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Description: Routine marburg: To estimate the AR parameters by Burg algorithm.
Input Parameters:
n : Number of data samples
ip : Order of autoregressive process
x : Array of complex data samples x(0) through x(n-1)
Output Parameters:
ep : Real variable representing driving noise variance
a : Array of complex AR parameters a(0) to a(ip)
ierror=0 : No error
=1 : ep<=0 .
ef : complex work array. ef[0] to ef[n-1]
eb : complex work array. eb[0] to eb[n-1]
in chapter 12-Routine marburg: To estimate the AR parameters by Burg algorithm. Input Parameters: n: Number of data samples ip: Order of autoregressive process x: Array of complex data samples x (0) through x (n-1) Output Parameters: ep: Real variable representing driving noise variance a: Array of complex AR parameters a (0) to a (ip) ierror = 0: No error = 1: ep <= 0. ef: complex work array. ef [0] to ef [n-1] eb: complex work array. eb [0] to eb [n-1] in chapter 12
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Size: 1024 |
Author: king_key |
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Description: 对偶复树小波以及去噪，
包含1维二维三维的程序实现-Dual tree complex wavelet and the noise, including the one-dimensional two-dimensional three-dimensional program
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Size: 1821696 |
Author: li |
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Description: 数字信号处理作业——小波变换的一个应用：
对一个图像加噪后再进行小波去噪、复小波去噪操作。-Digital signal processing operations- An application of wavelet transform: for an image noise after wavelet denoising, complex wavelet denoising operation.
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Size: 171008 |
Author: yoyo |
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Description: 包括0均值和给定方差的高斯白噪声生成函数，以及复高斯白噪声生成函数-Including the 0 mean and given variance Gaussian white noise generating function, as well as complex Gaussian white noise generating function
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Size: 1024 |
Author: 秦丽 |
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Description: ofdm信道特性
Channel transmission simulator
Channel transmission simulator
％
％ inputs:
％ sig2 - noise variance
％ Mt - number of Tx antennas
％ Mr - number of Rx antennas
％ x - vector of complex input symbols (for MIMO, this is a matrix, where each column
％ is the value of the antenna outputs at a single time instance)
％ H - frequency selective channel - represented in block-Toeplitz form for MIMO transmission
％ N - number of symbols transmitted in OFDM frame
％
％ outputs:
％ y - vector of channel outputs (matrix for MIMO again, just like x matrix)
％ create noise vector sequence (each row is a different antenna, each column is a
％ different time index) note: noise is spatially and temporally white-channel characteristics of OFDM Channel transmission simulatorChannel transmission simulator inputs: sig2- noise variance Mt- number of Tx antennas Mr- number of Rx antennas x- vector of complex input symbols (for MIMO, this is a matrix, where each column is the value of the antenna outputs at a single time instance) H- frequency selective channel- represented in block-Toeplitz form for MIMO transmission N- number of symbols transmitted in OFDM frame outputs: y- vector of channel outputs (matrix for MIMO again, just like x matrix) create noise vector sequence (each row is a different antenna, each column is a different time index) note: noise is spatially and temporally white
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Size: 1024 |
Author: sunxu |
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Description: 利用最小方差方法估计复正弦加白噪声得平稳信号的功率谱-Minimum variance estimation using complex sinusoidal plus a stationary white noise signal power spectrum
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Size: 1024 |
Author: yutan |
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Description: 对一个图像加噪后再进行小波去噪、复小波去噪操作。
测试数据必须是256色（8位）、灰度、正方形的raw格式图像（可由PhotoShop转换）。
为了显示时的美观，程序只显示了256像素宽度的图像，但输出时是完整的图像。
目录下附了两幅标准测试图像lena和camer-Noise of an image after wavelet denoising, wavelet denoising complex operation. Test data must be 256 colors (8 bit), gray, square format of the raw images (can be converted PhotoShop). In order to show at the time of appearance, the program shows only the image width of 256 pixels, but the output is a complete image. Directory under the laws of the two standard test images lena and camer
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Size: 171008 |
Author: 龙晓风 |
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Description: Mpsk simulation (m=2,4,8) from generation of bit stream through MPSK modulation and the creation of AWGN complex noise to the demodulation
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Size: 2048 |
Author: dave |
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Description: Add pilot symbols to the input data just before transmission
2. Implement cyclic prefix (CP), use 10 of symbol length. Divide the 64
input data points into 8-bit streams, generate and append the CP based
on each symbol
3. Include complex noise into transmitted signal
Organization of Transmitter end of OFDM System
a. Generate Data
b. Modulate
c. Serial to Parallel
d. Cyclic Prefix (CP)
e. Add Pilot points
f. IFFT
g. Parallel to Serial
-Add pilot symbols to the input data just before transmission
2. Implement cyclic prefix (CP), use 10 of symbol length. Divide the 64
input data points into 8-bit streams, generate and append the CP based
on each symbol
3. Include complex noise into transmitted signal
Organization of Transmitter end of OFDM System
a. Generate Data
b. Modulate
c. Serial to Parallel
d. Cyclic Prefix (CP)
e. Add Pilot points
f. IFFT
g. Parallel to Serial
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Size: 10240 |
Author: ibrahim |
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Description: 独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE.
