基于梯度阈值的太赫兹时域信号自适应稀疏算法

胶接结构广泛应用于航空航天等国防领域,但在工艺制作及使用过程可能会产生胶接界面脱粘缺陷和损伤,由于太赫兹无损检测技术对非金属材料良好的穿透性能,已被广泛应用于复合材料的无损检测中,太赫兹无损检测技术在多层胶接结构样件胶层内部缺陷的无损检测方面具有较大优势。利用反射式太赫兹时域光谱系统检测多层胶接结构样件,得到的具有样件内部材料信息的太赫兹时域信号,但信号中还包含了大量的冗余特征和噪声等无效信息,这些无效信息大大降低了信号处理和分析效率。针对这一问题,文中提出了基于二阶梯度法提取太赫兹时域信号有效特征,以飞行时间误差为限制条件基于信号的时域特征自适应确定阈值,稀疏太赫兹时域信号,减少信号中冗余无效信息,实现太赫兹时域信号的有效压缩。然后,通过二值化图像分割识别多高斯恢复信号和太赫兹时域光谱系统检测信号的太赫兹图像缺陷区域。最后,制备具有脱粘缺陷的多层胶接结构样件,开展太赫兹无损检测实验。结果表明：文中算法的数据压缩率达到了81%,相比传统压缩算法离散余弦变换提高了59%,相比主成分分析算法提高了75%,相比K-SVD字典学习算法提高了26%,缩短了约80%的数据计算时间,减小了约95%数...     

Minimisation of gradient artefacts in cardiac-MRI-gating using adaptive interference cancellation filter with synthesised reference

A simple and successful method for cardiac-MRI-gating is proposed. The adaptive interference cancellation filter (AICF) is used with a synthesised reference signal to reduce the gradient artefacts caused by the magnetic resonance (MR). The reference signals of the AICF were a combination of the noisy, three-channel ECG signals. In particular, the proposed method is based on a simple experimental setup and does not require any information from amplifiers of the MRI machine, such as shape, amplitude and rise time.     

Image quality assessment based on gradient similarity

In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes.     

基于FPGA的梯度自适应格型滤波器设计

对于高灵敏度的数字信号的处理需要使用数值特性优良的格型滤波器。利用EDA技术设计了梯度自适应格型滤波器。实验表明更新反射系数的步长应随着模块数的增加逐步减小。对梯度自适应格型滤波器的单独模块采用驰豫超前技术设计,显著提高了FPGA的运行时钟速率。     

A complex adaptive notch filter using modified gradient algorithm

A modified gradient algorithm is developed for improving the convergence speed of a first-order complex adaptive IIR notch filter, which is used for estimating an unknown frequency of a complex sinusoidal signal embedded in white Gaussian noise. The new cost function using new error criterion is presented and analyzed theoretically. The proposed technique can significantly improve the convergence speed as compared with a complex notch filter using plain gradient algorithm. The computer simulations are conducted to demonstrate the validity of the proposed complex adaptive notch filter.     

基于梯度特征的弱小目标检测北大核心CSCD

针对传统检测方法对低对比度和低信噪比弱小目标检测难度大的问题,提出一种基于梯度特征提取的弱小目标检测方法。首先利用弱小目标的各向同性及在梯度空间中的正负分布特性,提取两个方向梯度特征;然后通过改进的局部对比度算法分别抑制两个方向梯度特征的相似性,融合两个抑制相似性的方向梯度特征,增强目标同时抑制背景;最后通过自适应阈值对结果图进行分割,得到最终检测结果。实验结果表明,该算法不仅能够有效检测极低信噪比与对比度的目标,而且对复杂边缘场景具有很好的抑制效果,在信噪比、背景抑制因子以及检测率方面优于其他算法。     

A stochastic gradient adaptive filter with gradient adaptive stepsize

The step size of this adaptive filter is changed according to a gradient descent algorithm designed to reduce the squared estimation error during each iteration. An approximate analysis of the performance of the adaptive filter when its inputs are zero mean, white, and Gaussian noise and the set of optimal coefficients are time varying according to a random-walk model is presented. The algorithm has very good convergence speed and low steady-state misadjustment. The tracking performance of these algorithms in nonstationary environments is relatively insensitive to the choice of the parameters of the adaptive filter and is very close to the best possible performance of the least mean square (LMS) algorithm for a large range of values of the step size of the adaptation algorithm. Several simulation examples demonstrating the good properties of the adaptive filters as well as verifying the analytical results are also presented     

Stochastic gradient based third-order Volterra system identification by using nonlinear Wiener adaptive algorithm

The nonlinear Wiener stochastic gradient adaptive algorithm for third-order Volterra system identification application with Gaussian input signals is presented. The complete self-orthogonalisation procedure is based on the delay-line structure of the nonlinear discrete Wiener model. The approach diagonalises the autocorrelation matrix of an adaptive filter input vector which dramatically reduces the eigenvalue spread and results in more rapid convergence speed. The relationship between the autocorrelation matrix and cross-correlation matrix of filter input vectors of both nonlinear Wiener and Volterra models is derived. The algorithm has a computational complexity of O(M/sup 3/) multiplications per sample input where M represents the length of memory for the system model, which is comparable to the existing algorithms. It is also worth noting that the proposed algorithm provides a general solution for the Volterra system identification application. Computer simulations are included to verify the theory.     

