基于Duffing振子的BPSK载波信号检测方法研究

针对低压配电网具有噪声干扰强、信号衰减大等问题,提出了用Duffing振子来检测BPSK载波信号的新方法.Duffing振子对微弱信号敏感,信号的不同相位将引起系统在混沌态与大尺度周期态之间的相变,该方法就是利用载波信号的相位引起的Duffing振子的相变,通过对该相变的判别来检测载波信号的相位信息.同时还分析了Duf...     

最大Lyapunov特性指数在微弱信号检测中的应用

利用Duffing振子的运动状态来判断微弱信号的存在是一种常用的方法,通常此方法没有明确的门限。由于最大Lyapunov特性指数是指示动力学系统是否处于混沌状态的重要参数,所以将最大Lyapunov特性指数作为混沌判据引入基于Duffing振子的微弱信号检测中。利用最大Lyapunov特性指数指示Duffing振子所处的状态,如果最大Lyapunov特性指数大于1,测说明系统处于混沌状态,反之则处于周期状态,从而为此方法提供了更为直观的判断依据。仿真结果表明,采用最大Lyapunov指数作为判断依据可以更准确的判断微弱信号的存在,进而说明此方法的可行性。     

达芬振子激励分叉在多普勒频移检测中的应用

以达芬(Duffing)振子模型在单频外激励下的分叉原理为基础,提出一种可在强噪声背景下检测微弱水声探测信号多普勒频移的新方法。当达芬振子的激励振幅超过某一临界参数时系统由混沌状态突变为周期状态。根据这一原理,首先在无噪声条件下确定达芬振子由混沌状态向周期振荡转变的分叉参数阈值,然后根据噪声强度适当调节分叉参数,使得系统输出稳定在单一周期状态。当噪声中含有微弱的多普勒回波时,通过理论分析可知系统输出为阵发混沌状态,且可根据混沌状态的间隔周期较准确地求出回波信号的多普勒频移。     

5种新混沌弱信号检测系统设计

为了进一步改进混沌弱信号检测系统的性能,设计了5种新的高灵敏度复合混沌弱信号检测系统,并选择其中一种与目前广泛使用的Duffing混沌弱信号检测系统进行了对比分析,结果显示新复合混沌系统可以克服Duffing混沌弱信号检测系统出现的3个难题:被检测信号消失后混沌状态无法快速自动回复、大周期状态不稳定以及混沌状态和大周期状态难以区分。通过使用Jacobian矩阵和特征方程的分析,显示了新复合混沌系统比Duffing混沌系统更为有效。与传统弱信号检测方法相比,新复合混沌系统电路简单、具有更低的信噪比和检测信号门限。理论分析和实验显示了新复合混沌系统的特性。     

基于Melnikov方法的分数阶Duffing振子混沌阈值解析研究

研究了含有分数阶微分项的Duffing振子的分岔与混沌行为,利用等效刚度和等效阻尼的概念对分数阶微分项进行处理,将分数阶微分项等效成三角函数与指数函数的形式,用Melnikov方法分析了分数阶Duffing振子产生分岔与混沌的必要条件,得到了其解析结果。进行了解析解和数值解的比较,证明了解析结果的精确度,并通过仿真计算研究了分数阶的阶次和系数对系统产生混沌必要条件的影响。在数值模拟过程中,还发现分数阶Duffing振子中存在双稳态特性,从两个稳态解出发,随着外激励参数的变化都能通过倍周期分岔到达混沌的状态。通过分析系统的动力学响应验证了这一现象。     

基于混沌弱信号检测的轧机故障诊断研究

为检测强噪声背景下轧机的转子和齿轮故障,给出了一种Duffing方程检测微弱信号的算法.设计加入待检信号后的混沌振子方程,检测出转子发生早期碰摩以及齿轮偏心和单齿缺陷故障.故障诊断实验可以证明利用Duffing振子的间歇混沌现象对强噪声背景中轧机故障的微弱特征信号进行检测的有效性,与相关函数法比较,证明此方法的优越性.     

