Sparsity-inspired image reconstruction for electrical capacitance tomography

We present a new image reconstruction method for Electrical Capacitance Tomography (ECT). ECT image reconstruction is generally ill-posed because the number of measurements is small whereas the image dimensions are large. Here, we present a sparsity-inspired approach to achieve better ECT image reconstruction from the small number of measurements. Our approach for ECT image reconstruction is based on Total Variation (TV) regularization. We apply an efficient Split-Bregman Iteration (SBI) approach to solve the problem. We also propose three metrics to evaluate image reconstruction performance, i.e., a joint metric of positive reconstruction rate (PRR) and false reconstruction rate (FRR), correlation coefficient, and a shape and location metric. The results on both synthetic and real data show that the proposed TV-SBI method can better preserve the edges of images and better resolve different objects within reconstructed images, as compared to a representative state-of-the-art ECT image reconstruction algorithm, Projected Landweber Iteration with Linear Back Projection initialization (LBP-PLI).     

用迭代法消除数字图像放大后的模糊

用迭代法对数字图像经过插值放大后产生的模糊问题进行了研究，把数字图像插值放大造成的模糊看成是点扩散函数与清晰图像卷积的结果，根据插值算法可以得到点扩散函数，由于数字图像解卷积是典型的解线性方程组的问题，用雅可比（Jacobi）迭代法得到了很好的结果。与频谱空间变换的方法相比，迭代法没有分母为0的问题和空间变换过程造成的舍入误差。     

扇形束投影域重建—再投影迭代法

本文所提出的投影域重建——再投影迭代算法可以恢复不完整的扇形束投影数据,从而实现从有限的投影数据重建图象。这种算法把滤波——反投影图象重建过程和再投影的过程纳入一个公式,仅在投影域上估算未知的投影数据,因此避免了重建过程和反投影过程中的插值运算,改善了收敛性,提高了计算精度,缩短了运算时间。模拟实验表明:即使有70％的投影数据丢失,经过两次迭代就能以令人满意的精度恢复丢失的投影数据。     

两种求解可靠指标的实用算法

介绍了基于几何原理求解可靠指标、寻找设计验算点的具有较高实用价值的两种新算法： 图解渐近法和Monte Carlo递进法。推导了由标准正态变量反算常用分布类型的随机变量数值的计算公式，并且通过采取某些改善措施，简化了计算过程，大大提高了问题的求解效率。同时，两种算法可以结合使用，经算例验证，能够快速解决问题且满足高精度要求，说明了本文方法的可行性和有效性。     

Performance on agile teams: Relating iteration objectives and critical decisions to project management success factors

ContextWhile project management success factors have long been established via the golden triangle, little is known about how project iteration objectives and critical decisions relate to these success factors. It seems logical that teams’ iteration objectives would reflect project management success factors, but this may not always be the case. If not, how are teams’ objectives for iterations differing from the golden triangle of project management success factors?ObjectiveThis study identifies iteration objectives and the critical decisions that relate to the golden triangle of project management success factors in agile software development teams working in two-week iterations.MethodThe author conducted semi-structured interviews with members across three different agile software development teams using a hybrid of XP and Scrum agile methodologies. Iteration Planning and Retrospective meetings were also observed. Interview data was transcribed, coded and reviewed by the researcher and two independently trained research assistants. Data analysis involved organizing the data to identify iteration objectives and critical decisions to identify whether they relate to project management success factors.ResultsAgile teams discussed four categories of iteration objectives: Functionality, Schedule, Quality and Team Satisfaction. Two of these objectives map directly to two aspects of the golden triangle: schedule and quality. The agile teams’ critical decisions were also examined to understand the types of decisions the teams would have made differently to ensure success, which resulted in four categories of such decisions: Quality, Dividing Work, Iteration Amendments and Team Satisfaction.ConclusionThis research has contributed to the software development and project management literature by examining iteration objectives on agile teams and how they relate to the golden triangle of project management success factors to see whether these teams incorporate the golden triangle factors in their objectives and whether they include additional objectives in their iterations. What’s more, this research identified four critical decisions related to the golden triangle. These findings provide important insight to the continuing effort to better assess project management success, particularly for agile teams.     

机构位移仿真分析方法比较研究

机构位移分析是运动分析中最根本的问题,机构位移分析方程通常是非线性的,只有简单的二级机构才能列出输入变量和输出变量之间的显函数表达式,而在其他情况下,方程的求解就需要利用各种数值解法.以压床六杆机构为研究对象,推导了机构多位移矩阵迭代求解和单一位移精确求解的理论方程,并用上述两种方法对压床六杆机构进行了机构位移仿真计算比较分析,证实单一位移精确求解方法的计算比较简便,求解过程不用推导,一旦确定单一位移值后,可对其它位移进行精确求解,对机构位移仿真计算有一定的实用性,累积误差小,可用于机构运动学设计.     

WSN中基于超椭圆判决边界的异常检测的动态建模

为了实现无线传感器网络的动态数据流环境中的异常检测,本文提出了一种迭代超椭圆判决边界方法。首先建立起异常检测超椭圆模型,然后每个节点基于到当前时间为止的测量值来调整其超椭圆模型,最终收敛到覆盖正常和异常测量值的超椭圆边界;为了提高模型对监测环境中数据变化的跟踪能力,提出了一种采用遗忘因子并结合滑动窗口的基准估计和有效N跟踪的方法,从而实现对数据真实流属性的捕捉;仿真实验结果表明,本文提出的动态建模方法相比于目前先进的静态建模方法,不仅具有更高的准确性和异常检测能力,而且具有更强的数据变化的跟踪能力和检测能力。     

基于改进的背景模型的图像识别算法研究

当图像亮度不均匀、对比度低时,提取图像前景较困难。为此,提出一种图像分割方法,结合正弦基函数和绝对值距离测度构建背景模型,依据优化理论和迭代法求解背景模型,通过比较背景模型中各像素点亮度与实际图像中各像素点亮度来判别各像素点是背景还是前景。为应对图像亮度不均匀的情况,在图像分割前对图像进行分块,在分块图像中依据背景模型或相邻分块背景相似度进行图像分割。实验结果表明,在普适性方面,相对于经典的模糊C均值法和OTSU法,该方法的分割误差小,尤其是对亮度不均匀和对比度低的图像;在掌纹图像分割应用方面,与迭代线跟踪法和模糊粗糙集法相比,该方法的错误率低、信噪比高、处理时间短。最后将提出的分割算法应用在人脸识别上,实验结果表明了该算法的先进性。     

迭代函数系IFS吸引子的参数控制与树木的模拟

文章讨论了迭代函数系ＩＦＳ吸引子的参数控制,讨论了ＩＦＳ参数控制实现模拟树木的方法。