically coupled neuronal networks in a single spatial dimension our first goal is to establish a series of direct links between the abstract nature of the equations and their interpretation in terms of experimental findings in the cortex and other brain regions this is accomplished first by presenting a biophysically motivated derivation of the system and then by establishing a framework for comparison between numerical and experimental measures of activity propagation speed our second goal is to establish the existence of traveling pulse solutions using more rigorous methods two techniques are presented the first a shooting argument reduces the problem from finding a specific solution to an integro differential equation system to finding any solution to an ode system the second a singular perturbation argument provides a construction of traveling pulse solutions un

estimation of the parameters of sinusoidal signals in non gaussian noise accurate estimation of the amplitude and frequency parameters of sinusoidal signals from noisy observations is an important problem in many signal processing applications in this paper the problem is investigated under the assumption of non gaussian noise in general and laplace noise in particular it is proven mathematically that the maximum likelihood estimator derived under the condition of laplace white noise is able to attain an asymptotic cramer rao lower bound which is one half of that achieved by periodogram maximization and nonlinear least squares it is also proven that when applied to non laplace situations the laplace maximum likelihood estimator which may also be referred to as the nonlinear least absolute deviations estimator can achieve an even higher statistical efficiency especially when the noise distribution has heavy tails a computational procedure is proposed to overcome the difficulty of local extrema in the likelihood function simulation results are provided to validate the analytical findings

product of two envelopes taken from alpha mu kappa mu and eta mu distributions this paper presents exact novel formulations for the probability density function and cumulative distribution function for the product of two independent and non identically distributed alpha mu kappa mu and eta mu variates the expressions are given in terms of both 1 generalized fox h function and 2 easily computable series expansions the formulations derived can be directly used to explore the performance of a number of wireless communication processes including multihop systems cascaded channels radar communications multiple input multiple output links and others due to the high flexibility of the above mentioned distributions the results presented here comprise a substantial number of useful product distributions as application examples performance metrics for the cascaded fading channel are derived the validity of the expressions is confirmed via monte carlo simulation it is noteworthy that because any composite multipath shadowing fading model is obtained as a particular case of the product of two fading variables the results given here provide a plethora of composite multipath shadowing fading scenarios

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

penalties of sample and sum and weighted partial decision detectors in gaussian noise the deterioration in performance measured in the probability of error sense of sample and sum and weighted partial decision detectors are analyzed for arbitrary signal to noise ratios these suboptimal detectors have more modest computational requirements than the optimal digital matched filter making them amenable to simple digital implementations the effects on the penalties of the signaling waveform employed the number of samples processed and the signal to noise ratio are considered in detail included are the penalties for the optimum weighted partial decision detector the optimum weighted partial decision detector is the optimum detector in the minimum probability of error sense for hard limited samples the penalty of the optimum weighted partial decision detector relative to the digital matched filter detector represents the fundamental loss in signal detectability due to hard limiting in a sampled system

precise ber computation for binary data detection in bandlimited white laplace noise investigation into the characteristics and behaviours of laplace noise is of crucial importance for evaluating the performance of communication systems operating in impulsive noise as well as for ultra wideband wireless systems operating in the presence of multi user interference the bit error rate performances of a binary data communication system operating in the presence of additive bandlimited white laplace noise is analyzed theoretically a theoretical expression for the average bit error rate is derived using the beaulieu series for the optimal soft limiting detector and the matched filter detector when arbitrary pulse shapes are used the bit error rate performance of a reduced complexity version of the optimal detector the hard limiting detector and a reduced complexity version of the matched filter detector the sum of samples detector are also analyzed the analytical expressions for the bit error rate are validated by numerical examples

the evaluation of error probabilities for intersymbol and cochannel interference an efficient series that can be used to calculate the probability of error in a binary symmetric channel with intersymbol interference and additive noise is derived the series is derived by representing the noise complementary probability distribution function by an exact or approximate fourier series bounds on the accuracy of the estimate are derived for gaussian noise examples show that only a small numerical effort may be required to compute error probabilities of interest using the series applications to both finite and infinite intersymbol interference systems are discussed a similar technique is used to derive series representations for the probability of error of additive noise channels with cochannel interference or with combined intersymbol and cochannel interferences the accuracy of the results is bounded for gaussian noise

a novel unified expression for the capacity and bit error probability of wireless communication systems over generalized fading channels analysis of the average binary error probabilities abep and average capacity ac of wireless communications systems over generalized fading channels have been considered separately in past years this paper introduces a novel moment generating function mgf based unified expression for the abep and ac of single and multiple link communications with maximal ratio combining in addition this paper proposes the hyper fox s h fading model as a unified fading distribution of a majority of the well known generalized fading environments as such the authors offer a generic unified performance expression that can be easily calculated and that is applicable to a wide variety of fading scenarios the mathematical formulism is illustrated with some selected numerical examples that validate the correctness of the authors newly derived results

