搜索资源列表
pam
- pam我们考虑常见的插值(抽取)运算和滤波器级联的情形，在实际的插值(余 :)运算中，为了避免信号在经过插值(抽取)后出现混盛现象，保证能无失多 恢复信号，我们常常在插值运算之后(抽取运算之前)加上插值(抽取)滤公 时信号进行带限，如图2.10左边所示。 -we consider common interpolation (extraction) and Operational FILTER circumstances, In the actual interpolation
xilinx-qam-demodualater
- 本应用指南着重探讨了正交调幅 (QAM) 信号的基带解调，特别描述了分数率抽取电路模块的使用。本应用指南也对多相抽取滤波器结构进行了简介，讨论了分数率抽取电路及如何使用Xilinx System Generator 8.1i 实现它，并给出了实现结果。
time_varying_multipath_channel
- 单频信号经过多径时变信道后，输出信号的包络随时间随机起伏，输出信号的频谱从冲激谱变成一个窄带频谱。径数位20，变化频率从0—2HZ随机均匀抽取。-Single-frequency signal through time-varying multipath channel, the output signal envelope random fluctuations over time, the output signal from the impulse of the spectrum into
MATLAB1
- 声音信号的读入 抽取 及插值 （内附语音信号wav文件）-Sound signals read into the extraction and interpolation (with voice signals wav file)
Idddc_30mF
- 中频70M，30M带宽LFM信号，采样率为102.4M，，数字下变频后，还进行了三倍抽取，最后还得到I，Q两路信号 -IF 70M, 30M bandwidth LFM signal, the sampling rate 102.4M, under digital variable frequency after also carried out three times extracted, and finally also received the I and Q signals
wavepack-change
- 强大的小波包分析程序，将信号分频分析，在感兴趣的频率域内抽取信号信息。-Powerful wavelet packet analysis procedures, the signal of frequency analysis, frequency domain of interest extracted signal information.
Speech-signal-processing-source-code
- 对语音信号进行采样率的转换，如整数倍的内插和抽取，还可以进行非整数倍的采样率转换。-The sampling rate of the voice signal conversion, such as interpolation and decimation integer multiple of, can also be a non-integer multiple of the sampling rate conversion.
Multi-scale-analysis-
- 在matalab开发环境下的实现时域信号多尺度的分析，可用于信号的特征抽取和故障诊断-Multi-scale analysis of time-domain signal achieved in matalab development environment can be used for feature extraction and fault diagnosis signal
FFTorIFFT_MATLAB
- FFT和IFFT的MATLAB实现代码 用时间抽取的基2快速傅里叶变换实现序列卷积 对利用自编函数对信号进行DFT变换 对利用自编函数对信号进行DFT反变换-FFT IFFT dft
信道化信噪比程序
- 16个通道，16倍抽取，仿真结果是复信号输入情况下，将信号带宽分成了16路，采取F=2的方式，即采用1/2混叠的方式。信噪比计算
YGBSS
- 自己编的，基于自然梯度的盲源分离算法，如果想对自然梯度有所了解，可以参考Amari的经典文章。网络上一搜就行。(-own series, based on the natural gradient algorithm blind source separation, if you want to understand the natural gradient. Amari can refer to the classic article. Networks found on a trip.)