周期信号

基于DSP的低信噪比周期信号高速实时提取系统

DSP Based on High Speed Real Time System for Low SNR Periodic Signal Extracting

第二，基于Lyapunov指数的弱周期信号检测方法。

Second , weak periodic signal detection method based on Lyapunov exponent .

周期信号频谱分析演示CAI软件的编程实现

The Programming and Realization of the CAI Software for Periodic Signal Spectrum Analysis and Demonstration

FFT测量周期信号的幅值与相角修正

The Correct Value Of Scope and Phase Angle Using FFT Measure Periodic Signals

基于Lyapunov指数的弱周期信号检测

Weak Signal Detection Based on Lyapunov Exponents

基于ISP芯片的可编程周期信号测试仪的设计

Design of Programmable Period Signal Measuring Instrument Based on ISP

介绍了周期信号频谱分析演示CAI软件的设计思路和编程方法。

This paper introduces the designing way and the programming methods of the CAI software for periodic signal spectrum analysis and demo .

整个心动周期信号、P波、T波的高频谱无明显的规律性，而QRS波有一定的结构。

There was no regularity in the spectrum of entire cycle , P and T waves , but these was some structure in QRS wave .

利用DFT技术实现同频率周期信号相位差的高精度测量

A high precision measuring method by using the DFT technology to realize the phase difference of periodic signals with the same frequency

新生儿心动周期信号（HPS）的混沌特征、功率谱分析

Analysis for chaos characteristics and power spectrum of heart period signal ( hps ) on neonatal

在临床上运用心动周期信号（Heartperiodsignal，HPS）分析作为反映心脏自主神经系统活性（Autonomicnervoussystem，ANS）的一种无创性检测方法已得到广泛公认和应用。

Heart period signal ( HPS ) analysis is used as a non-invasive heart autonomic nervous system ( ANS ) function examination , which has been acknowledged and used extensively in clinic .

心动周期信号（HPS）具有混沌特征，携带有自主神经功能信息。HPS的功率谱分析可以定量地评价交感、副交感神经活动的强弱及其相互作用。

The heart period signal ( HPS ) has chaotic characteristics and it carries information about auto-nomic nervous system funtion .

目的探讨野生型p16β对体外喉癌Hep-2细胞周期信号传导的干预作用。

Objective To investigate the effect of p16 β interfering with the signal conduction of Hep-2 cell cycle in vitro .

复制因子C亚单位2（replicationfactorCsubunit2，RFC2）与DNA的复制和修复及细胞周期信号检查点的功能有关。

Replication factor C subunit 2 ( RFC2 ) was reported to be associated with DNA duplication , DNA repair , and the function of cellular checkpoint .

本文从理论上分析了频谱泄漏产生的原因，并设计了基于FPGA的128倍频系统，利用精确的频率跟踪技术实现了对周期信号整数点的采样，从而减小频谱泄漏。

This paper analyses firstly the reason of spectrum leakage in theory , then design a 128-multiple frequency system based on FPGA that obtain integer sampling points in a cycle and reduce spectrum leakage through accurate frequency pursuit .

研究了周期信号的采样理论，从理论上揭示了Shannon采样定理在应用于周期信号时的局限性。

The sampling theory of periodic signals has been studied in this paper . The limitations of Shannon Sampling theorem in its application to the periodic signals have been exposed .

文中以一组周期信号的DFT分析为例，比较了信号谱的幅度和相位误差，其结果表明频谱精度大大提高。

As an example of application of this device , the results of the DFT analysis on a multi frequency signal are given , and the accuracy of these measurements is improved highly .

分析了基于Melnikov法和相轨迹观察法的弱周期信号检测方法，针对该方法存在的检测精度低等不足，提出了基于Lyapunov指数法的弱周期信号检测方法。

The weak signal detection based on Melnikov method and the observation method is analyzed , and a new method of weak signal detection based on Lyapunov exponents is presented .

