A new approach for edge detection of noisy image based on CNN

A new approach for edge detection of noisy image by cellular neural network (CNN) is proposed in this paper. In order to get the reasonable template, the statistical characteristics of image are utilized, and Gibbs image model is employed to describe the stochastic dependence of an edge pixel on its neighbourhood. Based on stochastic edge image models, edge detection of noisy image is equivalent to seeking a minimum of a cost function. If the template of CNN is designed carefully, the energy function can be mapped properly to the cost function of stochastic edge image model, then CNN can be used for seeking the minimum of cost function. Genetic algorithm is efficient in the field of optimization, and we also utilized this algorithm to get the correct form of template. The results of computer simulation confirm that the new approach is very effective. Furthermore, this result also confirms that we can design template for many different questions based on statistical image model, and the area of application of CNN will be widened. Copyright © 2003 John Wiley & Sons, Ltd.     

CNN-based difference-controlled adaptive non-linear image filters

In this paper, we develop a common cellular neural network framework for various adaptive non-linear filters based on robust statistic and geometry-driven diffusion paradigms. The base models of both approaches are defined as difference-controlled non-linear CNN templates, while the self-adjusting property is ensured by simple analogic (analog and logic) CNN algorithms. Two adaptive strategies are shown for the order statistic class. When applied to the images distorted by impulse noise both give more visually pleasing results with lower-frequency weighted mean square error than the median base model. Generalizing a variational approach we derive the constrained anisotropic diffusion, where the output of the geometry-driven diffusion model is forced to stay close to a pre-defined morphological constraint. We propose a coarse-grid CNN approach that is capable of calculating an acceptable noise-level estimate (proportional to the variance of the Gaussian noise) and controlling the fine-grid anisotropic diffusion models. A combined geometrical–statistical approach has also been developed for filtering both the impulse and additive Gaussian noise while preserving the image structure. We briefly discuss how these methods can be embedded into a more complex algorithm performing edge detection and image segmentation. The design strategies are analysed primarily from VLSI implementation point of view; therefore all non-linear cell interactions of the CNN architecture are reduced to two fundamental non-linearities, to a sigmoid type and a radial basis function. The proposed non-linear characteristics can be approximated with simple piecewise-linear functions of the voltage difference of neighbouring cells. The simplification makes it possible to convert all space-invariant non-linear templates of this study to a standard instruction set of the CNN Universal Machine, where each instruction is coded by at most a dozen analog numbers. Examples and simulation results are given throughout the text using various intensity images. © 1998 John Wiley & Sons, Ltd.     

Estimating optical flow with cellular neural networks

The cellular neural network is a locally interconnected neural network capable of high-speed computation when implemented in analog VLSI. This work describes a CNN algorithm for estimating the optical flow from an image sequence. The algorithm is based on the spatio-temporal filtering approach to image motion analysis and is shown to estimate the optical flow more accurately than a comparable approach proposed previously. Two innovative features of the algorithm are the exploitation of a biological model for hyperacuity and the development of a new class of spatio-temporal filter better suited for image motion analysis than the commonly used space–time Gabor filter. © 1998 John Wiley & Sons, Ltd.     

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.     

APPLICATION OF FUZZY CELLULAR NEURAL NETWORK TO MORPHOLOGICAL GREY-SCALE RECONSTRUCTION

The fuzzy cellular neural network (FCNN) is a brand new branch of cellular neural network (CNN). In this paper, an additive structure of FCNN is proposed for the purpose of implementation of grey-scale mathematical morphology operators. Then this additive FCNN is used to implement morphological grey-scale reconstruction. Some applications of the FCNN to image processing are proposed based on morphological grey-scale reconstruction. Computer simulation results are given. © 1997 by John-Wiley & Sons, Ltd.     

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.     

EVALUATION OF CNN TEMPLATE ROBUSTNESS TOWARDS VLSI IMPLEMENTATION

In this paper a method for the evaluation of static robustness towards random variations in cellular neural network (CNN) templates is proposed. From this evaluation, circuit accuracy specifications for a VLSI implementation are derived which allow the designer to optimize the performance. Moreover, from this evaluation method, guidelines for robust template designs are derived and parametric testing templates are developed.     

