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Description: 1. 数学形态学在图像处理中的发展：
1.1 数学形态学在图像处理中的起源
1.2 二值数学形态学
1.3 灰度数学形态学
1.4 软数学形态学
2. 数学形态学在图像处理中的应用：
2.1 综合概述
2.2 介绍一种基于数学形态学的遥感图像边缘检测算法
-1. Mathematical Morphology in Image Processing of development:
1.1 Mathematical Morphology in Image Processing of the origin of
1.2 Binary mathematical morphology
1.3 gray scale mathematical morphology
1.4 soft mathematical morphology
2. Mathematical Morphology in Image Processing:
2.1 Comprehensive Overview
2.2 A mathematical morphology-based edge detection algorithm for remote sensing image
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Size: 342016 |
Author: lijinquan |
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Description: 。当各感知用户采用软判决方法,对于2
个感知用户合作和3个感知用户合作的情况,可对最优联合检测算法性能有015dB改善。-. When the user uses soft decision method of perception, perceptual user cooperation for 2 and 3 sensing user cooperation, the joint detection algorithm can best improve the performance of 015dB.
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Size: 449536 |
Author: 宝宝 |
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Description: 我们实验室一片新的关于SVM软测量的论文-a new paper about soft sensing based on SVM algorithm
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Size: 722944 |
Author: yan |
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Description: 本文首先介绍了粗糙集理论的基本概念，包括等价关系、不可分辨关系、上下近似、粗糙集和简约；并且从空间数据挖掘、遥感影像处理、GIS 不确定性、GIS 数据分析、模糊地理对象建模和粗糙集与其它软计算方法的结合等六方面概述了粗糙集理论在GIS 数据处理中应用的进展-This paper introduces the basic concepts of rough set theory, including the equivalence relation, can not distinguish between lower and upper approximation, Rough sets and simple and from spatial data mining, remote sensing image processing, GIS uncertainty, GIS data analysis, fuzzy geographical objects Modeling and rough sets and other soft computing methods outlined six aspects such as the combination of rough set theory in GIS progress in the application of data processing
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Size: 376832 |
Author: 胡伟 |
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Description: 本文首先介绍了粗糙集理论的基本概念，包括等价关系、不可分辨关系、上下近似、粗糙集和简约；并且从空间数据挖掘、遥感影像处理、GIS 不确定性、GIS 数据分析、模糊地理对象建模和粗糙集与其它软计算方法的结合等六方面概述了粗糙集理论在GIS 数据处理中应用的进展-This paper introduces the basic concepts of rough set theory, including the equivalence relation, can not distinguish between lower and upper approximation, Rough sets and simple and from spatial data mining, remote sensing image processing, GIS uncertainty, GIS data analysis, fuzzy geographical objects Modeling and rough sets and other soft computing methods outlined six aspects such as the combination of rough set theory in GIS progress in the application of data processing
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Size: 395264 |
Author: 胡伟 |
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Description: 实现神经网络逆软测量,通过构建内含传感器来构建神经网络逆软测量仪表-ANN soft-sensing method
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Size: 5120 |
Author: 鲁超 |
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Description: 软测量技术在打浆过程中的应用Soft Sensing Technology in Pulp Proce-Soft Sensing Technology in Pulp Process
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Size: 384000 |
Author: zee |
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Description: 软合并和检测中的认知无线电协作频谱感知,希望对你有用.-Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks.pdf
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Size: 220160 |
Author: ss |
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Description: Here, we simulate the multiuser sensing with soft information
fusion strategy. The total number of cooperating users N=5
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Size: 2048 |
Author: niru |
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Description: 利用最小二乘法进行拟合多元非线性方程，比较了软阈值，硬阈值及当今各种阈值计算方法,用MATLAB实现的压缩传感。- Multivariate least squares fitting method of nonlinear equations, Comparison of soft threshold and hard threshold and today various threshold calculation method, Using MATLAB compressed sensing.
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Size: 6144 |
Author: 许伟峰 |
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Description: Comparison of soft threshold and hard threshold and today various threshold calculation method, Using MATLAB compressed sensing, PLS PLS toolbox.
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Size: 150528 |
Author: 尤艳康
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Description: 软测量技术应用与发展。软测量技术的前景。(Application and development of soft sensing technology. The prospect of soft sensing technology.)
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Size: 219136 |
Author: 平行线123
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Description: DBN-ELM应用于软测量，可以预测精度，并且可以和其它软测量模型进行对比。(Application of DBN-ELM in Soft Sensing,it can predict the accuracy and compare with other soft sensor models.)
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Size: 7746560 |
Author: 轩西铎 |
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Description: 前已提出的频谱感知方法主要包括匹配滤波器检测、 能量检测、 循环平稳特征检测以及多分辨率频谱感知. 这些方法均为单节点感知方法.然而，在阴影和深度衰落情况下， 单个节点的感知结果并不可靠， 因此， 需要对多个节点的感知结果进行融合，以提高检测可靠性， 即协作感知技术. 文献采用“或” 准则对各个 CR 感知结果进行融合. 文献则提出了基于 D-S 证据理论的协作频谱感知算法，虽然该算法的性能比“或” 准则或“与”准则要好， 但需要存储大量历史信息， 算法的计算复杂度也很高. 文献中分析了采用似然比检测(likelihood ratio test， LRT) 的软判决与采用“与” 准则的硬判决的性能， 结果表明采用软判决的协作感知性能更优(Previously proposed spectrum sensing methods mainly include matched filter detection, energy detection, cyclostationary feature detection and multi-resolution spectrum sensing. These methods are all single node sensing methods. However, in the case of shadow and deep fading, the sensing results of single node are not reliable, so, It is necessary to fuse the sensing results of multiple nodes to improve the detection reliability, i.e. cooperative sensing technology. In the literature, "or" criterion is used to fuse the CR sensing results. In the literature, a cooperative spectrum sensing algorithm based on D-S evidence theory is proposed. Although the performance of the algorithm is better than "or" criterion or "and" criterion, a large amount of historical information needs to be stored, The computational complexity of the algorithm is also very high. In the literature, the performance of the soft decision based on the likelihood ratio test)
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Size: 6144 |
Author: UU仔 |
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