The Molgedey and Schuster decorrelation algorithm, having square mixing matrix and no noise . Truncation is used for the time shifted matrix, and it is forced to be symmetric . The delay Tau is estimated .
The number of independent components are calculated using Bayes Information Criterion (BIC), with PCA for dimension reduction.
he LPC932 can be used to create a Pulse Width Modulated PWM signal. That s an analog signal, with only 2 discrete levels, for example 0V and 5V and a constant period. The current value of this signal at a certain poiTnt of time is proportional to its Duty Cycle. That s the High Time during one period divided by the period. It can also be calculated as the average value during a particular period. That means after low pass filtering, (e.g. RC circuit) the signal becomes analog, with an actual value controlled by the microcontroller. The PWM functionality enables the LPC932 to control for example the speed of DC motors or the brightness of electric lighting.
Electromagnetic scattering from the trees above a tilted rough ground plane generated by the stochastic Lidenmayer system is studied by Monte Carlo simulations in this paper.The scattering coefficients are calculated in three methods:coherent addition approximation,tree-independent scattering,and independent scattering.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
//
// Histogram Sample
// This sample shows how to use the Sample Grabber filter for video image processing.
// Conceptual background:
// A histogram is just a frequency count of every pixel value in the image.
// There are various well-known mathematical operations that you can perform on an image
// using histograms, to enhance the image, etc.
// Histogram stretch (aka automatic gain control):
// Stretches the image histogram to fill the entire range of values. This is a "point operation,"
// meaning each pixel is scaled to a new value, without examining the neighboring pixels. The
// histogram stretch does not actually require you to calculate the full histogram. The scaling factor
// is calculated from the minimum and maximum values in the image.
he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization.
The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
Because of the poor observability of Inertial Navigation System on stationary base, the estimation
error of the azimuth will converge very slowly in initial alignment by means of Kalmari filtering, and making the
time initial alignment is longer. In this paper, a fast estimation method of the azimuth error is creatively proposed
for the initial alignment of INS on stationary base. On the basis of the the fast convergence of the leveling error,
the azimuth error can be directly calculated. By means of this fast initial alignment method, the time of initial
alignment is reduced greatly. The computer simulation results illustrate the efficiency of the method.
