System identification from multiple input/output data

For stationary random or transient data representing multicorrelated (multicoherent) input/output data occurring in physical systems, iterative computational algorithms are developed to identify the frequency response functions of optimum constant parameter linear systems connecting this data. Results are obtained from Fourier transform methods and optimum least-squares prediction techniques by changing original arbitrary input records into ordered sets of conditioned input records. These procedures provide the basis for efficient digital computer analysis of general multiple input/output problems. The Appendix contains useful error analysis results for the optimum frequency response estimates determined from measured data.     

用于单载波频域均衡水声通信的可分近似稀疏信道估计

针对单载波频域均衡水声通信中信道估计易受噪声干扰的问题,提出了一种低复杂度的信道估计方法。考虑水声信道的时域稀疏特性,导出频域输入、输出信号与信道冲激响应的关系式,并引入稀疏正则项,构造稀疏信道估计的目标函数。然后利用可分近似对目标函数进行迭代优化,再经过稀疏化与去偏处理,得到信道传递函数的最终估计。最后,利用数值仿真和海试数据对所提出方法的性能和运算效率进行评估。较之传统信道估计方法,所提出的方法在估计精度和计算复杂度方面具有一定的优势。      

High-order spectra-based deconvolution of ultrasonic NDT signals for defect identification

In ultrasonic nondestructive testing (NDT) of materials, pulse-echo measurements are masked by the characteristics of the measuring instruments, the propagation paths taken by the ultrasonic pulses, and noise. This measured pulse-echo signal is modeled by the convolution of the defect impulse response and the measurement system response, added to noise. The deconvolution operation, therefore, seeks to undo the effect of the convolution and extract the defect impulse response which is essential for defect identification. In this contribution, we show that the defect ultrasonic model can be formulated in the higher-order-spectra (HOS) domain in which the processing is more suitable to unravel the effect of the measurement system and the additive Gaussian noise. In addition, a new technique is developed to faithfully recover the impulse response signal from its HOS. Synthesized ultrasonic signals as well as real signals obtained from artificial defects are used to show that the proposed technique is superior to conventional second-order statistics-based deconvolution techniques commonly used in NDT.     

Adaptive Laguerre filters for active noise control

Feedforward controllers are used in many active noise control (ANC) systems to generate destructive interference in noise fields. An ideal feedforward ANC controller should have an infinite impulse response (IIR) transfer function, but most available feedforward ANC controllers have finite impulse responses (FIR) instead. The main reason is related to the adaptation algorithms of ANC systems. In general, adaptive FIR filters converge faster with guaranteed stability. In this study, the adaptive Laguerre filter is proposed and tested in an ANC application with positive experimental effects. The new ANC controller is an IIR filter, but its adaptation is similar to that of a FIR filter with fast convergence and guaranteed stability. Detailed explanations and analysis are presented in the main text.     

瞬态光谱测量中的触发与同步技术

设计加工了一套高速单脉冲触发电路,触发门限在150 mV～1.5 V范围内连续可调,输出脉冲宽度可自行调节,其响应时间小于50纳秒.提出了通过短脉冲半导体激光器测量触发延时的同步方案.在此基础上利用带像增强的ICCD探测器,建立了瞬态光谱测试系统,并实时测量了环氧丙烷爆燃转爆轰过程的瞬态光谱.触发与同步技术是研究的重点.     

Lattice form adaptive infinite impulse response filtering algorithm for active noise control

In some situations of active noise control, infinite impulse response (IIR) filters are more suitable than finite impulse response (FIR) filters owing to the poles in the transfer function. A number of algorithms have been derived for applying IIR filters in active noise control; however, most of them use the direct form IIR filter structure, which faces the difficulties of checking stability and relatively slow convergence speed for noise composed of narrow-band components with large power disparity. To overcome these difficulties along with using the direct form IIR filters, a new adaptive algorithm is proposed in this paper, which uses and updates the lattice form adaptive IIR filter in an active noise control system. Full mathematical derivations of the proposed algorithm are presented, and the comparison between the proposed algorithm and the commonly used filtered-u LMS and filtered-v LMS algorithms shows the superiority of the proposed algorithm.     

双向掺铒光纤放大器的特性分析

讨论了双向掺铒光纤放大器的结构方案,利用考虑放大的自发辐和北员耗影响的双向掺铒光纤放大器稳态放大速率方程模型分析的增益与掺铒光纤长度、输入信号光功率、帛运光功率及抽运方式等参数之间的关系,研究了单向和双向等功率抽运下正反向噪声系数随正反向信号光输入功率的变化行为。     

Passive and temporally continuous optical spectral analyzer of RF signals via wavelength coding

A novel approach realizing an optical spectrum analyzer for photonic detection of an unknown RF carrier signals is presented. The described module may be part of an electronic warfare system in which detection of a narrow band RF signal is required. Moreover, The RF signal is characterized by an unknown time varying carrier frequency embedded in wide band noise. The system uses a passive, fiber based photonic configuration. It allows the spectrum mapping of an incoming electronic RF signal modulated on an optical carrier. The spectral analyzer configuration uses a finite impulse response (FIR) filter that is realized by two different optical paths of parallel fibers which generate a spectral notch filter. Hence, a wavelength coding is realized by chromatic dispersion such that each wavelength is filtered by a different FIR filter. Therefore, the energy at a WDM demux output channels is actually proportional to the spectrum of the input RF signal. This spectral mapping is obtained without lose of temporal RF information.     