Application of a cellular neural network to facial expression animation and high-level image processing

The cellular neural network (CNN) is used to animate facial expressions of a human being. First, the change in facial expressions is regarded as a smooth 2D transformation which is restricted by a bending energy function and displacements of some key-points. Second, the parameters of the CNN are determined by comparing the bending energy function with the energy function of the CNN. Finally, the CNN is used to realize the transformation by minimizing its energy function. Also, the CNN is used to model some visual illusions which are frequently used in psychological tests. First, the retinal induction field is modelled by using a template. The comparison of this CNN model with the real physiological measurements is presented. Then, based on this template, the CNN universal machine is used to model four types of visual illusions: subjective contour illusion (Kanizsa illusion), size illusion (Mueller-Lyer illusion, Ponzo illusion), direction and location illusion (angular illusion of location, Poggendorff illusion) and contrast illusion (Herring illusion). Computer simulations are provided for animating facial expressions and modelling visual illusions.     

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.     

基于TensorFlow的高压输电线路异物识别

针对传统异物识别准确率较低的问题,提出一种基于TensorFlow的深度卷积神经网络的异物识别模型。将巡检图像进行图像灰度化和尺寸压缩等预处理,并采用三维块匹配滤波(BM3D)算法进行图像去噪得到实验所需的训练数据。提出基于TensorFlow的深度卷积神经网络框架,通过使用框架中的TensorBoard模块设计深度卷积神经网络模型结构与优选模型参数,并针对ReLU激活函数与特征权重进行理论分析。实验结果表明,经过15次迭代训练后,深度卷积神经网络比传统的支持向量机(SVM)、极限学习机(ELM)和BP神经网络算法具有更强的巡检图像识别能力;与经典的LeNet-5和VGGNet模型以及相关文献中的模型相比,所提模型更具有优越性。     

A rigorous design method for binary cellular neural networks

In order to be able to take full advantage of the great application potential that lies in cellular neural networks (CNNs) we need to have successful design and learning techniques as well. In almost any analogic CNN algorithm that performs an image processing task, binary CNNs play an important role. We observed that all binary CNNs reported in the literature, except for a connected component detector, exhibit monotonic dynamics. In the paper we show that the local stability of a monotonic binary CNN represents sufficient condition for its functionality, i.e. convergence of all initial states to the prescribed global stable equilibria. Based on this finding, we propose a rigorous design method, which results in a set of design constraints in the form of linear inequalities. These are obtained from simple local rules similar to that in elementary cellular automata without having to worry about continuous dynamics of a CNN. In the end we utilize our method to design a new CNN template for detecting holes in a 2D object. © 1998 John Wiley Sons, Ltd.     

Chaotic cellular neural network‐based true random number generator

This paper presents implementation of a chaotic cellular neural network (CNN)‐based true random number generator on a field programmable gate array (FPGA) board. In this implementation, discrete time model of the chaotic CNN is used as the entropy source. Random number series are generated for three scenarios. Obtained number series are tested by using NIST 800.22 statistical test suite. Also, the scale index technique is carried out for these three scenarios to determine the degree of non‐periodicity for key stream. Copyright © 2017 John Wiley & Sons, Ltd.     

基于改进U-net和CNN的绝缘子自爆检测方法研究

针对绝缘子自爆故障人工检测效率低,成本高的问题,基于改进U-net和卷积神经网络（CNN）模型,提出一种可有效识别绝缘子自爆故障的双阶段目标检测算法。首先,在语义分割阶段使用改进U-net模型,通过翻倍提高图像分辨率的方法有效提高图像分割精度。其次,在图像分类阶段提出更适合所提问题且有效提高分类准确度的新型CNN模型。最后,使用无人机拍摄的绝缘子图片为实验数据进行实验。实验结果表明所提算法识别精度较高。     

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.     

改进的联合型图像超分辨率重建算法

针对传统锚定邻域回归(anchored neighborhood regression, ANR)的图像超分辨率方法缺乏灵活性、且对图像的细节没有很好的恢复能力的缺点,提出一种锚定邻域回归和卷积神经网络(convolution neural network, CNN)相结合的图像重建方法。首先,在ANR中提出使用弹性网络回归模型,使算法具有特征选择的特点。其次,在CNN的图像预处理部分使用lanczos3插值方法,加快了运算速度,在特征提取中提出使用具有自门控特性的Swish函数作为激活函数,用于提高测试准确度。最后,在重建图像的评价方面提出了图像的相关系数,并用于对重建图像做进一步的有效性评估。实验结果证明,所提方法平均峰值信噪比(PSNR)达到了32.68,平均结构相似性(SSIM)达到0.938 0,平均相关系数达到0.982 8。算法有效地恢复了图像的细节部分,图像质量得到了进一步提升。     

