基于全变差和压缩感知的小带宽超分辨TOA估计

在一些被动雷达中,当目标回波信号的时宽带宽积较小时,采用传统脉冲压缩估计TOA的分辨率较低,对小时宽带宽积信号目标回波TOA进行有效估计是这些被动雷达面临的重要问题。对此提出了一种基于全变差和压缩感知的小时宽带宽积信号超分辨TOA估计方法。首先通过全变差滤波消除小时宽带宽积信号频带内的色噪声,然后利用压缩感知重构携带目标回波的TOA的稀疏信号,最后通过恒虚警检测获得目标的超分辨TOA估计。仿真实验证明了该方法的有效性。     

SNCK信号的循环自相关函数仿真

本文介绍了一种正弦型调频技术 正弦型非线性切普键控(Sinusoidal Nonlinear Chirp Keying,SNCK),阐述了SNCK技术的信号基本特征,诸如,波形样本的数学模型、时间频率调制曲线、已调信号的功率谱密度及SNCK信号相对于经典LFM信号的优点.同时,介绍了SCD技术及已调SNCK信号波形样本的循环自相关函数的仿真结果.     

基于离散多项式变换的宽带线性调频信号波达方向估计

为了解决宽带信号处理的问题,研究了一种宽带线性调频(LFM)信号的波达方向(DOA)估计方法。该方法采用离散多项式变换(DPT)将宽带的LFM信号变换成窄带的,经过变换后,即变换为单个正弦信号和新的噪声。这样可将时变的方向向量转化为时不变的方向向量,再采用常规的窄带信号处理方法--多信号分类(MUSIC)算法,对信号的波达方向进行估计。理论分析和仿真结果表明,该方法能够精确地估计信号的波达方向;不仅计算量小,易于实现,而且估计性能好。     

基于DPT的非线性调频信号的DOA估计

提出了一种基于DPT的宽带非线性调频信号的DOA估计算法.首先将非线性调频(NLFM)信号建模为高阶多项式相位信号(PPS)模型,然后通过高阶瞬时矩进行多项式相位变换.接收信号将变换为单个正弦信号和新的噪声.再利用ROOT-MUSIC或者ESPRIT算法对变换得到的正弦信号的波达方向进行估计.理论分析和仿真结果表明,该...     

高动态非线性调频直扩信号的伪码快捕算法

针对高动态非线性调频直接扩频(NLFM-DSS,nonlinear frequency modulation direct spread spectrum)信号伪码捕获的问题,提出一种基于离散多项式相位变换(DPT)的NLFM-DSS信号伪码快捕算法.首先通过瞬态矩确定信号的阶数,并将信号降阶成带残余频偏的直扩信号.然后采用PMF-FFT与功率谱相结合的方法实现伪码快速捕获.给出了基于该算法的信号模型和理论分析表达式,并通过计算机仿真验证了该方法的可行性,在扩频增益为1023时,利用功率谱非相关累积30次,可在信噪比为-20 dB时捕获到伪码.     

Riemann-Liouville型分数阶导数的非线性估计（英文）

本文主要研究任意有界连续信号的Riemann-Liouville分数阶导数估计问题.当分数阶α属于0到1时,首先利用滑模技术提出一种有界连续信号分数阶导数的非线性估计方法;然后将其结果推广至分数阶α∈R⁺的情况,并给出相应的非线性估计方案.借助Riemann-Liouville分数阶微积分频率分布模型,本文详细分析讨论了所给分数阶导数非线性估计的收敛性问题,并得到相应闭环系统是渐近稳定的结论.文中所提方法的主要优点是在事先未知给定信号分数阶导数上界的情况下,不仅能自适应地估计其Riemann-Liouville分数阶导数,而且当信号中含有随机噪声和不确定扰动时依然能正常工作.数值仿真实例验证了本文所给估计方法的可行性和有效性.     

基于公交GPS数据的交叉口信号配时参数估计

为了利用公交GPS数据估计交叉口信号配时参数,提出一种改进插值法来估计交叉口单车行程时间;通过先聚类再分类划分信号周期,给出采用停车线附近的GPS数据序列估计周期边界及周期时长的方法;结合周期边界估计结果,提出一种新的红灯时长估计方法.在不同交通流量下对估计方法进行模拟及现场测试,结果表明,基于公交GPS数据的信号配时参数估计方法适用于不同交通流量,且估计效果明显优于现有方法.     