Liu-cos混沌系统在声波弱信号检测中的应用

针对混沌三维弱信号检测系统的特点,设计了一种傅里叶变换和李雅普诺夫算法相结合的收敛性判别算法,证明了三维Liu-cos混沌系统对于声波弱信号检测具有广域性并且当输入声波信号幅值大于临界阈值时,系统变量x输出平衡于输入的周期摄动力信号, 系统变量y和z的输出收敛于零,临界阈值具有唯一性。解决了传统Duffing混沌系统应用于声波弱信号检测时,系统变量x和y输出不收敛、只能进入窄域检测等问题。构造了Duffing混沌系统和三维Liu-cos混沌系统的实际声波检测实验,分析了混沌系统在实际声波检测过程中的性能。     

混沌理论在车载超声测距系统中的应用研究

利用混沌系统敏感依赖于初始条件的特性,通过判断系统运动状态的变化,实现车载噪声环境下的微弱超声波信号的提取。系统的仿真实验结果表明混沌振子检测理论在抑制噪声、检测微弱信号方面具有很高的先进性,可以大大提高超声波测距的精度和性能。     

带有余弦函数的三维混沌弱信号检测系统及电路实现

从理论上分析了二维Duffing弱信号检测系统变量输出在混沌和周期态之间交替转换。发现一种新的三维混沌弱信号检测系统生成规则,以生成4种检测系统之一的三维Liu-cos混沌系统为例,采用傅里叶变换等方法从理论上验证了新生成的三维混沌弱信号检测系统输出信号具有广域收敛性,解决了二维Duffing混沌弱信号检测系统不能广域检测和输出信号不收敛问题;新设计的三维Liu-cos混沌弱信号检测电路空中声波实验显示可抗低频声波干扰,检测性能优于二维Duffing混沌电路和新设计的Duffing＋滤波器电路,水中实验显示三维Liu-cos混沌弱信号检测电路可抗高频水声干扰,可检测波形畸变的水声信号。     

基于Lorenz混沌同步系统的未知频率微弱信号检测

针对现有混沌类检测方法存在的不足,提出一种基于混沌同步系统测量强噪声背景下微弱信号频率值的新方法。该方法利用Lorenz混沌系统自身的初值敏感性、噪声免疫性和混沌系统的可同步性,采用驱动-响应法构建同步检测系统对微弱信号进行降噪处理,再结合多信号分类算法处理所得到的同步误差信号,最终实现微弱信号的频率测量。该方法有效解决了单Duffing振子参数设定复杂、运行状态转换时间长和状态判定困难的问题,也无需采用复杂的混沌系统阵列结构求解待测信号的频率值。仿真和实验结果表明新方法能够准确检测出微弱信号的频率,进一步完善了现有混沌类检测方法,为其应用于实际工程提供了新的思路。     

Improving performance of neurons by adding colour noise

Noise is not always an interfering signal which perturbs the system. On the contrary, noise signals can enhance the performance of some non‐linear systems such as stochastic resonance (SR). These systems can detect the weak input signal when it is added to the noise signal. According to this property, SR models play a significant role in the functioning of the brain for detecting weak input signals and synchronisation of neural connections. In this study, the authors model neurons as SR systems where different types of noise, i.e. white noise and pink noise, are employed to amplify the weak nervous signals. They demonstrate colour noise, in particular, pink noise enhances the performance of the SR system to amplify the input signal. Furthermore, pink noise has a wider range of optimum values in comparison to white noise. Therefore, they can conclude that neurons are more sensitive to detect the signals that carry pink noise than signals with white noise or without noise. Hence, the retrieving ability of neurons can be improved by adding pink noise.Inspec keywords: stochastic processes, white noise, neural nets, brain, noise, neurophysiologyOther keywords: interfering signal, particular noise, colour noise, weak nervous signals, pink noise, white noise, SR system, authors model neurons, SR models, noise signal, weak input signal, nonlinear systems     

