一种高速JPEG2000 MQ编码器的VLSI实现

JPEG2000采用的MQ编码器是一种优于Huffman编码的无损数据压缩算法。基于JPEG2000算法规程的MQ编码器速度较慢,限制了整个编码系统的实时性。本文采用部分并行算法,MQ编码器每个时钟最多可以编码2对数据,有效提高了编码器的数据吞吐率,并设计了基于3级流水的VLSI结构。试验结果表明,该算法平均每个时钟编码1.32bit,比普通算法的编码效率提高了约32%。     

一种基于可观测标准型的模型降阶算法

模型降阶方法在大规模集成电路的仿真中有着广泛的应用.由于对互联网络提取寄生参数后电路的规模巨大,使用传统的电路仿真方法将会消耗大量的资源,而模型降阶使得仿真计算量显著减小的同时精度并没有多少损失.文章针对互连线网络的MNA(Modified Nodal Analysis)矩阵通常可观测性较弱的特点,提出了一种基于可观测标准型的模型降阶算法:MOROC.文中推导证明了该算法能够匹配原系统的前q个矩,且具有形式简单和容易检验稳定性的特点.并给出了应用该算法对耦合互连线的降阶建模和仿真结果.     

A Practical Implementation of Parallel Dynamic Load Balancing for Adaptive Computing in VLSI Device Simulation

We present a new parallel semiconductor device simulation using the dynamic load balancing approach. This semiconductor device simulation based on the adaptive finite volume method with a posteriori error estimation has been developed and successfully implemented on a 16-PC Linux cluster with a message passing interface library. A constructive monotone iterative technique is also applied for solution of the system of nonlinear algebraic equations. Two different parallel versions of the algorithm to perform a complete device simulation are proposed. The first is a dynamic parallel domain decomposition approach, and the second is a parallel current-voltage characteristic points simulation. This implementation shows that a well-designed load balancing simulation can significantly reduce the execution time up to an order of magnitude. Compared with the measured data, numerical results on various submicron VLSI devices are presented, to show the accuracy and efficiency of the method.     

视频插值算法及其VLSI结构实现

提出一种基于三角分析和高频补偿的视频插值算法及其VLSI实现方法。三角分析能够检测并保护视频图像中的边缘信息;高频补偿技术用来进一步改善插值结果图像的视觉效果。算法的规则性决定了对应的VLSI结构的规则性、紧凑性和视频信号处理的高速度。此算法和它对应的VLSI结构有实用价值。用CMOS工艺实现VLSI结构。仿真实验结果表明,用此算法获得的插值结果图像在主观视觉效果和客观评价指标上优于传统的插值算法。VLSI芯片工作在100MHz频率、1.98V电压下,此结构的功耗为18.96mW。     

Handling forecasting problems based on fuzzy time series model and model error learning

Classical fuzzy time series forecasts are comprised of three steps: fuzzification, identification of fuzzy relation, and defuzzification. In this paper, we propose a new approach and add an error learning step to improve forecasts. In the fuzzification step, a hybrid method, based on the fuzzy c-means clustering and the fuzzy Silhouette criterion, is employed to determine the optimal number of intervals, which avoids time-consuming iterations of the whole algorithm. In the defuzzification step, an optimization model is set up to explain the rule of defuzzification. In the model structure, an error term is assembled into the traditional model to express model error, which is predicted by linear fitting and abnormal errors processing. Learning of model errors and considering of data characteristics guarantee good interpretability and accuracy. The numerical results show that the proposed approach has superior forecast performance to existing methods.     

Performance analysis of two-level structures on finite-precision machines

This paper presents a method for the performance analysis of two-level prediction algorithms when implemented on finite-precision digital machines. The analytical results are compared with an actual system response using simulation results. It is shown that the effect of finite-precision arithmetic is to increase the expected theoretical minimum cost and to slow down the two-level algorithm.     

A real-time neural system for color constancy

A neural network approach to the problem of color constancy is presented. Various algorithms based on Land's retinex theory are discussed with respect to neurobiological parallels, computational efficiency, and suitability for VLSI implementation. The efficiency of one algorithm is improved by the application of resistive grids and is tested in computer simulations; the simulations make clear the strengths and weaknesses of the algorithm. A novel extension to the algorithm is developed to address its weaknesses. An electronic system that is based on the original algorithm and that operates at video rates was built using subthreshold analog CMOS VLSI resistive grids. The system displays color constancy abilities and qualitatively mimics aspects of human color perception.     