一种雷达非线性调频信号检测及仿真方法

由于雷达非线性调频(NLFM)信号的时频非线性分布特性,常规检测算法难以实现信号能量的有效聚集,传统采用NLFM信号检测时准确率较低,在低信噪比下甚至会失效。针对上述问题,应用了一种基于广义时宽-带宽积的最优窗函数,提出了一种改进的短时分数阶傅立叶变换瞬时频率估计算法。应用改进算法可有效提高非线性调频信号的检测性能,提高检测的准确率。仿真结果表明,在相同信噪比下,提出的方法对NLFM信号的检测概率高于传统方法,性能较好。     

基于声源时延估计的欠定盲分离方法

提出一种基于声源时延估计的二元时频掩蔽方法.通过三个接收信号实现多于多个语音源信号的欠定盲分离.利用语音信号的W-分离正交性,在时频域估计各个源信号到达接收阵列的相对时延序列;进而基于信号时延序列的估计,采用最大似然算法将时频域划分为与源信号个数相同的互不重叠的时频点集合,每个集合(近似)只包含一个源信号的所有时频分量;再通过二元时频掩蔽依次恢复出各集合所对应的源信号.该方法性能通过主观试听得到了验证,其分段信噪比增益至少为13 dB.较之欠定解混迭估计技术DUET,本文方法得到的分离信号与实际声源信号的相异度降低约3 dB.     

基于AR模型的直扩信号循环平稳特性的分析

在信号处理中,传统的功率谱分析方法包含的信息量少,谱相关方法较前者具有更多优点.而一般的谱相关估计方法均基于周期图方法,分辨率低,估计方差大,需要的数据样本长.提出了一种基于AR模型,结合谱相关的方法进行信号的循环谱估计,克服了周期图方法的缺点.给出了算法的大致流程和建模思想.应用此方法在低信噪比条件下,进行直扩信号的循环谱分析,载波频率估计和码片时宽的估计.计算机仿真验证了此方法的有效性.     

纯量反馈系统稳定零极近似相消的积分约束

导出了单输入单输出反馈控制系统误差响应基于名义系统稳定的零、极点近似相消的时间域积分约束，此积分约束是任何反馈控制系统均应满足的．这一约束给出了单输入单输出反馈系统固有折中的新的观点．名义系统稳定的零、极点近似相消的存在导致反馈控制系统的调节时间延长或者误差响应的无穷范数变大．因此，在反馈控制系统设计中，尽量避免补偿器的零、极点与名义系统的极、零点近似相消(即使这些零、极点是稳定的)．     

区域生长辅助的地图配准在室内定位中的应用

地图匹配（ MM）算法通过粒子滤波（ PF）利用室内地图信息来抑制基于惯性传感器的室内定位系统的误差累计。利用区域生长（ RG）算法结合当前步长和方向信息在地图上找到合理的落脚范围,并以此来判断粒子的有效性。这种方法能有效改善地图配准算法的实用性和计算复杂度。提出一种改进的零速度（ ZV）检测算法能准确提取步伐信息,间接提升了零速度更新（ ZUPT）算法和地图配准算法的精度。实验结果表明：该算法的定位误差小于1.0%,定位精度比单纯的航位推算（ DR）算法平均提高了5.97%。     

基于KW、MW及DW的决策支持系统架构研究

SDSS是较为先进的决策支持系统，但由于其模型库中的模型固定不变，因此在实际应用中具有一定的局限性。提出了集成数据仓库DW、知识仓库KW和模型仓库MW作为底部架构的体系结构，能有效解决这一问题。该架构简化了模型获得和编码任务，增强了决策的有效性和智能性，提高了决策效率。     

基于多智体多库协同的一种虚拟现实仿真界面构建技术

一、引言计算机仿真接口界面,随着计算机软硬件的不断提高也在快速地变化着。从其发展趋势中我们不难看出这一点:从早期的命令行提示编辑Command Line,到全屏幕菜单编辑(Menu based Editor),再到图形用户界面Graphic User In-terface(GUI),界面在不断追求如何更好地适应用户、与用户更直接地交互。其具体特点包括自然而又丰富的色彩、逼真而又完美的几何造型、柔和而又动听的环境声响、质感而又具有力反馈的实物等。这些人们所需要的真实感,一种技术是难以胜任的,它需要各种软、硬件技术的综合与集成。从目前的趋     

The impact of intellectual capital on the competitive advantage: Applied study in Jordanian telecommunication companies

This research investigates the impact of intellectual capital components on the competitive advantage in the Jordanian telecommunication companies. The empirical findings indicate that the relational capital and the structural capital have positive impact on competitive advantage. Both the relational capital and the structural capital account for 48.4% of the competitive advantage. It is unexpected to find that the human capital does not have a significant direct impact on competitive advantage. However, it is valid to state that the human capital indirectly and significantly influences competitive advantage as it is embedded in the relational capital. The effect of the relational capital on competitive advantage is moderated by gender and age. The effect is strongest among younger men. In the case of the structural capital its effect is moderated by gender only such that the effect is slightly stronger for females rather than males.     

