Model-based estimation of knee stiffness

During natural locomotion, the stiffness of the human knee is modulated continuously and subconsciously according to the demands of activity and terrain. Given modern actuator technology, powered transfemoral prostheses could theoretically provide a similar degree of sophistication and function. However, experimentally quantifying knee stiffness modulation during natural gait is challenging. Alternatively, joint stiffness could be estimated in a less disruptive manner using electromyography (EMG) combined with kinetic and kinematic measurements to estimate muscle force, together with models that relate muscle force to stiffness. Here we present the first step in that process, where we develop such an approach and evaluate it in isometric conditions, where experimental measurements are more feasible. Our EMG-guided modeling approach allows us to consider conditions with antagonistic muscle activation, a phenomenon commonly observed in physiological gait. Our validation shows that model-based estimates of knee joint stiffness coincide well with experimental data obtained using conventional perturbation techniques. We conclude that knee stiffness can be accurately estimated in isometric conditions without applying perturbations, which presents an important step toward our ultimate goal of quantifying knee stiffness during gait.     

Model-based temporal object verification using video

An approach to model-based dynamic object verification and identification using video is proposed. From image sequences containing the moving object, we compute its motion trajectory. Then we estimate its three-dimensional (3-D) pose at each time step. Pose estimation is formulated as a search problem, with the search space constrained by the motion trajectory information of the moving object and assumptions about the scene structure. A generalized Hausdorff (1962) metric, which is more robust to noise and allows a confidence interpretation, is suggested for the matching procedure used for pose estimation as well as the identification and verification problem. The pose evolution curves are used to assist in the acceptance or rejection of an object hypothesis. The models are acquired from real image sequences of the objects. Edge maps are extracted and used for matching. Results are presented for both infrared and optical sequences containing moving objects involved in complex motions     

Extendability tests for multidimensional covariance sequences

The problem of extendibility of multidimensional covariance sequences and the equivalent problem of the existence of maximum entropy (ME) spectral estimates are analyzed using some recent results on the valid parameter space of Gaussian Markov random fields (GMRFs). For several nontrivial examples, explicit conditions for extendability and, using those conditions, sketches of the space of extendible covariance sequences are obtained. For the general case, a cutting-plane algorithm is proposed as an alternative to the two existing numerical procedures for ascertaining extendibility, namely, the linear programming procedure and the expanding-hull algorithm. The duality between the valid parameter space and the space of extendible covariances, and the relationship between those two spaces and the space of admissible covariances for finite-size data sequences, are examined. The connection between extendability and maximum-likelihood (ML) estimation is established, and some properties extendibility of covariances specified over increasing window sizes are presented     

Model-based estimation for dynamic cardiac studies using ECT

The authors develop a strategy for joint estimation of physiological parameters and myocardial boundaries using ECT (emission computed tomography). They construct an observation model to relate parameters of interest to the projection data and to account for limited ECT system resolution and measurement noise. The authors then use a maximum likelihood (ML) estimator to jointly estimate all the parameters directly from the projection data without reconstruction of intermediate images. They also simulate myocardial perfusion studies based on a simplified heart model to evaluate the performance of the model-based joint ML estimator and compare this performance to the Cramer-Rao lower bound. Finally, the authors discuss model assumptions and potential uses of the joint estimation strategy.     

Model-based quarter-pixel motion estimation with low computational complexity

Proposed is an interpolation-free quarter-pixel motion estimation method based on a mathematical model for video encoding. In this method, only three shift operators and four compare operators are required in each horizontal and vertical direction to find a quarter-pixel resolution motion vector. Experimental results show that the proposed method reaches almost the same performance as an interpolation-based full-search for quarter-pixel motion estimation with much less computational complexity.     

