This thesis focuses on the ica algorithm and its applications in image processing.
论文围绕ICA算法及其在图像处理中的应用进行了深入系统的研究。
The fast ICA algorithm is proposed to separate the rub-impact signal from the noise signal.
针对大背景噪声，本文提出采用快速ICA算法对碰摩与噪声信号进行分离。
Based on ICA algorithm with reference signals, a method of removing blinking artifacts was proposed in this paper.
本研究提出了一种基于带参考信号的ICA算法的脑电信号眨眼伪差的分离方法，可以得到纯净的脑电信号。
Finally, the experimental and analytical results show that in face recognition KICA algorithm outperforms ica algorithm.
实验和分析结果表明，在人脸识别中，基于KICA的方法优于基于ICA的方法。
After analyzing ICA algorithm, a new method of the radar signal sorting based on independent component analysis is proposed.
在深入分析ICA算法的基础上，提出了将其用于雷达信号分选的新思路。
To reduce the complexity, dimension reduction executes before ICA algorithm, and then after iteration of the orthogonal data processing.
为了降低复杂度，在进行ICA运算时，先对接收数据进行降维预处理，然后对迭代后的数据进行正交化处理。
Former ica algorithm generally supposes that the noises can be neglected, but practically, the additional noises are often included in surveyed data.
以往的ICA算法一般假设噪声可以忽略不计，而实际的观测数据中又常常包含一些加性噪声。
Using maximum approximation of differential Negentropy, an objective function for ICA is introduced and a Fast-ICA algorithm based on maximum Negentropy is presented.
使用差分最大负平均信息量的方法，一个为ica的目标函数和一个快速ica算法在文中被提出。
After several running of the ICA algorithm, according to the comparison of correlation coefficients the genes related with AD were selected based on frequency of the genes appearing.
首先运行多次ica算法，然后通过比较相关系数选出与AD相关的ICs，最后依据基因出现的频数选择相关基因。
Based on an analysis of EASI batch process algorithms for traditional blind source separation, a sliding window ICA algorithm is studied to deal with complex signals in the time variant mixing model.
通过对传统盲源分离批处理EASI算法的分析，针对时变信道中通信信号的复数形式，以平滑窗的形式实现了批处理算法在时变混合模型下的应用。
In this paper, proposed is the informax fast algorithm of ICA using information maximum likelihood estimation with the Newton iterate algorithm.
本文基于信息极大似然估计，采用牛顿迭代算法，建立了ICA的一种信息极大快速算法。
Based on the brief introductions of ICA theory and algorithm, we apply ICA to the removal of ocular artifacts from EEG recordings.
在简要分析ICA理论及其算法的基础上，提出将其应用到脑电中的眼电伪迹的去除任务。
In this paper, a simple gradient algorithm of ICA is developed using minimum mutual information.
本文基于互信息极小提出了ICA的梯度算法。
For the case that the measured data contain non-Gaussian latent variables, ICA is more efficient signal extracting algorithm than PCA.
在测量数据含有非高斯潜隐变量的情况下，ICA是比PC A更有效的特征提取算法。
In chapter 4, we introduce the higher order statistic analysis theory and information theory related to ICA theory, the object function of ICA, and several representative optimizing algorithm of ICA.
第四章介绍了与ICA理论有关的高阶统计理论，信息论理论，ICA的目标函数以及几种典型的优化算法。
This paper presents the principle, definition and EASI algorithm of ica, and describes the application of the algorithm in the field of speech signal processing.
介绍了ICA的原理、含义及EASI算法，并仿真了该算法在语音信号盲分离中的应用。
In this paper, kernel independent component analysis (KICA) 's principle and algorithm are introduced, and then the KICA comparison with some other ICA and principal component analysis (PCA) is given.
论文介绍了基于核空间的ICA的原理和基本算法，然后介绍了该算法与典型ICA和主成分分析(PCA)在盲源信号分离中的比较。
Simulation results indicate that the proposed algorithm is able to achieve better BER performance than the conventional matched filtering and conventional ica approach in the same environment.
仿真结果表明：此算法的误码率在相同的试验环境下可以取得比传统的匹配滤波器和传统的ICA算法更好的效果。
Simulation results indicate that the proposed algorithm is able to achieve better BER performance than the conventional matched filtering and conventional ica approach in the same environment.
仿真结果表明：此算法的误码率在相同的试验环境下可以取得比传统的匹配滤波器和传统的ICA算法更好的效果。
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