改进的基于卡尔曼滤波的非均匀性校正算法

本文研究的对象是针对红外焦平面阵列探测单元响应的非线性对非均匀性校正精度的影响,提出了改进的基于卡尔曼滤波的非均匀性校正算法.该算法的研究方法是采用探测器响应的分段线性模型,对基于卡尔曼滤波器非均匀性校正算法进行扩展和改进,其结果能有效地克服红外焦平面阵列探测单元响应特性的非线性对校正精度的影响.实验结论表明,改进后的算法在一定程度上解决了探测器偏置和增益随时间漂移以及响应非线性影响非均匀性校正性能的问题,获得了较好的非均匀性校正效果.     

基于读出电路结构的红外焦平面图像非均匀校正算法

为了改善基于图像配准的红外焦平面迭代非均匀校正算法的性能,在此基础上提出了一种新的红外焦平面图像非均匀校正算法.首先分析了真实图像的噪声规律,利用红外焦平面的读出电路结构特性建立了噪声模型.而后分两部分对算法原理进行分析,最后用仿真数据对算法的有效性给予了验证.通过与其他算法的校正结果比较,证实该算法具有收敛速度快、计算复杂度低,数据存储量小等优点.     

非均匀校正技术算法分析与实时系统设计

为获得良好的红外焦平面阵列非均匀校正效果,讨论了非均匀性的来源、噪声类型和目前基于定标,基于场景的常用非均匀校正方法.对修正的时域高通滤波、改进的神经元网络等利用场景信息来估计探测器参数的校正算法进行了仿真效果和实时性能的分析与评价.同时设计了一种以TMS320DM642为处理核心的小型低功耗DSP硬件系统平台,描述了系统流程和实时实现策略,为红外焦平面系统提供了一条有效的实现路径.     

自适应时间常数的时域高通滤波校正算法

针对时域高通滤波非均匀性校正算法中存在只对偏移响应系数进行校正、难以选取合适的时间常数以及滤波后图像细节不清晰等问题,本文通过分析时间常数对算法校正效果的影响,提出了一种改进的时域高通滤波非均匀性校正算法。首先采用离线采集的非均匀性噪声底图对红外焦平面阵列的原始输出图像进行预处理,去除部分固定模式噪声,保证了增益的均匀性;然后通过运动检测环节判断场景运动是否充分,根据运动程度自适应选取时间常数构造低通滤波器;最后运用高频提升滤波达到提升红外图像整体的亮度和增强目标细节的效果。对真实的红外视频图像实验表明,通过自适应选取时间常数有效地抑制了目标退化和伪像现象,且算法的收敛速度快,校正后图像细节清晰,有利于工程实时应用。     

红外焦平面阵列非均匀性非线性校正新方法

针对红外焦平面阵列非均匀性线性校正方法存在较大误差,而考虑非线性响应的校正算法又过于复杂,难于在实际工程中获得运用等难题,本丈提出一种易于硬件实时处理、校正精度较高的红外焦平面阵列非均匀性非线性校正新方法.在介绍非线性校正新方法原理的基础上,推导出其数学模型,并给出实验结果.实验结果表明该校正方法的校正精度达到1.2%,校正过程中需要存储的参数仅为4个/1象素,易于硬件实现实时校正.     

一种新的红外焦平面器件非均匀性自适应校正算法

红外焦平面阵列（IRFPA）器件普遍存在着响应度的非均匀性问题,而且这种非均匀性随时间和环境改变会发生缓慢变化,目前常用的一次性校正算法不能适应这种变化,同时由于图像场景的多样性,现有的统计校正算法也存在着一定的适应性问题。本文结合图像运动分析中的光流技术,提出了一种新的基于场景的连续校正算法,该算法具有较好的自适应性能。     

红外焦平面阵列非均匀性嵌入式实时校正技术

红外焦平面阵列(IRFPA)成像是当今红外成像技术发展的主流方向,然而IRFPA特有的非均匀性严重地限制了系统的成像质量.针对红外焦平面阵列在进行非均匀性校正中所涉及的运算量和数据量庞大、实时处理难于实现的特点,本文提出采用高速TMS320C6000系列DSP为核心的嵌入式硬件系统,结合分段线性和分段二次多项式算法,阐述了硬件设计和实现步骤,并给出实验结果,结果表明本系统完全满足实时高精度校正的要求.     

