optimization based differential kinematic modeling exhibits a velocity control strategy for dynamic posture determination in seated reaching movements abstract we proposed a velocity control strategy for dynamic posture determination that underlay an optimization based differential inverse kinematics odik approach for modeling three dimensional 3 d seated reaching movements in this modeling approach a four segment seven dof linkage is employed to represent the torso and right arm kinematic redundancy is resolved efficiently in the velocity domain via a weighted pseudoinverse weights assigned to individual dof describe their relative movement contribution in response to an instantaneous postural change different schemes of posing constraints on the weighting parameters by which various motion apportionment strategies are modeled can be hypothesized and evaluated against empirical measurements a numerical optimization procedure based on simulated annealing estimate the weighting parameter values such that the predicted movement best fits the measurement we applied this approach to modeling 72 seated reaching movements of three distinctive types performed by six subjects results indicated that most of the movements were accurately modeled time averaged rmse

stochastic modeling of the transform domain varepsilon rm lms algorithm this paper presents a statistical analysis of the transform domain least mean square tdlms algorithm resulting in a more accurate model than those discussed in the current open literature the motivation to analyze such an algorithm comes from the fact that the tdlms presents a higher convergence speed for correlated input signals as compared with other adaptive algorithms possessing a similar computational complexity such a fact makes it a highly competitive alternative to some applications approximate analytical models for the first and second moments of the filter weight vector are obtained the tdlms algorithm has an orthonormal transformation stage accomplishing a decomposition of the input signal into distinct frequency bands in which the interband samples are practically uncorrelated on the other hand the intraband samples are correlated the larger the number of bands the higher their correlation the model is then derived taking into account such a correlation requiring that a high order hyperelliptic integral be computed in addition to the proposed model an approximate procedure for computing high order hyperelliptic integrals is presented a regularization parameter is also considered in the model expressions permitting to assess its impact on the adaptive algorithm behavior an upper bound for the step size control parameter is also obtained through simulation results the accuracy of the proposed model is assessed

energy based event triggered state estimation for hidden markov models abstract in this work a problem of energy based event triggered remote state estimation for systems described by discrete finite state hidden markov models is investigated we consider energy harvesting sensors which absorb power from the environment or other resources and convert it to electrical power the event triggering condition etc considered depends on the sensor energy level which evolves according to a markov process the reference measure approach is used to obtain the optimal estimates of the state based on event triggered measurement information available at the remote estimator by introducing a reference probability measure and a map from the real world measure to the reference measure we derive the recursive expression of the unnormalized state conditional distribution under the reference measure which depends on the estimate of the energy level next we propose a second level of reference probability measure under which the state measurement energy level and event trigger are mutually independent so that the recursive form of unnormalized estimate of the energy level under this reference measure can be obtained with the help of the two reference measures the state estimate in the real world probability measure can be derived the effectiveness of the proposed method is illustrated with simulation results for a linear gaussian system quantized and parameterized into a hidden markov model

stability of markovian jump generalized neural networks with interval time varying delays this paper examines the problem of asymptotic stability for markovian jump generalized neural networks with interval time varying delays markovian jump parameters are modeled as a continuous time and finite state markov chain by constructing a suitable lyapunov krasovskii functional lkf and using the linear matrix inequality lmi formulation new delay dependent stability conditions are established to ascertain the mean square asymptotic stability result of the equilibrium point the reciprocally convex combination technique jensen s inequality and the wirtinger based double integral inequality are used to handle single and double integral terms in the time derivative of the lkf the developed results are represented by the lmi the effectiveness and advantages of the new design method are explained using five numerical examples

stability analysis for neural networks with time varying interval delay this letter is concerned with the stability analysis of neural networks nns with time varying interval delay the relationship between the time varying delay and its lower and upper bounds is taken into account when estimating the upper bound of the derivative of lyapunov functional as a result some improved delay interval dependent stability criteria for nns with time varying interval delay are proposed numerical examples are given to demonstrate the effectiveness and the merits of the proposed method

