Noise predictive belief propagation

We introduce iterative noise whitening for belief-propagation-based channel detectors over intersymbol interference channels with correlated additive noise. In particular, we consider longitudinal magnetic recording systems. The proposed "noise predictive belief propagation" (NPBP) detector iteratively whitens the noise samples by modifying the edge probability computation. NPBP detectors based on finite impulse response and infinite impulse response prediction filters are developed. In addition, we introduce a novel prediction filter optimization method leading to a better noise whitening performance. Simulation results for both coded and uncoded systems show that significant improvements can be obtained.     

Nondestructive detection of flaws in material using transducer arrays

In this paper, we describe a system which uses array filters for processing the recordings of an array of transducers for the purpose of locating possible flaws in the material being tested nondestructively. When applied, in order to focus the array outputs at a point inside the material, these filters are able to attenuate, very effectively, interfering residual energy scattered from adjacent flaws, as well as random noise. If there is a flaw at the point of focus, the summed output of the filters will have a large signal at a delay time corresponding to the total travel time for this point; otherwise the output will consist mainly of residual random noise. Thus, the location of flaws or even microflaws in material may be achieved by simple threshold detection.     

Multivariate optical computation for predictive spectroscopy

A novel optical approach to predicting chemical and physical properties based on principal component analysis (PCA) is proposed and evaluated using a data set from earlier work. In our approach, a regression vector produced by PCA is designed into the structure of a set of paired optical filters. Light passing through the paired filters produces an analog detector signal that is directly proportional to the chemical/physical property for which the regression vector was designed. This simple optical computational method for predictive spectroscopy is evaluated in several ways, using the example data for numeric simulation. First, we evaluate the sensitivity of the method to various types of spectroscopic errors commonly encountered and find the method to have the same susceptibilities toward error as standard methods. Second, we use propagation of errors to determine the effects of detector noise on the predictive power of the method, finding the optical computation approach to have a large multiplex advantage over conventional methods. Third, we use two different design approaches to the construction of the paired filter set for the example measurement to evaluate manufacturability, finding that adequate methods exist to design appropriate optical devices. Fourth, we numerically simulate the predictive errors introduced by design errors in the paired filters, finding that predictive errors are not increased over conventional methods. Fifth, we consider how the performance of the method is affected by light intensities that are not linearly related to chemical composition (as in transmission spectroscopy) and find that the method is only marginally affected. In summary, we conclude that many types of predictive measurements based on use of regression (or other) vectors and linear mathematics can be performed more rapidly, more effectly, and at considerably lower cost by the proposed optical computation method than by traditional dispersive or interferometric instrumentation. Although our simulations have used Raman experimental data, the method is equally applicable to Near-IR, UV-vis, IR, fluorescence, and other spectroscopies.     

分数阶差分滤波器及边缘检测

为了减少边缘检测中的噪声影响,设计了一种分数阶差分滤波器,给出了该滤波器的原理,分析了此类滤波器的幅频特性。该滤波器的系数可通过调节它的阶次来改变,在合适的阶次范围内,该滤波器具有一定的平滑功能。同时,它还克服了直接用分数阶微分检测边缘时,有边缘漂移的问题。最后,我们将其应用于边缘检测,结果表明,在无噪声情况下,其检测结果和其它方法一样好,在有噪声情况下,该滤波器具有一定的噪声免疫力。     

New algorithms for improved adaptive convex combination of LMS transversal filters

Among all adaptive filtering algorithms, Widrow and Hoff's least mean square (LMS) has probably become the most popular because of its robustness, good tracking properties and simplicity. A drawback of LMS is that the step size implies a compromise between speed of convergence and final misadjustment. To combine different speed LMS filters serves to alleviate this compromise, as it was demonstrated by our studies on a two filter combination that we call combination of LMS filters (CLMS). Here, we extend this scheme in two directions. First, we propose a generalization to combine multiple LMS filters with different steps that provides the combination with better tracking capabilities. Second, we use a different mixing parameter for each weight of the filter in order to make independent their adaption speeds. Some simulation examples in plant identification and noise cancellation applications show the validity of the new schemes when compared to the CLMS filter and to other previous variable step approaches.     