Model-based channel estimation for OFDM signals in Rayleigh fading

This paper proposes a robust pilot-assisted channel estimation method for orthogonal frequency division multiplexing (OFDM) signals in Rayleigh fading. Our estimation method is based on nonlinear regression channel models. Unlike the linear minimum mean-squared error (LMMSE) channel estimate, the method proposed does not have to know or estimate channel statistics like the channel correlation matrix and the average signal-to-noise ratio (SNR) per bit. Numerical results indicate that the performance of the proposed channel estimator is very close to the theoretical bit error propagation lower bound that is obtained by a receiver with perfect channel response information     

Model-based human circadian phase estimation using a particle filter

We present a method for tracking an individual's circadian phase that integrates dynamic models of circadian physiology with physiological measurements in a Bayesian statistical framework. A model of the circadian pacemaker's response to light exposure is transformed into a nonlinear state-space model with a circadian phase state. The probability distribution of the circadian phase is estimated by a particle filter that predicts changes over time based on the model, and performs updates with information gained from physiological measurements. Simulations demonstrate how probability distributions allow flexible initialization of model states and enable statistical quantification of entrainment and divergence properties of the circadian pacemaker. The combined use of sleep-wake scheduling data and physiological measurements is demonstrated in a case study highlighting advantages for addressing the challenge of noninvasive ambulatory monitoring of circadian physiology.     

Capon estimation of covariance sequences

Estimating the covariance sequence of a wide-sense stationary process is of fundamental importance in digital signal processing (DSP). A new method, which makes use of Fourier inversion of the Capon spectral estimates and is referred to as theCapon method, is presented in this paper. It is shown that the Capon power spectral density (PSD) estimator yields an equivalent autoregressive (AR) or autoregressive moving-average (ARMA) process; hence, theexact covariance sequence corresponsing to the Capon spectrum can be computed in a rather convenient way. Also, without much accuracy loss, the computation can be significantly reduced via an approximate Capon method that utilizes the fast Fourier transform (FFT). Using a variety of ARMA signals, we show that Capon covariance estimates are generally better than standard sample covariance estimates and can be used to improve performances in DSP applications that are critically dependent on the accuracy of the covariance sequence estimates.This work was supported in part by National Science Foundation Grant MIP-9308302, Advanced Research Project Agency Grant MDA-972-93-1-0015, the Senior Individual Grant Program of the Swedish Foundation for Strategic Research and the Swedish Research Council for Engineering Sciences (TFR).     

Background memory area estimation for multidimensional signalprocessing systems

Memory cost is responsible for a large amount of the chip and/or board area of customized video and image processing system realizations. In this paper, we present a novel technique-founded on data-flow analysis which allows one to address the problem of background memory size evaluation for a given nonprocedural algorithm specification, operating on multidimensional signals with affine indexes. Most of the target applications are characterized by a huge number of signals, so a new polyhedral data-flow model operating on groups of scalar signals is proposed. These groups are obtained by a novel analytical partitioning technique, allowing to select a desired granularity, depending on the application complexity. The method incorporates a way to tradeoff memory size with computational and controller complexity     

Nonparametric algorithm for local frequency estimation of multidimensional signals

Local frequency (LF) estimation of multidimensional (md) signals is considered. The md-Wigner distribution (WD) is used as the LF estimator. The LF is estimated based on the positions of the WD maxima. A nonparametric algorithm for the LF estimation is developed. It is based on the intersection of confidence intervals rule. This algorithm produces an adaptive window size in the WD which gives almost minimal mean squared error of the estimate. A simplified version of this algorithm is developed, with the starting estimate being produced with the WD of one-dimensional signals. Theory is illustrated in examples.     

Sampling multidimensional signals by a new class of quasi-random sequences

A new method of equidistributed (EQD) sampling of nD signals is proposed and investigated. Equidistributed or quasi-random (QR) sequences are deterministic sequences, which cover nD space more evenly than uniformly distributed random sequences. We propose to generate quasi-random nD sample sites using space-filling curves, which propagate their measure and neighborhood preserving properties to sample points. It is proved that these points are not only equidistributed but also well distributed, which results in a good volume approximation from samples of 2D and 3D images. A general way of reconstructing output of a linear system from QR samples of its input nD signals is also proposed and proved to be convergent. As an example of applications of the theory, a double radial basis neural network is proposed, which allows for the reconstruction of 2D cross-sections from samples of 3D image or from a video sequence of 2D images.     

