一种不同地面分辨率图像的拼接算法

航空遥感监测中变焦镜头的广泛应用以及飞行高度的变化会获得不同地面分辨率的航空影像,因此为了实时监控到飞行区域的整体情况,需要对不同地面分辨率的图像进行拼接。提出了针对不同地面分辨率图像进行拼接的方法。首先用四分法对图像进行分割,检测变化倍率,选择配准策略;然后用基于加速鲁棒性特征(Speeded-Up Robust Features,SURF)的算法对相邻图像进行拼接。获得了焦距变化从高倍过渡到低倍,或从低倍过渡到高倍的拼接图像。通过航空变焦模拟影像的拼接实验和地面变焦影像的拼接实验表明,该方法能够对不同地面分辨率的图像进行拼接,且拼接结果均以高分辨率为基准。     

On the estimation of nonlinear time series models

In the present paper, a framework for parametric estimation in nonlinear time series is developed. Strong consistency and asymptotic normality of minimum Hellinger distance estimates for a determined class of nonlinear models are investigated. The main Interest for these estimates is motivated by their robustness under perturbations as it has been emphazized in Beran [2]. The first part of the paper is devoted to the study of some probabilistic properties which ensure the existence and the optimal properties of the estimates     

Signal modeling using the gradient search

This letter presents a new method for continuous signal modeling. Firstly, the continuous signal can be represented as a function of the trigonometric functional extension (Fourier series). Fourier series of the signal are parameterized by the fundamental frequency and unknown parameters. Then, the gradient-based iterative identification algorithm is derived, for estimating parameters of the signal model with known and unknown frequencies, separately. Finally, the simulation results indicate that the proposed algorithm is effective.     

One-Step Generalized Estimating Equations With Large Cluster Sizes

Medical studies increasingly involve a large sample of independent clusters, where the cluster sizes are also large. Our motivating example from the 2010 Nationwide Inpatient Sample (NIS) has 8,001,068 patients and 1049 clusters, with average cluster size of 7627. Consistent parameter estimates can be obtained naively assuming independence, which are inefficient when the intra-cluster correlation (ICC) is high. Efficient generalized estimating equations (GEE) incorporate the ICC and sum all pairs of observations within a cluster when estimating the ICC. For the 2010 NIS, there are 92.6 billion pairs of observations, making summation of pairs computationally prohibitive. We propose a one-step GEE estimator that (1) matches the asymptotic efficiency of the fully iterated GEE; (2) uses a simpler formula to estimate the ICC that avoids summing over all pairs; and (3) completely avoids matrix multiplications and inversions. These three features make the proposed estimator much less computationally intensive, especially with large cluster sizes. A unique contribution of this article is that it expresses the GEE estimating equations incorporating the ICC as a simple sum of vectors and scalars.     

Bayesian Estimation for the Order of INAR(q) Mo del

In this paper, we consider the problem of determining the order of INAR(q) model on the basis of the Bayesian estimation theory. The Bayesian es-timator for the order is given with respect to a squared-error loss function. The consistency of the estimator is discussed. The results of a simulation study for the estimation method are presented.     

Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm

The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed.     

Comparison of scale factor estimators for ultrasonic temperature monitoring: Application to structural health monitoring

In Structural Health Monitoring (SHM) of materials, estimating the effects of environmental and operational conditions such as temperature is important. Indeed, temperature changes induce modifications of the mechanical properties of the material and therefore cause a dilation of the acoustic signals characterized by a scale factor. This paper described four scale factor estimators able to monitor changes in temperature: The short-time cross-correlation (STXC) method, the stretching method (STRE), the Minimum Variance Based Estimator method (MVBE) and the Scale Transform Based Estimator method (STBE). The first two methods have already been assessed in the literature while the latter two have been specifically developed for this study. First, closed-form for the Cramer-Rao bound on the estimates of the scale factor, from a simplified deterministic signal, are derived and simplified expressions are given. Then, a statistical evaluation of the quality of estimates is conducted through Monte-Carlo simulations using synthetic signals, based on a model taking into account the influence of temperature. A raw estimate of the computational complexity of signal processing methods also completes this evaluation phase. Finally, the experimental validation of estimation methods is conducted on an aluminum plate subjected to temperatures variations in a controlled thermal environment. The temperature estimates are then faced with an analytical model describing the material behavior.     

Robust multilevel methods for quadratic finite element anisotropic elliptic problems

This paper discusses a class of multilevel preconditioners based on approximate block factorization for conforming finite element methods employing quadratic trial and test functions. The main focus is on diffusion problems governed by a scalar elliptic partial differential equation with a strongly anisotropic coefficient tensor. The proposed method provides a high robustness with respect to non‐grid‐aligned anisotropy, which is achieved by the interaction of the following components: (i) an additive Schur complement approximation to construct the coarse‐grid operator; (ii) a global block (Jacobi or Gauss–Seidel) smoother complementing the coarse‐grid correction based on (i); and (iii) utilization of an augmented coarse grid, which enhances the efficiency of the interplay between (i) and (ii). The performed analysis indicates the high robustness of the resulting two‐level method. Moreover, numerical tests with a nonlinear algebraic multilevel iteration method demonstrate that the presented two‐level method can be applied successfully in the recursive construction of uniform multilevel preconditioners of optimal or nearly optimal order of computational complexity. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive multimedia streaming using a simulation test bed

The major purpose of this paper is to transmit an existing video above a complete simulated video streaming architecture based on Network Simulator (NS2). Thanks to our architecture, the visual quality evaluation of the distributed streaming platform under various conditions is simplified. Indeed, the received video can be easily visualized using a classic video client or be compared using the Peak Signal-Noise Ratio (PSNR) value and the Structural SIMilarity (SSIM) value. In the case study, we compare adaptive video congestion strategies using a transcoder, Datagram Congestion Control Protocol (DCCP) and TCP-Friendly Rate Control (TFRC).