Jade performs `Source Separation in the following sense:
X is an n x T data matrix assumed modelled as X = A S + N where
o A is an unknown n x m matrix with full rank.
o S is a m x T data matrix (source signals) with the properties
a) for each t, the components of S(:,t) are statistically
independent
b) for each p, the S(p,:) is the realization of a zero-mean
`source signal .
c) At most one of these processes has a vanishing 4th-order
cumulant.
o N is a n x T matrix. It is a realization of a spatially white
Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance
sigma. This is probably better than no modeling at a- Source separation of complex signals with JADE.
Jade performs `Source Separation in the following sense:
X is an n x T data matrix assumed modelled as X = A S+ N where
o A is an unknown n x m matrix with full rank.
o S is a m x T data matrix (source signals) with the properties
a) for each t, the components of S(:,t) are statistically
independent
b) for each p, the S(p,:) is the realization of a zero-mean
`source signal .
c) At most one of these processes has a vanishing 4th-order
cumulant.
o N is a n x T matrix. It is a realization of a spatially white
Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance
sigma. This is probably better than no modeling at all...
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Size: 7168 |
Author: 王庆香 |
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Description: qpsk+srrc+complex noise
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Size: 1024 |
Author: ramu |
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Description: 在复杂的电子系统中，影响信号接收的很多噪声为非高斯噪声，这些噪声是没有预料到的，以致破坏了接收机的功能，研究表明：这些噪声可以看成双模噪声，双模噪声从整体上讲属于非高斯噪声。-In complex electronic systems, many of the signal received by a non-Gaussian noise, the noise is not anticipated, resulting in damage to the receiver function, research shows that: the noise can be regarded as dual-mode noise, dual-mode noise As a whole is non-Gaussian noise.
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Size: 2048 |
Author: 廖畅 |
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Description: 对偶复树小波以及去噪， 包含1维二维三维的程序实现-Dual tree complex wavelet and the noise, including the one-dimensional two-dimensional three-dimensional program -The dual tree complex wavelet and denoising, including 1-dimensional 2D and 3D programs to achieve-Dual tree complex wavelet and the noise, including the one-dimensional two-dimensional three-dimensional program
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Size: 1821696 |
Author: gaoyuan |
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Description: This paper analyses impact of using averaging filtering on quality of SAR images. These are commonly corrupted by
multiplicative complex correlated noise (speckle) and by additive white Gaussian noise. Methods presented in this
paper: multilook algorithm and averaging filters are examined in the context of additive white Gaussian noise reduction
and computational demands. In order to obtain high cross-range resolution of SAR images Map-Drift Autofocus
algorithm is also employed
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Size: 698368 |
Author: yas |
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Description: 等间距线性阵列，阵元数M=8，阵元间距d= λ /2，等幅加权，目标方位φr =0°。产生空间白噪声矢量（复高斯分布）。
设阵列信号矢量元素s(m)=exp[j mψr]，其信号幅度为1，该阵元接收机附加的高斯噪声为n(m)=nmr+jnmi，其中实虚部均为独立同分布N(0, σ2)的高斯随机数，则该通道合成信号x(m)=s(m) + n(m)，其中信噪比为：
SNR = 10 lg[1/(2σ2)] = – 3 – 10 lg(σ2) (dB)
-The spacing of the linear array, and the array element number M = 8, the array element spacing d = λ/2, equal amplitude weighted target azimuth φR = 0. Space generated white noise vector (complex Gaussian distribution). Let array signal vector elements s (m) = exp [j mψr], the signal amplitude, the element receiver additional Gaussian noise n (m) = nmr+jnmi which the real and imaginary parts are independent and identically distributed N ( Gaussian random numbers of 0, σ2), the channel the synthesis signal x (m) = s (m) the PPLS n (m), where SNR is: SNR = 10 lg [1/(2σ2)] =- 3- 10 lg (σ2) (dB)
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Size: 5120 |
Author: 张 |
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Description: 在matlab中进行信道仿真，对噪声进行建模
该程序验证了加噪公式在复杂通信系统（扩频、过采样）中的正确性-The correctness of the channel simulation in matlab noise modeling program verification adding noise formula in complex communication system (Spread Spectrum oversampling)
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Size: 284672 |
Author: chenjie |
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Description: 1SAR图像降噪的双树复小波变换相位保持算法
-1 SAR image noise reduction phase maintain dual tree complex wavelet transform algorithm
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Size: 1496064 |
Author: fangsm |
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Description: This indeed is the core idea of MELP and is based on practical observations
where the prediction-error sequence is a combination of a pulse train with noise.
Thus, the MELP model is much more realistic than the LPC model, where the excitation
is either impulse train or noise.
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Size: 1024 |
Author: ehsan |
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Description: Robust Principal Component Analysis with Complex Noise
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Size: 3811328 |
Author: Tariq Sadad |
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Description: 复杂噪声背景下，微弱振动信号去噪方法之小波包软阀值去噪-Complex noise background, the weak vibration signal denoising method of wavelet package soft threshold denoising
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Size: 67584 |
Author: Liyer |
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