基于非线性变换的自适应时间延迟估计

含有脉冲性噪声的信号常使经典的时间延迟估计算法效果变差。给出了一种基于Sigmoid 变换的自适应时间延迟估计算法（SATDE）,采用 Sigmoid 函数,对含有脉冲性噪声的信号进行非线性变换;利用自适应滤波器对信号的时间延迟参量进行估计。计算机数值仿真实验结果表明,该算法在对信号的时间延迟进行估计时,不仅可以适用于符合高斯模型的信号噪声,更适用于含有脉冲噪声的信号。该算法是一种具有较好鲁棒性的时间延迟估计算法。     

基于改进的各向异性模型对IVUS图像降噪算法

针对血管内超声图像中的强背景噪声和边缘模糊等问题,提出了一种改进的各项异性扩散滤波算法.引入中值滤波,将滤波后的梯度模代替传统各向异性滤波中原始图像的梯度模,以控制扩散的过程,并能较好地保持边缘的特性;将各向异性扩散方程的常规扩散方向由4个方向扩展为8个方向,可保留更多的图像细节;并提出了一种自适应选取扩散门限的方法,解决了滤波和边缘保持的矛盾.实验结果表明,改进的算法在滤除噪声和保留边缘方面有着比较满意的效果,为以后血管内超声图像中外膜的提取提供了基础.     

A non-linear spatial hearing model based on bases pursuit algorithm

In the research on spatial hearing and realization of virtual auditory space, it is important to effectively model the head-related transfer functions (HRTFs) or head-related impulse responses (HRIRs). In our study, we managed to carry out adaptive non-linear approximation in the field of wavelet transformation. The results show that the HRIRs’ adaptive non-linear approximation model is a more effective data reduction model, is faster, and is 5 dB on average better than the traditional principal component analysis (PCA) (Karhunen-Loève transform) model based on relative mean square error (MSE) criterion. Furthermore, we also discussed the best bases’ choice for the time-frequency representation of HRIRs, and the results show that local cosine bases are more propitious to HRIRs’ adaptive approximation than wavelet and wavelet packet base. However, the improved effect of local cosine bases is not distinct. Here, for the sake of modeling the HRIRs more truthfully, we consider choosing optimal time-frequency atoms from redundant dictionary to decompose this kind of signals of HRIRs and achieve better results than all the previous models.     

SAR图像各向异性扩散滤波算法

为了抑制SAR图像中固有的乘性相干斑噪声,本文提出了一种基于各向异性扩散滤波的新算法。该算法首先将自蛇扩散引入经典的SRAD与DPAD算法各向异性扩散方程中,形成了一种带边缘增强功能的各向异性扩散新方程;然后利用改进Frost滤波系数作为扩散方程的新扩散函数,形成了一种既能有效抑制相干斑,又能较好保护边缘、抑制块效应现象的新扩散函数。实验表明,新扩散滤波算法在相干斑抑制、边缘保护、块效应抑制方面均比传统各向异性扩散滤波算法更优异。     

一种脉冲噪声下的鲁棒性自适应回波抵消方法

实际通信线路中脉冲性噪声常使得基于高斯噪声模型的经典自适应回波抵消方法的效果变差。针对该问题,提出了一种基于α稳定分布模型的鲁棒性自适应回波抵消方法（RAEC）。该方法采用p稳定分布建模脉冲噪声,在最小平均p范数准则下,利用自适应时间延迟估计器来抵消回波,不仅可以有效地抵消通信线路中含有高斯噪声的回波,而且对含有脉冲噪声的回波也有良好的抵消效果。理论分析和计算机仿真实验表明,RAEC是一种对脉冲噪声具有鲁棒性的自适应回波抵消方法。     

Robustness of cellular neural networks in image deblurring and texture segmentation