Frequency-Domain Interpretation of Oscillator Phase Stability

A frequency-domain interpretation of the phase stability of an oscillator is discussed. From a knowledge of the time dependence of an oscillator phase during a time interval T* it is possible to give the characteristics of this oscillator, not only for this time interval, but also for subsequent time intervals. Since the use of a Fourier transform for the computation of a continuous power spectrum is unrealistic, a discrete-spectrum approach will be taken. Usually, in the calculation of power spectra, stationarity of the fluctuations is assumed, although experiment indicates that this is often not the case. A more realistic approach is adopted. Analytical phenomena and random walk are separated from white noise on the basis of statistical criteria using discrete Fourier transforms. The white noise is then interpreted in the frequency domain. Both random walk and specific signals are studied in the time domain and can be separated by digital filtering. Two different sets of experimental results are analyzed by this method, one derived from measurements on a quartz-crystal oscillator locked to a low-frequency transmitter and the second from measurements on an ammonia maser. In both cases, measurement precision and ease of prediction of the behavior of the oscillator are improved.     

Asymptotic analysis and linearization of the randomly perturbed two-wells Duffing oscillator

The purpose of this paper is to approximate the dynamics of a non-linear oscillator with multiple static equilibrium excited by a white noise. An approach based on asymptotic expansions of dynamical systems driven by weak random perturbations is introduced. To illustrate the method, the Duffing oscillator with two symmetric potential wells is considered. An equivalent locally linear oscillator, which provides a good approximation of the dynamics of the studied oscillator, is obtained. This last approximation can be considered as a local linearization.     

以小波系数为特征值的Pi-sigma网络识别肌电信号

为了有效识别肌电信号EMG(Electromyography)的运动模式,利用小波分析的方法对采集的肌电信号进行消噪处理,最大限度地清除混杂在肌电信号中的噪声;然后提取各尺度小波系数最大值作为Pi-Sigma神经网络分类器的输入,完成基于EMG信号多运动模式的识别.与此同时,利用EMG信号的能量特性,对各模式的起始和终止时刻进行界定,配合模式分类器的识别结果控制电动假手完成相应的动作.实验表明,基于小波分析的二次消噪方法能很好地消除混杂在EMG信号中的噪声,在正确的运动模式识别情况下,依据提取的运动模式时间信息,能够方便地实现假手的实时控制.     

基于变形Lorenz系统和李雅普诺夫指数的微弱周期信号检测

提出了一种变形的Lorenz混沌检测系统,此系统主要具有混沌态和类周期态2种状态。利用最大李雅普诺夫指数作为判断混沌系统由混沌态趋于类周期态变化的量化依据,自动识别混沌系统的临界状态,更准确地判断微弱信号的存在。将互相关检测方法与混沌检测方法相结合,实现强噪声背景下微弱周期信号检测。仿真结果表明,该方法能有效地检测出强噪声中的微弱周期信号。     

Comparison of phase noise simulation techniques on a BJT LC oscillator

The phase noise resulting from white and flicker noise in a bipolar junction transistor (BJT) LC oscillator is investigated. Large signal transient time domain SPICE simulations of phase noise resulting from the random-phase flicker and white noise in a 2 GHz BJT LC oscillator have been performed and demonstrated. The simulation results of this new technique are compared with Eldo RF and Spectre RF based on linear circuit concepts and experimental result reported in the literature.     