posture prediction for static sagittal plane lifting three separate models are presented to predict the postures of humans performing static sagittal lifting tasks the models use a common inverse kinematics characterization to represent mathematically feasible postures but explore different criteria functions for selecting a final posture the first criterion assumes that subjects minimize the overall effort associated with a posture the second criterion expresses effort locally as opposed to globally and minimizes this value the third criterion maximizes body stability the postures predicted by these three models were compared to the postures assumed by 16 subjects performing 4 static sagittal lifting tasks the results showed that all of the prediction errors were significantly greater than zero but that the first objective function minimum total torque was more accurate than the other two criteria the models were in general less accurate for postures that had lower hand positions than for those with higher hand positions

sampled data state estimation for markovian jumping fuzzy cellular neural networks with mode dependent probabilistic time varying delays in this paper the problem of state estimation for markovian jumping fuzzy cellular neural networks fcnns using sampled data with mode dependent probabilistic time varying delays is investigated by developing a delay decomposition approach the information of the delayed states can be taken into full consideration by introducing a stochastic variable with a bernoulli distribution the information of probability distribution of the time varying delay is considered and transformed into one with deterministic time varying delay the main purpose of this paper is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally asymptotically stable in the mean square based on the lyapunov krasovskii functional including triple integral terms and decomposed integral intervals delay distribution dependent stability criteria are obtained in terms of linear matrix inequalities lmis finally two numerical examples are given to illustrate the effectiveness of the proposed theoretical results

wavelet transform domain adaptive fir filtering this paper presents and studies two new wavelet transform domain least mean square lms algorithms the algorithms exploit the special sparse structure of the wavelet transform of wide classes of correlation matrices and their cholesky factors in order to compute a whitening transformation of the input data in the wavelet domain and minimize computational complexity the procedures differ in the exact estimates they use and in the way they identify the data dependent whitening transformation the first approach explicitly computes a sparse estimate of the wavelet domain correlation matrix of the input process it then computes the cholesky factor of that matrix and uses its inverse to whiten the input the complexity of this first approach is o n log sup 2 n in contrast the second approach computes a sparse estimate of the cholesky factor of the wavelet domain correlation matrix of the input process directly this second approach has a computational complexity of o n log n floating point operations however it requires a more complex bookkeeping procedure both algorithms have a convergence rate that is faster than that of time domain lms and discrete fourier transform dft or discrete cosine transform dct based lms procedures the paper compares the two procedures and analyzes their mean and mean square performance

realization of adaptive digital filters using the fermat number transform an efficient realization of the block least mean square adaptive digital filters using fast transforms is discussed the computational complexities of the fermat number transform realization and the fixed point fast fourier transform realization are compared and their convergence properties are studied through the computer simulation of practical applications

block realization of multirate adaptive digital filters in this paper we study multirate transversal adaptive digital filters adf s based on the block least mean square blms algorithms these include an adf with decimation adfd and an adf with interpolation adfi we first formulate these filters based on the block mean squared error bmse criterion and derive blms multi rate weight adjustment algorithms and then study efficient realization of those filters it is shown that the blms adfd can be realized efficiently in the direct form or in the filter bank structure both using the fast fourier transform fft and an appropriate sectioning procedure according to our analysis of computational complexity the fft realization of the blms adfd in the filter bank structure is more efficient than in the direct form and the two structures using the fft become more efficient in comparison to the lms adfd as the number of weights increases unlike the direct form realization the filter bank realization of the blms adfd using the fft becomes more efficient as the decimation ratio increases similar results have been obtained for the blms adfi finally we investigate by computer simulation the effects of different weight adjustment algorithms and several system parameters on the performances of adfd s the results of simulation indicate that as expected the convergence speed of the blms adfd can be significantly improved by self orthogonalization of weight adjustment in the frequency domain furthermore the convergence factor of the self orthogonalizing blms adfd can be chosen such that the steady state performance of the new adfd is the same as that of the existing lms adfd

performance of transform domain lms adaptive digital filters in this paper we analyze the performance particularly the convergence behavior of the transform domain least mean square lms adaptive digital filter adf using the discrete fourier transform and discrete orthogonal transforms such as discrete cosine and sine transforms we first obtain the optimum wiener solution and the minimum mean squared error mse in the transform domain it is shown that the two minimum mse s in the time and transform domains are identical independently of the transforms used we then study the convergence conditions and the steady state excess mse s of the transform domain lms trlms algorithms both for the cases of having a constant and a time varying convergence factors when a constant convergence factor is used the convergence behaviors of the lms and trlms adf s appear to be almost identical provided that each has an appropriate value of the convergence factor depending on the transform used also based on the concept of a self orthogonalizing algorithm in the transform domain it is shown that the convergence speed of the trlms adf can be improved significantly for the same excess mse as that of the lms adf in addition we compare the computational complexities of the lms and trlms adf s finally we investigate by computer simulation the effects of system parameter values and different transforms on the convergence behavior of the trlms adf

