Propagated guided image filtering for edge-preserving smoothing

This paper proposes an edge-preserving smoothing filtering algorithm based on guided image filter (GF). GF is a well-known edge-preserving smoothing filter, but is ineffective in certain cases. The proposed GF enhancement provides a better solution for various noise levels associated with image degradation. In addition, halo artifacts, the main drawback of GF, are well suppressed using the proposed method. In our proposal, linear GF coefficients are updated sequentially in the spatial domain by using a new cost function, whose solution is a weighted average of the neighboring coefficients. The weights are determined differently depending on whether the pixels belong to the edge region, and become zero when a neighborhood pixel is located within a region separated from the center pixel. This propagation procedure is executed twice (from upper-left to lower-right, and vice versa) to obtain noise-free edges. Finally, the filtering output is computed using the updated coefficient values. The experimental results indicate that the proposed algorithm preserves edges better than the existing algorithms, while reducing halo artifacts even in highly noisy images. In addition, the algorithm is less sensitive to user parameters compared to GF and other modified GF algorithms.     

Asymptotic smoothing errors for hidden Markov models

In this paper, the asymptotic smoothing error for hidden Markov models (HMMs) is investigated using hypothesis testing ideas. A family of HMMs is studied parametrised by a positive constant /spl epsiv/, which is a measure of the frequency of change. Thus, when /spl epsiv//spl rarr/0, the HMM becomes increasingly slower moving. We show that the smoothing error is O(/spl epsiv/). These theoretical predictions are confirmed by a series of simulations.     

Recursive filtering and smoothing for reciprocal Gaussianprocesses-pinned boundary case

The least square estimation problem for pinned-to-zero discrete-index reciprocal Gaussian processes in additive white noise is solved, thus completing and extending some previous results available in the literature. In particular, following the innovations approach a (finite) set of recursive equations is obtained for the filter and for the three standard classes of smoothers (fixed-point, fixed-interval, fixed-lag). Recursive expressions for the mean square performance of the proposed estimators are also given     

Elliptic filtering in continuous-time sigma-delta modulator

An improved continuous-time sigma-delta (/spl Sigma//spl Delta/) architecture with elliptic signal transfer function is presented. It is demonstrated that the UTRA/FDD band 1 interferer filtering can be fully achieved within the sigma-delta loop.     

An EM Algorithm for Markov Modulated Markov Processes

An expectation–maximization (EM) algorithm for estimating the parameter of a Markov modulated Markov process in the maximum likelihood sense is developed. This is a doubly stochastic random process with an underlying continuous-time finite-state homogeneous Markov chain. Conditioned on that chain, the observable process is a continuous-time finite-state nonhomogeneous Markov chain. The generator of the observable process at any given time is determined by the state of the underlying Markov chain at that time. The parameter of the process comprises the set of generators for the underlying and conditional Markov chains. The proposed approach generalizes an earlier approach by RydÉn for estimating the parameter of a Markov modulated Poisson process.      

Robust peak-to-peak filtering for Markov jump systems

The peak-to-peak filtering problem is studied for a class of Markov jump systems with uncertain parameters. By re-constructing the system, the dynamic filtering error system is obtained. The objective is to design a peak-to-peak filter such that the induced L_∞ gain from the unknown inputs to the estimated errors is minimized or guaranteed to be less or equal to a prescribed value. By using appropriate stochastic Lyapunov–Krasovskii functional, sufficient conditions are initially established on the existence of mode-dependent peak-to-peak filter which also guarantees the stochastic stability of the filtering error dynamic systems. The design criterions are presented in the form of linear matrix inequalities and then described as an optimization problem. Simulation results demonstrate the validity of the proposed approaches.     

A Kalman filtering technique for certain Markov chains

In this paper an efficient decoupling Kalman filtering technique is applied to certain Markov chains with finite-dimensional stationary state-transition matrices. For optimal estimates of a Markov chain with ann-dimensional stationary statetransition matrix, the resultant computational algorithm consists ofn-1 simple one-dimensional recursive formulas.This research was supported by the ONR Contract N00014-88-K-0127 and the State of Texas grant TATP 2892.     

基于蒙特卡洛滤波平滑的语音增强算法

本文在分析统计信号贝叶斯模型和语音信号的时变自回归(TVAR)模型的基础上,利用蒙特卡洛滤波及平滑方法,对语音信号的TVAR模型参数进行了估计,提出了一种有效的针对非平稳加性噪声影响下的语音增强算法.该算法可以很好的跟踪非平稳信号,同时引入对反射系数的判断,保证了跟踪的稳定性.实验表明,本文方法能很好的抑制背景噪声,提高信噪比,改善语音信号的听觉质量.     