Analysis of stochastic gradient tracking of time-varying polynomialWiener systems

This paper presents analytical and Monte Carlo results for a stochastic gradient adaptive scheme that tracks a time-varying polynomial Wiener (1958) system [i.e., a linear time-invariant (LTI) filter with memory followed by a time-varying memoryless polynomial nonlinearity]. The adaptive scheme consists of two phases: (1) estimation of the LTI memory using the LMS algorithm and (2) tracking the time-varying polynomial-type nonlinearity using a second coupled gradient search for the polynomial coefficients. The time-varying polynomial nonlinearity causes a time-varying scaling for the optimum Wiener filter for Phase 1. These time variations are removed for Phase 2 using a novel coupling scheme to Phase 1. The analysis for Gaussian data includes recursions for the mean behavior of the LMS algorithm for estimating and tracking the optimum Wiener filter for Phase 1 for several different time-varying polynomial nonlinearities and recursions for the mean behavior of the stochastic gradient algorithm for Phase 2. The polynomial coefficients are shown to be accurately tracked. Monte Carlo simulations confirm the theoretical predictions and support the underlying statistical assumptions     

Application of orthogonal perturbation sequences to adaptive beamforming

The application of orthogonal perturbation sequences to adaptive beamforming is considered. The perturbation approach is relevant when all the array element signals in an array are not accessible but the array weights are independently adjustable. The perturbation sequences can be used to provide an estimate of the gradient required for adaptive beamforming. A unified analysis of the performance of orthogonal perturbation sequences is presented, and the performance of some specific sequences is investigated. It is demonstrated that for time multiplex perturbation sequences the penality incurred because of the use of perturbations for gradient estimation in adpative beamforming is an increase by a factorNin the system time constants.     

改进的自然梯度盲源分离算法在非平稳环境中的应用

对于传统的自然梯度算法,在处理非平稳信号时,在步长更新迭代过程中,非平稳信号变化幅度过快而导致分离矩阵幅度变化的不稳定,从而影响分离效果。针对此问题,结合变步长的思想,本文提出了基于正交约束的自然梯度盲分离算法,该算法主要对恢复信号进行约束,通过使用瞬时误差有目的地控制变步长,从而加快算法收敛速度且提高了分离精度,同时保证了非平稳环境下分离过程的稳定性。结果表明,正交约束下的盲源分离算法可以高效地分离出非平稳环境下的源信号。     

基于面积约束和自适应梯度修正的分水岭图像分割

图像中的噪声或非规则细节干扰易导致形态学 分水岭产生较严重的过分割,为了在消除过分割的同时尽可能 保持图像目标边界的准确定位,提出了一种基于面积约束和自适应梯度修正的分水岭图像分 割方法。首先对图像进行梯 度变换,采用区域面积约束滤除狭小高梯度尖峰对应的噪声和非规则细节;然后建立梯度级 与结构元素大小之间的函数 关系,并以相对应的结构元素对梯度图像进行粘性形态学(VM)闭运算,消除低梯度噪声及非 规则细节,实现梯度图像的自适 应修正,由于VM闭运算对梯度图像进行修正时,对目标仅作轻度或不作修正,因 而能够最大限度的保持目标轮 廓的准确定位,而对噪声和非规则细节则采用较大尺寸的结构元素进行较大幅度修正,从而 消除产生过分割的因素;最 后对修正图像进行分水岭分割。实验结果表明,本文方法能够在消除过分割的同时,保持目 标轮廓的准确定位。     

A gradient descent implementation of the adaptive robust narrowband constrained LMS beamformer

Classical adaptive beamforming strategies have been shown to exhibit a substantial performance degradation if an error exists in the estimation of the angle of arrival (AOA). The optimization problem can be modified to incorporate an improved constraint allowing for robustness to AOA imperfections. In this paper, we propose a gradient descent implementation of the robust adaptive beamformer whereby a direct relation is established between the Lagrange multiplier and the adaptation step size. This allows for the heuristic approximation of one of the parameters while establishing a valid range for the other. Computer simulations have been used to confirm the findings of this study and illustrate the performance of the developed algorithm.     

A modified spectral conjugate gradient projection method for signal recovery

In this paper, signal recovery problems are first reformulated as a nonlinear monotone system of equations such that the modified spectral conjugate gradient projection method proposed by Wan et al. can be extended to solve the signal recovery problems. In view of the equations’ analytic properties, an improved projection-based derivative-free algorithm (IPBDF) is developed. Compared with the similar algorithms available in the literature, an advantage of IPBDF is that the search direction is always sufficiently descent as well as being close to the quasi-Newton direction, without requirement of computing the Jacobian matrix. Then, IPBDF is applied into solving a number of test problems for reconstruction of sparse signals and blurred images. Numerical results indicate that the proposed method either can recover signals in less CPU time or can reconstruct the images with higher quality than the other similar ones.     

