一种OFDM系统中的符号定时同步方法

正交频分调制（OFDM）是一种高效的数据传输技术，具有良好的抗频率选择性衰落能力，并且提高了频谱利用率。但是OFDM对同步误差十分敏感，特别是符号同步误差。传统的MLE算法虽对符号定时估计很有效，但在ISI干扰情况下性能不高。针对MLE算法的不足，提出了一个基于新的算法。同时通过Matlab的模拟仿真表明，在信噪比较高时，新的算法定时估计性能优于MLE算法。     

On the indirect approaches for CARMA model identification

In this paper, we study the problem of reconstructing a continuous-time (CT) model from an identified discrete-time (DT) model for a continuous-time stochastic process. We present a new necessary and sufficient condition for the existence of the solution. We also show that the solution is unique if it exists. Our results are useful in modeling multivariable processes as well. These results are then used to develop an algorithm where the intermediate discrete-time model estimation is not necessary. The performance of our algorithm is illustrated using numerical simulations.     

OFDM系统中基于全相位算法的最大似然信道估计器

在OFDM系统中设计了一种基于全相位快速傅里叶变换算法的最大似然信道估计器。全相位傅里叶变换相对于快速傅里叶变换呈现平方的幅度增益性质,在高信噪比情况下可以抑制最大似然估计器中的自带噪声,由此能够更准确地估计出信道冲击响应,并用来均衡信号。同时,OFDM采用全相位傅里叶变换作为解调算法克服了系统晶振不匹配以及信道传输过程中产生的频偏。在3GPP的空间信道模型下,设计了基于全相位最大似然估计器的OFDM系统,并与传统的最大似然估计器系统比较,使用蒙特卡洛方法仿真证明:信道冲击响应估计的均方误差和系统误码率均有所下降。     

Meta-heuristic algorithms for parameter estimation of semi-parametric linear regression models

Consider the semi-parametric linear regression model Y=β^′X+ε, where ε has an unknown distribution F₀. The semi-parametric MLE of β under this set-up is called the generalized semi-parametric MLE(GSMLE). Although the GSML estimation of the linear regression model is statistically appealing, it has never been attempted due to difficulties with obtaining the GSML estimates of β and F until recent work on linear regression for complete data and for right-censored data by Yu and Wong [2003a. Asymptotic properties of the generalized semi-parametric MLE in linear regression. Statistica Sinica 13, 311-326; 2003b. Semi-parametric MLE in simple linear regression analysis with interval-censored data. Commun. Statist.—Simulation Comput. 32, 147-164; 2003c. The semi-parametric MLE in linear regression with right censored data. J. Statist. Comput. Simul. 73, 833-848]. However, after obtaining all candidates, their algorithm simply does an exhaustive search to find the GSML estimators. In this paper, it is shown that Yu and Wong's algorithm leads to the so-called dimension disaster. Based on their idea, a simulated annealing algorithm for finding semi-parametric MLE is proposed along with techniques to reduce computations. Experimental results show that the new algorithm runs much faster for multiple linear regression models while keeping the nice features of Yu and Wong's original one.     

Inferring Minimal Functional Dependencies in Horn and q-Horn Theories

In this paper, we study various problems related to the inference of minimal functional dependencies in Horn and q-Horn theories. We show that if a Horn theory is represented by a Horn CNF, then there exists an incrementally polynomial algorithm for inferring all minimal functional dependencies. On the other hand, if a Horn theory is represented as the Horn envelope of a given set of models, then there exists a polynomial total time algorithm for this inference problem if and only if there exists such an algorithm for dualizing a positive CNF. Finally, we generalize our results to the case of q-Horn theories, and show that all the considered problems can be reduced in polynomial time to the corresponding problems for Horn theories.     

Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification

Surface Electromyography (sEMG) is a non-invasive, easy to record signal of superficial muscles from the skin surface. The sEMG is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. Considering the nonlinear and non-stationary characteristics of sEMG, hand gesture recognition using sEMG signals necessitate designers to use Maximal Lyapunov Exponent (MLE) or ensemble Empirical Mode Decomposition (EMD) based MLEs. In this research, we propose a hand gesture recognition method of sEMG based on nonlinear multiscale MLE. The aim is to increase the classification accuracy of sEMG features while reducing the complexity of EMD. The nonlinear MLE features are classified using Flexible Neural Tree (FNT), which can solve highly structured dependent problems of the Artificial Neural Network (ANN). The testing has been conducted using several experiments with five participants. The classification performance of nonlinear multiscale MLE method is compared with MLE and EMD-based MLE through simulations. Experimental results demonstrate that the former algorithm outperforms the two latter algorithms and can classify six different hand gestures up to 97.6% accuracy.     

Robust maximum likelihood estimation for stochastic state space model with observation outliers

The objective of this paper is to develop a robust maximum likelihood estimation (MLE) for the stochastic state space model via the expectation maximisation algorithm to cope with observation outliers. Two types of outliers and their influence are studied in this paper: namely,the additive outlier (AO) and innovative outlier (IO). Due to the sensitivity of the MLE to AO and IO, we propose two techniques for robustifying the MLE: the weighted maximum likelihood estimation (WMLE) and the trimmed maximum likelihood estimation (TMLE). The WMLE is easy to implement with weights estimated from the data; however, it is still sensitive to IO and a patch of AO outliers. On the other hand, the TMLE is reduced to a combinatorial optimisation problem and hard to implement but it is efficient to both types of outliers presented here. To overcome the difficulty, we apply the parallel randomised algorithm that has a low computational cost. A Monte Carlo simulation result shows the efficiency of the proposed algorithms.     