基于样本熵的DNA序列相似性分析

针对传统方法在分析DNA序列相似性方面的不足,提出了一种基于样本熵的DNA序列相似性分析方法.以5种东亚钳蝎神经毒素的基因序列作为分析对象,首先通过DNA序列的图形表示把DNA序列转换为时间序列,然后运用样本熵算法计算出时间序列的样本熵值,将样本熵的互值大小作为分析序列之间相似性的依据,最后将样本熵方法与DTW(Dynamic Time Warping,动态时间弯曲)方法的实验结果进行比较.实验结果表明,样本熵分析方法能有效分析序列之间的相似性,与DTW分析方法相比较,显示出更强的相似性和区别度,可将其进一步应用于生物序列的分析.     

Model-based parameter estimation in electromagnetics. I. Backgroundand theoretical development

This article first provides a background and motivation for using model based parameter estimation (MBPE) in electromagnetics, focusing on the use of fitting models that are described by exponential and pole series. How data obtained from various kinds of sampling procedures can be used to quantify such models, i.e., to determine numerical values for their coefficients is also presented. The paper continues by illustrating applications of MBPE to various kinds of EM observables. It concludes by discussing how MBPE might be used to improve the efficiency of first-principles models based on frequency-domain integral equations     

Model-based estimation of wind fields over the ocean from windscatterometer measurements. II. Model parameter estimation

For pt.I see ibid., vol.28, no.3, p.349-360 (1990). The feasibility of a model-based approach to wind field estimation is discussed. In this approach the parametric model for near-surface mesoscale wind fields developed in pt.I is used. The parameters of the model are estimated from the wind scatterometer measurements; the wind field estimate is then computed from the estimated model parameters. Unlike the traditional pointwise approach, this approach takes advantage of the inherent correlation in the winds at different sample points to estimate the wind field more accurately and resolve directional ambiguity. The accuracies of wind field estimates obtained using the traditional pointwise estimation scheme and the model-based approach using simulated scatterometer measurements are compared     

Channel estimation using aperiodic binary sequences

Estimating a channel impulse response using a known aperiodic sequence is considered. The problem can be reduced to minimizing the trace of the inverse of a Toeplitz matrix. An efficient algorithm for computing this trace is developed and optimal binary sequences up to length 32 are found and tabulated. The use of complementary sequences in this context is also investigated. It is shown that the eigenvalues of the autocorrelation matrices of a pair of complementary sequences sum to a known constant     

Complementary sequences for ISI channel estimation

A merit factor based on the sequence autocorrelation function, whose minimization leads to the reduction in the Cramer-Rao lower bound (CRLB) for the variance of “two-sided” intersymbol interference (ISI) channel estimation is introduced. Pairs of binary pilot symbol sequences (a preamble and a postamble) for channel estimation are jointly designed to minimize this merit factor. Given that the number of channel taps is L and the length of a pilot symbol sequence is (N+L-1), where N⩾L, we distinguish between the case when N is even and the case when it is odd. For even N, we show that complementary sequences not only minimize the merit factor, but also the CRLB. For a subset of odd N we construct almost-complementary periodic sequence pairs that minimize the merit factor. The optimal pilot symbol block signaling requires alternating between two (in most cases) different binary sequences that form the merit-minimizing pair     

基于时间段的时序规则发现

提出了一个新的基于时间段的频繁闭模式的挖掘算法,采用时间段的概念,利用频繁闭模式的特点,生成相应的时序规则.算法通过使用闭模式的性质进行剪枝优化,不生成冗余的候选序列,降低了时序规则发现的时间与空间复杂度,提高了效率.     

Parameter estimation using pseudorandom binary sequences

The instrumental-variable technique for parameter estimation is applied when the input is a pseudorandom binary sequence (p.r.b.s.). The instrumental variables proposed are time-weighted elements of a p.r.b.s., and this is shown to be equivalent to obtaining the mean value of several estimates obtained from noisy measurements. The estimates of the parameters are asymptotically unbiased.     

Model-based parameter estimation in electromagnetics. II.Applications to EM observables

Discusses spectral domain model based parameter estimation (MBPE), waveform-dominated MBPE, and adapting and optimizing the sampling of the generating model. These are discussed with reference to antenna theory including scattering