Description: turbo码系统的仿真，包括编码、交织、不同译码、穿孔等各个部分的程序。仿真结果在图中表现。译码算法为log-map。-turbo code system simulation, including encoding, interleaving, different decoder, such as perforation of part of the process. Simulation results are shown in Fig. Decoding algorithm for log-map. Platform: |
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Author:赵欣 |
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Description: 该代码不仅实现了编码的仿真，还在多种条件下实现了译码的仿真，包括MAP,LOG-MAP,SOVA下的单双滑动窗口。-The code not only to achieve a coding simulation, is also a wide range of conditions to achieve the decoding of the simulation, including MAP, LOG-MAP, SOVA under single-and double-sliding window. Platform: |
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Author: |
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Description: MFCC (Mel Frequent Cepstral Coefficient) in M-File.
epresentation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency.
MFCCs derived as follows:
1. Take the Fourier transform of (a windowed excerpt of) a signal.
2. Map the powers of the spectrum obtained above onto the mel scale, using triangular overlapping windows.
3. Take the logs of the powers at each of the mel frequencies.
4. Take the discrete cosine transform of the list of mel log powers, as if it were a signal.
5. The MFCCs are the amplitudes of the resulting spectrum.
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Author:Mitha |
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Description: 当前论文主要考虑的是非信号依赖的高斯噪声下的图像恢复，本程序实现了泊松噪声下的图像恢复，泊松噪声为信号依赖噪声，能够更加有效逼近实际成像系统噪声。- This is the code that was used in the papers "A Nonnnegatively Constrained Convex Programming Method for Image Reconstruction", "Total Variation-Penalized Poisson Likelihood Estimation for Ill-Posed Problems", "Tikhonov Regularized Poisson Likelihood Estimation: Theoretical Justification and a Computational Method", "An Efficient Computational Method for Total Variation with Poisson Negative-Log Likelihood", "An Analysis of Regularization by Diffusion for Ill-Posed Poisson Likelihood Estimation," "An Iterative Method for Edge-Preserving MAP Estimation when Data-Noise is Poisson", and finally, "Regularization Parameter Selection Methods for Ill-Posed Poisson Maximum Likelihood Estimation". See my publications page for more details. The main algorithm is for nonnegatively constrained, regularized Poisson likelihood estimation. At this point you can choose Tikhonov, total variation regularization, and diffusion regularization. A number of other methods are also implemented. Regularizatio Platform: |
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Author:sun |
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Description: LogMAP译码算法,可用于递归卷积编码和非递归卷积编码的译码，代码质量高，供参考-<1> Log MAP decoder for RSC and NSC convolutional codes
<2> Based on Lalit Bahl s original BCJR algorithm and its logarithmic version (Hanzo & Woodard).
<3> Test-bench code is also included. Platform: |
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Author:Leo Tsao |
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Description: 运用Log-Map迭代译码算法实现Turbo码的简化译码，改善了码率，提高译码速度，减小了运算量。-The use of Log-Map iterative decoding algorithm decoding Turbo codes to simplify and improve the bit rate, improve decoding speed and reduce the computational complexity. Platform: |
Size: 118784 |
Author:何天玲 |
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Description: 在Matlab图像处理工具箱中的Phantom函数，可以产生Shepp－Logan的大脑图,利用RADON变换重建图像-In the Matlab Image Processing Toolbox functions in the Phantom can generate Shepp-Logan brain map, the use of RADON transform image reconstruction Platform: |
Size: 1024 |
Author:xinluo |
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Description: BCJR算法是在Turbo码的译码中广泛使用的一种重要算法。程序实现了BCJR的具体译码方法。程序是MAP-LOG的实现算法。-BCJR Algorithm for Turbo decoding is widely used in a key algorithm. The specific procedures implemented BCJR decoding method. MAP-LOG program is the realization algorithm. Platform: |
Size: 1024 |
Author:jack |
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Description: In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.-In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.
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Author:loossii |
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Description: this is a simulation for Log-MAP decoder in channel coding systems-this is a simulation for Log-MAP decoder in channel coding systems... Platform: |
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Author:hossein |
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