Robust techniques for signal processing: A survey

In recent years there has been much interest in robustness issues in general and in robust signal processing schemes in particular. Robust schemes are useful in situations where imprecise a priori knowledge of input characteristics makes the sensitivity of performance to deviations from assumed conditions an important factor in the design of good signal processing schemes. In this survey we discuss the minimax approach for the design of robust methods for signal processing. This has proven to be a very useful approach because it leads to constructive procedures for designing robust schemes. Our emphasis is on the contributions which have been made in robust signal processing, although key results of other robust statistical procedures are also considered. Most of the results we survey have been obtained in the past fifteen years, although some interesting earlier ideas for minimax signal processing are also mentioned. This survey is organized into five main parts, which deal separately with robust linear filters for signal estimation, robust linear filters for signal detection and related applications, nonlinear methods for robust signal detection, nonlinear methods for robust estimation, and robust data quantization. The interrelationships among many of these results are also discussed in the survey.     

神经网络在图像编码中的应用

神经网研究的再度兴起及其在图像编码中的应用，开辟了图像压缩的新途径。本文论述了用于图像编码的神经网络模型、算法、应用效果及进展，对该领域尚未解决的一些基本理论问题，如神经网络实现图像数据压缩的机理，基于神经网络图像编码方法的分类等进行了探讨。最后，展望了需要进一步研究的方向。     

三容水箱的单神经元自适应PID控制研究

针对一些过程控制的动态过程所具有的大惯性、大延时，及非现性问题，结合PID和人工神经元的特点，提出了一种改进的三容水箱单神经元自适应PID控制方法。该方法具有自学习和自适应能力，以及较强的鲁棒性。仿真结果表明该方法对三容水箱这种复杂对象具有好的控制效果，而且适合工程应用。     

Practical control for improving power density and efficiency of the BLDC generator

This work presents an advanced control technique for maximizing power density and efficiency of the brushless dc generator by using a linear tracking method and also suggests a sub-optimal control method in practical applications. In a generator of given rating, the weight and size of the system affect the fuel consumption directly; therefore, power density is one of the most important issues in a standalone generator. Conventional rectification methods cannot achieve the maximum power possible because of a nonoptimal current waveform. The optimal control scheme to maximize power density and minimize machine size and weight in a nonsinusoidal power supply system has been derived, incorporated in a control system, and verified by simulation and experimental work. A new simple algebraic method has been proposed to accomplish the proposed control without a fast Fourier transform which is time consuming and complicated.     

Intelligent modeling, observation, and control for nonlinearsystems

We present identification methods for nonlinear mechatronic systems. First, we consider a system consisting of a known linear part and an unknown static nonlinearity. With this approach, using an intelligent observer, it is possible to identify the nonlinear characteristic and to estimate all unmeasurable system states. The identification result of the nonlinearity and the estimated system states are used to improve the controller performance. Secondly, the first approach is extended to systems where both the linear parameters and the nonlinear characteristic are unknown. This is achieved by implementing the intelligent observer as a structured recurrent neural network     

求解超定线性方程组L_1─范数最优解的神经网络方法

基于线性规划对偶理论，本文给出一种求解超定线性方程组Ｌ１─范数解的神经网络方法。这一方法由两部分组成，首先利用ＬＳＳＭ神经网络求出Ｌ１问题的近似对偶解，然后利用改进的Ｔ－Ｈ网络求Ｌ１─问题的解，当参数选择适当时，Ｔ－Ｈ网络的全局渐近稳定点就是问题的精确解，模拟试验也表明了这一方法的可行性。     

基于支持向量机的线性轮廓图控制方法研究

在质量控制的应用中,一些过程或产品质量可以由响应变量与多个解释变量之间的关系（轮廓图）更好地表征.提出了一种基于支持向量机的控制方法来检测第二阶段中线性轮廓图的变化.通过计算机仿真模拟实验与其他几种传统方法以及神经网络的方法做比较发现,仿真得出所提出的控制方法在检测截距和斜率的变化方面表现优异.     

