Description: 用小波变换实现对BMP图像的灰度处理，迅速分离空间域与频率域-Using wavelet transform to achieve grayscale BMP image processing, rapid separation of the space domain and frequency domain Platform: |
Size: 25600 |
Author:征征 |
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Description: 该程序是盲源分离算法中时域—频域分离算法，该算法应用范围广泛。-The procedure is blind source separation algorithm in time domain- the frequency domain separation algorithm, which a wide range of applications. Platform: |
Size: 57344 |
Author:雷衍斌 |
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Description: 定点频域ICA，使用高斯函数、负熵最大化来处理语音信号分离问题的演示-FIXED-POINT FREQUENCY DOMAIN ICA with
GENERALIZED GAUSSIAN FUNCTION BASED
NEGENTROPY APPROXIMATION for SPEECH SIGNAL
SEPARATION Platform: |
Size: 847872 |
Author:leo |
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Description: 功能十分强大的数字图像处理程序，具有基本的图像输入和变换，图形的即时绘制，彩色图像处理，图像旋转平移缩放，分离滤除RGB三原色，边缘检测与提取，频域变换（FFT，DCT），中值滤波等功能-Function is very powerful digital image processing procedures, have a basic input and transform images, graphics, real-time rendering, color image processing, image rotation scaling translation, the three primary colors RGB separation filter, edge detection and extraction, frequency domain transform (FFT, DCT ), median filtering and other functions Platform: |
Size: 4040704 |
Author:Myrrhy |
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Description: In frequency-domain blind source separation (BSS) for speech with independent component analysis (ICA), a practical parametric Pearson distribution system is used to model the distribution of frequency-domain source signals.-In frequency-domain blind source separation (BSS) for speech with independent component analysis (ICA), a practical parametric Pearson distribution system is used to model the distribution of frequency-domain source signals. Platform: |
Size: 21504 |
Author:刘海 |
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Description: 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 series of independent noise signal of the component (sysu), so that we can to each individual independent component analysis and research. First describes the characteristics of diesel engine noise signal. Forecasting model shows: the engine noise signal satisfy the requirements. ICA calculation Then introduces the related theoretical model of ICA. Illustrate the effectiveness of the method of ICA and separated signal to noise ICA method of separation of energy efficiency. Continuous wavelet transform to display the independent components in time-frequency domain sysu properties. By gathering signal noise signal can be obtained as the engine of the theoretical prediction and design basis. Platform: |
Size: 15193088 |
Author:王霞 |
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Description: 本程序为基于FFT快速傅里叶积分的程序，可以通过改程序将所需频率的信号在频域进行分离。-This program is based on FFT Fast Fourier integral program, you can change the program by the required frequency of the signal in the frequency domain separation. Platform: |
Size: 1024 |
Author:田博文 |
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Description: 完成两个混合正弦信号的分离，分离是从频域的角度-Complete separation of the two mixed sine signal, separating from the perspective of frequency domain Platform: |
Size: 2048 |
Author:lucent |
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Description: 一种新的频域盲源分离方法，用皮尔逊系统去模拟不同频率上的信号分布，通过信号特征，选择合适的皮尔逊类型的函数去优化分离矩阵，从实现盲源分离.-A new frequency-domain blind source separation method using the Pearson system to simulate a signal on a different frequency distribution, through the signal characteristics, select the appropriate type of function to optimize Pearson separation matrix, from the implementation of blind source separation. Platform: |
Size: 784384 |
Author:刘梦蝶 |
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Description: 小波变换是一种线性运算 , 它对信号进行不同尺度的分解 , 可有效地应用于如
信噪分离 , 提高时频两域的分辩率等 。本文讨论小波变换用于心电 Q RS 波形中细微特征
( 即高频成份特征 ) 提取的方法.-Wavelet transform is a linear operation, its signal is decomposed at different scales, can be effectively used as the signal to noise separation, the two time-frequency domain, such as to improve the resolution. This article discusses the wavelet transform of ECG waveform Q RS subtle features (ie, features high-frequency components) extraction method. Platform: |
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Author:张春竹 |
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Description: 本代码主要提供了在频域使用fastica进行盲源分离，并且解决了频域的排列和增益两个歧义性问题。-This code mainly provides the use of fastica in the frequency domain for blind source separation, and solves the frequency domain arrangement and gain of two ambiguity problems. Platform: |
Size: 97280 |
Author:纪璇 |
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Description: 直接运行Demo文件即可，本算法案例是两源信号卷积混合，基于同一信号相邻频点能量相关的方法对频域盲源分离信号进行排序(The demo file can be run directly. The case of this algorithm is the convolution mixing of two source signals. The Blind Source Separation (BSS) signals in frequency domain are sorted based on the energy correlation method of the adjacent frequency points of the same signal) Platform: |
Size: 10240 |
Author:高大帅V5 |
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Description: vmd变分模态分解，是一种自适应、完全非递归的模态变分和信号处理的方法。该技术具有可以确定模态分解个数的优点，其自适应性表现在根据实际情况确定所给序列的模态分解个数，随后的搜索和求解过程中可以自适应地匹配每种模态的最佳中心频率和有限带宽，并且可以实现固有模态分量（IMF）的有效分离、信号的频域划分、进而得到给定信号的有效分解成分，最终获得变分问题的最优解。(VMD variational modal decomposition is an adaptive, completely non-recursive modal variational and signal processing method. This technique has the advantage that the number of modal decompositions can be determined. Its adaptability manifests itself in determining the number of modal decompositions for a given sequence according to the actual situation. The subsequent search and solution process can adaptively match the number of each modal decomposition. It has the best center frequency and limited bandwidth, and can achieve effective separation of inherent modal components (IMF), frequency domain division of signals, and then obtain effective decomposition components of a given signal, and finally obtain the optimal solution of the variational problem.) Platform: |
Size: 3072 |
Author:张先生1234 |
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Description: VMD（Variational mode decomposition）是一种自适应、完全非递归的模态变分和信号处理的方法。该技术具有可以确定模态分解个数的优点，其自适应性表现在根据实际情况确定所给序列的模态分解个数，随后的搜索和求解过程中可以自适应地匹配每种模态的最佳中心频率和有限带宽，并且可以实现固有模态分量（IMF）的有效分离、信号的频域划分、进而得到给定信号的有效分解成分，最终获得变分问题的最优解。(VMD (variable mode decomposition) is an adaptive and non recursive method for modal variation and signal processing. This technique has the advantage of determining the number of mode decomposition. Its self adaptability is that the number of mode decomposition of the given sequence can be determined according to the actual situation. In the subsequent search and solution process, it can adaptively match the optimal central frequency and finite bandwidth of each mode, and realize the effective separation of intrinsic mode components (IMF), frequency domain division of signals, and then get the Given the effective decomposition component of the signal, the optimal solution of the variational problem is obtained.) Platform: |
Size: 2048 |
Author:hoppder |
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Search - frequency domain separation