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- A new soft decision metric for QAM is presented in this paper.Unlike traditional soft decision metric based on minimum Euclidean distance, the proposed metric is based on the bit confidences. 传统的软判决度量大多是基于最小欧氏距离准则生成，该文提出一种基于比特置信度快速生成QAM软判决度量的算法，通过将比特置信度引入到QAM软判决度量的计算中来减少计算的复杂性。
- Minimum Euclidean distance 最小欧氏距离
- The global features and local features will be recognized and sorted by weighting Euclidean distance and the minimum distance, respectively. 通过加权欧几里德距离和最小距离分别对全局特征和局部特征进行分类识别。
- The minimum squared Euclidean distance (MSED) of 2-ary multi-k continuous-phase frequency-shift keying (CPFSK) signal is presented. 本文详细讨论了二进制多h连续相位移频键控(CPFSK)信号的最小平方欧几里德距离(MSED),提出了决定MSED的信号隔离度的概念。
- We choose the Euclidean distance as a similarity coefficient. 将相似性系数E引人菌株相 似性分析，大大提高了耐药谱分型的辨别力和重复性。
- Finally, weighted Euclidean distance classifier is utilizedin recognition. 最后利用加权欧氏距离分类器进行识别。
- The similarity is decided by the Euclidean distance of the gray histograms. 利用各区域灰度直方图的欧氏距离判定相似区域。
- The relative Weighted Euclidean Distance(WED) classifier is proposed as an improvement of the classifier. 通过对WED(Weighted Euclidean Distance)分类器的改进研究,提出了相对WED分类器的新概念,进一步提高了系统的识别能力。
- This method include 3D Euclidean distance transform, computer the Hessian matrix in every voxel,and visibility test. 主要步骤包括:三维欧几里德距离变换,求Hessian矩阵,可视化检测几个步骤。
- The technique supports Euclidean distance measure and L-shift Euclidean distance measure. 这种方法支持欧几理德距离标准和 L -平移欧几理德距离标准 .
- All the process of clustering based on the Euclidean distance among data vectors. 聚类过程都是根据数据之间的Euclidean(欧几里得)距离。
- During the recognition phase, Euclidean Distance representing VQ distortion in this project is calculated. 在认识阶段期间，通过对欧几里德距离代表VQ的计算来减少失真。
- With the minimum square sum of weighted Euclidean distances as the objective function, the unified theory and cyclical iteration model of fuzzy cluster, recognition and optimum decision are founded. 通过以全体样本对全体类别加权广义欧氏权距离平方和最小为目标函数，建立了模糊聚类、识别与优选决策统一的理论与循环迭代模型。
- First, the counterparts of the points on image in camera coordinate system (CCS) are found by utilizing two properties, namely collinearity and Euclidean distance invariability. 首先,利用共线性和欧氏距离不变性这两个特性可以求得图像中的点在相机坐标系(CCS)中的坐标。
- Euclidean distance transform is one of most useful distance transform algorithms. It defines the distance of the line between two points in the space. 其中，欧几里德距离转定义了空间两点间的直线距离，它是一种最常见的距离转换方式，在相关领域中，尤其在图像处理中，它的应用十分广泛。
- Experimental results show that retrieval effectiveness is the highest for E+Gibbs and the lowest for the Euclidean distance. 实验结果证明E+吉布斯的检索效果最好而欧氏距离的检索效果最差。
- Finally the algorithm discovers all time -series patterns by computing Euclidean distance between any two subsequences in each box. 最后通过计算每个盒子中任意两个子序列间的欧几里德距离来发现所有的模式。
- Finally, with the step-by-step scheme, we implemented the fast hardware algorithm for Euclidean distance transform based on two-dimension images. 最后，根据硬件算法的实现原理，本文采用stp-by-step的设计方案，选择相关硬件，实现了一种基于二维图像的欧几里德距离转换算法电路。
- The Euclidean distance is usually chosen as the similarity measure in the conventional K-NN algorithm, which usually relates to all attributes. 传统的K-近邻算法选择的相似性度量通常是欧几里德距离的倒数,这种距离通常涉及所有的特征。
- Similarity measurement is the key to solving the clustering problem, while similarity of categorical attributes can'be measured by Euclidean distance. 相似性的度量是解决聚类问题的关键;而分类属性的相似性无法用欧几里德(Euclidean)距离来度量.
