基于IBIS模型的FPGA信号完整性仿真验证方法

讨论了SRAM型FPGA信号完整性验证的必要性,提出了一种基于IBIS模型和HyperLynx软件的针对SRAM型FPGA器件信号完整性仿真验证方法,并以Stratix-2和Virtex-4两种类型的FPGA为例进行了实际仿真验证,分析了信号的有效数据宽度、电平幅值和传输速率等仿真验证结果,比较了这两种器件的信号完整性优劣,通过该仿真实例也验证了这种FPGA信号完整性仿真验证技术的可行性。随后对模型参数进行了仿真对比,得出造成器件信号完整性差异的内在机理,从而在设计上指导优化器件信号完整性性能。     

高速数字设计中的信号完整性研究

信息完整性问题始终贯穿于整个高速数字系统设计中,并且对设计领域影响巨大.针对高速数字电路中典型的信号完整性问题,剖析了各类破坏信号完整性的原因,提出了避免PCB设计中出现信号完整性问题的处理方法.     

波形测试在信号完整性分析中的应用

信号完整性是指电路系统中信号的质量,如果在要求的时间内,信号能够不失真的从源端传送到接收端,那么该信号是完整的。由于系统时钟频率的增长,信号完整性测试变得越来越重要。本篇介绍了电子产品生产过程中高速电路信号完整性波形测试的基本方法和原理分析。     

信号完整性分析之分支线的阻抗控制

随着电子系统和电路设计全面进入高速信号设计领域,信号完整性问题已经成为电子设计的一个瓶颈.针对分支线结构电气网络所遇到的信号完整性问题进行了深入分析,指出了通过阻抗控制来解决信号反射问题的方法,并且用电路模型仿真验证了方法的有效性.     

高速PCB信号完整性仿真分析

随着PCB设计越来越复杂,设计周期越来越短,信号完整性仿真分析正变得越来越重要.本文简介了信号完整性针对的基本问题.介绍了基于信号完整性仿真分析的高速PCB设计方法,并结合一个高速PCB设计案例,给出仿真分析的流程和分析结果.     

高速PCB的仿真技术

利用IBIS模型进行板级信号完整性分析是一种简单、易用的分析方法。结合PCB设计的SI模型,介绍了几种板级信号完整性分析的方法,讨论了各种分析方法的利弊,确定了使用IBIS模型进行信号完整性分析和EMC分析。通过加载IBIS模型对P4主板的DDR信号线进行了仿真,并对仿真结果进行了分析,达到了验证设计规范的目的。     

高速数字系统中的信号完整性及实施方案

描述了高速数字电路中典型的信号完整性问题,分析了各种破坏信号完整性的原因及解决方案,并结合一个实际的高速DSP系统,阐述实现信号完整性的具体方法。     

电路板设计中信号完整性问题探讨

随着电子信息科学的发展,人们对信号完整性的要求越来越高.如何更好的确保信号的完整性成了一项新的研究课题.本文介绍了电路板设计中信号完整性(SI)的定义和保证信号完整性的电路板设计准则.     

高速视频处理系统中的信号完整性分析

结合高速DSP图像处理系统讨论了高速数字电路中的信号完整性问题,分析了系统中信号反射、串扰、地弹等现象破坏信号完整性的原因,通过先进IS工具的辅助设计,找出了确保系统信号完整性的具体方法。     

高速电路设计和信号完整性分析

高速电路设计对PCB设计者提出了新的要求和挑战,高速电路中的信号完整性问题变得越来越突出,传统的设计方法已经不能适应,利用IBIS模型进行信号完整性分析正是为了迎接这种挑战而提出的新方法。介绍了IBIS模型的构成要素、基本的建模原理,以及利用IBIS模型进行信号完整性分析及其在高速电路设计中的应用,最后用一个实例讲述了分析的具体步骤和过程。     

基于话者无关模型的说话人转换方法

提出一种基于话者无关模型的说话人转换方法.考虑到音素信息共同存在于所有说话人的语音中,假设存在一个可以用高斯混合模型来描述的话者无关空间,且可用分段线性变换来描述该空间到各说话人相关空间之间的映射关系.在一个多说话人的数据库上,用话者自适应训练算法来训练模型,并在转换阶段使用源目标说话人空间到话者无关空间的变换关系来构造源与目标之间的特征变换关系,快速、灵活的构造说话人转换系统.通过主观测听实验来验证该算法相对于传统的基于话者相关模型方法的优点.     

