基于近似高斯金字塔的视觉注意模型快速算法

利用输入图像的近似高斯金字塔,将经典的基于显著性的视觉注意模型改造为时空开销更小的版本,从而使其更加适合在嵌入式实时系统中实现.首先采用矩形窗口近似圆形窗口,矩形平均算子近似高斯卷积核;然后采用“先做行累加,再做列累加”的方法来实现矩形平均算子,并直接采样计算出各个特征通道的显著性分布图,该算法关于输入图像像素点个数具有线性时间复杂度;最后,还给出了在显著性分布图中抑制已提取区域显著性的快速算法.在Berkeley分割图像库上的实验结果表明,该方法极大地减小了系统实现的时空开销,且输出结果的误差在可接受范围内.提出的用矩形窗口近似圆形窗口,用矩形平均算子近似高斯卷积核的方法,还适用于其他需要在嵌入式实时系统中实现的图像处理问题.     

无线传感器网络中基于信息收益的多目标跟踪

目标跟踪是无线传感器网络应用的一个很重要的研究领域,节点有限的通信能力、处理能力、存储能力限制了传统跟踪算法的使用.为了提高网络的信息感知能力和降低能量消耗,提出基于信息收益的加权质心多目标跟踪算法,算法通过信息收益函数选取并唤醒节点对目标感知,通过传感器属性信息解模糊,并利用加权质心算法实现了多目标跟踪.仿真结果表明,与Bayes滤波协同多目标跟踪算法相比,算法虽精度略低,但时间复杂度低、失跟率低、实时性高,总体性能优于前者.     

A kernel-based centroid classifier using hypothesis margin

The centroid-based classifier is both effective and efficient for document classification. However, it suffers from over-fitting and linear inseparability problems caused by its fundamental assumptions. To address these problems, we propose a kernel-based hypothesis margin centroid classifier (KHCC). First, KHCC optimises the class centroids via minimising hypothesis margin under structural risk minimisation principle; second, KHCC uses the kernel method to relieve the problem of linear inseparability in the original feature space. Given the radial basis function, we further discuss a guideline for tuning the value of its parameter. The experimental results on four well-known data-sets indicate that our KHCC algorithm outperforms the state-of-the-art algorithms, especially for the unbalanced data-set.     

On the combinatorial complexity of fuzzy pattern matching in music analysis

In music analysis it is a common requirement to search a musical score for occurrences of a particular musical motif and its variants. This tedious and time-consuming task can be carried out by computer, using one of several models to specify which variants are to be included in the search. The question arises: just how many variants must be explicitly considered? The answer has a profound effect on the computer time needed. In this paper, recurrence relations and closed form analytic expressions are derived for the run time complexity of two models of fuzzy pattern matching for use in music analysis; each model assumes the existence of an atomic exact pattern matching operation. The formulae so obtained are evaluated and tabulated as a function of their independent parameters. These results enable a priori estimates to be made of the relative run times of different music searches performed using either model. This is illustrated by applying the results to an actual musical example.Richard E. Overill, BSc, PhD, C.Math, FIMA, C.Eng, MBCS, MIEE, is Lecturer in Computer Science at King's College London. His research includes the design, analysis, and practical implementation of algorithms on supercomputers. He has also given lecture-recitals on the keyboard music of the Tudor composers Thomas Tallis (1985), John Blitheman (1991), and William Byrd (1993).     

时滞系统问题的一种近似解方法

本文对线性时滞微分方程边值函数问题提出一种级数近似解方法。其基本思想是利用正交多项式级数的近似性质把它近似地简化为一线性代数方程组的求解问题     

病态线性系统的控制模型设计及其迭代算法

建立一种解决病态线性系统的控制模型，得到求解病态线性系统近似解的一种新算法．该算法为病态线性系统利用计算机迭代求解提供了直接的算法依据，同时也适合于常态线性系统近似解的计算．最后将新算法与传统的Gauss—Seidel算法进行比较．     

自反传递粗集中近似算子与拓扑算子的复合

在内部算子、闭包算子和近似算子概念的基础上，研究了内部算子、闭包算子与自反传递粗集中近似算子的复合以及交叉复合，得到了它们之间的一些关系．     

Identifying cell-to-cell variability in internalization using flow cytometry

Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as the internalization of material by cells. Given that internalization is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalization is of high biological interest. Yet, it is common for studies of internalization to neglect cell-to-cell variability. We develop a simple mathematical model of internalization that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data of internalization of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. This approach provides information relating to the region of the parameter space, and consequentially the nature of cell-to-cell variability, that produces model realizations consistent with the experimental data. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from internalization assays and similar experiments that probe cellular dynamical processes.     

一种改进的气动弹性时域推进方法

针对气动弹性控制方程,结构项采用标准龙格库塔方法推进,广义气动力项采用时间上的插值技术,发展了一种在每一时间步内只用计算一次非定常气动力的近似四阶龙格库塔推进方法。通过计算非线性Bernoulli方程和某一超音速翼面的气动弹性响应,证明该方法的时间准确度远高于采用气动力冻结技术的四阶龙格库塔方法。其准确度与标准龙格库塔方法相差不大,而其调用非定常气动力求解器的次数仅为其四分之一,大大提高了气动弹性数值模拟的效率。对于一般的气动弹性响应而言,该时间推进方法只用保证每个周期内的时间步在20～30个左右就能满足较高的准确度要求。     

基于推拉策略的文本分类增量学习研究

学习算法是否具有增量学习能力是衡量其是否适合于解决现实问题的一个重要方面。增量学习使学习算法的时间和空间资源消耗保持在可以管理和控制的水平,已被广泛应用于解决大规模数据集问题。针对文本分类问题,本文提出了增量学习算法的一般性问题。基于推拉策略的基本思想,本文提出了文本分类的增量学习算法ICCDP,并使用该算法对提出的一般性问题进行了分析。实验表明,该算法训练速度快,分类精度高,具有较高的实用价值。