Analysis of time-varying cellular neural networks for quadratic global optimization

The algorithm for quadratic global optimization performed by a cellular neural network (CNN) with a slowly varying slope of the output characteristic (see References 1 and 2) is analysed. It is shown that the only CNN which finds the global minimum of a quadratic function for any values of the input parameters is the network composed by only two cells. If the dimension is higher than two, even the CNN described by the simplest one-dimensional space-invariant template Â=[A₁, A₀, A₁], fails to find the global minimum in a subset of the parameter space. Extensive simulations show that the CNN described by the above three-element template works correctly within several parameter ranges; however, if the parameters are chosen according to a random algorithm, the error rate increases with the number of cells. © 1998 John Wiley & Sons, Ltd.     

Membrain: A cellular neural network model based on a vibrating membrane

This paper introduces the Membrain model describing a neural network architecture which is similar to the architecture underlying the class of cellular neural networks (CNNs). the main difference pertains to the characteristic processing equation, which is based on a wave equation instead of a heat equation. Within the CNN framework, a cellular Membrain model may be obtained by replacing the neuron output function by a first-order state equation. Furthermore, the network-cloning templates are chosen such that the CNN behaves like a system of coupled harmonical oscillators. Since the energy of such a system is bounded, the piecewise linear neuron characteristic function may be chosen such that it always operates in the linear regime. Our starting point is the analytical and general solution for forced vibrations with damping. This solution applies to a Membrain neural network whose functional architecture is based on the specialized solution for a network of coupled harmonic oscillators. In particular, we present a Membrain CNN (MCNN) having a toroidal connection structure such that the natural modes of vibration of the net are translation-invariant. Moreover, depending on the point group of the network, some rotation invariance can also be obtained. Identifying the input of such a network with the initial state of the oscillators gives rise to an output which is in essence a transversally travelling wave made up of components which are coupled harmonic neuronal oscillators; that is, the wave is a superposition of natural modes of vibration of the network. the temporal wave pattern may be transformed into a one-dimensional temporal signal which is invariant under translation of the initial deflection pattern of the MCNN. the amplitudes of the components in the temporal signal correspond to the power spectrum of the natural vibration modes in the MCNN. Interpreting the initial deflection pattern as a grey-level image, the temporal signal can be viewed as a modulation of a translation-invariant ‘fingerprint’ of the image. the signal may be sampled such that the modulated ‘fingerprint’ can be classified using some of the traditional neural network models. In particular we show that (1) a self-organizing feature map clusters correlated images and (2) a back-propagation neural network extracts position-invariant features.     

基于多源数据和模型融合的超短期母线负荷预测方法

母线负荷预测对于电网调度运行的安全性和在线分析决策的准确性具有重要的意义.为了进一步提高母线负荷预测精度,提出了一种基于多源数据和模型融合的超短期母线负荷预测方法.结合当前电力大数据,首先将历史负荷数据、日期信息以及天气信息等多类型数据作为预测模型的输入特征,并建立基于BP-ANN(back propagation)神...     

基于CNN和LightGBM的新型风电功率预测模型

考虑到风力发电具有波动和不确定的特点,难以预测,文章提出了基于卷积神经网络和LightGBM算法相结合的新型风电功率预测模型.通过分析风电场与相邻风电场原始数据的时序特征,构建出新的特征集;应用卷积神经网络(CNN)从输入数据中提取信息,基于数据间的对比结果调整相应参数;为了提高预测结果的准确性和鲁棒性,将LightGBM分类算法加入模型中.对比所提模型与支持向量机以及单一的LightGBM和CNN模型仿真结果,证明所提模型具有更好的精度和相率.     

Multi‐template approach to realize central pattern generators for artificial locomotion control

Biologically inspired control of artificial locomotion often makes use of the concept of central pattern generator (CPG), a network of neurons establishing the locomotion pattern within a lattice of neural activity. In this paper a new approach, based on cellular neural networks (CNNs), for the design of CPGs is presented. From a biological point of view this new approach includes an approximated chemical synapse realized and implemented in a CNN structure. This allows to extend the results, previously obtained with a reaction‐diffusion‐CNN (RD‐CNN) for the locomotion control of a hexapod robot, to a more general class of artificial CPGs in which the desired locomotion pattern and the switching among patterns are realized by means of a spatio‐temporal algorithm implemented in the same CNN structure. Copyright © 2002 John Wiley & Sons, Ltd.