An efficient soft-computing technique for extraction of EEG signal from tainted EEG signal

Electroencephalography (EEG) is the recording of electrical activity of neurons within the brain and is used for the evaluation of brain disorders. But, EEG signals are contaminated with various artifacts which make interpretation of EEGs clinically difficult. In this research paper, we use a soft-computing technique called ANFIS (Adaptive Neuro-Fuzzy Inference System) for the removal of EOG artifact, combined EOG and EMG artifact. Improvement in the output signal to noise ratio and minimum mean square error are used as the performance measures. The outputs of the proposed technique are compared with the outputs of techniques such as neural network, based on ADALINE (Adaptive Linear Neuron) and adaptive filtering method, which makes use of RLS (Recursive Least Squares) algorithm through wavelet transform (RLS-Wavelet). The obtained results show that the proposed method could significantly detect and suppress the artifacts.     

A divide and conquer matheuristic algorithm for the Prize-collecting Steiner Tree Problem

The Prize-collecting Steiner Tree Problem (PCSTP) is a well-known problem in graph theory and combinatorial optimization. It has been successfully applied to solve real problems such as fiber-optic and gas distribution networks design. In this work, we concentrate on its application in biology to perform a functional analysis of genes. It is common to analyze large networks in genomics to infer a hidden knowledge. Due to the NP-hard characteristics of the PCSTP, it is computationally costly, if possible, to achieve exact solutions for such huge instances. Therefore, there is a need for fast and efficient matheuristic algorithms to explore and understand the concealed information in huge biological graphs. In this study, we propose a matheuristic method based on clustering algorithm. The main target of the method is to scale up the applicability of the currently available exact methods to large graph instances, without loosing too much on solution quality. The proposed matheuristic method is composed of a preprocessing procedures, a heuristic clustering algorithm and an exact solver for the PCSTP, applied on sub-graphs. We examine the performance of the proposed method on real-world benchmark instances from biology, and compare its results with those of the exact solver alone, without the heuristic clustering. We obtain solutions in shorter execution time and with negligible optimality gaps. This enables analyzing very large biological networks with the currently available exact solvers.     

一种改进的心电信号预处理方法研究

心电信号是典型的强噪声下的非平稳微弱信号,减小噪声的干扰对心电信号的分析有着十分重要的意义,因此,有效的滤波方法一直是该领域学者关注的热点问题。本文在基于小波变换心电信号分析研究基础上,针对小波去噪时分解只作用于低频部分,从而忽略了高频区域中一部分有用信号的问题,提出了一种采用改进小波包理论实现心电信号去噪的方法,利用小波包在消除信号噪声方面具有更为精确的局部分析能力的特点,采用了‘db4’小波和＂最优基＂选择的方法,对心电信号进行消噪。以MIT-BIH心电数据库中心律失常数据仿真实验,得到了较理想的去噪效果。对比该方法与小波滤波去噪,发现基于小波包的心电信号去噪具有更优良的去噪性能。     

VoIP认证与计费的设计与实现

基于RADIUS的VoIP认证系统，采用分散受理、集中管理的接入认证管理体系，数据集中存放在认证中心(RADIUS服务器)，用户身份认证由PC向网守发起，网守通过RADIUS协议向认证中心的认证服务器发起认证请求。这样，可以保证用户安全地使用网络资源，以确保用户身份的合法性。同时其落地话单经过处理，可进行计费及其它帐务处理。文中论述了RADIUS对VoIP的支持，提出了一个Gatekeeper与RADIUS结合的整体解决方案。     

Wavelet-based envelope features with automatic EOG artifact removal: Application to single-trial EEG data

In this study, we propose an analysis system for single-trial classification of electroencephalogram (EEG) data. Combined with automatic EOG artifact removal and wavelet-based amplitude modulation (AM) features, the support vector machine (SVM) classifier is applied to the classification of left finger lifting and resting. Automatic EOG artifact removal is proposed to eliminate the EOG artifacts automatically by means of independent component analysis (ICA) and correlation coefficient. The features are then extracted from the discrete wavelet transform (DWT) data by the AM method. Finally, the SVM is used for the discriminant of wavelet-based AM features. Compared with EEG data without EOG artifact removal, band power features and LDA classifier, the proposed system achieves promising results in classification accuracy.