Reduced-complexity filtering for partially observed nearly completely decomposable Markov chains

This paper provides a systematic method of obtaining reduced-complexity approximations to aggregate filters for a class of partially observed nearly completely decomposable Markov chains. It is also shown why an aggregate filter adapted from Courtois' (1977) aggregation scheme has the same order of approximation as achieved by the algorithm proposed in this paper. This algorithm can also be used systematically to obtain reduced-complexity approximations to the full-order fitter as opposed to algorithms adapted from other aggregation schemes. However, the computational savings in computing the full-order filters are substantial only when the large scale Markov chain has a large number of weakly interacting blocks or "superstates" with small individual dimensions. Some simulations are carried out to compare the performance of our algorithm with algorithms adapted from various other aggregation schemes on the basis of an average approximation error criterion in aggregate (slow) filtering. These studies indicate that the algorithms adapted from other aggregation schemes may become ad hoc under certain circumstances. The algorithm proposed in this paper however, always yields reduced-complexity filters with a guaranteed order of approximation by appropriately exploiting the special structures of the system matrices.     

光学图像高斯平滑滤波的DSP优化

高斯平滑滤波方法具有优良的噪声平滑性能和边缘保留能力,但运算量相对较大,限制了其在实时图像处理系统中的应用。本文基于高斯平滑掩膜的可分解性,提出了一种单次遍历实现两次卷积的高斯平滑滤波DSP优化方法。首先,依次打包读取4×8图像区域作为基本运算单元,有效降低对数据的重复访问;其次,在基本运算单元内,利用内联函数并行计算横向模板的一次卷积;然后,重组并复用横向模板卷积单元直接进行纵向模板的二次卷积。最后,以基本运算单元为单位遍历处理图像,计算平滑滤波结果。实验表明,利用TMS320C6455定点DSP对一幅320×240×8bit图像进行5×5高斯模板滤波耗时0.187ms,是优化前滤波耗时的1/35,具有较大的工程应用价值。     

平滑滤波在卫星导航抗干扰天线中的应用

为解决实际工程中卫星导航抗干扰天线鲁棒性不强的问题,提出了利用正交多项式平滑滤波算法对抗干扰权值进行滤波修正,以降低扰动对抗干扰天线的影响。文中建立了平滑滤波的数学模型,确定了滤波函数的参数,论述了抗干扰权系数正交多项式平滑滤波的过程,并给出了试验效果。     

Polyharmonic smoothing splines and the multidimensional Wiener filtering of fractal-like signals.

Motivated by the fractal-like behavior of natural images, we develop a smoothing technique that uses a regularization functional which is a fractional iterate of the Laplacian. This type of functional was initially introduced by Duchon for the approximation of nonuniformily sampled, multidimensional data. He proved that the general solution is a smoothing spline that is represented by a linear combination of radial basis functions (RBFs). Unfortunately, this is tedious to implement for images because of the poor conditioning of RBFs and their lack of decay. Here, we present a much more efficient method for the special case of a uniform grid. The key idea is to express Duchon's solution in a fractional polyharmonic B-spline basis that spans the same space as the RBFs. This allows us to derive an algorithm where the smoothing is performed by filtering in the Fourier domain. Next, we prove that the above smoothing spline can be optimally tuned to provide the MMSE estimation of a fractional Brownian field corrupted by white noise. This is a strong result that not only yields the best linear filter (Wiener solution), but also the optimal interpolation space, which is not bandlimited. It also suggests a way of using the noisy data to identify the optimal parameters (order of the spline and smoothing strength), which yields a fully automatic smoothing procedure. We evaluate the performance of our algorithm by comparing it against an oracle Wiener filter, which requires the knowledge of the true noiseless power spectrum of the signal. We find that our approach performs almost as well as the oracle solution over a wide range of conditions.     

基于马尔可夫决策过程的维修决策模型研究

提出了一种基于设备状况进行维修的模型.该模型包括随机失效和针对失效进行有计划检查、维修的过程.运用马尔可夫决策模型,提出了以费用-效果为指标的维修策略.     

Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions

We present an efficient particle filtering method to perform optimal estimation in jump Markov (nonlinear) systems (JMSs). Such processes consist of a mixture of heterogeneous models and possess a natural hierarchical structure. We take advantage of these specificities in order to develop a generic filtering methodology for these models. The method relies on an original and nontrivial combination of techniques that have been presented recently in the filtering literature, namely, the auxiliary particle filter and the unscented transform. This algorithm is applied to the complex problem of time-varying autoregressive estimation with an unknown time-varying model order. More precisely, we develop an attractive and original probabilistic model that relies on a flexible pole representation that easily lends itself to interpretations. We show that this problem can be formulated as a JMS and that the associated filtering problem can be efficiently addressed using the generic methodology developed in this paper. Simulations demonstrate the performance of our method compared to standard particle filtering techniques.     

基于马尔可夫决策过程的网格资源发现

传统的网格资源发现方法没有考虑节点和资源本身性能的优劣性。针对这一问题,提出了基于马尔可夫决策过程(MDP)的网格资源发现方法,利用其对固定目标的最优搜索理论建立MDP模型实现报酬最优的资源发现,并对该模型仿真分析,验证其性能。     

Multiscale Fitting Procedure Using Markov Modulated Poisson Processes

This paper proposes a parameter fitting procedure using Markov Modulated Poisson Processes (MMPPs) that leads to accurate estimates of queuing behavior for network traffic exhibiting long-range dependence behavior. The procedure matches both the autocovariance and marginal distribution of the counting process. A major feature is that the number of states is not fixed a priori, and can be adapted to the particular trace being modeled. The MMPP is constructed as a superposition of L 2-MMPPs and one M-MMPP. The 2-MMPPs are designed to match the autocovariance and the M-MMPP to match the marginal distribution. Each 2-MMPP models a specific time-scale of the data. The procedure starts by approximating the autocovariance by a weighted sum of exponential functions that model the autocovariance of the 2-MMPPs. The autocovariance tail can be adjusted to capture the long-range dependence characteristics of the traffic, up to the time-scales of interest to the system under study. The procedure then fits the M-MMPP parameters in order to match the marginal distribution, within the constraints imposed by the autocovariance matching. The number of states is also determined as part of this step. The final MMPP with M2 ^L states is obtained by superposing the L 2-MMPPs and the M-MMPP. We apply the inference procedure to traffic traces exhibiting long-range dependence and evaluate its queuing behavior through simulation. Very good results are obtained, both in terms of queuing behavior and number of states, for the traces used, which include the well-known Bellcore traces.     

H∞ inferential filtering, prediction and smoothing problems

A new type of H_∞ optimal linear estimation problem is considered where no direct measurement of the output to be estimated is available. The optimal filter, predictor and smoother are derived for this case where outputs must be inferred from available measurements. The results cover the usual discrete time filtering problems and also optimal deconvolution estimation problems. They also apply to the situation, often found in industry, where estimates of signals are required which can only be determined from secondary measurements. An equalizing solution to the H_∞ problem is obtained, ensuring that the estimation error spectrum is determined directly by the choice of the dynamic cost-weighting function.     

基才隐马尔可夫模型的蛋白质序列筛选算法

对蛋白质序列筛选算法进行了研究,针对蛋白质数据库中存在大量的冗余序列,提出了一种基于隐马尔可夫模型的蛋白质序列筛选算法。详细阐述了在蛋白质数据库中如何建立隐马尔可夫模型。对模型中主要参数的求解和估计给出了主要计算公式。然后利用所建立的模型对蛋白质数据库进行的序列筛选测试,测试结果表明本文设计的算法对同源蛋白质序列筛选精...     

基于自适应衰减因子Kalman 滤波的GPS 相位平滑伪距算法

载波相位平滑伪距的主要目的是通过高精度的载波相位测量值作为辅助量,使伪距测量值中、大随机误差得以消减。针对GPS 伪距测量中未知时变的噪声,提出基于极大后验时变噪声统计估计器的自适应衰减因子Kalman 滤波算法(AFKF),采用衰减的加权因子,使估计器逐渐忘记陈旧数据的作用,同时增加新数据的比重,避免滤波过程的发散。结合载波相位平滑伪距原理,利用AFKF 算法对全球导航卫星系统(GNSS)的国际GNSS 服务组织(IGS)的跟踪站实测数据进行仿真分析,并提出利用伪距双差值及伪距三差值来直观体现不同算法的效果比较,结果表明:与标准Kalman 滤波相比,AFKF 算法在伪距平滑应用中取得很好的效果。