Template parameters of cellular neural networks (CNNs) should be robust enough to random variability of VLSI tolerances and noise. Using the CNN for image processing, one of the main problems is the robustness of a given task in a real VLSI chip. It will be shown that very different tasks such as 2D or 3D deconvolution and texture segmentation can be solved in a real VLSI CNN environment without significant loss of efficiency and accuracy under low precision (about 6–8 bits) and random variability of the VLSI parameters. The CNN turns out to be very robust against template noise, image noise, imperfect estimation of templates and parameter accuracy. The parameters of a template are tuned using genetic learning. These optimized parameters depend on the precision of the architecture. It was found that about 6–8 bits of precision is enough for a complicated multilayer deconvolution, while only 4 bits of precision is enough for difficult texture segmentation in the presence of noise and parameter variances. The tolerance sensitivity of template parameters is considered for VLSI implementation. Theory and examples are demonstrated by many results using real-life microscopic images and natural textures.     

自适应SUSAN扩散的超声图像去噪算法

为了降低超声图像斑点噪声的影响,提出一种自适应SUSAN(ASUSAN)扩散的超声图像去噪算法.通过引入SUSAN算法检测出图像的边缘,然后采用K均值法对模板区域特征值进行分类,自适应获得图像的全斑点噪声区域和相关参数,最后将SUSAN算法与非线性扩散方法相结合,提出一种新的扩散方程对图像进行非线性去噪.实验证明,相比于其他的各向异性扩散算法,本算法能得到更高的PSNR值和FOM值以及更小的MSE值,且在细节保留方面也能取得更好的效果.     

Cellular neural networks with non-linear and delay-type template elements and non-uniform grids

The cellular neural network (CNN) paradigm is a powerful framework for analogue non-linear processing arrays placed on a regular grid. In this paper we extend the current repertoire of CNN cloning template elements (atoms) by introducing additional non-linear and delay-type characteristics. In addition, architectures with non-uniform processors and neighbourhoods (grid sizes) are introduced. With this generalization, several well-known and powerful analogue array-computing structures can be interpreted as special cases of the CNN. Moreover, we show that the CNN with these generalized cloning templates has a general programmable circuit structure (a prototype machine) with analogue macros and algorithms. the relations with the cellular automaton (CA) and the systolic array (SA) are analysed. Finally, some robust stability results and the state space structure of the dynamics are presented.     

Compact CMOS implementation of a low‐power,current‐mode programmable cellular neural network

We report on the design and characterization of a full‐analog programmable current‐mode cellular neural network (CNN) in CMOS technology. In the proposed CNN, a novel cell‐core topology, which allows for an easy programming of both feedback and control templates over a wide range of values, including all those required for many signal processing tasks, is employed. The CMOS implementation of this network features both low‐power consumption and small‐area occupation, making it suitable for the realization of large cell‐grid sizes. Device level and Monte Carlo simulations of the network proved that the proposed CNN can be successfully adopted for several applications in both grey‐scale and binary image processing tasks. Results from the characterization of a preliminary CNN test‐chip (8×1 array), intended as a simple demonstrator of the proposed circuit technique, are also reported and discussed. Copyright © 2001 John Wiley & Sons, Ltd.     

Nonlinear filtering and fast ray tracing of 3D image data.Improving visualization in medical imaging

This article deals with the problem of nonlinear filtering of magnetic resonance (MR) tomograms based on geometry-driven diffusion and speeding up the visualization of volumetric data by ray tracing. The authors present the basic motivation of this research, which was recently carried out in the Institute of Measurement Science. Locally adaptive diffusion conductivity, effective iteration stopping criterion, and fast ray tracing algorithms are discussed. Also, the results of computer experiments are described and the improvement of 3-D visualization is demonstrated     

用于非线性电阻片通流容量试验的冲击方波电流源的研究

对非线性电阻片能量耐受试验用的冲击方波电流源进行了参数设计和仿真计算,得出了回路及负载参数和回路输出波形参数之间的关系。计算和试验结果表明:将人工传输线的各链电感依次由大到小按一定比例进行选择,既可以抑制波头的过冲,又可以减少方波持续期间的平顶降落,得到符合标准规定的冲击方波电流波。     

