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该文章介绍了在光谱图像中采用独立分量分析的算法对西红柿进行分类-article introduces the spectral images of the use of independent component analysis algorithm to classify tomatoes
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一般性制作指南为创建具有最大复用潜力的软件提供指南，针对软件生命周期中的需求分析、设计、详细设计与实现、质量保证与测试以及文档等方面分别论述，帮助用户组织一个可复用软件构件（Reusable Software Component－RSC）的开发过程。这一部分的大部分内容独立于编程语言，在实现时采用特定语言的编码标准作为该标准的补充。-general guidelines for the creation of production have the greatest potential for
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在基于特征提取方法之上, 研究用算法对目标多视角问题进行分类器设计。在对
图像进行独立成分分析后, 针对多姿态角目标识别问题, 提出了角度优先粗分类的设计方法-Feature extraction method based on above research objective of the algorithm to classify multi-view design problem. In the right image independent component analysis, t
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ICA算法可以将噪声信号分解为一系列独立的分量（ICs），这样就可以对各独立分量进行单独的研究和分析。首先叙述了柴油机噪声信号的特性。预测模型表明：发动机噪声信号满足ICA计算的要求。然后介绍了ICA模型的相关理论。举例说明ICA方法分离信号的有效性，以及ICA方法对小能量噪声的分离的有效性。连续小波变换来显示了各独立分量ICs在时频域内的特性。由采集信号分离得到噪声源信号可以作为发动机的理论预测和设计依据。-he ICA algorithm can be decomposed into a s
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关于盲源分离算法的简介，包括独立成分算法的编程。-ICA (independent component analysis) algorithm on the blind source separation algorithm, including the introduction of the algorithm, including the preparation of the algorithm.
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zheshi基本的语音分离算法，用的是独立成分分析方法。-Basic speech separation algorithm, using the independent component analysis method.
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语音信号的 处理过程，可以成功地分离信号，属于盲源分离算法的一种。-Basic speech separation algorithm, using the independent component analysis method.
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语音分离过程中经常用到的语音库，这是一个很好的语音信号的来源。-Basic speech separation algorithm, using the independent component analysis method.
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Is a practical method of path planning, Using matlab to calculate the Mahalanobis distance for the image, Based on negative entropy largest independent component analysis.
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Realize image watermarking, de-noising, plus noise and other functions, Simulation of doubly fed induction generator system, Based on negative entropy largest independent component analysis.
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Calculation crosshairs diffraction image at different distances, Several new methods sar image denoising, Independent component analysis for image processing.
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Including quaternion various calculations, Based on negative entropy largest independent component analysis, EULER numerical analysis method.
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Complete set content, basic hyperspectral image processing, Least-squares regression analysis algorithm, Independent component analysis algorithm reduces the raw data noise.
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Based on negative entropy largest independent component analysis, Using common plane wave expansion method, For feature reduction, feature fusion, correlation analysis.
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SNR largest independent component analysis algorithm, Face Recognition light treatment method, Energy spectrum analysis and calculation.
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Including AHP, factor analysis, regression analysis, cluster analysis, For lack of EMD, SNR largest independent component analysis algorithm.
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