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Description: 文件夹中NPFMain.m为滤波算法主运行程序，CRLBCompute.m为计算CRLB并且画出CRLB、NPF、EKF/IMM-EKF滤波误差(均值和均方差)曲线。-folder NPFMain.m filtering algorithm for the main operating procedures, CRLBCompute.m estimator to calculate and paint estimator, NPF, EKF / IMM - EKF filter error (mean and variance) curve.
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Author: 赵辉 |
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Description: MYW - ARMA 算法的MATLAB代码, 是频谱分析(通常是在高级DSP这门课中会用到的)的常用算法-MYW- ARMA algorithm MATLAB code, Analysis of the spectrum (usually at the senior DSP This class will be used) to the commonly used algorithm
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Author: Frankie |
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Description: 文件夹中NPFMain.m为滤波算法主运行程序，CRLBCompute.m为计算CRLB并且画出CRLB、NPF、EKF/IMM-EKF滤波误差(均值和均方差)曲线。-folder NPFMain.m filtering algorithm for the main operating procedures, CRLBCompute.m estimator to calculate and paint estimator, NPF, EKF/IMM- EKF filter error (mean and variance) curve.
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Size: 52224 |
Author: 赵辉 |
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Description: 代码用于估计关联维数。包括G-P算法(corrint.m)，高斯核关联算法(gka.m) 和Judd算法(judd.m)-Correlation dimension estimation code. Algorithms for estimating the correlation dimension using the grassberger-Proccacia approach (corrint.m), the Gaussian-Kernel algorithm (gka.m) and Judd s estimator (judd.m) are provided.
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Size: 18432 |
Author: 彭跃华 |
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Description: 有用的几个动力学物理量。包括自相关函数(acorr.m)，复杂度(complexity.m)，最近邻点(fnn.m)，局部非线性预测误差(nlpe.m，Shannon复杂度（Shannon.m）,嵌入窗估计(window.m)-Several dynamic invariants and measures that may be useful. Included are algorithms for autocorrelation (acorr.m), complexity (complexity.m), false nearest neighbours (fnn.m), local nonlinear prediction error (nlpe.m), Shannon complexity (shannon.m) and the embedding window estimator (window.m).
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Size: 14336 |
Author: 彭跃华 |
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Description: As integrated circuits are migrated to more advanced technologies, it
has become clear that crosstalk is an important physical
phenomenon that must be taken into account. Crosstalk has
primarily been a concern for ASICs, multi-chip modules, and
custom chips, however, it will soon become a concern in FPGAs. In
this paper, we describe the first published crosstalk-aware router that
targets FPGAs. We show that, in a representative FPGA architecture
implemented in a 0.18μm technology, the average routing delay in
the presence of crosstalk can be reduced by 7.1 compared to a
router with no knowledge of crosstalk. About half of this
improvement is due to a tighter delay estimator, and half is due to an
improved routing algorithm.-As integrated circuits are migrated to more advanced technologies, it
has become clear that crosstalk is an important physical
phenomenon that must be taken into account. Crosstalk has
primarily been a concern for ASICs, multi-chip modules, and
custom chips, however, it will soon become a concern in FPGAs. In
this paper, we describe the first published crosstalk-aware router that
targets FPGAs. We show that, in a representative FPGA architecture
implemented in a 0.18μm technology, the average routing delay in
the presence of crosstalk can be reduced by 7.1 compared to a
router with no knowledge of crosstalk. About half of this
improvement is due to a tighter delay estimator, and half is due to an
improved routing algorithm.
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Size: 199680 |
Author: sia |
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Description: fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML - fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
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Author: resident e |
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Description: Projection Based M-Estimator ,一个基于M
-Estimator估计器的投影程序，能够很好的估计，计算机图像领域的线性、异方差（椭圆和 基础矩阵）和子空间等。- using the base class for linear, heteroscedastic (ellipse and fundamental matrix) and subspace estimation are included in the program.
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Size: 4879360 |
Author: top |
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Description: 递归核最小二乘算法，来至MIT大学的wingate教授，含6个源码，有实例！-dict_init.m- Part of the dictionary implementation used by KRLS algorithm. Can stand alone.
dict.m- Part of the dictionary implementation used by the KRLS algorithm. Can stand alone.
krls_init.m- Kernel recursive least squares initializer.
krls.m- Main KRLS function. Repeatedly called with new data points.
krls_query.m- Query the resulting estimator.
krls_example.m- An example script showing the different parts.
