Description: Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to
％ Baddour s work: \"Autoregressive modeling for fading channel simulation\"-Program to simulate using Rayleigh fading a p-th order autoregressive model AR (p) accord % ing to Baddour's work : "autoregressive modeling for fading channel's imulation " Platform: |
Size: 1088 |
Author:cqj |
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Description: Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to Baddour s work: \"Autoregressive modeling for fading channel simulation\", IEEE Transaction on Wireless Communications, July 2005. Platform: |
Size: 1169 |
Author:Feng |
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Description: Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to
％ Baddour s work: "Autoregressive modeling for fading channel simulation"-Program to simulate using Rayleigh fading a p-th order autoregressive model AR (p) accord % ing to Baddour's work : "autoregressive modeling for fading channel's imulation " Platform: |
Size: 1024 |
Author:cqj |
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Description: 一个用MATLAB写的关于锐利信道仿真的例子，希望对通信开发的同志们有用-a MATLAB write on the sharp channel simulation examples, and I hope to the development of the communications useful comrades Platform: |
Size: 9216 |
Author:左虎 |
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Description: Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to Baddour s work: "Autoregressive modeling for fading channel simulation", IEEE Transaction on Wireless Communications, July 2005.-Program to simulate Rayleigh fading using a p-th order autoregressive model AR (p) according to Baddour s work: Autoregressive modeling for fading channel simulation , IEEE Transaction on Wireless Communications, July 2005. Platform: |
Size: 1024 |
Author:Feng |
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Description: This toolbox is Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands, Vector Autoregressive modeling and Detection. It requires ARMASA toolbox. This toolbox can be downloaded from the Matlab Central file exchange at www.mathworks.com. Platform: |
Size: 88064 |
Author:Morteza Babaee |
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Description: 自动谱分析：可用于丢失/采样/子束光谱分析；矢量自动迭代，可用于建模，故障诊断；-The applications of this additional toolbox are:
- Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands,
- Vector Autoregressive modeling and Detection [uses ARMASA]
- Reduced statistics ARMAsel: A compact yet accurate ARMA model is obtained based on a given power spectrum. Can be used for generation of colored noise with a prescribed spectrum.
- ARfil algorithm: The analysis of missing data/irregularly sampled signals
- Subband analysis: Accurate analysis of a part of the power spectrum
- Detection: Generally applicable test statistic to determine whether two signals have been generated by the same process or not. Based on the Kullback-Leibler index or Likelihood Ratio.
- Analysis of segments of data, possibly of unequal length. Platform: |
Size: 302080 |
Author:王佳 |
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Description: 基于MATLAB的ARIMA模型的源代码。ARIMA模型是自回归滑动平均求和模型，是时间序列分析模型，可以用于时间序列的预测。该代码实现了ARIMA模型的建模和谱分析过程-The ARIMA model based on MATLAB source code. ARIMA model is the sum of autoregressive moving average model is time series analysis models, can be used for time series prediction. The code to achieve the ARIMA models and spectral analysis of the modeling process Platform: |
Size: 2048 |
Author:王二 |
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Description: The early detection of arrhythmia is very important
for the cardiac patients. This done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data.-The early detection of arrhythmia is very important
for the cardiac patients. This is done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data. Platform: |
Size: 273408 |
Author:Amit Majumder |
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Description: 针对户外视频监控存在光照变化这一问题, 提出一个用于准确完成目标检测的实时背景建模框架. 考虑到目标检测
的准确性要求, 建立基于帧间像素亮度差统计直方图的像素亮度扰动阈值. 在此基础上, 针对背景建模的实时性要求, 提出一种基于自回归背景模型的参数快速更新方法. 鉴于不同光照变化的适应性要求, 定义对光照变化不敏感的背景纹理模型. 上述模型统称为自回归{ 纹理(Auto regression and texture, ART) 模型, 该模型适应于户外光照变化. 基于该模型构建像素亮度和纹理置信区间用于目标检测. 实验结果表明, 该框架能适应和实时跟踪户外背景的光照变化, 并对目标进行准确检测.-Considering the appearance of illumination variation in outdoor video surveillance, a real-time background
modeling framework, which is also composed of accurate foreground detection, is established. In view of the accuracy
of foreground detection, a threshold based on the histogram of pixel0s intensity di® erence between neighboring frames is
proposed. On account of the real-time background modeling, a fast estimation approach on parameters of autoregressive
model is presented. Considering the adaptability to variable illumination, a texture background model insensitive to
outdoor illumination variation is designed. Thus, a uniform model named auto regression and texture (ART) is obtained.
According to the established con¯ dence intervals with perturbation of pixel s intensity and its local texture, foreground in
scenes with di® erent illumination variations is successfully detected. The experimental results indicate that the framework
is adaptive to and can exactly Platform: |
Size: 3717120 |
Author: |
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Description: 依据《Autoregressive modeling for fading channel simulation》一文，基于P阶AR模型仿真瑞利衰落-Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to Baddour s work: Autoregressive modeling for fading channel simulation . Platform: |
Size: 1024 |
Author:Potter |
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Description: 一老外写的采用p阶AR模型实现的瑞利衰落matlab仿真程序，参考文献为 Autoregressive modeling for fading channel simulation , IEEE Transaction on Wireless Communications, July 2005. -Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to
Baddour s work: Autoregressive modeling for fading channel simulation , IEEE Transaction on Wireless Communications, July 2005. Platform: |
Size: 1024 |
Author:王新宇 |
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Description: Wind speed modeling using a vector autoregressive process with a time-dependent intercept term Platform: |
Size: 1541120 |
Author:aria ahmadi |
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Description: The code performs the simulation of time series with autoregressive fractionally integrated moving average (ARFIMA) models that generalize ARIMA (autoregressive integrated moving average) and ARMA autoregressive moving average models. ARFIMA models allow non-integer values of the differencing parameter and are useful in modeling time series with long memory. The model is generally represented as RFIMA(p,d,q) model where d is the differencing parameter and p and q are the order of the autoregressive and moving average parts of the model respectively. -The code performs the simulation of time series with autoregressive fractionally integrated moving average (ARFIMA) models that generalize ARIMA (autoregressive integrated moving average) and ARMA autoregressive moving average models. ARFIMA models allow non-integer values of the differencing parameter and are useful in modeling time series with long memory. The model is generally represented as RFIMA(p,d,q) model where d is the differencing parameter and p and q are the order of the autoregressive and moving average parts of the model respectively. Platform: |
Size: 3072 |
Author:ammar_muayad |
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Description: Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to
Baddour s work: Autoregressive modeling for fading channel simulation , IEEE Transaction on Wireless Communications, July 2005.
-Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to
Baddour s work: Autoregressive modeling for fading channel simulation , IEEE Transaction on Wireless Communications, July 2005.
Platform: |
Size: 1024 |
Author:amar |
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Search - autoregressive modeling