CNN‐based architecture for real‐time object‐oriented video coding applications

This paper presents a new CNN‐based architecture for real‐time video coding applications. The proposed approach, by exploiting object‐oriented CNN algorithms and MPEG encoding capabilities, enables low bit‐rate encoder/decoder to be designed. Simulation results using Claire video sequence show the effectiveness of the proposed scheme. Copyright © 2005 John Wiley & Sons, Ltd.     

Pixel‐level snakes on the CNNUM: algorithm design,on‐chip implementation and applications

In this paper, a new algorithm for the cellular active contour technique called pixel‐level snakes is proposed. The motivation is twofold: on the one hand, a higher efficiency and flexibility in the contour evolution towards the boundaries of interest are pursued. On the other hand, a higher performance and suitability for its hardware implementation onto a cellular neural network (CNN) chip‐set architecture are also required. Based on the analysis of previous schemes the contour evolution is improved and a new approach to manage the topological transformations is incorporated. Furthermore, new capabilities in the contour guiding are introduced by the incorporation of inflating/deflating terms based on the balloon forces for the parametric active contours. The entire algorithm has been implemented on a CNN universal machine (CNNUM) chip set architecture for which the results of the time performance measurements are also given. To illustrate the validity and efficiency of the new scheme several examples are discussed including real applications from medical imaging. Copyright © 2005 John Wiley & Sons, Ltd.     

Guiding a mobile robot with cellular neural networks

We show how cellular neural networks (CNNs) are capable of providing the necessary signal processing needed for visual navigation of an autonomous mobile robot. In this way, even complex feature detection and object recognition can be obtained in real time by analogue hardware, making fully autonomous real‐time operation feasible. An autonomous robot was first simulated and then implemented by simulating the CNN with a DSP. The robot is capable of navigating in a maze following lines painted on the floor. Images are processed entirely by a CNN‐based algorithm, and navigation is controlled by a fuzzy‐rule‐based algorithm. Copyright © 2002 John Wiley & Sons, Ltd.     

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.     

基于卷积神经网络的暂态电压稳定快速评估

随着特高压直流输电的发展和负荷构成及特性的变化,暂态电压问题严重威胁系统的安全稳定运行。基于卷积神经网络(CNN),提出一种交直流受端电网分区暂态电压稳定快速评估方法。计及系统快速动态响应元件影响,基于暂态电压时序信息构建暂态电压跌落面积矩阵,利用基于t分布的随机近邻嵌入(t-SNE)算法将其映射到二维平面,对受端电网进行分区。依据节点相对距离选择各分区稳态潮流特征。构建线路故障严重度指标,据其对故障线路号进行编码,将编码结果与故障线路号共同作为故障特征。采用粒子群优化算法确定各分区CNN最优卷积核大小和数量,提升CNN性能。实际多馈入交直流电网的仿真结果表明了方法的有效性。     

Robot vision with cellular neural networks: a practical implementation of new algorithms

Cellular neural networks (CNNs) are well suited for image processing due to the possibility of a parallel computation. In this paper, we present two algorithms for tracking and obstacle avoidance using CNNs. Furthermore, we show the implementation of an autonomous robot guided using only real‐time visual feedback; the image processing is performed entirely by a CNN system embedded in a digital signal processor (DSP). We successfully tested the two algorithms on this robot. Copyright © 2006 John Wiley & Sons, Ltd.     

Preliminary results from an analog implementation of first‐order TDCNN dynamics

This work falls into the category of linear cellular neural network (CNN) implementations. We detail the first investigative attempt on the CMOS analog VLSI implementation of a recently proposed network formalism, which introduces time‐derivative ‘diffusion’ between CNN cells for nonseparable spatiotemporal filtering applications—the temporal‐derivative CNNs (TDCNNs). The reported circuit consists of an array of Gm‐C filters arranged in a regular pattern across space. We show that the state–space coupling between the Gm‐C‐based array elements realizes stable and linear first‐order (temporal) TDCNN dynamics. The implementation is based on linearized operational transconductance amplifiers and Class‐AB current mirrors. Measured results from the investigative prototype chip that confirms the stability and linearity of the realized TDCNN are provided. The prototype chip has been built in the AMS 0.35 µm CMOS technology and occupies a total area of 12.6 mm sq, while consuming 1.2 µW per processing cell. Copyright © 2010 John Wiley & Sons, Ltd.     

