激光对CCD器件破坏时几种阈值的测量

本文简要回顾了近十年来激光CVD(LCVD)技术的发展概况及其在金属、电介质和半导体薄膜生长方面的应用情况。阐明了这种新发展起来的成膜技术不仅因其生长的低温化能够给器件带来优良的电学特性,同时也可利用其高精度的膜厚控制特性获得新结构的材料和器件。作者还对该技术的广泛的应用前景予以展望和肯定。     

双光子Jaynes－Cummings模型的亚泊松光子统计特性研究

本文利用量子电动力学理论和缀饰原于方法，对双光子Ｊａｍｅｓ－Ｃｕｍｍｉｎｇｓ模型中单模激光场的稳态亚泊松光子统计性质进行了系统研究。结果表明：激光上能级相对泵浦参量Ｘａ具有一个确定的阈值Ｘａｔ；当Ｘａ＞Ｘａｔ时，光场呈现亚泊松光子统计；而当Ｘａ＞＞Ｘａｔ时，光场将呈现出深度且完全恒定的亚泊松光子统计。     

双相位共轭镜的工作条件参数分析

本文讨论了纯扩散机制的光折变晶体用作单作用区的双相位共轭镜（ＤＰＣＭ）时的阈值耦合强度和允许的输入光强比范围，并与双作用区的情况进行比较后，给出了在输入光强比远离１的条件下，双相位共轭镜的正常工作的条件参数。     

基于LabVIEW的超声信号小波阈值去噪方法

图形化编程软件LabVIEW具有强大的信号采集功能,但对于需要进行大量数据运算处理的复杂应用就显得力不从心。数学工具软件MATLAB强大的计算功能及较高的编程效率可以弥补LabVIEW开发的不足。文中结合两者软件的优点,采用混合编程的方法进行实际工程的去噪处理,同时针对超声缺陷回波信号信噪比低、易于被噪声淹没的特点,选取小波阈值去噪的方法,通过调用LabVIEW里的MATLAB Script节点对信号进行仿真实验,仿真结果表明采用stein的无偏风险阈值去噪的方法消噪效果最为明显,得到较高的信噪比。     

一种改进的基于多项式滤波的稳态检测方法

化工过程的数据中常常含有较多的随机误差和粗差干扰,导致传统的稳态检测方法无法得到准确的结果,从而降低系统故障诊断的可靠性。针对实际的工业过程数据,提出一种融合自适应平滑技术的稳态检测方法,该方法首先以基于导数分析的自适应平滑算法进行降噪处理,消除随机误差的影响,然后引入阈值拟合技术进一步抑制粗差干扰,以多项式滤波方法对数据进行稳态检测,根据测量信号的趋势特征确定过程是否处于稳态。仿真实验研究表明:融合自适应平滑技术的稳态检测方法能够克服传统稳态检测方法中随机误差和粗差干扰对检测结果的影响,进而显著提高稳态检测处理的准确性,检测结果明显优于传统的基于多项式滤波的检测方法。     

Majority gates vs. general weighted threshold gates

In this paper we study small depth circuits that contain threshold gates (with or without weights) and parity gates. All circuits we consider are of polynomial size. We prove several results which complete the work on characterizing possible inclusions between many classes defined by small depth circuits. These results are the following:

基于阈值分割的松材线虫病树计数与定位

松材病虫病是最紧迫的威胁之一,最近对中国针叶林造成了严重破坏,为了阻止松材病虫病的快速传播,必须在早期准确检测和计数受感染的树木。提出了一种基于密度图的方法,用于从高分辨率航拍图像中估计具有松材病虫病的树木数量,方法引入了阈值分割、形态处理和超像素技术,以最大限度地减少由于建筑物和岩石等相似背景物体引起的误差。实验表明,所提出的方法优于密度学习 算法,平均绝对误差、均方根误差和绝对误差方差分别从21.3、22.2和127.2减少到8.0、11.7和59.6,计数准确率从54.7%提高到81.3%。     

弯道岸坡上粗细泥沙统一起动流速公式

该文基于概率论与泥沙起动基本理论,考虑了起动概率、相对暴露度与细颗粒泥沙间黏结力和附加下压力对泥沙起动的影响,采用滑动起动模型,从理论上推导出了适用于弯道岸坡上粗、细泥沙颗粒起动的统一流速公式,完善了已有研究成果对泥沙起动概率考虑的不足。公式综合了弯道岸坡、顺直岸坡、正负坡及平坡等条件下粗细泥沙颗粒起动流速计算,可简化为各简单条件下泥沙起动流速公式。通过各简单条件下泥沙起动流速实测资料验证,该文公式与实测数据符合良好。     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

EMD-IT去噪算法的阈值估计方法

为解决EMD-IT去噪算法中阈值难以确定的问题,提出一种基于高斯白噪声能量分布的阈值估计方法。将含噪信号进行经验模态分解并估计各固有模态函数(IMF)中噪声的能量;根据模态单元阈值的含义,在各IMF中利用去除掉的模态单元包含的总能量等于噪声能量这一准则估计阈值。合成数据和实际心电信号的去噪仿真实验验证了该方法的有效性,其是自适应的且避免了阈值选择的主观性。