一种基于通用DSP的快速余弦变换算法

在对传统快速DCT算法进行分析的基础上 ,充分考虑到通用DSP乘加速度相同等指令特点 ,并且结合DCT变换的性质 ,同时还考虑了对于通用DSP实现相当重要的存取操作的因素 ,提出了一种新的基于通用DSP的快速余弦变换 (FDCT)算法 ,以使总操作数最少。与传统快速DCT相比 ,速度有了明显的提高 ,并且易于实现。给出了该算法在数码相机中的实现结果     

一种混沌神经网络及其在优化计算中的应用

研究了一种具有混沌特性的神经网络 ,该网络具有瞬态混沌响应 ,类似于Hopfield网络的结构 ,但有比Hopfield网络更加丰富的动力学特征、更强的全局搜索能力。通过把混沌动力学与收敛动力学相结合 ,使网络逐渐由混沌神经网络向Hopfield网络过渡 ,达到控制混沌的目的 ,并且提供一个在全局最优解附近的初值 ,有效地解决了Hopfield网络的局部极值问题。该网络模型可以用来解决复杂的非线性优化问题。     

基于质量评价技术的多信道语音选优决策系统

在分析远距离多信道同源语音信道特性的基础上 ,用若干特征参数来表征信道传输的语音质量并建立了语音质量客观评价模型 ,结合人的听觉特性等因素开发出一种基于语音质量客观评价技术的优质语音信道选优决策系统。该系统能够确保语音通信始终在最佳信道上 ,且在切换过程中无跳变感觉 ,不影响听觉效果 ,现已在实际工程中得到成功运用并取得了满意效果。     

基于神经网络的一类非线性系统的变结构控制

在已知名义系统的基础上 ,将小脑关节模型控制器 (CMAC)神经网络用于一类状态反馈可线性化的多输入多输出连续时间非线性系统的变结构控制中。利用自适应技术估计了估计误差的大小 ,减小了系统的不确定性 ,并利用模糊控制技术调整了变结构增益 ,改善了系统的性能。在很弱的假设条件下 ,应用Lyapunov稳定性定理证明了闭环系统内的所有信号为均匀最终有界。算法在导弹控制系统中的应用进一步证明了本文方法的有效性。     

轨道动力学可视化仿真研究

研究了在机车车辆一轨道耦合动力学视景仿真系统中，轨道系统动态行为仿真的逼真性和实时性问题，提出了一种能准确模拟轨道系统振动行为的有限长轨道单元法，考虑到钢轨以梁的形式参与振动的实质，通过实时建立具有一定垂向、横向和扭转振动形态的钢轨模型来模拟钢轨的振动行为。仿真结果表明，在保证优良的实时性的同时，利用该方法不仅可以建立更加符合实际的轨道模型，而且还能真实地模拟轨道子系统各组成部件的各种特殊振动行为。     

线性定常模糊随机系统的响应分析

证明了线性定常模糊随机系统的输出在均方意义下的收敛性,并给出了系统响应的模糊概率特征关系方程。     

Group consensus for multiple networked Euler-Lagrange systems with parametric uncertainties

In this paper,a group consensus problem is investigated for multiple networked agents with parametric uncertainties where all the agents are governed by the Euler-Lagrange system with uncertain parameters.In the group consensus problem,the agents asymptotically reach several different states rather than one consistent state.A novel group consensus protocol and a time-varying estimator of the uncertain parameters are proposed for each agent in order to solve the couple-group consensus problem.It is shown that the group consensus is reachable even when the system contains the uncertain parameters.Furthermore,the multi-group consensus is discussed as an extension of the couple-group consensus,and then the group consensus with switching topology is considered.Simulation results are finally provided to validate the effectiveness of the theoretical analysis.     

Multi-dimensional scattering properties diagnosis system of scale aircraft model in an anechoic chamber

Aiming at the concept of ＂diagnosis＂, a simple and effective broadband radar cross section （RCS） measurement system is constructed, and some multi-dimensional scattering properties diagnosis techniques are presented based on the system. Firstly, a stepped-frequency signal is employed to achieve high range resolution, combining with a variety of signal processing tech- niques. Secondly, cross-range resolution is gained with a rotating table, and the high-resolution two-dimensional （2-D） imaging of the scale model is obtained by the microwave imaging theory. Finally, two receiving antennas with a small distance in altitude are used, and the three-dimensional （3-D） height distribution of scattering points on the scale model is extracted from the phase of images. Some typical bodies and a scale aircraft model are diagnosed in an anechoic chamber. The experimental results show that, after scaling with a metal sphere, the accurate one- dimensional （l-D） RCS pattern of the model is obtained, and it has a large dynamic range. When the bandwidth of the transmitting signal is 4 GHz, the resolution of the 2-D image can reach to 0.037 5 m. The 3-D height distribution of scattering points is given by interferometric measurement. This paper provides a feasible way to obtain high-precision scattering properties parameters of the scale aircraft model in a conventional rectangular anechoic chamber.     

Image separation using wavelet-complex shearlet dictionary

This paper presents a new method for image separation through employing a combined dictionary consisting of wavelets and complex shearlets. Because the combined dictionary sparsely represents points and curvilinear singularities respectively, the image can be decomposed into pointlike and curvelike parts as accurate as possible. The proposed method based on the geo- metric separation theory introduced by Donoho in 2005 shows that accurate geometric separation of the morphologically distinct fea- tures of points and curves can be achieved by l1 minimization. The experimental results show that the proposed method can not only be effective but also greatly reduce the computing time.     

Empirical likelihood inference for logistic equation with random perturbation

Empirical likelihood (EL) combined with estimating equations (EE) provides a modern semi-parametric alternative to classical estimation techniques such as maximum likelihood estimation (MLE). This paper not only uses closed form of conditional expectation and conditional variance of Logistic equation with random perturbation to perform maximum empirical likelihood estimation (MELE) for the model parameters, but also proposes an empirical likelihood ratio statistic (ELRS) for hypotheses concerning the interesting parameter. Monte Carlo simulation results show that MELE and ELRS provide competitive performance to parametric alternatives.