zemax源碼：
This DLL models a standard ZEMAX surface type, either plane, sphere, or conic
The surface also demonstrates a user-defined apodization filter
The filter is defined as part of the real ray trace, case 5
The filter can be used at the stop to produce x-y Gaussian apodization similar to the Gaussian pupil apodization in ZEMAX but separate in x and y.
The amplitude apodization is of the form EXP[-(Gx(x/R)^2 + Gy(y/R)^2)]
The transmission is of the form EXP[-2(Gx(x/R)^2 + Gy(y/R)^2)]
where
x^2 + y^2 = r^2
R = semi-diameter
The tranmitted intensity is maximum in the center.
T is set to 0 if semi-diameter < 1e-10 to avoid division by zero.
Procedure TSPSA:
begin
init-of-T { T為初始溫度}
S={1，……，n} {S為初始值}
termination=false
while termination=false
begin
for i=1 to L do
begin
generate(S′form S) { 從當前回路S產生新回路S′}
Δt:=f(S′))-f(S) {f(S)為路徑總長}
IF(Δt<0) OR (EXP(-Δt/T)>Random-of-[0,1])
S=S′
IF the-halt-condition-is-TRUE THEN
termination=true
End
T_lower
End
End
Carrier-phase synchronization can be approached in a
general manner by estimating the multiplicative distortion (MD) to which
a baseband received signal in an RF or coherent optical transmission
system is subjected. This paper presents a unified modeling and
estimation of the MD in finite-alphabet digital communication systems. A
simple form of MD is the camer phase exp GO) which has to be estimated
and compensated for in a coherent receiver. A more general case with
fading must, however, allow for amplitude as well as phase variations of
the MD.
We assume a state-variable model for the MD and generally obtain a
nonlinear estimation problem with additional randomly-varying system
parameters such as received signal power, frequency offset, and Doppler
spread. An extended Kalman filter is then applied as a near-optimal
solution to the adaptive MD and channel parameter estimation problem.
Examples are given to show the use and some advantages of this scheme.
Hybrid Monte Carlo sampling.SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo
algorithm to sample from the distribution P ~ EXP(-F), where F is the
first argument to HMC. The Markov chain starts at the point X, and
the function GRADF is the gradient of the `energy function F.