经验模态分解中多种边界处理方法的比较研究

经验模态分解(EMD)的一个关键问题是处理边界效应。尽管目前除了Huang申请了NASA专利的边界处理方法,仍没有一个最终的解决方案,但工程上已经提出了多种处理方法。本文实现了工程上常用的5种EMD边界处理方法:线性外延,多项式拟合,镜像法,径向基(RBF)神经网络预测和AR预测方法,设计了一套消除了EMD处理中信号的相互作用及模式混淆影响的测试方法,并利用准周期信号和随机信号对它们的边界效应处理结果进行了定量测试。结果表明镜像法是目前相对最优的EMD边界处理方法。     

MRL-filters: a general class of nonlinear systems and their optimaldesign for image processing

A class of morphological/rank/linear (MRL)-filters is presented as a general nonlinear tool for image processing. They consist of a linear combination between a morphological/rank filter and a linear filter. A gradient steepest descent method is proposed to optimally design these filters, using the averaged least mean squares (LMS) algorithm. The filter design is viewed as a learning process, and convergence issues are theoretically and experimentally investigated. A systematic approach is proposed to overcome the problem of nondifferentiability of the nonlinear filter component and to improve the numerical robustness of the training algorithm, which results in simple training equations. Image processing applications in system identification and image restoration are also presented, illustrating the simplicity of training MRL-filters and their effectiveness for image/signal processing.     

ATM业务流控制的一些有效方法

文章叙述了实现ATM业务流控制的一些可行的方法，即应用模糊控制技术来控制呼叫路由，用神经网络技术来预测输入业务流实现动态带宽分配控制、以及用神经网络技术实现业务呼叫的带宽分配控制。     

Time multiplexed color image processing based on a CNN withcell-state outputs

A practical system approach for time-multiplexing cellular neural network (CNN) implementations suitable for processing large and complex images using small CNN arrays is presented. For real size applications, due to hardware limitations, it is impossible to have a one-on-one mapping between the CNN hardware cells and all the pixels in the image involved. This paper presents a practical solution by processing the input image, block by block, with the number of pixels in a block being the same as the number of CNN cells in the array. Furthermore, unlike other implementations in which the output is observed at the hard-limiting block, the very large scale integrated (VLSI) architecture hereby described monitors the outputs from the state node. While previous implementations are mostly suitable for black and white applications because of the thresholded outputs, our approach is especially suitable for applications in color (gray) image processing due to the analog nature of the state node. Experimental complementary metal-oxide-semiconductor (CMOS) chip results in good agreement with theoretical results are presented     

Chip-scale packaging

Uniting the advantages of bare die and mainstream packaging, these minimal IC supports are racing to the aid of portable yet powerful products. Rigorously defined, the perimeter of such a package is no more than 1.2 times the perimeter of the die it contains, so that few other IC packages are any smaller. This reduction in size is the key driver of the popularity of the approach. But because the term chip-scale has a marketing value, some manufacturers have extended it to cover other sizes, too. Chip-scale packaging technology is still taking its first steps into the marketplace, and issues of standards, design, and reliability remain to be solved. Even so, an infrastructure for the technology is beginning to develop, and its potential market seems to be guaranteed, not least by consumer thirst for portable electronic applications, for which the small, light package is a natural     

An adaptive step size for backpropagation using linear lowerbounding functions

An adaptive step size is presented for the backpropagation algorithm in feedforward neural nets using linear lower bounding functions. Basically, a linear lower bounding function (LLBF) for a given function over an interval is a linear function that lies below the given function and matches the original function value at one end point. To search for an adaptive step size, an LLBF for the error function, which is expressed in terms of the step size, is derived. Since the error in a neural net can never be smaller than zero, it is plausible not to take a step larger than the step size when the associated LLBF reaches zero. In the paper, an adaptive learning algorithm based on the above idea is given. Numerical examples are used to illustrate its feasibility and to compare it with some previous results     