移动环境下OFDM系统定时同步算法

针对移动环境TOFDM系统的定时同步问题,提出了一种解决算法。该算法基于0FDM系统的Preamble实现,包括定时捕获与定时跟踪两部分。同时,在考虑符号定时误差和样值定时误差的条件下,建立了OFDM系统的数学模型,分析了两种定时误差对系统接收性能的影响。最后阐述了定时同步算法的实施流程,给出了一种同步捕获位置和跟踪位置的判决方法,分析了门限值的设定对算法锁定精度的影响。仿真结果表明,所提出的定时同步算法具有同步精度高和易于实现的特点。     

一种面向并行设计规划的快速布局模型与算法

面向并行设计规划的快速布局是高性能计算机概念设计不可缺少的重要手段,布局目标、布局对象、布局结果等方面的特殊性,决定了传统布局方法难以满足并行设计规划需求。针对巨型机概念设计需求,在传统芯片级与系统级布局规划方法基础上,提出一种面向并行设计规划的快速布局模型与算法。该算法是基于有向图分解模型的分级布局算法,将定时驱动布局思想引入结群过程,是集定时驱动布局、基于结群的布局、基于划分的布局和有向图分解为一体的快速布局算法。该算法被成功应用到某巨型机概念设计的并行设计规划过程。     

Application of fuzzy logic in resistive fault modeling and simulation

Real defects (e.g., resistive stuck at or bridging faults) in very large-scale integration (VLSI) circuits cause intermediate voltages which cannot be modeled as ideal shorts. In this paper, we first show that the traditional zero-resistance model is not sufficient for fault simulation. Then, we present a resistive fault model for real defects and use fuzzy logic techniques for fault simulation and test pattern generation at the gate level. Our method uses Takagi-Sugeno (TS) fuzzy system to accurately model digital VLSI circuits and produces much more realistic fault coverage compared to the conventional methods. The experimental results include the fault coverage and test-pattern statistics for the ISCAS85 benchmarks.     

基于改进的最大熵均值聚类方法在文本分类中的应用*

针对传统的文本分类算法存在着各特征词对分类的结果影响相同、分类准确率较低、造成算法时间复杂度增加的问题,提出了一种改进的最大熵C-均值聚类文本分类方法。该方法充分结合了C-均值聚类和最大熵值算法的优点,以香农熵作为最大熵模型中的目标函数,简化分类器的表达形式,然后采用C-均值聚类算法对最优特征进行分类。仿真实验结果表明,与传统的文本分类方法相比,提出的方法能够快速得到最优分类特征子集,大大提高了文本分类准确率。     

Experimental investigation of stochastic parafoil guidance using a graphics processing unit

Control of autonomous systems subject to stochastic uncertainty is a challenging task. In guided airdrop applications, random wind disturbances play a crucial role in determining landing accuracy and terrain avoidance. This paper describes a stochastic parafoil guidance system which couples uncertainty propagation with optimal control to protect against wind and parameter uncertainty in the presence of impact area obstacles. The algorithm uses real-time Monte Carlo simulation performed on a graphics processing unit (GPU) to evaluate robustness of candidate trajectories in terms of delivery accuracy, obstacle avoidance, and other considerations. Building upon prior theoretical developments, this paper explores performance of the stochastic guidance law compared to standard deterministic guidance schemes, particularly with respect to obstacle avoidance. Flight test results are presented comparing the proposed stochastic guidance algorithm with a standard deterministic one. Through a comprehensive set of simulation results, key implementation aspects of the stochastic algorithm are explored including tradeoffs between the number of candidate trajectories considered, algorithm runtime, and overall guidance performance. Overall, simulation and flight test results demonstrate that the stochastic guidance scheme provides a more robust approach to obstacle avoidance while largely maintaining delivery accuracy.     