Cellular neural networks for real-time DNA microarray analysis

A new methodology for real-time processing of DNA chip images is proposed. The idea developed here is to use the cellular neural network (CNN) array to analyze the DNA microarray. A CNN is an analog dynamic processor array that reflects this property: the processing elements interact directly within a finite local neighborhood. Due to its architecture, a two-dimensional CNN array is widely used to solve image processing and pattern recognition problems; moreover, the parallelism characteristic of this structure allows one to perform the most computationally expensive image analysis tasks three orders of magnitude faster than a classical CPU-based computer. This approach, thanks to the supercomputing capabilities of the CNN architecture, makes the whole DNA chip methodology fully parallel and also makes the processing phase, until now very time consuming, a real-time step. We discuss the results of testing an algorithm based on the CNN universal machine (CNN-UM) that has been designed to classify the image data. The algorithm is implemented in an analogic (analog and logic) microprocessor.     

Image denoising using pulse coupled neural network with an adaptive Pareto genetic algorithm

Recently, pulse coupled neural network (PCNN) attracts much attention in image denoising as a nonlinear filtering technique. The PCNN‐based anisotropic diffusion (PCNN‐AD) method has been proposed previously for flicker noise reduction and its effectiveness has been demonstrated. Using the visual characteristics of PCNN, PCNN‐AD has also solved the problem of AD that AD is not able to suppress the isolate noise. However, there are still two drawbacks in PCNN‐AD, that is, time consuming and PCNN parameters' estimation. In order to improve the efficiency and the denoising performance of PCNN‐AD, a PCNN‐based method with an adaptive Pareto genetic algorithm (GA‐PCNN) has been proposed to restrain from additive white Gaussian noise (AWGN) in this paper. GA‐PCNN firstly integrates the PCNN and AD as a parallel system, then, optimizes the parameters of a simplified PCNN by the adaptive Pareto GA. Experimental results indicate that GA‐PCNN has better performances than the previous denoising techniques, i.e. median filter, Wiener filter, AD filter, and PCNN‐AD. The effectiveness of GA‐PCNN on AWGN reduction and edge preservation are shown finally. The results will also contribute to denoising in CMOS image sensors in the future. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A unifying framework for the analysis of proportionate NLMS algorithms

Despite being a de facto standard in sparse adaptive filtering, the two most important members of the class of proportionate normalised least mean square (PNLMS) algorithms are introduced empirically. Our aim is to provide a unifying framework for the derivation of PNLMS algorithms and their variants with an adaptive step‐size. These include algorithms with gradient adaptive learning rates and algorithms with adaptive regularisation parameters. Convergence analysis is provided for the proportionate least mean square (PLMS) algorithm in both the mean and mean square sense and bounds on its parameters are derived. An alternative, more insightful approach to the convergence analysis is also presented and is shown to provide an estimate of the optimal step‐size of the PLMS. Incorporating the so obtained step‐size into the PLMS gives the standard PNLMS together with a unified framework for introducing other adaptive learning rates. Simulations on benchmark sparse impulse responses support the approach. Copyright © 2014 John Wiley & Sons, Ltd.     

General rational approximation of Gaussian wavelet series and continuous-time gm-C filter implementation

A general method of rational approximation for Gaussian wavelet series and Gaussian wavelet filter circuit design with simple g_m-C integrators is presented in this work. Firstly, the multiorder derivatives of Gaussian function are analyzed and proved as wavelet base functions. Then a high-accuracy general approximation model of Gaussian wavelet series is constructed, and the transfer function of first-order derivative of Gaussian wavelet filter is obtained using quantum differential evolution (QDE) algorithm. Thirdly, as an example, a fifth-order continuous-time analog first-order derivative of Gaussian wavelet filter circuit is designed based on multiple loop feedback structure with a simple g_m-C integrator as the basic blocks. Finally, simulation results demonstrate that the proposed method is an excellent way for the wavelet transform implementation. The designed first-order derivative of Gaussian wavelet filter circuit operates from a 0.53-V supply voltage and a bias current 2.5 nA. The power dissipation of the wavelet filter circuit at the basic scale is 41.1 nW. Moreover, the high-accuracy QRS detection based on the designed wavelet filter has been validated in application analysis.     