transform domain lms algorithm the concept of transform domain adaptive filtering is introduced in certain applications filtering in the transform domain results in great improvements in convergence rate over the conventional time domain adaptive filtering the relationship between several existing frequency domain adaptive filtering algorithms is established applications of the discrete fourier transform dft and the discrete cosine transform dct domain adaptive filtering algorithms in the areas of speech processing and adaptive line enhancers are discussed

wirtinger based integral inequality application to time delay systems in the last decade the jensen inequality has been intensively used in the context of time delay or sampled data systems since it is an appropriate tool to derive tractable stability conditions expressed in terms of linear matrix inequalities lmis however it is also well known that this inequality introduces an undesirable conservatism in the stability conditions and looking at the literature reducing this gap is a relevant issue and always an open problem in this paper we propose an alternative inequality based on the fourier theory more precisely on the wirtinger inequalities it is shown that this resulting inequality encompasses the jensen one and also leads to tractable lmi conditions in order to illustrate the potential gain of employing this new inequality with respect to the jensen one two applications on time delay and sampled data stability analysis are provided

delay slope dependent stability results of recurrent neural networks by using the fact that the neuron activation functions are sector bounded and nondecreasing this brief presents a new method named the delay slope dependent method for stability analysis of a class of recurrent neural networks with time varying delays this method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed lyapunov krasovskii functional then some improved delay dependent stability criteria with less computational burden and conservatism are obtained numerical examples are given to illustrate the effectiveness and the benefits of the proposed method

biomechanical simulation of manual lifting using spacetime optimization previous optimization techniques for the prediction of lifting motion patterns often require a change in either the number of variables or the order of the mathematical functions used to express the angular displacement of selected joints in response to change in variant conditions the resolution of predicted results can also be seriously constrained by the number of variables used these restrictions may often limit the applicability of these methodologies in this paper we proposed a new methodology for generating the optimum motion patterns for para sagittal lifting tasks a detailed description of this methodology is introduced an example of an analysis using this methodology is presented the computer program generated lifting motion patterns with a reduction of the overall objective function values the actual versus predicted lifting motion patterns are compared using this method constraints can be added anywhere within the lifting cycle without the need of rewriting the whole program these features provide for a more flexible and efficient prediction of the lifting motion

impulsive control for existence uniqueness and global stability of periodic solutions of recurrent neural networks with discrete and continuously distributed delays in this paper a class of recurrent neural networks with discrete and continuously distributed delays is considered sufficient conditions for the existence uniqueness and global exponential stability of a periodic solution are obtained by using contraction mapping theorem and stability theory on impulsive functional differential equations the proposed method which differs from the existing results in the literature shows that network models may admit a periodic solution which is globally exponentially stable via proper impulsive control strategies even if it is originally unstable or divergent two numerical examples and their computer simulations are offered to show the effectiveness of our new results

event triggered generalized dissipativity filtering for neural networks with time varying delays this paper is concerned with event triggered generalized dissipativity filtering for a neural network nn with a time varying delay the signal transmission from the nn to its filter is completed through a communication channel it is assumed that the network measurement of the nn is sampled periodically an event triggered communication scheme is introduced to design a suitable filter such that precious communication resources can be saved significantly while certain filtering performance can be ensured on the one hand the event triggered communication scheme is devised to select only those sampled signals violating a certain threshold to be transmitted which directly leads to saving of precious communication resources on the other hand the filtering error system is modeled as a time delay system closely dependent on the parameters of the event triggered scheme based on this model a suitable filter is designed such that certain filtering performance can be ensured provided that a set of linear matrix inequalities are satisfied furthermore since a generalized dissipativity performance index is introduced several kinds of event triggered filtering issues such as h infty filtering passive filtering mixed h infty and passive filtering q s r dissipative filtering and mathcal l 2 mathcal l infty filtering are solved in a unified framework finally two examples are given to illustrate the effectiveness of the proposed method