Hysteresis-induced long-time tails

It is shown analytically that the output of the standard model for complex, nonlocal hysteresis, the Preisach model, exhibits long-time tails under quite general conditions. For uncorrelated input signals the exponent of the algebraic output correlation decay is determined solely by the tails of the input and the Preisach density. Correspondingly we identify universality classes leading to identical algebraic tails. These results predict the occurrence of 1/f noise for a large class of hysteretic systems.     

A computational algorithm for computing nonlinear auditory frequency selectivity.

Computational algorithms that mimic the response of the basilar membrane must be capable of reproducing a range of complex features that are characteristic of the animal observations. These include complex input output functions that are nonlinear near the site's best frequency, but linear elsewhere. This nonlinearity is critical when using the output of the algorithm as the input to models of inner hair cell function and subsequent auditory-nerve models of low- and high-spontaneous rate fibers. We present an algorithm that uses two processing units operating in parallel: one linear and the other compressively nonlinear. The output from the algorithm is the sum of the outputs of the linear and nonlinear processing units. Input to the algorithm is stapes motion and output represents basilar membrane motion. The algorithm is evaluated against published chinchilla and guinea pig observations of basilar membrane and Reissner's membrane motion made using laser velocimetry. The algorithm simulates both quantitatively and qualitatively, differences in input/output functions among three different sites along the cochlear partition. It also simulates quantitatively and qualitatively a range of phenomena including isovelocity functions, phase response, two-tone suppression, impulse response, and distortion products. The algorithm is potentially suitable for development as a bank of filters, for use in more comprehensive models of the peripheral auditory system.     

Spectral analysis of non-linear systems involving square-law operations

This paper contains a number of useful theoretical formulas to analyze the frequency domain properties of Gaussian input data passing through non-linear square-law systems. Special bispectral density functions are defined and applied that are functions of a single variable. From measurements of input data and output data only, results are obtained to identify the separate frequency response functions for two models of linear systems in parallel with non-linear square-law systems. Non-linear coherence functions are defined from these models which determine the proportion of the output spectrum due to the non-linear operations. Together with ordinary coherence functions, a measured output spectrum for these models can be decomposed into three components representing the linear operations, the non-linear operations, and the remaining uncorrelated noise effects. This material indicates also how to analyze other types of non-linear models by employing similar techniques.     

Non-Gaussian,non-dynamical stochastic resonance

The classical model revealing stochastic resonance is a motion of an overdamped particle in a double-well fourth order potential when combined action of noise and external periodic driving results in amplifying of weak signals. Resonance behavior can also be observed in non-dynamical systems. The simplest example is a threshold triggered device. It consists of a periodic modulated input and noise. Every time an output crosses the threshold the signal is recorded. Such a digitally filtered signal is sensitive to the noise intensity. There exists the optimal value of the noise intensity resulting in the “most” periodic output. Here, we explore properties of the non-dynamical stochastic resonance in non-equilibrium situations, i.e. when the Gaussian noise is replaced by an α-stable noise. We demonstrate that non-equilibrium α-stable noises, depending on noise parameters, can either weaken or enhance the non-dynamical stochastic resonance.     

Effects of input noise on a simple biochemical switch

Many biological processes are controlled by biomolecular switches which themselves are regulated by various upstream chemical molecules (the input). Understanding how input noise affects the output stochastic switching process is of significant interest in various biophysical systems like gene regulation, chemosensing, and cell motility. Here, we propose an exactly solvable model where the noisy input signal arises from a simple birth-death process and directly regulates the transition rates of a downstream switch. We solve the joint master equations to analyze the statistical properties of the output switching process. Our results suggest that the conventional wisdom of an additive input-output noise rule fails to describe signaling systems containing a single molecular switch, and, instead, the most important effect of input noise is to effectively reduce the on rate of the switch.     

Stochastic resonance in the FitzHugh-Nagumo model from a dynamic mutual information point of view

Stochastic resonance(SR) in a FitzHugh-Nagumo neuron model is investigated based on a dynamic mutual information (DMI) between the input and the corresponding output signals. The DMI is expressed in terms of the (cross)power spectra of the input and output time series. Both stochastic-periodic and aperiodic SR are treated based on the DMI and our results are in good accord with the SR measured by the signal to noise ratio(SNR) for the case of the stochastic-periodic input and the power norm for the case of the aperiodic input.     