On the universe of stable cellular neural networks

A cellular neural network (CNN) is a novel analogue circuit architecture with many desirable features. This paper extends previous stability results of CNNs to include classes of strictly sign-symmetric and acyclic templates. We show that most of the 3×3 strictly sign-symmetric templates are stable almost everywhere, with the unknown templates reduced to three classes. We also introduce template graphs and CNN graphs and utilize them to obtain results concerning stability and irreducibility of CNN templates.     

SAR图像相干斑噪声的各向异性扩散滤波算法

传统各向异性扩散滤波算法以梯度作为边缘检测与扩散系数生成的主要依据,当其用于抑制SAR图像中的乘性相干斑噪声时,存在对噪声敏感,边缘检测非恒虚警,扩散不均衡等问题,为此提出了一种新的各向异性扩散滤波算法.该算法利用平行窗通过比率运算生成边缘强度映射(ESM),然后利用ESM代替梯度作为边缘检测与扩散系数生成的主要参数,...     

激光引信电流采样处理系统设计与应用

激光引信是一种对目标的高精度主动检测方案,广泛应用于空空导弹中。提出一种对激光引信电流采样信号进行调理和数字信号处理相结合的方案,处理后的信号将可用于调节激光器供电电压,以实现激光器恒流驱动的目的。为分析此方法的性能,给出了精确分析模型。结果表明,与传统的激光引信电源采样处理方式相比,对信号采样处理系统的硬件和软件进行优化设计,能够对采样的信号噪声幅值降低50%,可减弱激光引信发射功率抖动,大大提高激光引信的稳定性和精度。该处理系统成功应用于某型激光引信发射驱动电路中,经充分考核,验证了其有效性。     

Reference Model with an Adaptive Hermite Fuzzy Neural Network Controller for Tracking a Synchronous Reluctance Motor

This article presents a reference adaptive Hermite fuzzy neural network controller for a synchronous reluctance motor. Although synchronous reluctance motors are mathematically and structurally simple, they perform poorly under dynamic modes of operation because certain parameters, such as the external load and non-linear friction, are difficult to control. The proposed adaptive Hermite fuzzy neural network controller overcomes this problem, as using the Hermite function instead of the conventional Gaussian function shortens the training time. Furthermore, the proposed adaptive Hermite fuzzy neural network controller uses an online self-tuning fuzzy neural network to estimate the system's lumped uncertainty. The estimation method involves a fuzzy controller with expert knowledge of the initial weight of the neural network. Finally, the Lyapunov stability theory and adaptive update law were applied to guarantee system convergence. In this article, the responsiveness of the adaptive Hermite fuzzy neural network controller and an adaptive reference sliding-mode controller is compared. The experimental results show that the adaptive Hermite fuzzy neural network controller markedly improved the system's lumped uncertainty and external load response.     

Second‐order blind algorithms for adaptive recursive linear equalizers: analysis and lattice implementation

Adaptive recursive linear equalizers present important advantages in terms of performance and robustness compared to more standard finite impulse response structures, and provide a means for blindly initializing the decision feedback structure. We present an analysis of a pair of algorithms for the adaptation of the recursive part of the equalizer, which are based on the second‐order statistics of the received signal, in a multichannel complex‐valued setting with spatially coloured noise. When the number of equalizer poles is no less than the channel order, both algorithms enjoy a unique stationary point, which in addition is locally convergent; global convergence properties, on the other hand, can be quite different. When the optimum setting presents poles close to the stability boundary, the lattice structure is preferred for ease of stability monitoring. Lattice versions of the two algorithms are developed and their convergence properties discussed. Copyright © 2006 John Wiley & Sons, Ltd.