一种对3G网络无线链路上ROHC的改进方法

介绍了ROHC这种目前公认应用于无线链路上较为理想的头部压缩方式。通过对其模式转换的分析，发现了其中鲁棒性与压缩效率未能兼顾的缺点，从而讨论了一种新的模式转换的改进方法，进一步提高了ROHC的鲁棒性与压缩效率的平衡性。最后，用仿真实验验证了改进方法的有效性。     

光束质量稳健估计非线性拟合求解方法

激光光束质量M2因子在测量过程中,数据不可避免的存在随机和非随机两类误差,为了减小测量过程中受到误差的影响以提高系统的测量精度和稳定性,针对不同误差影响进行分析并提出相应的解决方法。在随机误差情况下,对传统双曲线拟合的正规方程组解法进行了改进并提出了加权拟合的正规方程组解法;而对于非随机误差情况,双曲线拟合尚未发现有效的解决方案,为此提出了基于稳健估计的双曲线拟合方法,并对此进行了深入的理论研究。实验表明,在数据存在非随机的情况下采用稳健估计的方法,可以克服非随机误差对参数估值产生的影响,使其拟合优度接近最优值,稳健估计的误差要比正规方程组的误差低一个数量级。因此,稳健估计可有效地提高测量M2因子的测量精度,对评价光束质量方面有着重要的应用价值。     

截集型特征加权模糊C-均值聚类算法

已有的特征加权型模糊C-均值（WFCM）聚类算法可以有效地提取数据的相关特征,WFCM存在的主要问题是收敛速度慢和对噪声敏感。借助模糊集的截集方式对WFCM的隶属度值进行修改,提出截集型特征加权模糊C-均值聚类算法：SWFCM。SWFCM不仅具有良好的特征提取能力,而且具有收敛速度快和对噪声稳健的优点。实验结果表明,SWFCM的总体性能优于原有的WFCM聚类算法和截集模糊C一均值聚类算法。     

Robust Stability of Multivariate Polynomials. Part 1: Small Coefficient Perturbations

This paper presents a detailed analysis of some classes of stable multivariate polynomials. The main aim of the analysis is to give conditions under which polynomials preserve stability when they are subjected to small coefficient variations. The maximal class of such polynomials is introduced. Some basic properties of polynomials from this class are obtained.     

基于SVD-SURF的宽基线鲁棒景象匹配算法

提出一种基于SVD-SURF的宽基线鲁棒景象匹配算法。首先,在实时图与基准图奇异值分解的基础上构建SURF尺度空间,运用快速Hessian矩阵定位极值点;然后,计算出图像的64维SURF描述子;最后,通过Hessian矩阵迹进行特征点匹配,并利用RANSAC参数估计方法剔除出格点,从而实现位置参数的精确估计。实测航空图像序列位置估计实验表明了该景象匹配算法对图像的旋转、尺度变换及噪声不敏感,具有较强的实时性、精确性和鲁棒性。     

印刷作品版权保护中的数字图像水印技术

数字水印一个重要的应用扩展就是对纸质图像产品的保护。数字水印技术应用于音频、图像、视频和文本的版权保护,在该领域的各种不同分支都有很多学者做了大量研究。论文主要讨论了抵抗打印扫描信道的数字图像水印技术,并根据数据嵌入域的不同对这些技术进行了分类讨论。     

汉语大词汇量连续语音识别系统研究进展

本文综述了近年来大词汇量连续语音识别技术进步和发展,描述了大词汇量连续汉语语音识别系统的设计方法.对语音识别系统中的一些关键技术和原理进行了详细地分析和讨论,并对语音识别技术进一步发展中存在的问题和近年语音识别研究发展动向进行了讨论.     

基于SURF的图像匹配算法改进

在图像匹配中,SURF算法采用最邻近与次邻近欧式距离比查寻匹配点,当阈值过大时,这种方法会产生大量误匹配点,从而严重影响图像配准的精度。RANSAC算法是一种有效剔除误匹配点的方法,但需要人工确定部分参数值。提出一种多层次图像匹配方法,不仅能够剔除误匹配点还可以无需人工参与完成图像匹配。实验表明,该匹配方法能够精确提取匹配点和实现自动匹配,很好地满足图像配准的要求,具有一定的应用价值。     

Adaptive robust control of fully-constrained cable driven parallel robots

In this paper, adaptive robust control (ARC) of fully-constrained cable driven parallel robots is studied in detail. Since kinematic and dynamic models of the robot are partly structurally unknown in practice, in this paper an adaptive robust sliding mode controller is proposed based on the adaptation of the upper bound of the uncertainties. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The proposed controller not only keeps all cables under tension for the whole workspace of the robot, it is chattering-free, computationally simple and it does not require measurement of the end-effector acceleration. The stability of the closed-loop system with proposed control algorithm is analyzed through Lyapunov second method and it is shown that the tracking error will remain uniformly ultimately bounded (UUB). Finally, the effectiveness of the proposed control algorithm is examined through some experiments on a planar cable driven parallel robot and it is shown that the proposed controller is able to provide suitable tracking performance in practice.     

Amplitude information based robust tracking method for multiple marine targets

A robust multi-Bernoulli filter based on the amplitude information of the target in sea clutter was proposed,in which the amplitude information of the target and sea clutter was exploited to improve the measurement likelihood.Therefore,the proposed approach could distinguish targets from clutter better when they were spatially close,which further enhanced the ability to track targets in marine radar detection applications.The simulation results of Swerling type Ⅰfluctuating targets tracking in K distributed sea clutter show that,the proposed algorithm is superior in performance to the robust multi-Bernoulli filter in terms of state estimation,cardinality estimation,as well as clutter rate estimation.