基于有限更新率的非带限信号采样与重建算法

针对Shannon采样定理中要求信号带宽有限的缺陷,提出了一种基于有限更新率的非带限信号采样和重建的方法.该方法借助奇异值分解技术,从空间变换的角度实现了对与狄拉克流有关的一类特殊有限更新率信号的采样与重建.它用采样点的离散傅里叶变换系数构成一个Hankel矩阵,通过对该矩阵进行奇异值分解求得狄拉克流的位置信息,然后再求解范德蒙方程组求取狄拉克流的权信息,重建出原信号.计算机仿真结果表明,只要以不低于信号更新率的速率对信号进行采样,利用该算法就可以准确地重建出原信号,而且具有很好的抗噪性能.     

医学CAD系统中降低假阳性率的解决方案

如何提高乳腺癌计算机辅助诊断系统（CAD）中的灵敏度一直是众多学者研究的热点,特别是针对亚洲女性及年轻妇女的致密组织图像的检测。尽管之前已经提出了针对该类图像的解决方法,实验也表明,该方法可以提高系统的灵敏度（真阳性率TP）,但人们发现随着TP的提高也伴随了假阳性率（FP）的增长。所以,本文的研究目的是在前续研究的基础上,即保证CAD系统的灵敏度的同时尽可能地降低假阳性率。     

故障自动防护微机械角速度传感器

微机械角速度传感器是目前市场上能够进行批量生产的最复杂的传感器之一。它由在真空中做复杂运动的惯性质量块和驱动该设备、分析其响应的多种复杂电路组成,并集中于一个在极狭小的空间内。本文介绍了由奥地利SensorDynamics公司成功研制的一种MEMS角速率传感器,可以满足现代应用的全部要求,尤其是汽车工业所要求的小尺寸、坚固的机械结构、长期稳定性、不受限制的故障自动防护性和AEC-Q100认证。     

基于车辆路径问题的蚁群算法参数的优化

蚁群算法是一种解决组合优化问题的有效算法,已得到日益深入的研究,并逐渐得到应用。蚁群算法的一个不足是,算法参数的设置往往凭借经验,缺乏充足的依据。文章以车辆路径问题（vehicleroutingproblem,VRP）为例,从一个烟草配送的智能决策系统中抽取一定量的数据,对蚁群算法中各参数与算法收敛性之间的关系进行了大量的仿真实验,通过对实验结果的分析,给出了解决此类问题时的一种优化算法参数的方法。     

基于U-正交变换的图像编码算法

为了更有效地进行图像编码,先用U-正交函数系构造出一类新型的U-正交变换, 并以三次U-正交变换为例,研究了基于U-正交变换的图像编码算法。该编码算法首先通过离散U-正交函数系的基函数构造U-正交变换的变换矩阵,并根据U-正交矩阵的对称性给出了U-正交变换的快速算法; 然后应用三次U-正交变换对图像实施2维变换, 再用JPEG标准中的量化矩阵、Huffman码表与熵编码方法对图像的三次U-变换系数进行量化与编码, 实现了基于三次U-正交变换的图像编码算法。实验结果表明, 三次U-正交变换的编码增益、去相关效率与DCT基本相同,而编码效果却与JPEG编码效果非常接近, 且计算复杂度与基于FFT的快速DCT算法基本一致。由此可见, 应用U-正交变换对图像进行编码压缩是一类行之有效的方法,并有望在视频编码中得到应用。     

电视制导武器的导引律选择与仿真

制导性能直接影响命中目标的精度。在电视末制导武器跟踪过程中,为了保证有充足视场来满足制导要求,通常希望有较大的俯冲落角;同时为了保证有较好的图像处理环境,希望制导过程中的目标视线角速度也不能太大。对两方面的要求需要通过选择合适的导引律来实现。通过对常见的几种导引律的理论分析、仿真建模,重点对导引律的导引弹道、需用过载和视线角速度变化进行仿真对比分析。最终得出采用最优导引律可以较好地发挥电视制导性能,提高武器跟踪精度。     