Watermarking on CNN‐UM for image and video authentication

In this paper a new approach to fragile watermarking technique is introduced. This problem is particularly interesting in the field of modern multimedia applications, when image and video authentication are required. The approach exploits the cellular automata suitability to work as pseudorandom pattern generators and extends the related algorithms under the framework of the cellular non‐linear networks (CNNs). The result is a novel way to perform watermarking generation in real time, using the presently available CNN‐universal chip prototypes. In this paper, both the CNN algorithms for fragile watermarking as well as on‐chip experimental results are reported, confirming the suitability of CNNs to successfully act as real‐time watermarking generators. The availability of CNN‐based visual microprocessors allows to have powerful algorithms to watermark in real time images or videos for efficient smart camera applications. Copyright © 2004 John Wiley & Sons, Ltd.     

External inputs,stable equilibria and complete stability of CNNs

We construct two cellular neural networks (CNNs) of three cells to show that a CNN can have stable equilibria, but is not completely stable and that the complete stability of CNN depends on the choice of external inputs. These phenomena cannot occur for two‐cell CNNs. Copyright © 2003 John Wiley & Sons, Ltd.     

Complete stability of feedback CNNs with dynamic memristors and second‐order cells

The paper considers a feedback cellular neural network (CNN) obtained by interconnecting elementary cells with an ideal capacitor and an ideal flux‐controlled memristor. It is supposed that during the analogue computation of the CNN the memristors behave as dynamic elements, so that each dynamic memristor (DM)‐CNN cell is described by a second‐order differential system in the state variables given by the capacitor voltage and the memristor flux. The proposed networks are called DM‐CNNs, that is CNNs using a dynamic (D) memristor (M). After giving a foundation to the DM‐CNN model, the paper establishes a fundamental result on complete stability, that is convergence of solutions toward equilibrium points, when the DM‐CNN has symmetric interconnections. Because of the presence of dynamic memristors, a DM‐CNN displays peculiar and basically different dynamic properties with respect to standard CNNs. First of all a DM‐CNN computes during the time evolution of the memristor fluxes, instead of the capacitor voltages as for a standard CNN. Furthermore, when a steady state is reached, the memristors keep in memory the result of the computation, that is the limiting values of the fluxes, while all memristor currents and voltages, as well as all currents, voltages, and power in the DM‐CNN vanish. Instead, for standard CNNs, currents, voltages, and power do not drop off when a steady state is reached. Copyright © 2016 John Wiley & Sons, Ltd.     

基于 CNN 时-空卷积优化的 EM-EEG 识别方法研究

针对当前情绪脑电信号(emotion electroencephalogram, EM-EEG)识别研究中时间域信息的时间尺度难以把握和空间域信息易被忽视致使辨识率停滞不前,以及采集EM-EEG时通道过多导致信息冗余和信息处理成本增加等问题,提出了基于CNN的时-空卷积优化融合网络进行EM-EEG识别研究。该融合网络由提取EM-EEG时域信息的长卷积(long convolution, L-Conv)CNN和提取EM-EEG空域信息的CNN并联组成,在CNN模型时-空优化中使用粒子群算法(particle swarm optimization, PSO)对时域CNN中的L-Conv尺度进行了优化,并使用短时功率谱(short time power spectrum, STPS)的相关分析方法进行空域CNN模型通道数目优化,深层且有效地提取了EEG中的时间域和空间域特征。结果表明,提出的时-空卷积优化融合CNN在SEED IV数据集上对平和、悲伤、恐惧、高兴4种情绪最终准确率可以达到90.13%,相比传统单一CNN的识别准确率提高了4.76%,并且通道数目由62路降低至33路,缩减了46...     

Output‐capacitorless segmented low‐dropout voltage regulator with controlled pass transistors

This article presents a low quiescent current output‐capacitorless quasi‐digital complementary metal‐oxide‐semiconductor (CMOS) low‐dropout (LDO) voltage regulator with controlled pass transistors according to load demands. The pass transistor of the LDO is segmented into two smaller sizes based on a proposed segmentation criterion, which considers the maximum output voltage transient variations due to the load transient to different load current steps to find the suitable current boundary for segmentation. This criterion shows that low load conditions will cause more output variations and settling time if the pass transistor is used in its maximum size. Furthermore, this situation is the worst case for stability requirements of the LDO. Therefore, using one smaller transistor for low load currents and another one larger for higher currents, a proper trade‐off between output variations, complexity, and power dissipation is achieved. The proposed LDO regulator has been designed and post‐simulated in HSPICE in a 0.18 µm CMOS process to supply a stable load current between 0 and 100 mA with a 40 pF on‐chip output capacitor, while consuming 4.8 μA quiescent current. The dropout voltage of the LDO is set to 200 mV for 1.8 V input voltage. The results reveal an improvement of approximately 53% and 25% on the output voltage variations and settling time, respectively. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