红外焦平面阵列非均匀性嵌入式实时校正技术

红外焦平面阵列（IRFPA）成像是当今红外成像技术发展的主流方向,然而IRFPA特有的非均匀性严重地限制了系统的成像质量。针对红外焦平面阵列在进行非均匀性校正中所涉及的运算量和数据量庞大、实时处理难于实现的特点,本文提出采用高速TMS320C6000系列DSP为核心的嵌入式硬件系统,结合分段线性和分段二次多项式算法,阐述了硬件设计和实现步骤,并给出实验结果,结果表明本系统完全满足实时高精度校正的要求。     

利用FPGA实现红外焦平面阵列实时非均匀性校正

实时非均匀性校正是红外成像的一项关键技术。根据红外焦平面阵列探测元光谱响应的特点和基于参照源的两点温标非均匀性校正理论，提出一种利用FPGA硬件实现红外焦平面阵列实时非均匀性两点校正的新方法。该方法动态范围大、处理速度快，适用于红外成像系统实时图像处理场合。仿真和实验结果证明是可行的。     

制冷红外焦平面阵列响应特性的研究

文章分析了制冷红外焦平面阵列（IRFPA）的响应模型及响应特性,利用恒温黑体采集某320＊256长波红外焦平面阵列在不同辐射下的响应数据得到其非线性响应曲线,然后讨论分析响应曲线,在实际应用中划分不同的线性响应段,通过调整合适的积分时间结合两点温度定标法对图像的非均匀性进行线性校正,效果良好。     

Flaw signature estimation in ultrasonic nondestructive evaluation using the Wiener filter with limited prior information

Flaw signals measured in ultrasonic testing include the effects of the measurements system and are corrupted by noise. The measurement system response is both bandlimited and frequency dependent within the bandwidth, resulting in measured signals which are blurred and distorted estimates of actual flaw signatures. The Wiener filter can be used to estimate the flaw's scattering amplitude by removing the effect of the measurement system in the presence of noise. A method is presented for implementing an optimal form of the Wiener filter that requires only estimates of the noise distribution parameters. The theoretical error for scattering amplitude estimation, assuming various levels of available prior information, is analyzed. Three estimation techniques, one a maximum-likelihood based method and the other two residual-sum-of-squares methods, are formulated and tested. The results demonstrate that any of the three approaches could be used to optimally implement the alternative form of the Wiener filter with limited prior information.     

An algebraic study of non classical fullerenes

The Wiener index is the oldest topological index based on the sum of distances between any pair of vertices in given graph. An algebraic approach for generalizing the Wiener index was proposed by Graova? and Pisanski for the first time. In this paper, we compute the modified Wiener index and then the differences between Wiener and modified Wiener indices of an infinite class of non-classical fullerenes.     

Real‐time Reliability Evaluation with a General Wiener Process‐based Degradation Model

The aim of this paper is to investigate the issue of real‐time reliability evaluation based on a general Wiener process‐based degradation model. With its mathematical tractability, the Wiener process with a linear drift has been widely used in the literature, to characterize the dynamics of the degradation process or its transformation. However, the nonlinear degradation process, which can't be properly linearized, exists in practice. The dynamics of such a degradation process can't be accurately captured by linear models. Here, a general Wiener process‐based degradation model is proposed, which covers a variety of Wiener process‐based models as its limiting cases. A two‐stage method is presented to estimate the unknown parameters. Two real‐time reliability evaluation procedures are presented for different conditions: one is the analytical evaluation procedure, and the other is the simulated evaluation procedure. It is shown that when new degradation information is available, the evaluation results can be adaptively updated. Moreover, to check out the proposed degradation model, a graphical method is provided. Finally, the validity of the proposed evaluation method is illustrated by a numerical example and two real‐world examples. Copyright © 2013 John Wiley & Sons, Ltd.     