Quantifying requirements volatility effects

In an organization operating in the bancassurance sector we identified a low-risk IT subportfolio of 84 IT projects comprising together 16,500 function points, each project varying in size and duration, for which we were able to quantify its requirements volatility. This representative portfolio stems from a much larger portfolio of IT projects. We calculated the volatility from the function point countings that were available to us. These figures were aggregated into a requirements volatility benchmark. We found that maximum requirements volatility rates depend on size and duration, which refutes currently known industrial averages. For instance, a monthly growth rate of 5% is considered a critical failure factor, but in our low-risk portfolio we found more than 21% of successful projects with a volatility larger than 5%. We proposed a mathematical model taking size and duration into account that provides a maximum healthy volatility rate that is more in line with the reality of low-risk IT portfolios. Based on the model, we proposed a tolerance factor expressing the maximal volatility tolerance for a project or portfolio. For a low-risk portfolio its empirically found tolerance is apparently acceptable, and values exceeding this tolerance are used to trigger IT decision makers. We derived two volatility ratios from this model, the π-ratio and the ρ-ratio. These ratios express how close the volatility of a project has approached the danger zone when requirements volatility reaches a critical failure rate. The volatility data of a governmental IT portfolio were juxtaposed to our bancassurance benchmark, immediately exposing a problematic project, which was corroborated by its actual failure. When function points are less common, e.g. in the embedded industry, we used daily source code size measures and illustrated how to govern the volatility of a software product line of a hardware manufacturer. With the three real-world portfolios we illustrated that our results serve the purpose of an early warning system for projects that are bound to fail due to excessive volatility. Moreover, we developed essential requirements volatility metrics that belong on an IT governance dashboard and presented such a volatility dashboard.     

A minimum Hellinger distance estimator for stochastic differential equations: An application to statistical inference for continuous time interest rate models

A minimum disparity estimator minimizes a φ-divergence between the marginal density of a parametric model and its non-parametric estimate. This principle is applied to the estimation of stochastic differential equation models, choosing the Hellinger distance as particular φ-divergence. Under an hypothesis of stationarity, the parametric marginal density is provided by solving the Kolmogorov forward equation. A particular emphasis is put on the non-parametric estimation of the sample marginal density which has to take into account sample dependence and kurtosis. A new window size determination is provided. The classical estimator is presented alternatively as a distance minimizer and as a pseudo-likelihood maximizer. The latter presentation opens the way to Bayesian inference. The method is applied to continuous time models of the interest rate. In particular, various models are tested using alternatively tests and their results are discussed.     

拟概率空间上学习理论的关键定理和学习过程一致收敛速度的界

进一步讨论了拟概率的一些性质,给出了拟概率空间上的拟随机变量及其分布函数、期望和方差的概念及若干性质;证明了拟概率空间上的Markov不等式、Chebyshev不等式和Khinchine大数定律;给出并证明了拟概率空间上学习理论的关键定理和学习过程一致收敛速度的界,把概率空间上的学习理论的关键定理和学习过程一致收敛速度的界推广到了拟概率空间,为系统地建立拟概率上的统计学习理论与构建支持向量机奠定了理论基础.     

基于子带的AOFDM系统自适应调制方式选择

提出了一种基于子带的自适应正交频分复用(AOFDM)系统的调制方式的自适应选择方法,不同于传统AOFDM系统采用固定的门限,该方法基于子带中所有子载波的后验信噪比(SNR),计算得到当前信道状况下不同调制方式该子带的误码率(BER),并与系统所要求的目标BER对比从而确定子带所采用的调制方式。理论分析和计算机仿真表明,该方法能够同时兼顾系统可靠性和有效性,并在保证系统BER性能的前提下较大地提升了AOFDM系统的吞吐量。