further results on the beaulieu series a frequent problem in digital communications is the computation of the probability density function pdf and cumulative distribution function cdf given the characteristic function chf of a random variable rv this problem arises in signal detection equalizer performance equal gain diversity combining intersymbol interference and elsewhere often it is impossible to analytically invert the chf to get the pdf and cdf in closed form beaulieu 1990 1991 has derived an infinite series for the cdf of a sum of rvs that has been widely used we rederive his series using the gil pelaez 1951 inversion formula and the poisson sum formula this derivation has several advantages including both the bridging of the well known sampling theorem with beaulieu s series and yielding a simple expression for calculating the truncation error term it is also shown that the pdf and cdf can be computed directly using a discrete fourier transform

spontaneous motion on two dimensional continuous attractors attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons the firing rate profile or the neuronal activity is thought to carry information continuous attractor neural networks canns describe the neural processing of continuous information such as object position object orientation and direction of object motion recently it was found that in one dimensional canns short term synaptic depression can destabilize bump shaped neuronal attractor activity profiles in this article we study two dimensional canns with short term synaptic depression and spike frequency adaptation we found that the dynamics of canns with short term synaptic depression and canns with spike frequency adaptation are qualitatively similar we also found that in both kinds of canns the perturbative approach can be used to predict phase diagrams dynamical variables and speed of spontaneous motion

an infinite series for the computation of the complementary probability distribution function of a sum of independent random variables and its application to the sum of rayleigh random variables the properties of the series are studied for both bounded and unbounded random variables the technique is used to find efficient series for computation of the distributions of sums of uniform random variables and sums of rayleigh random variables a useful closed form expression for the characteristic function of a rayleigh random variable is presented and an efficient method for computing a confluent hypergeometric function is given an infinite series for the probability density function of a sum of independent random variables is also derived the inversion of characteristic functions a trapezoidal rule for numerical integration and the sampling theorem in the frequency domain are related to and interpreted in terms of the results

statistical analysis and characterization of the indoor propagation channel this paper presents statistical analysis of data obtained by measuring narrowband path loss at dect digital enhanced cordless telecommunications frequency 1 89 ghz in an indoor environment specific goodness of fit tests are applied to the data the tests assess whether the data generating source belongs to a known family of random variables results obtained support that local path loss distribution can be represented as weibull or nakagami in most environments the close resemblance between such distributions and rice distribution with rayleigh as a special case confirms that rice rayleigh description of fading can be applied to indoor environments that similarity leads to a general simple approximate expression for the cumulative distribution function of the signal to interference ratio the expression can be used without limitations to the number and the parameters of the rice interferers

off line recognition of large set handwritten characters with multiple hidden markov models there are many uncertainties in handwritten character recognition stochastic modeling is a flexible and general method for modeling such problems and entails the use of probabilistic models to deal with uncertain or incomplete information this paper presents an efficient scheme for off line recognition of large set handwritten characters in the framework of stochastic models the first order hidden markov models hmms to facilitate the processing of unconnected patterns and patterns with isolated noises four types of feature vectors based on the regional projection contour transformation rpct are employed the recognition system consists of two phases for each character in the training phase multiple hmms corresponding to different feature types of rpct are built in the classification phase the results of individual classifiers to produce the final recognition result for an input character are integrated where each individual hmm classifier produces one score that is the probability of generating the test observation sequence for each character model in this paper several methods for integrating the results of different classifiers are considered so that a better result could be obtained in order to verify the effectiveness of the proposed scheme the most frequently used 520 types of hangul characters in korea have been considered in the experiments experimental results indicate that the proposed scheme is very promising for the recognition of large set handwritten characters with numerous variations

on adaptive hmm state estimation new online adaptive hidden markov model hmm state estimation schemes are developed based on extended least squares els concepts and recursive prediction error rpe methods the best of the new schemes exploit the idempotent nature of markov chains and work with a least squares prediction error index using a posterior estimates more suited to markov models than traditionally used in identification of linear systems these new schemes learn the set of n markov chain states and the a posteriori probability of being in each of the states at each time instant they are designed to achieve the strengths in terms of computational effort and convergence rates of each of the two classes of earlier proposed adaptive hmm schemes without the weaknesses of each in these areas the computational effort is of order n implementation aspects of the proposed algorithms are discussed and simulation studies are presented to illustrate convergence rates in comparison to earlier proposed online schemes

data aided and non data aided estimation of signal amplitude for binary data communication in laplace noise the estimation of signal amplitude for a binary data communication system in the presence of laplace noise is considered maximum likelihood estimators are derived for both data aided and non data aided cases an absolute median estimator with a simple implementation is also proposed the performances of the proposed estimators are examined and compared to well known previous estimators in terms of mean squared error numerical results quantify the superiority of the proposed estimators over the conventional estimators