基于联合估计的Wyner-Ziv系统边信息重建

在不显著增加解码复杂度的前提下,为提高Wyner-Ziv视频编码系统的边信息(SI)重建质量,提出一种基于SI信度的整像素/半像素联合估计算法.该算法引入一个SI质量判定因子判断边信息的重建质量,根据SI置信度是否超过设定的判别阈值进行整像素和半像素边信息重建方法的自适应切换.实验结果表明,在保持甚至超过传统亚像素边信...     

Design and development of a unified framework towards swarm intelligence

The application of swarm intelligence (SI) in the optimization field has been gaining much popularity, and various SI algorithms have been proposed in last decade. However, with the increased number of SI algorithms, most research focuses on the implementation of a specific choice of SI algorithms, and there has been rare research analyzing the common features among SI algorithms coherently. More importantly, no general principles for the implementation and improvement of SI algorithms exist for solving various optimization problems. In this research, aiming to cover such a research gap, a unified framework towards SI is proposed inspired by the in-depth analysis of SI algorithms. The unified framework consists of the most frequently used operations and strategies derived from typical examples of SI algorithms. Following the proposed unified framework, the intrinsic features of SI algorithms can be understood straightforwardly and the implementation and improvement of SI algorithms can be achieved effortlessly, which is of great importance in practice. The numerical experiments examine the effects of the possible strategies employed in the unified framework, and provide pilot attempts to validate the performance of different combinations of strategies, which can not only facilitate specific SI algorithm application, but also can motivate SI algorithm innovation.     

Nonlinear identification of a spark ignition engine torque based on ANFIS with NARX method

Spark ignition (SI) engines have a nonlinear dynamic system with inherent uncertainties and unpredictable disturbances. The identification of a nonlinear system is vital in many fields of engineering. In this study, SI engine torque is identified from an input–output measurement. This study aims to propose a dynamic nonlinear model that uses an adaptive neuro‐fuzzy inference system and a nonlinear auto‐regressive with exogenous input structure to identify the dynamic nonlinear behavior of an SI engine. Considerable good performance is achieved using the adaptive neuro‐fuzzy inference system nonlinear auto‐regressive with exogenous input method. For model validation, the proposed method is compared with the more conventional identification approach called the Hammerstein method. The results show that the two methods are in excellent agreement. The Hammerstein model was chosen because its identification result of the SI system was studied previously by the author. Validation results prove that the ability of the proposed model can capture the highly nonlinear behavior of the SI system.     

用开关电流技术实现连续小波变换的改进方法

提出了一种用开关电流技术实现连续小波变换的改进方法，即采用梯形公式逼近数值积分的原理改进用于逼近连续小波变换的带通滤波器组，并用开关电流电路实现，还详细分析了这种改进对于减小逼近误差的优势所在．以开关电流的双二次滤波器实现八通道的Marr小波为例，简述了设计过程和仿真结果，从而证实了这种改进的可行性．     

Application of a Bayesian hierarchical model to system identification of structural parameters

