By building a nonlinear function relationship between an d the error signal，this paper presents a no—
vel variable step size LMS(Least Mean Square)adaptive filtering algorithm．
SOUNDSC(Y,...) is the same as SOUND(Y,...) except the data is
scaled so that the sound is played as loud as possible without
clipping. The Mean of the data is removed.
Spikes can be taken as absolute quantities of measuring values which are large than approximately four (expressed as variable [Times_SD] in the program)times of the standard deviation of the time series, and can be removed by repeating 3 times with each time series. When a measuring value with the deviation from the Mean larger than four times of the standard deviation, the variable can be taken as NO_VALUE, and the number of spikes is saved into the variable [SpikeNum].
If the variable [Times_SD] is taken as four, many records will be removed, so the variable [Times_SD] can be taken as larger, for example eight.
圖像處理的關于Snakes : Active Contour Models算法和水平集以及GVF的幾篇文章，文章列表為：
[1]Snakes Active Contour Models.pdf
[2]Multiscale Active Contours.pdf
[3]Snakes, shapes, and gradient vector flow.pdf
[4]Motion of level sets by Mean curvature I.pdf
[5]Spectral Stability of Local Deformations Spectral Stability of Local Deformations.pdf
[6]An active contour model for object tracking using the previous contour.pdf
[7]Volumetric Segmentation of Brain Images Using Parallel Genetic AlgorithmsI.pdf
[8]Segmentation in echocardiographic sequences using shape-based snake model.pdf
[9]Active Contours Without Edges.pdf
學習圖像處理的人必看的幾篇文章
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the Mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
本程序實做MLP(Multi-layer perceptron)算法，使用者可以自行設定訓練數據集與測試數據集，將訓練數據集加載，在2、3維下可以顯示其分布狀態，并分別設定鍵節值、學習率、迭代次數來訓練其類神經網絡，最后可觀看辨識率與RMSE（Root Mean squared error）來判別訓練是否可以停止。
The Fuzzy Clustering and Data Analysis Toolbox is a collection of Matlab
functions. Its propose is to divide a given data set into subsets (called
clusters), hard and fuzzy partitioning Mean, that these transitions between
the subsets are crisp or gradual.
KMeanS Trains a k Means cluster model.CENTRES = KMeanS(CENTRES, DATA, OPTIONS) uses the batch K-Means
algorithm to set the centres of a cluster model. The matrix DATA
represents the data which is being clustered, with each row
corresponding to a vector. The sum of squares error function is used.
The point at which a local minimum is achieved is returned as
CENTRES.
