Description: 非平稳信号时频分析中的S变换实现的例程及应用-Non-stationary signal analysis of time-frequency S transform realization and application of routine Platform: |
Size: 263168 |
Author:陈实 |
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Description: matlab非常有用的工具箱，可对非平稳信号进行分析与处理。-matlab toolbox very useful, may be non-stationary signal analysis and processing. Platform: |
Size: 460800 |
Author:tieking |
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Description: 非平稳时变分析工具箱。现代信号处理的重要工具。-Non-stationary time-dependent analysis toolbox. An important tool in modern signal processing. Platform: |
Size: 379904 |
Author:赵宜楠 |
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Description: 离散分数阶傅里叶变换，可以处理非平稳信号，也可以很好的对抗多普勒频移，用在OFDM系统中尤为重要。-Discrete fractional Fourier transform, can handle non-stationary signal can also be very good against the Doppler frequency shift, used in the OFDM system is particularly important. Platform: |
Size: 1024 |
Author:suokun |
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Description: TFBSS是一种基于短时傅里叶时频分析的盲源分离算法，算法基于卷积混合。处理非平稳源信号。-TFBSS performs Blind Source Separation of (over)determined multiplicative mixtures of non-stationary real valued sources.
TFBSS is based on the joint-diagonalization of whitened and noise-compensated Spatial Time-Frequency Distribution (STFD) matrices of the observations, corresponding to single auto-terms positions, as described in:
C. Févotte and C. Doncarli. "Two contributions to blind source separation using time-frequency distributions", IEEE Signal Processing Letters, 2004. IEEE Signal Processing Letters, vol. 11, no. 3, Mar. 2004. pdf
and
A. Holobar, C. Févotte, C. Doncarli, and D. Zazula. "Single autoterms selection for blind source separation in time-frequency plane". In Proc. 11th EUSIPCO, Toulouse, France, 2002 (Special Session on Source Separation). pdf
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Size: 25600 |
Author:范文涛 |
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Description: 张贤达的经典书籍非平稳信号分析与处理，完全版本-Xian-up of the classic books of non-stationary signal analysis and processing, full version Platform: |
Size: 10238976 |
Author:郭琳婕 |
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Description: 本文利用小波以及小波包在数字信号处理中表现出来的优势，将其应用到变换域通信(TDCS)中。针对基于傅里叶变换估计方法无法对非平干扰进行有效估计的缺点，于是小波技术被应用于谱估计中。作为TDCS通信前端的频谱感知阶段所应用的数学工具，进行频谱感知，小波变换可以确定干扰所在频段以及创建小波基函数，用于数据传输。并且将其与其他变换方式的应用场合以及性能进行对比。并且针第二代小波良好的数据压缩性能，拟将其应用于TDCS中，对干扰频谱进行进一步压缩-The article utilized the advantages of wavelet and wavelet packet synthesis applying in the digital signal processing domain, and applied it into the Transform Domain Communication System (TDCS). To composite the shortcomings in Fourier Transform in non-stationary noise, wavelet transform are introduced. As a mathematical tool before transformation, wavelet transform will sensing the environment, locate the interference and create a fundamental modulation waveform（FMW）。Comparison with other kinds of transforms is made in this article as well. As the same time, the second generation wavelet has a good performance in data comprising, we could consider utilizing it into further lifting in the quality of the sensing period. Platform: |
Size: 698368 |
Author:mini |
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Description: 经验模态分解（Empirical Mode Decomposition, EMD）方法是由美国NASA的黄锷博士提出的一种信号分析方法。它依据数据自身的时间尺度特征来进行信号分解，无须预先设定任何基函数。EMD方法在理论上可以应用于任何类型的信号的分解，因而在处理非平稳及非线性信号序列上具有很高的信噪比，体现出非常明显的优势。-Empirical Mode Decomposition (EMD) is a signal analysis method proposed by the U.S. NASA s Dr. Huang E. It is based on the time-scale features of the data itself to decompose the signal, there is no need to pre-set any function. EMD method in theory can be applied to any type of signal decomposition and therefore non-stationary and nonlinear signal sequence with high signal-to-noise ratio, reflecting a very distinct advantage. Platform: |
Size: 1024 |
Author:Sharon |
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Description: time-invariant degradation models and stationary
signals and noise, the classical Fourier domain Wiener
filter, which can be implemented in O(N logN) time, gives the
minimum mean-square-error estimate of the original undistorted
signal. For time-varying degradations and nonstationary processes,
however, the optimal linear estimate requires O(N2) time
for implementation. We consider filtering in fractional Fourier
domains, which enables significant reduction of the error compared
with ordinary Fourier domain filtering for certain types
of degradation and noise (especially of chirped nature), while
requiring only O(N logN) implementation time. Thus, improved
performance is achieved at no additional cost. Expressions for
the optimal filter functions in fractional domains are derived,
and several illustrative examples are given in which significant
reduction of the error (by a factor of 50) is obtained. Platform: |
Size: 382976 |
Author:baibai |
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