腐蚀声发射信号降噪方法

为了从复杂背景噪声中有效地检测出腐蚀声发射信号,采用短时分形维数和离散分数余弦变换相结合的降噪方法,利用声发射检测系统,对6% FeCl₃·6H₂O溶液中Q235钢板的全面腐蚀和局部腐蚀声发射信号进行了降噪处理。实验结果表明,腐蚀声发射信号分别加入白噪声、有色噪声和粉红噪声,在输入信噪比为0~15dB的条件下,此方法降噪效果与标准离散余弦、离散分数余弦变换方法相比,输出信噪比最高可提升8 dB。所述降噪方法对检测腐蚀声发射信号以及对金属剩余寿命的评价具有一定意义。      

机械抖动激光陀螺的高分辨率信号处理

 机械抖动激光陀螺（MDRLG）信号是两路相位差为π/2的正弦拍频信号,一般采用4倍频鉴相输出计数脉冲,利用FIR滤波和抖动剥除等方法解调抖动信号,从而得到外界输入的角速度信息。高速采集MDRLG信号,并细分为相位依次相差π/16的16路信号,实现MDRLG的8倍频、16倍频和32倍频鉴相输出。输出角速率的均方差随着倍频数的提高而减小,信号处理的分辨率得到提高。实验测试和Allan方差分析表明：32倍频时的量化误差Q从4倍频鉴相时的0.327″减小到0.170″,增加MDRLG信号鉴相的倍频数可以减小量化误差。在对MDRLG信号进行抖动剥除解调时,由于量化误差是主要误差源之一,高分辨率信号处理能够有效提高MDRLG角速度测量的精度。     

Non-linear filtering of ultrasonic signals using neural networks

Structure noise from inhomogeneous micro-structures makes the detection of flaws present in highly scattering materials difficult. Several techniques have been applied to improve the signal-to-noise ratio (SNR) in order to make flaw detection easier. Linear filtering does not provide good results because both structure noise and flaw signal concentrate energy in the same frequency band. Non-linear filtering can be used to reduce the structure noise of ultrasonic signals. Therefore, neural networks are applied in this work for this purpose. In order to use neural networks for non-linear filtering, dynamic structures must be applied. The easiest way to implement a neural network with the capability of processing temporal patterns is to consider them spatial ones, applying the signal into a tapped delay line of finite extension, that is the input of a static neural network (for example, a multi-layer perceptron). In this work, a dynamic neural network has been built to filter ultrasonic signals with structure noise, and has been trained with the real-time back-propagation algorithm, using as inputs 3000 synthetic ultrasonic signals of 896 samples each. Target signals for training are the same as the ones used as inputs but without noise. The neural network is trained in order to generate as output the target signal when the noisy input one is applied. For testing the performance of the non-linear filter, a new set of 500 noisy signals has been used. The SNR improvement is about 6 dB average. The results show that this non-linear filtering method is quite useful as pre-processing stage in flaw detection systems.     

三稳系统的动态响应及随机共振

以平衡点参数p, q构造出一类对称三稳势函数, 进而提出微弱信号和噪声共同驱动的三稳系统模型. 深入研究并总结参数p, q对势垒高度ΔU₁, ΔU₂及两势垒高度差的影响. 从定常输入的角度提出了系统稳态解曲线的概念, 并进一步研究低频谐波信号输入时系统的输出动态响应. 引入噪声, 三稳系统在合适的参数条件下实现随机共振, 从稳态解曲线的角度分析了噪声诱导的三稳系统随机共振机理. 最后研究了阻尼比k和平衡点参数p, q对系统随机共振的影响.     

基于中值滤波和提升小波分析的图像去噪方法研究

针对现有算法大多对单一高斯噪声或脉冲噪声进行图像滤波的问题,在对二维图像平滑去噪的过程中,采用基于中值滤波和提升小波变换相结合的图像去噪方法。在中值滤波基础上,构造基于脉冲检测的中值滤波器,找出混合噪声中脉冲噪声并进行滤波;与此同时,对原始小波进行提升,构造提升小波,然后采用提升小波阈值去噪方法抑制高斯噪声。实验结果表明:采用本文方法,混合噪声得到有效抑制,去噪效果好。     

Robust modeling of acoustic paths using a sparse adaptive algorithm

Acoustic impulse response functions are generally sparse in nature and traditionally these are modeled by adaptive finite impulse response (FIR) filters trained using a least mean square (LMS) algorithm. The conventional LMS algorithm is not effective in modeling sparse systems and sparse LMS algorithms have been recently developed to improve the modeling in such scenarios. However, the traditional sparse LMS algorithms are not robust to disturbances at the error sensor and may diverge in some scenarios. With an objective to overcome this limitation of conventional sparse adaptive algorithm, this paper presents a robust sparse adaptive algorithm. The new algorithm has been shown to effectively model sparse systems in a robust manner. In addition, the algorithm has been successfully applied in modeling the acoustic feedback path in a behind the ear digital hearing aid.