The gradient adaptive split lattice algorithm

The authors present a new algorithm for adaptive filtering of autoregressive processes, based on a three-term recurrence that replaces the lattice equations of the gradient adaptive lattice (GAL) algorithm of Griffiths (1977) and requires only half as many multiplications per recursion. They also present a single-stage convergence analysis and a numerical example comparing the new algorithm to the GAL. Its performance is similar to the GAL, while requiring less than 2/3 as many multiplications and additions     

Analysis of stochastic gradient identification ofWiener-Hammerstein systems for nonlinearities with Hermite polynomialexpansions

This paper investigates the statistical behavior of a sequential adaptive gradient search algorithm for identifying an unknown Wiener-Hammerstein (1958) system (WHS) with Gaussian inputs. The WHS nonlinearity is assumed to be expandable in a series of orthogonal Hermite polynomials. The sequential procedure uses (1) a gradient search for the unknown coefficients of the Hermite polynomials, (2) an LMS adaptive filter to partially identify the input and output linear filters of the WHS, and (3) the higher order terms in the Hermite expansion to identify each of the linear filters. The third step requires the iterative solution of a set of coupled nonlinear equations in the linear filter coefficients. An alternative scheme is presented if the two filters are known a priori to be exponentially shaped. The mean behavior of the various gradient recursions are analyzed using small step-size approximations (slow learning) and yield very good agreement with Monte Carlo simulations. Several examples demonstrate that the scheme provides good estimates of the WHS parameters for the cases studied     

A fully adaptive normalized nonlinear gradient descent algorithm for complex-valued nonlinear adaptive filters

A fully adaptive normalized nonlinear complex-valued gradient descent (FANNCGD) learning algorithm for training nonlinear (neural) adaptive finite impulse response (FIR) filters is derived. First, a normalized nonlinear complex-valued gradient descent (NNCGD) algorithm is introduced. For rigour, the remainder of the Taylor series expansion of the instantaneous output error in the derivation of NNCGD is made adaptive at every discrete time instant using a gradient-based approach. This results in the fully adaptive normalized nonlinear complex-valued gradient descent learning algorithm that is suitable for nonlinear complex adaptive filtering with a general holomorphic activation function and is robust to the initial conditions. Convergence analysis of the proposed algorithm is provided both analytically and experimentally. Experimental results on the prediction of colored and nonlinear inputs show the FANNCGD outperforming other algorithms of this kind.     

Dual slew-rate limiting for reducing gradient field interference in cardiac gated magnetic resonance imaging

A method is presented for minimising gradient field interference on the electrocardiogram (ECG) of a patient undergoing cardiac magnetic resonance imaging. A two lead ECG amplifier uses a single slew-rate limiter in the main signal path, this path providing the ECG signal plus slew-rate limited gradient interference from the scanner. A secondary signal path, also containing a slew-rate limiter, is derived from the main path and provides only slew-rate limited gradient interference. This signal can then be subtracted from the main path to cancel the original interference. This simple arrangement operates in real-time, needs no control signals from the scanner and has been successfully employed in a clinical environment.     

Adaptive stochastic resonance

This paper shows how adaptive systems can learn to add an optimal amount of noise to some nonlinear feedback systems. This “stochastic resonance” (SR) effect occurs in a wide range of physical and biological systems. The noise energy can enhance the faint periodic signals or faint broadband signals that force the dynamical systems. Fuzzy and other adaptive systems can learn to induce SR based only on samples from the process. The paper derives the SR optimality conditions that any stochastic learning system should try to achieve. The adaptive system learns the SR effect as the system performs a stochastic gradient ascent on the signal-to-noise ratio. The stochastic learning scheme does not depend on a fuzzy system or any other adaptive system. Simulations test this SR learning scheme on the popular quartic-bistable dynamical system and on other dynamical systems. The driving noise types range from Gaussian white noise to impulsive noise to chaotic noise     

Adaptive polynomial filters

Adaptive nonlinear filters equipped with polynomial models of nonlinearity are explained. The polynomial systems considered are those nonlinear systems whose output signals can be related to the input signals through a truncated Volterra series expansion or a recursive nonlinear difference equation. The Volterra series expansion can model a large class of nonlinear systems and is attractive in adaptive filtering applications because the expansion is a linear combination of nonlinear functions of the input signal. The basic ideas behind the development of gradient and recursive least-squares adaptive Volterra filters are first discussed. Adaptive algorithms using system models involving recursive nonlinear difference equations are then treated. Such systems may be able to approximate many nonlinear systems with great parsimony in the use of coefficients. Also discussed are current research trends and new results and problem areas associated with these nonlinear filters. A lattice structure for polynomial models is described