神经网络在ATM流量控制中的应用

以ATM流量控制的三层结构模型为基础，介绍神经网络在接入允许控制（CAC）中的五种算法——学习控制方法、hybrid允许方法、移动窗口训练方法，BPHT算法和时间-差分学习方法。讨论基于神经网络的流量测量、流量管理和拥塞控制方法。性能分析结果表明神经网络提供了快速、自适应、简单的智能控制。     

模拟神经元电路实现研究现状与进展

人工神经网络是现代信息处理领域的一个重要的方法。相对于软件实现 ,硬件实现方式能充分发挥神经网络并行处理的特点。用模拟电路实现神经网络电路形式简单、功耗低、速度快、占用芯片面积小 ,可以提高在神经网络芯片上神经元的集成度 ,神经元电路适合用模拟电路实现。文中综述了当前神经网络单元的模拟 VLSI实现的成果、新技术以及作者的工作成果。针对应用最广泛的线性和平方突触神经元 ,详细从权值存储单元、突触电路和阈值函数电路三方面来叙述。对各种实现方式的优缺点进行了比较 ,同时指出了神经网络实现电路中需要考虑的因素。最后 ,展望了用集成电路技术实现自学习神经网络的发展方向     

Kinematisch hyperredundante Greifersysteme mit neuronaler Steuerung

Conventional robot grippers show a limited performance in case that the objects are soft or variably structured. Objects difficult to handle are, e. g. liquid filled bags or amorphous goods. Additional issues arise if the object position is weakly defined in the working-space, as it may be the case for micro- or nano-robotic applications. The following article introduces the concept of a two-dimensional tentacle-shaped gripper with distance sensors which accomplishes an automatic approach coordination and grasping sequence for the handling of objects, coordinated by a neural controller.     

Stochastic stability analysis of fuzzy hopfield neural networks with time-varying delays

The ordinary Takagi-Sugeno (TS) fuzzy models have provided an approach to represent complex nonlinear systems to a set of linear sub-models by using fuzzy sets and fuzzy reasoning. In this paper, stochastic fuzzy Hopfield neural networks with time-varying delays (SFVDHNNs) are studied. The model of SFVDHNN is first established as a modified TS fuzzy model in which the consequent parts are composed of a set of stochastic Hopfield neural networks with time-varying delays. Secondly, the global exponential stability in the mean square for SFVDHNN is studied by using the Lyapunov-Krasovskii approach. Stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages.     

Applications of micromechatronics in minimally invasive surgery

Many surgical procedures are now performed using the techniques of minimally invasive surgery (MIS), in which unnecessary trauma is limited by reducing the size of incisions to less than about 1 cm or by using catheters or endoscopes threaded through vessels, the gastrointestinal tract, or other tubular structures. Micromechatronic technologies have great potential to allow access to regions now inaccessible, or to enhance the surgeon's abilities in applications where current minimally invasive techniques do not permit the full range of human dexterity and perception. Key needs and applications in MIS are identified, and relevant technologies, methods, and systems issues in mechatronics are discussed. The authors' millirobotic system for MIS of the abdomen is used as an example     

Machine learning-based offline signature verification systems: A systematic review

The offline signatures are the most widely adopted biometric authentication techniques in banking systems, administrative and financial applications due to its simplicity and uniqueness. Several automated techniques have been developed to anticipate the genuineness of the offline signature. However, the recapitulate of the existing literature on machine learning-based offline signature verification (OfSV) systems are available in a few review studies only. The objective of this systematic review is to present the state-of-the-art machine learning-based models for OfSV systems using five aspects like datasets, preprocessing techniques, feature extraction methods, machine learning-based verification models and performance evaluation metrics. Thus, five research questions were identified and analysed in this context. This review covers the articles published between January 2014 and October 2019. A systematic approach has been adopted to select the 56 articles. This systematic review revealed that recently, the deep learning-based neural network attained the most promising results for the OfSV systems on public datasets. This review consolidates the state-of-the-art OfSV systems performances in selected studies on five public datasets (CEDAR, GPDS, MCYT-75, UTSig and BHSig260). Finally, fifteen open research issues were identified for future development.