Hybrid order statistic filter and its application to image restoration

We introduce a new nonlinear filter for signal and image restoration, the hybrid order statistic (HOS) filter. Because it exploits both rank- and spatial-order information, the HOS realizes the advantages of nonlinear filters in edge preservation and reduction of impulsive noise components while retaining the ability of the linear filter to suppress Gaussian noise. We show that the HOS filter exhibits improved performance over both the linear Wiener and the nonlinear L filters in reducing mean-squared error in the presence of contaminated Gaussian noise. In many cases it also performs favorably compared with the Ll and rank-conditioned rank selection filters.     

DDNP生产及其污水处理新工艺

介绍了DDNP废水处理及其制造新工艺。并进行了有关性能和经济效益的对比,提出了有关DDNP起爆药的新见解。     

Measurement of static and vibration-induced phase noise in UHF thin-film resonator (TFR) filters

Measurements of the static phase noise and vibration sensitivity of thin-film resonator (TFR) filters operating at 640 and 2110 MHz have been made. They show that the short-term frequency instability of the filters is small compared with that induced in the oscillator signal by the sustaining stage amplifier PM (phase modulation) noise. In-oscillator measurement of filter performance under vibration indicates that fractional frequency vibration sensitivities (δf ₀/f₀) are on the order of several parts in 10^-9/g. Because the percentage bandwidth and order (number of poles) of the filters was fairly constant, so was the product of the center frequency and group delay. Thus, the fractional frequency vibration sensitivity of the filters can be expressed alternatively as carrier signal phase sensitivity to vibration. The τ-ω₀ product for the filters that were tested was on the order of 300 rad, so that the equivalent phase sensitivity to vibration was approximately 1 grad/g     

DDNP废水处理方法研究综述

DDNP废水的治理是一大难题.文中归纳整理出现有处理DDNP废水的方法,并在此基础上对其优缺点进行了讨论,旨在对其做进一步的研究,以找到一种治理效果好、成本低廉的可靠方法.     

无粉尘球状DDNP球形化工艺研究

文章研究了无粉尘球状DDNP工艺的还原和重氮。还原采用硫化钠直接还原苦味酸,加上特殊的控制结晶技术使生产的氨基苦味酸钠颗粒均匀;重氮采用加入连苯三酚作为晶型控制剂,X1作为晶型引导剂,使DDNP成球状,无粉尘。文章还试验研究了球状DDNP的特性。与传统工艺相比,新工艺生产的球状DDNP的极限药量为60mg,略高于传统工艺;耐压性能在53MPa以上,略高于传统工艺;撞击感度、摩擦感度低于传统工艺;静电火花感度与传统工艺相当。     

一种新的自适应非线性卡尔曼滤波算法

为避免由于系统噪声统计特性不准确所导致的滤波性能下降问题,改进了一种基于新息的系统噪声方差调整方法,并将其与扩展卡尔曼滤波、Unscented 卡尔曼滤波和差分滤波相结合,形成自适应非线性卡尔曼滤波.将此方法应用到非线性测量光电跟踪系统中,并与采用基本非线性卡尔曼滤波进行性能对比.仿真实验结果证明该方法可以实时调整系统噪声方差,有效地避免由于系统噪声统计特性不准确所带来的滤波性能下降的问题,而且其性能明显优于基本非线性卡尔曼滤波.     

Sensing strategies for a flexible manipulator

In this paper, we investigate the combination schemes using multisensors for Kalman filtering for modal estimation of a flexible manipulator. In addition to observability, the performance indices regarding the global estimation error covariance, the sensitivity to the initial estimate errors, and the robustness to the round-off errors of the relative noise intensity between process and measurements, are utilized to evaluate the quality of the Kalman filters. The aim is to identify well-conditioned multisensor combinations for which Kalman filters can achieve a specified level of design performance. Case studies for the assessment of the multisensor combination schemes are carried out for Kalman filters with various relative noise intensities. The useful guidelines for achieving the well-conditioned combinations of multisensors are presented     

A Comparison of Integration and Low-Pass Filtering

A comparison of the ideal integrator and low-pass filters has been made. Many experimenters use an integrator to filter out high-frequency noise from their data, whereas a properly designed low-pass filter will remove more noise and distort the signal less. Viewed as a low-pass filter, the integrator is superior to the RC filter but inferior to higher order filters. An integrating digital voltmeter (DVM) integrates and samples the input signal. If the signal has a varying component and is mixed with noise, it would be more accurate to use a low-pass filter followed by a sampler, than the integrating DVM.     