On stability and performance of sampled-data systems subject towordlength constraint

In this paper we study the effects of finite-precision controller implementation on the stability and performance of sampled-data systems. A framework is presented in which these effects can be analyzed from a statistical point of view. We analyze the behavior of finite-precision controllers at fast sampling rates and consider the problem of ℒ ₂-induced norm minimization, subject to a wordlength constraint     

基于聚类的数据预处理对模糊决策树产生的影响

在模糊决策树归纳过程中,数据的模糊化预处理通常使用三角形隶属函数,该隶属函数的中心点参数将决定数据模糊化的效果,进而影响模糊决策树的执行效率、精度和规模。Kohonen'sfeature-maps聚类算法能够用来选取连续属性值的中心点。实验研究表明,该算法选取的中心点使模糊子集之间的覆盖范围不再相同,因而能够更合理地表示模糊概念之间的重叠关系。通过与其它算法比较证明该算法使模糊决策树可以获得更高的分类精度。     

A simulated annealing algorithm for multi-agent systems: a job-shop scheduling application

In this paper, a parallel implementation of the modular simulated annealing algorithm for classical job-shop scheduling is presented. The implementation is for a multi agent system running on the distributed resource machine, which is a novel, scalable, distributed virtual machine based on Java technology. The problems tackled are well known, difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit time. The empirical results obtained show that the method proposed is successful in comparison with a sequential version of modular simulated annealing algorithm and other methods described in the literature.     

Estimating the Focus of Expansion in Analog VLSI

In the course of designing an integrated system for locating the focus of expansion (FOE) from a sequence of images taken while a camera is translating, a variety of direct motion vision algorithms based on image brightness gradients have been studied (McQuirk, 1991, 1996b). The location of the FOE is the intersection of the translation vector of the camera with the image plane, and hence gives the direction of camera motion. This paper describes two approaches that appeared promising for analog very large scale integrated (VLSI) circuit implementation. In particular, two algorithms based on these approaches are compared with respect to bias, robustness to noise, and suitability for realization in analog VLSI. From these results, one algorithm was chosen for implementation. This paper also briefly discuss the real-time analog CMOS/CCD VLSI architecture realized in the FOE chip.     

一种基于FPGA的SOM神经网络算法的并行实现

分析了SOM神经网络算法在FPGA实现过程中要考虑的2个主要问题：并行性和有限字长效应。通过分析，提出了一种实现该算法的高并行体系结构并给出了该体系结构中关键模块的具体实现电路。根据计算机仿真以及在FPGA上的实现所得到的结果表明，该体系结构在保证神经网络性能的同时，可以使电路具有较高的处理速度。     

无人机PCA故障检测与诊断技术研究

无人机（UAV）飞控系统传感器故障检测和诊断常采用基于解析模型的方法,但飞行控制系统（FCS）的精确数学模型往往获取困难。针对此问题,提出了一种UAV-PCA算法。该算法在传统主成分分析（PCA）方法的基础上结合方差敏感自适应阈值的故障检测方法和基于特征方向的故障诊断方法,实现UAV飞控系统传感器的故障检测和诊断。算法不需要系统的数学模型,解决了应用传统PCA方法进行FCS故障检测与诊断时易出现暂态过程虚警和误诊断的问题。仿真结果证明该算法可以快速准确地检测传感器故障,而且可以有效地降低暂态过程虚警和提高诊断结果准确度。     

一种模糊逻辑推理神经网络的结构及算法设计

建立了一种基于模糊逻辑推理的神经网络．由样本获取的初始规则确定规则层神经元个数,并确立模糊化层与规则层之间的连接．利用黄金分割法确定模糊化层隶属度函数的初始中心和宽度;根据初始规则的结论确定清晰化层的初始权值;针对网络结构提出了改进的BP算法．仿真实例表明,网络结构合理。具有较好的非线性映射能力,改进的BP算法适合于此网络,与另一种模糊神经网络相比较具有较快的训练速度和较好的泛化能力．     

基于自步数据重构正则化的模糊C均值聚类算法改进

为了有效降低模糊C均值算法对奇异值和噪声点的敏感性,本文提出一种自步数据重构正则化模糊C均值聚类算法。传统算法是在C均值算法的目标函数中引入加权参数来实现对数据的模糊性划分,而本文提出的方法则是通过对C均值的目标函数进行数据重构正则化来实现,并以自步学习的方式逐步对数据点进行聚类。实验结果表明,本文算法在模拟数据、实际数据以及在图像分割中都能显著降低算法对奇异值和噪声数据的敏感性,聚类更为准确高效。