噪声在非线性系统中最显著的作用就是随机共振（SR），即在非线性系统中，内噪声或外噪声的存在，可以增强系统对外界弱周期信号的响应。

The constructive effects of noise in nonlinear systerms is stochastic resonance ( SR ), it is that the existence of noise can evoke the best correlation between a weak external signe and the response of the nonlinear system .

给出了用FFT变换和相关函数法来测量两个周期信号之间相位差的两种方法，并在实际测量中进行了应用，证明对测量相位差是行之有效的方法。

This paper presents two methods of using FFT converter and correlated function to measure the phase difference between two periodical signals . And through practical application , it proves that the two methods are effective in measuring phase difference .

讨论：①基于windows开发的系统具有较好的交互性和移植性，②获得胎儿心电后可用我们已开发的成熟技术实现对胎儿心动周期信号的混沌特征分析，以估计胎儿自主神经系统功能。

Discussions : ① The system developed on the base of Windows was much better in interaction and transportability or portability ,② Whenever extracting FECS , the sophisticated technique developed by our laboratory should be used to accomplish analyzing fetal heart period signal and estimating fetal autonomic nervous system function .

本文利用Duffing方程的动力学特性，即混沌振子对白噪声具有免疫力，同时由于干扰周期信号的频率与混沌振子的固有频率相差较大，故而混沌振子对这些干扰信号也具有免疫力。

In this paper , according to the characteristic of Duffing equation : chaotic oscillator is immune against the white noise and the periodical signal whose frequency is different to the inherent frequency of chaotic oscillator .

混沌理论检测微弱周期信号利用了Duffing系统的动力学行为特点，在系统处于混沌临界状态时向系统中加入微弱周期信号，使得系统相空间状态发生改变。

The model makes use of dynamics behavior characteristics of Duffing system , which is in chaos critical state . We add weak periodic signal to such a system , changing the systematic phase space state .

在此基础上，以已知Hurst指数的分形高斯噪声（fGn）序列为主要研究对象，利用逆向方法，分别研究了周期信号以及高斯白噪声影响下的Hurst指数估计算法的估计性能。

Based on this , taking the fractal Gaussian noise ( fGn ) series with a prior known Hurst exponent into account , the performance of these Hurst algorithms impacting on the periodical signal and the Gaussian white noise respectively using reverse method is evaluated .

给出了采用Morlet小波变换并结合集合平均检测叠加有平稳随机噪声周期信号基频初相位的方法，为旋转机械故障定位提供新手段。

A new method for detecting the initial phase of the fundamental frequency component of a periodic signal mixed with stationary random noise by using the Morlet wavelet transform and ensemble average is presented , which provides a more effective means for the fault location in rotary machinery .

利用555电路产生周期信号，然后对该信号进行分频，产生时序控制信号，用来控制ADC0809模块完成A/D转换，此外，使用锁存器对转换后的数字信号进行锁存以便单片机对其读入保存。

The paper firstly uses the 555 circuit to produce the periodic signal , and then provides the control signals to control the A / D transformation , the PC can read and save the data .

SCN有两大主要功能：一是在外界没有周期信号下，能够保持近日节律的周期在24小时左右；二是当外界周期信号在一定范围时，能够与外界保持周期一致。

SCN has two main functions . One is that when there is no external periodic signal , SCN has the ability to keep circadian rhythms with the period close to 24h . The other one is that SCN can keep the same period as outside under proper periodic signal .

通过对LVQ网络的一些改进，实现了LVQ对周期信号的识别，并且可以通过调整相似的程度来限制结果，对于低于规定相似程度的输入，网络可以把它作为样本储存下来。

Artificial neural networks model of LVQ is mended in this article so that it realize the periodical signal recognizing . Furthermore , the degree of similarity can be rectified to confine the result . The input under the limit will be remembered by the net as a standard sample .

计算机辅助非正弦周期信号的幅频谱分析

Computer Assistant Analysis on Magnitude Spectrum for the Non-Sinusoidal Periodic Signals

精确确定周期信号的方法研究

Study on the Method of Precise Determination of a Periodic Signal