further results on exponential stability for impulsive switched nonlinear time delay systems with delayed impulse effects this paper is concerned with the problem of exponential stability for a class of impulsive switched nonlinear time delay systems with delayed impulse effects by using the multiple lyapunov krasovskii functionals technique some exponential stability criteria are obtained respectively for two kinds of impulsive signals destabilizing impulsive signal and stabilizing impulsive signal the derived results not only characterise the effects of delayed impulse time delay and switching on nonlinear systems but also remove some restriction conditions furthermore a more precise bound of system state is given compared with existing results on related problems the obtained results are less conservative three examples are provided to illustrate the effectiveness and the generality of the proposed results

event triggered state estimation for discrete time multidelayed neural networks with stochastic parameters and incomplete measurements in this paper the event triggered state estimation problem is investigated for a class of discrete time multidelayed neural networks with stochastic parameters and incomplete measurements in order to cater for more realistic transmission process of the neural signals we make the first attempt to introduce a set of stochastic variables to characterize the random fluctuations of system parameters in the addressed neural network model the delays among the interconnections are allowed to be different which are more general than those in the existing literature the incomplete information under consideration includes randomly occurring sensor saturations and quantizations for the purpose of energy saving an event triggered state estimator is constructed and a sufficient condition is given under which the estimation error dynamics is exponentially ultimately bounded in the mean square it is worth noting that the ultimate boundedness of the error dynamics is explicitly estimated the characterization of the desired estimator gain is designed in terms of the solution to a certain matrix inequality finally a numerical simulation example is presented to illustrate the effectiveness of the proposed event triggered state estimation scheme

reciprocally convex approach to stability of systems with time varying delays whereas the upper bound lemma for matrix cross product introduced by park 1999 and modified by moon park kwon and lee 2001 plays a key role in guiding various delay dependent criteria for delayed systems the jensen inequality has become an alternative as a way of reducing the number of decision variables it directly relaxes the integral term of quadratic quantities into the quadratic term of the integral quantities resulting in a linear combination of positive functions weighted by the inverses of convex parameters this paper suggests the lower bound lemma for such a combination which achieves performance behavior identical to approaches based on the integral inequality lemma but with much less decision variables comparable to those based on the jensen inequality lemma

the use of the modified escalator algorithm to improve the performance of transform domain lms adaptive filters this paper describes a new algorithm that improves the convergence performance of the transform domain least mean square trlms algorithm the algorithm exploits the sparse structure of the correlation matrix of the transformed input process to derive a data dependent gram schmidt orthogonalization type transform of the process we show its faster convergence compared with the time domain least mean square lms algorithm and the dct or the dwt based trlms algorithm the gram schmidt orthogonalization is realized using a modified adaptive escalator algorithm the modification significantly reduces the computational complexity of the adaptive escalator algorithm and determines the computational complexity of the proposed algorithm

transform domain adaptive filters an analytical approach transform domain adaptive filters refer to lms filters whose inputs are preprocessed with a unitary data independent transformation followed by a power normalization stage the transformation is typically chosen to be the discrete fourier transform dft although other transformations such as the cosine transform dct the hartley transform dht or the walsh hadamard transform have also been proposed in the literature the resulting algorithms are generally called dft lms dct lms etc this preprocessing improves the eigenvalue distribution of the input autocorrelation matrix of the lms filter and as a consequence ameliorates its convergence speed in this paper we start with a brief intuitive explanation of transform domain algorithms we then analyze the effects of the preprocessing performed in dft lms and dct lms for first order markov inputs in particular we show that for markov 1 inputs of correlation parameter spl rho spl isin 0 1 the eigenvalue spread after dft and power normalization tends to 1 spl rho l 1 spl rho as the size of the filter gets large whereas after dct and power normalization it reduces to 1 spl rho for comparison the eigenvalue spread before transformation is asymptotically equal to 1 spl rho sup 2 1 spl rho sup 2 the analytical method used in the paper provides additional insight into how the algorithms work and is expected to extend to other input signal classes and other transformations

