Person-independent expression recognition based on person-similarity weighted expression feature

A new method to extract person-independent expression feature based on higher-order singular value decomposition (HOSVD) is proposed for facial expression recognition. Based on the assumption that similar persons have similar facial expression appearance and shape, the person-similarity weighted expression feature is proposed to estimate the expression feature of test persons. As a result, the estimated expression feature can reduce the influence of individuals caused by insufficient training data, and henc...     

一种新型神经网络控制器的研究

提出了一种新型神经网络控制器,其控制机理与PID控制相类似,但解决了常规PID控制存在的快速性与超调量的矛盾.给出了该神经网络控制器的自适应学习算法,分析讨论了控制系统的稳定性.仿真实验表明,这种新型神经网络控制器构成的自适应控制系统具有较强的适应性和鲁棒性.     

一种SAR图像目标识别方法

提出了一种新的合成孔径雷达 (SAR)图像目标检测和识别方法 ,该方法根据SAR图像统计分布特性 ,结合恒虚警检测算法和小波变换提取感兴趣的SAR图像目标特征 ,检测得到目标 ,采用马氏距离从杂波背景中识别该目标。实际SAR图像测试结果表明了该方法的有效性     

一种高分辨率谱估计与数据外推的快速算法

利用信号频谱幅度分布的先验信息,对短时数据序列信号提出了一种实现高分辨率谱估计和数据序列外推的快速算法。该算法利用了算子矩阵的特殊结构,采用简单、快速的矩阵求逆算法,使算法的运算量和数据存储容量大幅度下降。给出了算法在高分辨率谱估计及数据序列外推中的应用实例和仿真结果,证实了该算法的优良性能。     

野生和栽培红毛五加的比较研究

为比较野生和栽培红毛五加形态、光合、光谱及生理特征差异,制定合理的红毛五加人工栽培方案提供技术支持,对野生与栽培红毛五加叶片的外部形态与解剖特征、光合气体交换参数、叶绿素荧光参数、叶片及冠层光谱特性及以刺五加苷E、绿原酸为代表的次生代谢产物含量进行比较。①与野生红毛五加叶片相比,栽培红毛五加叶片厚度、叶面积明显增加,叶片长宽比明显减少,栅栏组织较厚而海绵组织较薄;②栽培红毛五加光合速率及光能利用率较高;③野生和栽培红毛五加都具有典型的植株反射光谱特征,但同野生红毛五加相比,栽培红毛五加叶片和冠层光谱在近红外波段的反射率都较高,一阶导数光谱中的红边振幅和红边面积也较大,红边位置红移10 nm;④2009年所测的栽培红毛五加茎皮中的绿原酸、刺五加苷E含量较野生红毛五加,栽培6年后,上述两种成分含量有所上升,且刺五加苷E含量符合《四川省中药材标准》相关标准。结果表明光照条件的改变影响着红毛五加的叶片形态,充足的光照有利于红毛五加的光合作用和正常生长,研究结果将有助于指导红毛五加的人工驯化栽培工作。     

Improved chirp scaling algorithm for parallel-track bistatic SAR

The curvature factor of the parallel-track bistatic SAR is range dependent, even without variation of the effective velocity. Accounting for this new characteristic, a parallel-track chirp scaling algorithm (CSA) is derived, by introducing the method of removal of range walk (RRW) in the time domain. Using the RRW before the CSA, this method can reduce the varying range of the curvature factor, without increasing the computation load obviously. The azimuth dependence of the azimuth-FM rate, resulting from the RRW, is compensated by the nonlinear chirp scaling factor. Therefore, the algorithm is extended into stripmap imaging. The realization of the method is presented and is verified by the simulation results.     

Robust H∞ filtering for discrete-time systems with Markovian switching and time-delays

The robust H∞ filtering problem for uncertain discrete-time Markovian jump linear systems with modedependent time-delays is investigated. Attention is focused on designing a Markovian jump linear filter that ensures robust stochastic stability while achieving a prescribed H∞ performance level of the resulting filtering error system, for all admissible uncertainties. The key features of the approach include the introduction of a new type of stochastic Lyapunov functional and some free weighting matrix variables. Sufficient conditions for the solvability of this problem are obtained in terms of a set of linear matrix inequalities. Numerical examples are provided to demonstrate the reduced conservatism of the proposed approach.     

Capacity analysis of high-rank line-of-sight MIMO channels

Matrix perturbation theory is utilized to investigate high-rank line of sight multiple input multiple output channels in a microwave relay system. The upper and lower bounds of channel capacity are derived based on space time block codes technique and singular values decomposition. A useful constraint for designing LOS MIMO channels is developed by the use of the condition number of the MIMO channel matrix. The theoretical analysis of channel capacity is confirmed by the simulation. The results show that the proposed method is able to give a physical explanation of the high-rank LOS MIMO channel matrix characteristics.     

改进的支持向量机低分辨雷达目标分类算法

针对雷达目标识别系统中存在库外目标、各特征属性分类性能不一致,低分辨雷达不同类别目标特征值分布区间混叠的问题。文中基于支持向量机分类算法,对目标样本特征值进行置信度分析,获得置信区间,利用代价函数确定拒识门限,对库外目标以及混叠区域目标进行拒识分析;并根据训练样本先验识别率使用注水原理分配特征属性权重;在此基础上利用支持向量机进行目标识别。雷达实测数据实验结果表明,引入置信拒识分析与注水分配加权后,分类器性能得到了有效改善。     

Dual hesitant fuzzy information aggregation with Einstein t-conorm and t-norm

Dual hesitant fuzzy set (DHFS) is a new generalization of fuzzy set (FS) consisting of two parts (i.e., the membership hesitancy function and the non-membership hesitancy function), which confronts several different possible values indicating the epistemic degrees whether certainty or uncertainty. It encompasses fuzzy set (FS), intuitionistic fuzzy set (IFS), and hesitant fuzzy set (HFS) so that it can handle uncertain information more flexibly in the process of decision making. In this paper, we propose some new operations on dual hesitant fuzzy sets based on Einstein t-conorm and t-norm, study their properties and relationships and then give some dual hesitant fuzzy aggregation operators, which can be considered as the generalizations of some existing ones under fuzzy, intuitionistic fuzzy and hesitant fuzzy environments. Finally, a decision making algorithm under dual hesitant fuzzy environment is given based on the proposed aggregation operators and a numerical example is used to demonstrate the effectiveness of the method.