k-step ahead predictions determined by simulation of the
％ one-step ahead neural network predictor. For NNARMAX
％ models the residuals are set to zero when calculating the
％ predictions. The predictions are compared to the observed output.
％
//使用gray code的解法
#include <iostream>
#include <cmath>
using namespace std
#define ZERO 0
#define ONE 1
#define ODD 1
#define EVEN 0
#define RIGHT 1
#define LEFT 0
#define MAX 10
We obtained the energy transport velocity distribution for a three dimensional ideal cloak
explicitly. Near the operation frequency, the energy transport velocity has rather peculiar
distribution. The velocity along a line joining the origin of the cloak is a constant, while
the velocity approaches zero at the inner boundary of the cloak. A ray pointing right into
the origin of the cloak will experience abrupt changes of velocities when it impinges on the
inner surface of the cloak. This peculiar distribution causes long time delays for beams
passing through the ideal cloak within a geometric optics description.
SQL中的單記錄函數
1.ASCII
返回與指定的字符對應的十進制數
SQL> select ascii( A ) A,ascii( a ) a,ascii( 0 ) zero,ascii( ) space from dual
A A ZERO SPACE
--------- --------- --------- ---------
65 97 48 32
2.CHR
給出整數,返回對應的字符
SQL> select chr(54740) zhao,chr(65) chr65 from dual
ZH C
-- -
OTSU Gray-level image segmentation using Otsu s method.
Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes
by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection
Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern.
9:62-66 1979). Thresholds are computed to maximize a separability
criterion of the resultant classes in gray levels.
OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the
corresponding Iseg is therefore a binary image. The pixel values for
Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ...
[Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability
criterion within the range [0 1]. Zero is obtained only with images
having less than n gray level, whereas one (optimal value) is obtained
only with n-valued images.