state estimation for markovian jump linear systems with bounded disturbances in this paper we investigate the state estimation problem for a class of markovian jump linear systems mjlss in the presence of bounded polyhedral disturbances a set membership estimation algorithm is first proposed to find the smallest consistent set of all possible states which is shown to be expressed by a union of multiple polytopes the posterior probabilities of the system jumping modes are then estimated by introducing the lebesgue measure based on which the optimal point estimate is further provided moreover a similarity relationship for polytopes is defined and an approximate method is presented to calculate the minkowski sum of polytopes which can help reduce the computational complexity of the overall estimation algorithm

development of a set of equations describing joint trajectories during para sagittal lifting given a lifting task with predetermined starting and ending positions the angular trajectories are usually very consistent with a distinctive pattern this paper derives a set of equations that can describe the joint trajectories during a para sagittal lifting task three optimal motion patterns were also expressed by the polynomials minimal hand jerk minimal center of gravity cg jerk and minimal muscle utilization rate mur the variability of the joint movements were synthesized by overlapping the optimal patterns

spatial resolution of spontaneous accelerations in reaching tasks reaching tasks are considered well executed if they appear smooth a quality that is typically quantified by its opposite jerk the rate of change of acceleration while jerk is a theoretically sound measure its application to spastic individuals sometimes yields counter intuitive results and does not reveal motor impairment across the workspace to more generally quantify spontaneous accelerative transients sats within a movement a pseudo wavelet transform was devised that iteratively compared angular trajectories to a series of straight line approximants cumulative linear fit errors were expressed in terms of flexion angle yielding an sat map of the entire motion to compare sat maps with traditional smoothness measures two scalar indices were extracted from them residual excursion deviation red representing the integral over deltatheta and the ratio of peak error to mean error peme on the map fifteen subjects including five subjects with chronic stroke performed elbow flexions throughout their entire ranges of motion deltatheta at a comfortable pace with their arms supported in the transverse plane maps revealed that stroke subjects were significantly less coordinated than controls as measured both by red 8 0 2 9 x 10 3 versus 3 1 0 8 x 10 3 and peme 6 6 0 9 versus 12 1 1 9 both p 0 001 comparable jerk metrics including integrated average jerk did not report a significant performance deficit at the p 0 05 level map metrics for all subjects were independent of average velocity correlation with theta rho0 31 but jerk based metrics for stroke subjects were spuriously co variant with velocity rho 0 85 which may relate to the significantly higher mean arrest period ratio in stroke subjects 0 26 0 19 versus 0 09 0 08 p 0 001 we conclude that sat maps provide reliable information on regional movement impairments at a wide range of proficiency levels

magnitude squared conherence function estimation an adaptive approach this paper concerns an adaptive approach for estimating the magnitude squared coherence msc function via widrow s least mean square lms algorithm analytical expressions for the bias and variance of the estimate so obtained are derived for the case when the input signals are stationary some computer simulation results are also included for both stationary and nonstationary signals

self orthogonalizing adaptive equalization in the discrete frequency domain a self orthogonalizing discrete adaptive equalizer for synchronous data transmission is presented based on the overlapsave filtering technique self orthogonalization in the discrete frequency domain is adaptively performed by premultiplying by a diagonal matrix the mse gradient estimates before projecting by means of rosen s gradient projection method the diagonal of the matrix is the inverse of the power spectrum of the received sequence taken at equally spaced frequencies and estimates are obtained by using bartlett s procedure of periodograms averaging for spectrum estimation projection is accomplished by means of an off line derived projection matrix confidence of gradient estimates is improved by means of a block correlated estimation technique using available dft s of blocks of data this equalizer is compared to time domain self orthogonalizing algorithms as regards speed of convergence and ease of implementation during the short startup phase convergence is competitive with that of godard s algorithm which is the fastest algorithm known and in the decision directed mode fast convolution performed by blocks results in a considerable reduction of the number of multiplications with respect to the time domain algorithms a computationally simpler unconstrained startup algorithm is also examined obtained by removing the projection while retaining gradient block estimates in the dfd

