Comments on the shortcomings of predicting the precision of Cavalieri volume estimates based upon assumed measurement functions

It is well known that the estimation of an object's volume by means of serial cross-sections, the so-called Cavalieri method, yields an unbiased estimate. But by itself it provides no means by which to estimate how precise this estimate is unless the shape of the volume is fully known beforehand. This knowledge can only be partially determined from the serial section information that is collected. Methods have been developed that claim to surmount this difficulty by using the serial section data to create a mathematical model of the volume's shape properties. The model then is used to estimate (predict) the precision of the volume estimate (its CE) from the single set of data available. Unfortunately, the theory underlying the model is flawed and so the model itself amounts to no more than an unsubstantiated guess about the shape of the volume. Therefore, the precision of the volume estimates that one obtains from the method is only as good as the model and this cannot be ascertained from the single set of acquired data. In this letter I explain the inadequacies of the modelling method. I suggest that it be used only with caution, if at all. Instead I suggest two alternative ways to predict the CE, one that is based upon a rule-of-thumb approach to the object's shape, and another that is based upon spectral analysis of the measurement function and that is easy to implement with available computer software.     

测量输入光窗台阶指标的指示表的选择

阐述了对近贴聚焦型微通道板像增强管中输入光窗台阶高度和平整度指标进行测量时，所用指示仪表的选择依据，并详细分析了指示表在检定过程中的误差来源，进一步计算出其合成标准不确定度和扩展不确定度，从而选定可用于测试的指标表。     

提高曲面近似展开精度的方法与实现

通过对不可展曲面近似展开精度的研究，提出了根据曲面曲率的大小对曲面自动进行网格划分，通过对综合展开误差数据场进行数据拟合来选取最优展开基点，基于空间曲面与其展开图间近似的等距变换关系进行展开样形的误差矫正等3种方法，这几种方法的实现在不同程度上有效地减小了曲面的近似展开误差，提高了曲面的近似展开精度。     

一种适用于有限长度混合相位未知脉冲的最小平方反褶积新方法

普通的最小平方反褶积只有在子波为最小相位脉冲和反射序列为白噪声的假设下才是成立的,而对于混合相位未知脉冲来说,这种最小平方反褶积就不再适用了。本文提出一种适合于有限长度混合相位未知脉冲的最小平方反褶积方法。运用此法只需预先估计一个混合相位子波的长度。合成数据的试验结果表明,该方法有较好的效果,且对预先估计出的子波长度不敏感。文中还证明混合相位未知脉冲最小平方反褶积等效于一种有间隙的平滑误差滤波器的作用,而这种有间隙的平滑误差滤波又可分解为一个前向多步预测误差滤波与一个后向多步预测误差滤波之和。可以预计这种新的反滤积方法将在地震数据处理中得到有效的应用。     

静态电子轨道衡组成部分解析及其合理选择

阐述静态电子轨道衡诸多优点;工作原理、允许误差及误差分配;轨道衡的结构组成部分、解析各部分的工作特性及技术指标要求;合理选配及推荐实例.     

How Meta-Analysis Increases Statistical Power.

One of the most frequently cited reasons for conducting a meta-analysis is the increase in statistical power that it affords a reviewer. This article demonstrates that fixed-effects meta-analysis increases statistical power by reducing the standard error of the weighted average effect size (T?.) and, in so doing, shrinks the confidence interval around T?.. Small confidence intervals make it more likely for reviewers to detect nonzero population effects, thereby increasing statistical power. Smaller confidence intervals also represent increased precision of the estimated population effect size. Computational examples are provided for 3 effect-size indices: d (standardized mean difference), Pearson's r, and odds ratios. Random-effects meta-analyses also may show increased statistical power and a smaller standard error of the weighted average effect size. However, the authors demonstrate that increasing the number of studies in a random-effects meta-analysis does not always increase statistical power. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

利用田口技术论证非标测量设备最佳校准系统值

通过利用田口技术对非标测量设备校准参数的计算，可以更好控制产品质量，以满足产品的需要。     

Foveation-Based Error Resilience and Unequal Error Protection over Mobile Networks

By exploiting new human-machine interface techniques, such as visual eyetrackers, it should be possible to develop more efficient visual multimedia services associated with low bandwidth, dynamic channel adaptation and robust visual data transmission. In this paper, we introduce foveation-based error resilience and unequal error protection techniques over highly error-prone mobile networks. Each frame is spatially divided into foveated and background layers according to perceptual importance. Perceptual importance is determined either through an eye tracker or by manually selecting a region of interest. We attempt to improve reconstructed visual quality by maintaining the high visual source throughput of the foveated layer using foveation-based error resilience and error correction using a combination of turbo codes and ARQ (automatic reQuest). In order to alleviate the degradation of visual quality, a foveation based bitstream partitioning is developed. In an effort to increase the source throughput of the foveated layer, we develop unequal delay-constrained ARQ (automatic reQuest) and rate compatible punctured turbo codes where the punctual pattern of RCPC (rate compatible punctured convolutional) codes in H.223 Annex C is used. In the simulation, the visual quality is significantly increased in the area of interest using foveation-based error resilience and unequal error protection; (as much as 3 dB FPSNR (foveal peak signal to noise ratio) improvement) at 40% packet error rate. Over real-fading statistics measured in the downtown area of Austin, Texas, the visual quality is increased up to 1.5 dB in PSNR and 1.8 dB in FPSNR at a channel SNR of 5 dB.     

Projection center calibration by motion

Camera calibration is the first step of three-dimensional machine vision. A fundamental parameter to be calibrated is the position of the camera projection center with respect to the image plane. This paper presents a method for the computation of the projection center position using images of a translating rigid object, taken by the camera itself.

Many works have been proposed in literature to solve the calibration problem, but this method has several desirable features. The projection center position is computed directly, independently of all other camera parameters. The dimensions and position of the object used for calibration can be completely unknown.

This method is based on a geometric relation between the projection center and the focus of expansion. The use of this property enables the problem to be split into two parts. First a suitable number of focuses of expansion are computed from the images of the translating object. Then the focuses of expansion are taken as landmarks to build a spatial back triangulation problem, the solution of which gives the projection center position.