this m file can Find a (near) optimal solution to the Traveling Salesman Problem (TSP) by setting up a Genetic Algorithm (GA) to search for the shortest path (least distance needed to travel to each city exactly once)
Notes:
1. Input error checking included
2. Inputs can be specified in any order, so long as the Parameter pairs are specified as a Parameter , value
The source code for this package is located in src/gov/nist/sip/proxy. The proxy
is a pure JAIN-SIP application: it does not need proprietary nist-sip
classes in addition of those defined in JAIN-SIP 1.1, you can substitute
the NIST-SIP stack by another JAIN-SIP-1.1 compliant stack and it should
interoperate.
he proxy can act as presence server and be able to process NOTIFY and
SUBSCRIBE requests. If this Parameter is disabled, the proxy will simply
forward those kind of requests following the appropriate routing decision.
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.
The equation is written as a system of two first order ODEs. These are evaluated for different values of the Parameter Mu. For faster integration, we choose an appropriate solver based on the value of the Parameter Mu.
This R2.9 revision of the CLID detector provides the TYPE 1 (on-hook, between first and second ring, or
before first ring) signal detection and returns the message raw byte data without parsing of particular fields
such as Message Type, Parameter(s) Type(s), etc. The decoding of the message meaning should be performed by the user application.主叫號碼識別CID算法for TI DSP
This little Program allows you to send commands to the CardReader (CT-BCS) or to the Card itself.
First you will be ask to what Port the Reader is connected (0=COM1, 1=COM2).
Then the Class-Byte (CLA), Instruction-Byte (INS), Parameter 1 (P1), Parameter 2 (P2).
If wou don愒 want to send Parameter 3 (P3) press Ctrl-d (^d).
If you enter a number then you have to the Bytes of the Datafield.
After the last Byte of the Datafield is entered the whole APDU is send the replay is displayed. After that you can send the next APDU.
runs Kalman-Bucy filter over observations matrix Z
for 1-step prediction onto matrix X (X can = Z)
with model order p
V = initial covariance of observation sequence noise
returns model Parameter estimation sequence A,
sequence of predicted outcomes y_pred
and error matrix Ey (reshaped) for y and Ea for a
along with inovation prob P = P(y_t | D_t-1) = evidence
Kalman filter toolbox written by Kevin Murphy, 1998.
See http://www.ai.mit.edu/~murphyk/Software/kalman.html for details.
Installation
------------
1. Install KPMtools from http://www.ai.mit.edu/~murphyk/Software/KPMtools.html
3. Assuming you installed all these files in your matlab directory, In Matlab type
addpath matlab/KPMtools
addpath matlab/Kalman
Demos
-----
See tracking_demo.m for a demo of 2D tracking.
See learning_demo.m for a demo of Parameter estimation using EM.
較早版本的kalman濾波matlab源碼，適合研讀。