GSA-based maximum likelihood estimation for threshold vector error correction model

The log-likelihood function of threshold vector error correction models is neither differentiable, nor smooth with respect to some parameters. Therefore, it is very difficult to implement maximum likelihood estimation (MLE) of the model. A new estimation method, which is based on a hybrid algorithm and MLE, is proposed to resolve this problem. The hybrid algorithm, referred to as genetic-simulated annealing, not only inherits aspects of genetic-algorithms (GAs), but also avoids premature convergence by incorporating elements of simulated annealing (SA). Simulation experiments demonstrate that the proposed method allows to estimate the parameters of larger cointegrating systems. Additionally, numerical results show that the hybrid algorithm does a better job than either SA or GA alone.     

约束条件下BN参数最大熵模型扩展学习算法

在很多智能系统的参数建模时,用户往往面对建模样本稀少的困境。针对在小数据集条件下贝叶斯网络（BN）参数建模的问题,提出了一种约束数据最大熵BN参数学习算法（CDME）。首先利用小数据集估算BN参数,随后把定性的专家经验转换为不等式约束,并利用Bootstrap算法生成满足约束的一组参数候选集,再根据信息最大熵进行加权计算出BN参数。实验结果表明,当数据量充分时,CDME参数学习算法与经典的MLE算法的学习精度近似,表明了算法的正确性;在小数据集条件下,利用CDME算法,可以对BN进行参数建模,学习精度优于MLE算法和QMAP算法。CDME算法在实际故障诊断样本数据相对稀缺的条件下,获取了诊断BN模型参数,在此基础上完成的诊断推理结果也印证了算法的有效性,为小数据集条件下的参数建模提供了一条新途径。     

期望最大算法及其应用(1)

EM算法是实现极大似然估计的一种有效方法,主要用于非完全数据的参数估计。它通过假设隐变量的存在,极大地简化了似然方程;对于一些特殊的参数估计问题,利用EM算法也很容易实现。而极大似然估计是一种常用的参数估计方法,EM算法使其应用更加广泛。文章从应用者的角度出发,内容是自包含的。     

A fault tolerant routing algorithm based on cube algebra for hypercube systems

We propose an approach to determine the shortest path between the source and the destination nodes in a faulty or a non-faulty hypercube. The number of faulty nodes and links may be rather large and if any path between the nodes exists, the developed algorithm determines it. To construct this algorithm, some properties of the cube algebra are considered and some transformations based on this algebra are developed.     

基于变权重迁移学习的BN参数学习算法

针对小数据集条件下的贝叶斯网络(Bayesian network,BN)参数估计困难问题,提出了一种基于变权重迁移学习(DWTL)的BN参数学习算法。首先,利用MAP和MLE方法学习得到目标域初始参数和各源域参数;然后根据不同源域数据样本贡献的不同计算源权重因子;接着基于目标域样本统计量与小数据集样本阈值的关系设计了目标域初始参数和源域参数的平衡系数;最后,基于上述参数、源权重因子和平衡系数计算得到新的目标参数。在实验研究中,通过对经典BN模型的参数学习问题验证了DWTL算法的有效性;针对小数据集下的轴承故障诊断问题,相较于传统迁移学习(LP)算法,DWTL算法学习精度提高了10%。实验结果表明:所提出的算法能够较好地解决样本数据集在相对稀缺条件下的目标参数建模问题。     

Medical image fusion using multi-level local extrema

The fusion of data for medical imaging has become a central issue in such biomedical applications as image-guided surgery and radiotherapy. The multi-level local extrema (MLE) representation has been shown to have many advantages over conventional image representation methods. In this paper, we propose a new fusion algorithm for multi-modal medical images based on MLE. Our method enables the decomposition of input images into coarse and detailed layers in the MLE schema, and utilizes local energy and contrast fusion rules for coefficient selection in the different layers. This preserves more detail in the source images and further improves the quality of the fused image. The final fused image is obtained from the superposition of selected coefficients in the coarse and detailed layers. We illustrate the performance of the proposed method using three groups of medical images from different sources as our experimental subjects. We also compare our method with other techniques using cumulative mutual information, the objective image fusion performance measure, spatial frequency, and a blind quality index. Experimental results show that our method achieves a superior performance in both subjective and objective assessment criteria.     

A sound and complete algorithm for distributed commerce transactions

Summary. In a multi-party transaction (also called a distributed commerce transaction) agents face risks from dealing with untrusted agents. These risks are compounded in the face of deadlines, e.g., an agent may fail to deliver purchased goods by the time the goods are needed. We characterize the risks, and present a distributed algorithm that mitigates these risks, by using pairwise exchanges and trusted intermediaries. The algorithm generates a safe sequence of actions that completes a commerce transaction without risk, if such a sequence exists. We show that the algorithm is sound (produces only safe multi-agent action sequences) and complete (finds a safe sequence whenever one exists). The initial restriction of guaranteeing safety even when none of the principals trusts another can be relaxed in some cases, so we show how to handle principals that do trust each other and interact directly rather than through a trusted intermediary. Received: September 1997 / Accepted: December 1998