A fast customizable function for locating and measuring the peaks in noisy time-series signals. Adjustable parameters allow discrimination of "real" signal peaks from noise and background. Determines the position, height, and width of each peak by least-squares curve-fitting.
This paper studies the problem of categorical data clustering,
especially for transactional data characterized by high
dimensionality and large volume. Starting from a heuristic method
of increasing the height-to-width ratio of the cluster histogram, we
develop a novel algorithm – CLOPE, which is very fast and
scalable, while being quite effective. We demonstrate the
performance of our algorithm on two real world
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.
圖形顯示技巧,這是其中一段代碼
procedure TForm1.Button1Click(Sender: TObject)
var
newbmp:TBitmap
i,bmpheight,bmpwidth:integer //推拉
begin
newbmp:=TBitmap.Create
newbmp.Width:=image1.Width
newbmp.Height:=image1.Height
bmpheight:=image1.Height
bmpwidth:=image1.Width
for i:=0 to bmpheight do
begin
newbmp.Canvas.CopyRect(Rect(0,bmpheight-i,bmpwidth,bmpheight),image1.Canvas,Rect(0,0,bmpwidth,i))
form1.Canvas.Draw(120,100,newbmp)
end
newbmp.free
end
A new cable fault location method based on
wavelet reconstruction is proposed. In this method the
difference between the currents of faulty phase and sound
phase under the high voltage pulse excitation is used as the
measured signal and is decomposed in multi-scale by wavelet
transform, then reconstructed in single scale. Comparing with
traditional fault location method by travelling wave, the
presented method will not be interfered by the reflected wave
from the branch joint of cables or from other positions where
the impedances are not matched and not be influenced by fault
types, otherwise, the reflected waves can be recognized even
the faulty position is near to the measuring terminal, at the
same time, the influence of the wave speed uncertainty can be
reduced. The correctness of the proposed method is proved by
simulation results.
Make a graph from database record and either send it to a printer
directly selecting many print options or copy it in any file on disc.
This project also gives you print preview option for A4 size paper and
you can set your graph anywhere on the page.Also if you want to change
graph s height or width you can change.Zooming effect is also given
using hsrollbar.