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Author: jiang |
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Description: 本文针对SMM中著名的交互式多模型(IMM)估计器，通过将IMM看作是输人交互和子滤波器申联，分析了具有m个参数的MTP矩阵，给出了六条不依赖于应用环境及子滤波器设计的结论-In this paper, SMM Interacting Multiple Model (IMM) estimator, analyzed by the IMM as input interaction and sub-filters Shenlian MTP matrix with m parameters, six does not depend on the application environment and sub-filter design to the conclusion
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Author: tc |
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Description: This code estimates the intrinsic dimension of a dataset. It
calculates three estimates. The estimator by the authors which was
proposed in
M. Hein, J-Y. Audibert
Intrinsic dimensionality estimation of submanifolds in Euclidean space
Proceedings of the 22nd ICML, 289-296, Eds. L. de Raedt and S. Wrobel, 2005
and two classical estimators: the correlation dimension and the Takens estimator
(see the paper for references).
Two possible implementations are available. The first one is C-Code and the other
one is a mex-file for the usage in MATLAB.
The usage of this code is allowed for scientific purposes. If you use this code
please cite the above paper.
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Size: 18432 |
Author: dadashi |
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Description: 2D2D image registration based on Hausdorff distance and M estimator
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Author: deokman |
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Description: to simulate the water level in the tanks. to verify its stability, controllability and observability.
design kalman filter and define and simulate the Kalman estimator-Kalman filter of a two tank system
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Size: 2048 |
Author: Lu |
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Description: 基于IMCRA单通道噪声估计,基于Omlsa(optimally-modified log-spectral amplitude)语音增强-Isreal Cohen- omlsa : Single Channel OM-LSA with IMCRA noise estimator.
Noise psd estimation & Speech enhencement
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Size: 4096 |
Author: lixiaofei |
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Description: Correlation windows estimator for MAtlab
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Size: 1024 |
Author: mario |
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Description: 基于投影M估计量的稳健回归方法，用于估计计算机视觉立体像对间的Fundamental Matrix。Fundamental Matrix，参考文献：H. Chen, P. Meer, Robust regression with projection based M-estimators. 9th International Conference on Computer Vision (ICCV), Nice, France, October 2003, 878-885.PS：这是作者11年前本科毕设的源代码，时间很久远了，希望还有点参考价值。-VC++ code for estimating Fundamental Matrix using projection based M-estimator (pbM)
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Size: 6087680 |
Author: 梦回钱塘 |
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Description: M估计子，计算机视觉大牛Peter Meer出品。-Generalized Projection based M-estimator
C++ code to find the robust estimate derived without using any user supplied scale. The theory is described in Generalized Projection Based M-stimator.
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Size: 13588480 |
Author: 何儒汉 |
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Description: Capon Bartlet Music
MUSIC estimates the frequency content of a signal or autocorrelation matrix using an eigenspace method. This method assumes that a signal, x(n), consists of p complex exponentials in the presence of Gaussian white noise. Given an M \times M autocorrelation matrix, \mathbf{R}_x, if the eigenvalues are sorted in decreasing order, the eigenvectors corresponding to the p largest eigenvalues (i.e. directions of largest variability) span the signal subspace. The remaining M-p eigenvectors span the orthogonal space, where there is only noise. Note that for M = p + 1, MUSIC is identical to Pisarenko harmonic decomposition. The general idea is to use averaging to improve the performance of the Pisarenko estimator.
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Size: 1024 |
Author: Said |
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Description: MUSIC estimates the frequency content of a signal or autocorrelation matrix using an eigenspace method. This method assumes that a signal, x(n), consists of p complex exponentials in the presence of Gaussian white noise. Given an M \times M autocorrelation matrix, \mathbf{R}_x, if the eigenvalues are sorted in decreasing order, the eigenvectors corresponding to the p largest eigenvalues (i.e. directions of largest variability) span the signal subspace. The remaining M-p eigenvectors span the orthogonal space, where there is only noise. Note that for M = p + 1, MUSIC is identical to Pisarenko harmonic decomposition. The general idea is to use averaging to improve the performance of the Pisarenko estimator.
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Size: 1024 |
Author: Said |
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