System identification (SI) is a key step in the process of evaluating the status or condition of physical structures and of devising a scheme to sustain their structural integrity. SI is typically carried out by updating the current structural parameters used in a computational model based on the measured responses of the structure. In the deterministic approach, SI has been conducted by minimizing the error between calculated responses (using the computational model) and measured responses. However, this brought about unexpected numerical issues such as the ill-posedness of the inverse problem, which likely results in non-uniqueness of the solutions or non-stability of the optimization operation. To address this issue, Bayesian updating enhanced with an advanced modeling technique such as a Bayesian network (BN) was introduced. However, it remained challenging to construct the quantitative relations between structural parameters and responses (which are placed in conditional probability tables: CPTs) in a BN setting. Therefore, this paper presented a novel approach for conducting the SI of structural parameters using a Bayesian hierarchical model (BHM) technique. Specifically, the BHM was integrated into the Bayesian updating framework instead of utilizing a BN. The primary advantage of the proposed approach is that it enables use of the existing relations between structural parameters and responses. This can save the computational effort needed to construct CPTs to relate the parameter and response nodes. The proposed approach was applied to two experimental structures and a realistic soil-slope structure. The results showed that the proposed SI approach provided good agreement with actual measurements and also gave relatively robust estimation results compared to the traditional approach of maximum likelihood estimation. Hence, the proposed approach is expected to be utilized to address SI problems for complex structural systems and its computational model when integrated with a statistical regression approach or with various machine learning algorithms.     

基于多假设运动补偿预测的边信息改进方法

根据分布式系统中边信息和原信息之间的噪声模型,提出一种基于多假设运动补偿的边信息改进方法.使用传统方法生成原始的边信息,对其进行双向运动估计产生补偿块,将补偿块线性组合成新的边信息.实验结果表明,该算法具有复杂度较低的优点,能提高边信息的质量,从而有效地改善分布式视频压缩的率失真性能.     

Development of a safety index for manual lifting tasks

The concept of a safety index (SI) for assigning a worker to a particular manual lifting task is developed, and a simple formula for its calculation is presented. The proposed index is based upon the combined measure of acceptability of the biomechanical and physiological stress responses of the worker to a lifting task. Individual capacity norms, as opposed to the norms usually given based on population percentiles, are also defined. Numerical examples are given to illustrate the SI approach.     

Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units

Data clustering is related to the split of a set of objects into smaller groups with common features. Several optimization techniques have been proposed to increase the performance of clustering algorithms. Swarm Intelligence (SI) algorithms are concerned with optimization problems and they have been successfully applied to different domains. In this work, a Swarm Clustering Algorithm (SCA) is proposed based on the standard K-Means and on K-Harmonic Means (KHM) clustering algorithms, which are used as fitness functions for a SI algorithm: Fish School Search (FSS). The motivation is to exploit the search capability of SI algorithms and to avoid the major limitation of falling into locally optimal values of the K-Means algorithm. Because of the inherent parallel nature of the SI algorithms, since the fitness function can be evaluated for each individual in an isolated manner, we have developed the parallel implementation on GPU of the SCAs, comparing the performances with their serial implementation. The interest behind proposing SCA is to verify the ability of FSS algorithm to deal with the clustering task and to study the difference of performance of FSS-SCA implemented on CPU and on GPU. Experiments with 13 benchmark datasets have shown similar or slightly better quality of the results compared to standard K-Means algorithm and Particle Swarm Algorithm (PSO) algorithm. There results of using FSS for clustering are promising.     

降低滤波器组多载波信号峰均比的边信息嵌入选择性映射方法

针对现有选择性映射（SLM）方法抑制滤波器组多载波（FBMC）信号峰均比（PAPR）的性能不佳及边信息错误率（SIER）高的问题,提出了一种边信息（SI）嵌入的SLM方法来抑制PAPR。在发送端,设计了一组嵌入SI的相位旋转矢量,并将相位旋转矢量同发送数据块相乘产生备选数据块;利用备选数据块的实部和虚部分量的逆离散傅里叶变换（IDFT）输出,设计了基于循环时移的候选FBMC信号,并选择具有最小PAPR的候选信号进行发送。在接收端,利用SI子载波数据的旋转相位不同提出了与发送符号调制阶数无关的低复杂度SI检测器。仿真结果表明,所提方法在发送端能够有效降低FBMC信号的PAPR,在接收端具有良好的SI检测和误比特率（BER）性能。