Noise robustness of nonlinear filters for image recognition

We analyze the performance of the Fourier plane nonlinear filters in terms of signal-to-noise ratio (SNR). We obtain a range of nonlinearities for which SNR is robust to the variations in input-noise bandwidth. This is shown both by analytical estimates of the SNR for nonlinear filters and by experimental simulations. Specifically, we analyze the SNR when Fourier plane nonlinearity is applied to the input signal. Using the Karhunen-Loève series expansion of the noise process, we obtain precise analytic expressions of the SNR for Fourier plane nonlinear filters in the presence of various types of additive-noise processes. We find a range of nonlinearities that need to be applied that keep the output SNR of the filter stable relative to changes in the noise bandwidth.     

时域模态参数辨识的状态滤波算法

本文针对结构模态参数辨识的噪声干扰问题,采用一种状态滤波方法削弱测量噪声的影响,避免了传统Kalman 滤波法中对系统模型的较高精度要求,并与特征系统实现算法(ERA)相结合,有效地克服了ERA 方法在信噪比较低的情况下对非零奇异值判断的困难,并更精确地识别出结构的模态参数。     

Design and efficient implementations of predictive FIR filters

Two new approaches to the design of predictive FIR filters are presented. First, we discuss the design of predictors and estimators for narrow-band signals based on the interpolated FIR (IFIR) filter approach. The transfer function of the IFIR predictor is of the form H(z)=P(z^L)G(z) where P(z) is a predictor and G(z) is an interpolating estimator. The general-purpose design procedure for efficient IFIR predictors is described, and demonstrated for polynomial predictors. The resulting predictors, optimized for white noise attenuation, have much lower computational complexity than the corresponding direct-form FIR predictors. Secondly, an IIR filter-based implementation of sinusoidal FIR predictors is presented. As an application, a zero-crossing detector for 50 Hz thyristor drives is designed     

Impulse noise reduction using hybrid neuro-fuzzy filter with improved firefly algorithm from X-ray bio-images

Noise filtering performance in medical images is improved using a neuro-fuzy network developed with the combination of a post processor and two neuro-fuzzy (NF) filters. By the fact, the Sugeno-type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro-fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.     

Sinusoidal frequency estimation at low signal‐to‐noise ratio

Abstract

In this paper, an algorithm is developed to estimate the frequencies of the sinusoids corrupted by white noise. First, the input data is analyzed by a large order autoregressive (AR) spectral estimator, and then we root the prediction error filter (PEF) to get a set of zeros. Using a zero‐clustering algorithm, we partition the zeros into clusters. From these zero clusters, we construct filters to enhance the sinusoids. Then, using frequency estimators, we estimate the frequencies of the sinusoids from the filtered data. The results presented in this paper demonstrate that the proposed algorithm provides a better resolution ability and estimate accuracy than that of the conventional AR spectral estimator, especially for close sinusoids at low SNR.     

New spin filters for interferometric fringe patterns and grating patterns

The basic spin filter for interferometric fringe patterns is improved and developed into several new versions for different applications. These spin filters can filter off random noise efficiently and have almost no blurring effect and phase distortion for the fringe patterns. First, they find the local fringe tangent direction, and then they apply a one-dimensional low-pass filter on this direction. In this way the spin filters can separate easily and clearly high-frequency noise from a real fringe signal with nearly zero frequency. The new spin filters are suitable not only for various fringe patterns but also for wrapped-phase, line-grating, and cross-grating patterns, which are impossible by common filters.     

Data-Aided Timing Recovery for Recording Channels With Data-Dependent Noise

In high-density data storage systems, noise becomes highly correlated and data dependent as a result of media noise, channel nonlinearities, and front-end filters. In such environments, conventional timing recovery schemes will exhibit large residual timing jitter and, especially, data-dependent timing jitter. This paper presents a new data-aided timing recovery algorithm for data storage systems with data-dependent noise. We derive a maximum-likelihood timing recovery scheme based on a data-dependent Gauss-Markov model of the noise. The timing recovery algorithm incorporates data-dependent noise prediction parameters in the form of linear prediction filters and prediction error variances. Moreover, because noise can be nonstationary in practice, we propose an adaptive algorithm to estimate and track the noise prediction parameters. Simulation results, for an idealized optical storage channel incorporating a simple model of media noise, illustrate the merits of our algorithm