一个新颖的轮廓线跟踪算法

提出了一个新颖的基于隐马尔科夫模型与光流的轮廓线跟踪算法。曲线描绘由B样条形状空间向量来表达,能够捕捉全局和局部变形。提出应用沿曲线的光流计算来预测曲线在下一帧的位置,在预测曲线的基础上,提出应用隐马尔科夫模型来准确定位曲线的位置。隐马尔科夫模型提供了一种有效的概率手段来融合多种量测特征比如边缘,曲线平滑性,区域灰度或颜色统计信息等等,能够更准确的定位曲线位置。基于仿射形状空间的实验了表明本文所提出算法的有效性。     

基于MMD算法的HMM参数重估计的矩阵表示

本文提出的MMD算法用于提高模型区别错误信息和正确信息的能力．利用该算法在对模型的参数进行重估计时．涉及到复杂的目标函数的梯度运算．击运用矩阵运算使得梯度运算变得简单明了，因此本文给出了MMD算法下的HMM参数重估计的矩阵表示形式并给出了证明．     

Single photon and nonlocality

In a paper by Home and Agarwal [1], it is claimed that quantum nonlocality can be revealed in a simple interferometry experiment using only single particles. A critical analysis of the concept of hidden variable used by the authors of [1] shows that the reasoning is not correct.      

汽车滚装码头泊位调度优化模型与算法研究

本文研究滚装码头混合泊位分配和劳动力分配的联合调度优化问题。首先,考虑潮汐时间窗约束、装卸劳动力约束、泊位缆桩分布约束以及泊位不规则布局因素,建立以最小化船舶总服务时间为目标的混合整数规划模型。其次,采用内外嵌套算法设计策略,提出求解该类问题的组合算法。其中,外层是多种群并行进化的遗传算法,生成多种船舶计划顺序,内层为基于规则的启发式算法,用于计算给定计划顺序的目标函数值。然后,基于实际运营数据,生成多组不同规模的算例进行全面数值实验,结果表明所提出的算法可在10分钟内求解包含50艘船、100个泊段的算例。最后,开展基于真实滚装码头运营实例的案例分析,对所提模型和算法在实际码头调度问题中的适用性与高效性进行验证。     

Closed‐form rectangular atomic wave functions of an N‐dimensional atom

A closed‐form expression has been derived to obtain the N‐dimensional rectangular atomic wave functions. The wave functions can be obtained straightforwardly with the knowledge of the angular momentum quantum numbers and the dimension and there is no need to employ recursive methods as in previous works. © 2002 Wiley Periodicals, Inc. Int J Quantum Chem 88: 263–274, 2002     

声音转换技术的研究与进展

声音转换是一项改变说话人声音特征的技术,可以将一人的语音模式转换为与其特性不同的另一人语音模式.声音转换算法的目标是确定一个什么样的模式转换规则,使转换语音保持第一个说话人原有语音信息内容不变,而具有第二个说话人的声音特点.本文介绍了当前声音转换技术领域的研究状态,主要分析现有声音转换技术中各种转换算法的实现原理,描述声音转换系统性能的各种评估方法,最后给出了对声音转换技术的简要评述和展望.     

Fast switching of vertically aligned negative liquid crystals by optically hidden relaxation

We propose a method for fast switching of vertically aligned (VA) negative liquid crystals (LCs) by hiding the relaxation process of LCs. During the turn-off process, a strong in-plane electric field is applied for a short duration of time instead of relying solely on the slow relaxation of LCs. The LC molecules are rotated to the transmission axis of one of the polarisers by the applied in-plane electric field, resulting in turn-off switching that is 5.8 times faster than that of a conventional VA cell. By applying an overdriving scheme, we experimentally obtained a total response time of 3.3 ms.     

Unsupervised segmentation of hidden semi-Markov non-stationary chains

The Bayesian segmentation using Hidden Markov Chains (HMC) is widely used in various domains such as speech recognition, acoustics, biosciences, climatology, text recognition, automatic translation and image processing. On the one hand, hidden semi-Markov chains (HSMC), which extend HMC, have turned out to be of interest in many situations and have improved HMC-based results. On the other hand, the case of non-stationary data can pose an important problem in real-life situations, especially when the model parameters have to be estimated. The aim of this paper is to consider these two extensions simultaneously: we propose using a particular triplet Markov chain (TMC) to deal with non-stationary hidden semi-Markov chains. In addition, we consider a recent particular HSMC having the same computation complexity as the classical HMC. We propose a related parameter estimation method and the resulting unsupervised Bayesian segmentation is validated through experiments; in particular, a real radar image segmentations are provided.     

基于隐马尔科夫模型的CPM信号盲Turbo均衡算法

针对高阶连续相位调制信号(CPM)盲均衡中存在的均衡性能较差以及不容易收敛等问题,从隐马尔科夫模型(HMM)的角度出发,建立一种新的EM-SOVA信道盲均衡方法,并结合Turbo均衡思想,提出了一种适用于高阶CPM信号的盲Turbo均衡算法。该算法通过将SOVA内嵌到EM算法的迭代过程中,有效改善了信道均衡效果,同时使用Turbo均衡的软信息迭代处理来进一步提高低信噪比条件下系统性能。理论分析和仿真结果表明,所提算法具有良好的盲均衡性能以及收敛性。     

基于N阶近邻路网的车辆行程时间估计模型

车联网的提出为智能交通的研究提供了新的交通信息收集技术.针对短时交通中车辆的路网行程时间估计问题,提出了基于N阶近邻的隐马尔科夫模型,利用马尔科夫性质来解决道路行程时间的前后关联性问题,同时考虑不同道路的异构性构建了N阶近邻路网模型来模拟路网间的交互影响.针对短时交通中实时数据更新的问题,提出基于道路关联性算法,并结合车联网的采集技术给出了迭代更新模型的方法.实验表明,本文提出的方法在短时交通车辆行程时间预测中精度较高,能够在车辆行进中做出实时预测.