低信噪比环境下语音识别的鲁棒性方法研究

针对噪声环境下语音识别率急剧下降的问题，提出了一种基于语音时频域稀疏性原理的改进最小方差无畸变响应波束形成与改进维纳滤波结合的算法。该算法首先利用麦克风阵列语音信号的空间信息，通过基于时频掩蔽的改进最小方差无畸变响应波束形成器，增强目标声源方向的语音信号，抑制其他方向噪声的干扰，然后再使用改进的维纳滤波器去除残留噪声并提高语音可懂度，对增强后的语音信号提取梅尔频率倒谱系数作为特征参数，使用隐马尔可夫模型搭建语音识别系统。实验结果表明，该方法能够有效提高低信噪比环境下的语音识别率，具有较好的鲁棒性。     

Optimal burn-in policy by using an integrated Wiener process

The conventional burn-in test, which only collects go/no go (or time-to-failure) data over short testing periods becomes rather ineffective for highly reliable products. This decision problem can be solved if there exists a suitable quality characteristic whose degradation over time can be related to the product's lifetime. An innovative approach to determine the burn-in policy by using a suitable degradation model has been suggested in the literature. However, a major disadvantage of this procedure is that it only uses information contained in current degradation data, and it ignores any information given by the entire sequence of observations. Thus, it becomes relatively insensitive to detecting weak items if the degradation path of the product degrades very slowly. To remedy this weakness, similar to the CUSUM control charting scheme in statistical process control, we propose an integrated Wiener process to model the cumulative degradation path of the product's quality characteristic. Then the optimal burn-in policy can be easily obtained. In addition, we also use an analytic approach to compare the efficiency of our proposed procedure with the method that is previously reported in the literature.     

Fractional Wiener filter

The fractional correlation is a new operation that can easily be implemented by optical means. This operation might be useful for shift-variant pattern recognition and for image restoration. The Wiener filter is the optimal filter according to the minimal square-error criterion. For a given spectral noise that distorts the reference image, this filter is optimal for restoring an image in noise. A fractional Wiener filter is suggested for restoring reference objects in a fractional correlation system. The new filter sometimes performs better than the conventional Wiener filter.     

Remaining Life Prediction Based on Wiener Processes with ADT Prior Information

Precisely predicting the remaining life for an individual plays an important role in condition‐based maintenance, so Bayesian inference method, which can integrate useful data from several sources to improve the prediction accuracy, has became a research hot. Aiming at the situation that accelerated degradation tests have been widely applied to assess the reliability of products, a remaining life prediction method based on Bayesian inference by taking accelerated degradation data as prior information is proposed. A Wiener process with random drift, diffusion parameters is used to model degradation data, and conjugate prior distributions of random parameters are adopted. To solve the problem that it is hard to estimate the hyper parameters from accelerated degradation data using an Expectation Maximization algorithm, a data extrapolation method is developed. With acceleration factors, degradation data are extrapolated from accelerated stress levels to the normal use stress level. Acceleration factor constant hypothesis is used to deduce the expression of acceleration factor for a Wiener degradation model. Besides, simulation tests are designed to validate the proposed method. The method of constructing the confidence levels for the remaining life predictions is also provided. Finally, a case study is used to illustrate the application of our developed method. Copyright © 2015 John Wiley & Sons, Ltd.     

Wiener filter in the gyrator domain

The Wiener filter is the optimal filter according to the minimal mean square error criterion. For data distorted by noise with given spectral density, the Wiener filter is the optimal filter for restoring the noisy image. In this paper we will define a gyrator convolution operator and its optical implementation. The new generalized gyrator Wiener filter was defined using the new operator. The new filter was compared to the conventional Wiener filter and showed improved performance. An improvement of up to 40?dB in the mean square error at the optimal rotation angle in comparison to the conventional Wiener filter is demonstrated.     

A Wiener‐based degradation model with logistic distributed measurement errors and remaining useful life estimation

Degradation data analysis, which investigates degradation processes of products to extrapolate the lifetime properties, is an effective method for reliability analysis. But degradation data that reflect a product's inherent randomness of degradation are often contaminated by measurement errors. To deal with the problem, this paper proposes a Wiener‐based model with an assumption of logistic distributed measurement errors and adopts the Monte Carlo expectation‐maximization method together with the Gibbs sampling for parameter estimation. Based on the model and parameter estimates, an efficient algorithm is proposed for a quick calculation of maximum likelihood value. Also, the estimation of remaining useful lifetime is discussed. Simulation results show that the proposed model is relatively better and more robust in comparison with the Wiener process with Gaussian noises. Finally, the application of the proposed model is illustrated by an example.