unconstrained frequency domain adaptive filter an unconstrained frequency domain least mean squares uflms algorithm is presented the algorithm is based on the overlap save technique used in frequency domain filtering the proposed algorithm converges to the wiener solution without the time domain constraint on the impulse response as proposed by ferrara for a large number of taps geq32 the uflms algorithm offers a significant reduction in computation another advantage is its fast convergence for highly correlated input signals the performance of the algorithm is illustrated by a simulation

finite time state estimation for recurrent delayed neural networks with component based event triggering protocol this paper deals with the event based finite time state estimation problem for a class of discrete time stochastic neural networks with mixed discrete and distributed time delays in order to mitigate the burden of data communication a general component based event triggered transmission mechanism is proposed to determine whether the measurement output should be released to the estimator at certain time point according to a specific triggering condition a new concept of finite time boundedness in the mean square is put forward to quantify the estimation performance by introducing a settling like time function the objective of the addressed problem is to construct an event based state estimator to estimate the neuron states such that in the presence of both mixed time delays and external noise disturbances the dynamics of the estimation error is finite time bounded in the mean square with a prescribed error upper bound sufficient conditions are established via stochastic analysis techniques to guarantee the desired estimation performance by solving an optimization problem with some inequality constraints the explicit expression of the estimator gain matrix is characterized to minimize the settling like time finally a numerical simulation example is exploited to demonstrate the effectiveness of the proposed estimator design scheme

fast positive definite linear system solvers the authors show that the m band wavelet transforms of a wide class of covariance matrices consist of subblocks that are essentially banded furthermore they prove that the cholesky factors of the transformed covariance matrices also consist of subblocks that are essentially banded they combine these two observations to construct a fast o n sup 2 algorithm for solving the n spl times n linear positive definite systems of equations that arise in statistical signal processing finally they provide an error analysis of the proposed linear positive definite system solver

state estimation on positive markovian jump systems with time varying delay and uncertain transition probabilities investigate the positive discrete time mjs with disturbance input time varying delay and uncertain transition probabilities together for the first time where the transition probability matrix is described by a polytope set provide a necessary and sufficient condition of stochastic stability with regard to the resulting error dynamic system present the exact computation of l gain index for the resulting stochastically stable error dynamic systems firstly and derive a necessary and sufficient condition of the l gain performance correspondingly provide a sufficient condition on the existence of a desired observer to solve the state estimation problem such that the error dynamic system is stochastically stable with a prescribed l gain performance all the proposed conditions are expressed in linear programming forms which can be solved effectively by using some existing softwares and the observer gain matrices can be directly obtained by an effective algorithm this paper is concerned with the state estimation problem based on an l observer for a class of positive discrete time markovian jump systems mjss the system under consideration consists of time varying delay and uncertain transition probabilities where the transition probability matrix is described by a polytope set necessary and sufficient conditions are presented in linear programming lp form for the resulting error dynamic system to be stochastically stable and stochastically stable with an l gain performance respectively furthermore an l observer design scheme is presented to solve the state estimation problem such that the resulting error dynamic system is stochastically stable and achieves a prescribed l gain performance all the proposed conditions are solvable in terms of lp with additional parameters finally some examples are given to demonstrate the validity of the developed technique

impulsive differential equations this paper deals with the periodic solutions problem for impulsive differential equations by using lyapunov s second method and the contraction mapping principle some conditions ensuring the existence and global attractiveness of unique periodic solutions are derived which are given from impulsive control and impulsive perturbation points of view as an application the existence and global attractiveness of unique periodic solutions for hopfield neural networks are discussed finally two numerical examples are provided to demonstrate the effectiveness of the proposed results

event triggered state estimation for markovian jumping impulsive neural networks with interval time varying delays abstractthis paper investigates the event triggered state estimation problem of markovian jumping impulsive neural networks with interval time varying delays the purpose is to design a state estimator to estimate system states through available output measurements in the neural networks there are a set of modes which are determined by markov chain a markovian jumping time delay impulsive neural networks model is employed to describe the event triggered scheme and the network related behaviour such as transmission delay data package dropout and disorder the proposed event triggered scheme is used to determine whether the sampled state information should be transmitted the discrete delays are assumed to be time varying and belong to a given interval which means that the lower and upper bounds of interval time varying delays are available first we design a state observer to estimate the neuron states second based on a novel lyapunov krasovskii functional lkf with triple integral terms and us