Extremely low phase noise UHF oscillators utilizing high-overtone, bulk-acoustic resonators

High-overtone, bulk acoustic resonators (HBAR) have been designed that exhibit 9-dB insertion loss and loaded Q values of 80000 at 640 MHz with out-of-phase resonances occurring every 2.5 MHz. These resonators have been used as ovenized frequency-control elements in very low phase noise oscillators. The oscillator sustaining stage circuitry incorporates low-1/f noise modular RF amplifiers, Schottky-diode ALC, and a miniature 2-pole helical filter for suppression of HBAR adjacent resonant responses. Measurement of oscillator output signal flicker-of-frequency noise confirms that state-of-the-art levels of short-term frequency stability have been obtained. Sustaining stage circuit contribution to resulting oscillator flicker-of-frequency noise is 7-10 dB below that due to the resonators themselves. At 16-dBm resonator drive, an oscillator output signal white phase noise floor level of -175 dBc/Hz is achieved.     

Stochastic analysis of strongly non-linear elastic impact system with Coulomb friction excited by white noise

The stochastic response of frictionally damped strongly non-linear elastic impact oscillator subjected to white noise excitation and its stochastic bifurcation are considered. By the stochastic averaging method based on generalized harmonic function, one can obtain the stationary probability density function of this system. The effects of system parameters on the responses are investigated and the analytical results were verified by comparing with numerical results from Monte Carlo simulations. Stochastic bifurcations are discussed through a qualitative change of the stationary probability distribution, which indicates that the coefficient of friction, damping constant of the elastic impact force respectively, can be treated as bifurcation parameters.     

Sub-threshold signal processing in arrays of non-identical nanostructures

Weak input signals are routinely processed by molecular-scaled biological networks composed of non-identical units that operate correctly in a noisy environment. In order to show that artificial nanostructures can mimic this behavior, we explore theoretically noise-assisted signal processing in arrays of metallic nanoparticles functionalized with organic ligands that act as tunneling junctions connecting the nanoparticle to the external electrodes. The electronic transfer through the nanostructure is based on the Coulomb blockade and tunneling effects. Because of the fabrication uncertainties, these nanostructures are expected to show a high variability in their physical characteristics and a diversity-induced static noise should be considered together with the dynamic noise caused by thermal fluctuations. This static noise originates from the hardware variability and produces fluctuations in the threshold potential of the individual nanoparticles arranged in a parallel array. The correlation between different input (potential) and output (current) signals in the array is analyzed as a function of temperature, applied voltage, and the variability in the electrical properties of the nanostructures. Extensive kinetic Monte Carlo simulations with nanostructures whose basic properties have been demonstrated experimentally show that variability can enhance the correlation, even for the case of weak signals and high variability, provided that the signal is processed by a sufficiently high number of nanostructures. Moderate redundancy permits us not only to minimize the adverse effects of the hardware variability but also to take advantage of the nanoparticles' threshold fluctuations to increase the detection range at low temperatures. This conclusion holds for the average behavior of a moderately large statistical ensemble of non-identical nanostructures processing different types of input signals and suggests that variability could be beneficial for signal processing. We demonstrate also that circuits composed of coupled non-identical nanoparticles can act as elementary nano-oscillators that show synchronization properties for sub-threshold stimuli. The results obtained should be of conceptual interest for the design of reliable signal processing schemes with non-identical nanostructures.     

差分振子时间历程在微弱调制信号检测中的应用

单一的差分振子仅可实现对周期信号中某一频率成分进行检测,对于强噪声背景下的边频带,尽管可以利用多个差分振子组成差分阵列进行逐个检测,进而确定边频带的间隔,但这种做法无疑会带来巨大的计算量。在对调制信号进行Hilbert变换包络分析时,所得到的时域信号是原始调制信号中的低频分量,亦是调制波信号,若该低频分量仍然包含较强的噪声成分,传统的频谱分析将会失效。此时,我们可借助差分振子时间历程对含较强的噪声的包络进行检测。因此,提出基于差分振子时间历程的微弱调制信号检测方法,即首先对信号进行Hilbert包络解调,然后利用差分振子时间历程对含较强的噪声的幅值包络进行检测,并成功应用于风机早期故障检测中。