Description: DVB-T（2K模式）同步，仿真最大似然相关（MLE）算法，在符号开始位置出现谱峰-DVB-T (2K mode) synchronization, simulation Maximum Likelihood correlation (MLE) algorithm. Symbol began in the peaks of position Platform: |
Size: 1522 |
Author:陈栩秋 |
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Description: 最大似然法德matlab仿真程序，用在ofdm系统中很好！！！与大家一起享用-Maximum Likelihood France and Germany Matlab simulation program used in ofdm system good! ! ! Together with everyone to enjoy! Platform: |
Size: 1591 |
Author:崔世耀 |
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Description: 提供实现了（2，1，7）卷积码的维特比译码的源程序，采用了最大似然算法，介绍了软判决维特比译码算法过程的三个步骤：初始化、度量更新和回溯译码。-for achieving a (2,1,7) Convolutional Codes Viterbi decoding of the source, using the maximum - likelihood algorithm, introduced a soft-decision Viterbi decoding algorithm of the three steps : initialization, Metric update and backtracking decoding. Platform: |
Size: 1261 |
Author:王雪松 |
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Description: A one-dimensional calibration object consists of three or more collinear points with known relative positions.
It is generally believed that a camera can be calibrated only when a 1D calibration object is in planar motion or rotates
around a ¯ xed point. In this paper, it is proved that when a multi-camera is observing a 1D object undergoing general
rigid motions synchronously, the camera set can be linearly calibrated. A linear algorithm for the camera set calibration
is proposed，and then the linear estimation is further re¯ ned using the maximum likelihood criteria. The simulated and
real image experiments show that the proposed algorithm is valid and robust.-A one-dimensional calibration object con sists of three or more points with Conic kno wn relative positions. It is generally believe d that a camera can be calibrated only when a 1D ca libration object is in planar motion or rotates around a Platform: |
Size: 2402644 |
Author:王峰 |
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Description: LDPC码译码相关文献
Bounds on the maximum likelihood decoding error probability of low density parity check codes Platform: |
Size: 99273 |
Author:xzm |
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Description: Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique. Platform: |
Size: 134537 |
Author:zhang |
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Description: A stack-based sequential depth-first decoder that returns Maximum-Likelihood solutions to spherical LAST coded MIMO system-type problems Platform: |
Size: 21435 |
Author:xzl |
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Description: * acousticfeatures.m: Matlab script to generate training and testing files from event timeseries.
* afm_mlpatterngen.m: Matlab script to extract feature information from acoustic event timeseries.
* extractevents.m: Matlab script to extract event timeseries using the complete run timeseries and the ground truth/label information.
* extractfeatures.m: Matlab script to extract feature information from all acoustic and seismic event timeseries for a given run and set of nodes.
* sfm_mlpatterngen.m: Matlab script to extract feature information from esmic event timeseries.
* ml_train1.m: Matlab script implementation of the Maximum Likelihood Training Module.
?ml_test1.m: Matlab script implementation of the Maximum Likelihood Testing Module.
?knn.m: Matlab script implementation of the k-Nearest Neighbor Classifier Module. Platform: |
Size: 10081 |
Author:陈延军 |
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Description: he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization.
The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
Platform: |
Size: 7730 |
Author:薛耀斌 |
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Description: matlab在系统辨识中的应用此处为递推的极大似然法应用的源代码及运行后结果（包括图像）-Matlab system identification in the application of recursive here Maximum Likelihood of the application's source code and running after the results (including images) Platform: |
Size: 47198 |
Author:于瑞 |
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Description: ICA算法The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.-ICA algorithm:The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction. Platform: |
Size: 563873 |
Author:陈互 |
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Description: Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motion-based tracking framework which makes no such assumptions. Object is represented by a set of local invariant features, whose motions are observed by a feature correspon-
dence process. A generative model is proposed to depict
the relationship between local feature motions and object
global motion, whose parameters are learned efciently by
an on-line EM algorithm. And the object global motion is estimated in term of maximum likelihood of observations.Then an updating mechanism is employed to adapt object representation. Experiments show that our framework is
exible and robust in dealing with appearance changes,background clutter, illumination changes and occlusion Platform: |
Size: 424705 |
Author:chenjieke |
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Description: Bradley A. Hanson
Date: October 28, 1998
Revised: September 27, 2000
Summary: These are notes to accompany a lecture I gave in a seminar at the University of Iowa taught by Mike Kolen. A detailed description is given of using the EM algorithm to compute maximum likelihood parameter estimates in IRT models for dichotomous items. An example of applying the procedures described is given using a generalized Guttman scale model. Brief descriptions of how to use the EM algorithm to compute Bayes modal parameter estimates and maximum likelihood parameter estimates for polytomous IRT models are given in the final section.
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Size: 16183136 |
Author:sherry.zxling@gmail.com |
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Description: matlab在系统辨识中的应用此处为递推的极大似然法应用的源代码及运行后结果（包括图像）-Matlab system identification in the application of recursive here Maximum Likelihood of the application's source code and running after the results (including images) Platform: |
Size: 47104 |
Author:于瑞 |
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Search - maximum likelihood