An Adaline based arcing fault detection algorithm for single-pole autoreclosers

In this paper, a new Adaline based adaptive single-pole autorecloser algorithm is proposed to discriminate permanent and transient faults in HV transmission lines. The proposed algorithm is implemented by processing only terminal voltages and also used to estimate secondary arc extinction time. The algorithm is simulationally analyzed using ATP version of EMTP by varying fault locations and pre fault loading conditions to demonstrate the capabilities and limitations of the method. In addition to that, measured data, which are taken from an actual power system, are also used for testing the algorithm. Results show that the method can successfully be implemented for real time application and computationally less expensive when compared with other methods.     

适用于区域电网的三级电能质量综合监测系统

传统的电能质量监测方法存在工作量大、数据采集不全面、不能实时掌握电网电能质量指标情况等缺点。讨论了电能质量实时在线监测的实施方法,设计了一种新型电能质量综合监测系统。该系统总体采用3级分布式结构,将多个以不同监测目标为对象的子系统集成在1个综合平台上,提高了系统的可扩展能力和大区域电网电能质量的监测能力。实际运行表明,该系统能够准确、可靠、实时地监测各项电能质量参数,并具有良好的暂态电能质量事件处理功能,大大增强了电力部门对电网的电能质量监管能力。     

A Technique of Automatically Tuning Decision Parameters for Rule‐Based Defect Classifier

In automatic defect classification of semiconductor wafers using scanning electron microscope images, we propose a technique of tuning decision parameters for rule‐based defect classifiers. The proposed method adopts a coarse‐to‐fine search for reduction in processing time. However, due to search leakage, there is no guarantee that the same solutions as in a full parameter search can be obtained. In order to prevent leakage in the coarse search theoretically, the proposed method evaluates a candidate parameter set based on the estimated range of classification accuracy attained by not only the candidate but the surrounding solutions eliminated by the coarse search. Experiments on real image data demonstrate the effectiveness of the proposed method. The proposed method can extract the same solutions as the full parameter search within almost the same processing time as the conventional coarse‐to‐fine search. When the sampling step of the coarse search is three to six, while the tuning time of the conventional coarse‐to‐fine search is 1 to 21 s, that of the proposed method is 5 to 35 s.     

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.     

Analytical transient response of MOS current mirrors

The analytical transient response of MOS current mirrors is derived for the case when the input current is a saturating ramp. The closed form solution obtained for the voltages and currents in the circuit is expressed using Airy's functions. The settling time is then calculated using the closed‐form current equation. For the case when the settling time is greater than the transition time of the input current, a closed form solution is obtained. When the settling time is less than the input transition time, a very simple fitting function is found to accurately model the dependence of the settling time on the various parameters in the circuit, including the transition time of the input current. Simulations show excellent agreement of the estimates of the settling time macromodel with LEVEL‐1 SPICE results. For LEVEL‐3 MOSFET models, the error in the macromodel is within 20% of detailed circuit simulation. Copyright © 2003 John Wiley & Sons, Ltd.     

一种基于嵌入式技术的新型电能质量检测装置

利用DSP强大的数据处理功能和ARM优良的控制功能,设计了具有暂态扰动检测、分析等功能的基于嵌入式技术的新型电能质量检测装置.仿真结果显示该装置无论作为独立的电能质量检测系统,还是广域电能质量检测系统的前端机,都能够实时、全面、高精度检测和管理电能质量参数.     

Long‐Term Generation Scheduling Method with Constrained Numbers of Unit Commitment and Fuel Constraints on Thermal Units

The long‐term generation scheduling in power utilities is aimed at maintaining power supply sufficiency and estimating fuel consumption. Multiperiod constraints, such as the allowable number of unit commitments of thermal units and fuel consumption with a given total amount of fuel, must be taken into appropriate consideration for practical economic scheduling in the long‐term scheduling horizon. Due to the large size and complexity of such scheduling problems, it is difficult to obtain solutions collectively in terms of stability and processing time in operational situations. We propose a new scheduling method which consists of “preparative processing” and “detailed optimization processing.” The former acts to divide the long‐term problem with multiperiod constraints into time units of a week according to quick and simplified scheduling results, and the latter acts to minimize the total operation cost on the basis of those weekly partitioned problems. The processing starts with an optimal scheduling result excluding the aforementioned two multi‐period constraints and proceeds to sequentially resolve the violation of each constraint with quantitative consideration of interrelationships between them. This paper describes the validation and effectiveness of the proposed method through the real world example of Chubu Electric Power Company.