The package contains a Reed-Solomon coding and decoding program, derived
partly from Phil Karn/Robert Morelos-Zaragoza "new_rs_erasures.c".
In particular the Berlekamp-Massey algorithm has not been modified. New
features compared to "new_rs_erasures.c" are:
- fully parameterized: code Parameters (n,k,m) can be selected via
command line options.
- decoding optional by Euclid or Belekamp-Massey algorithm
- efficient support of shortened codes
- extensive verbose levels for hardware verification
Accurate estimates of the autocorrelation or power spectrum can be obtained with a parametric model (AR, MA or ARMA). With automatic inference, not only the model Parameters but also the model structure are determined from the data. It is assumed that the ARMASA toolbox is presen
300 km 傳輸線和功率補償仿真
The circuit below represents an equivalent power system feeding a 300 km transmission line. The line is compensated by a shunt inductor at its receiving end. A circuit breaker allows energizing and de-energizing of the line. To simplify matters, only one of the three phases is represented. The Parameters shown in the figure are typical of a 735 kV power system.
This programme is to control DC motor in a certain speed using PWM.
The target speed is "r", it is the speed in 1s.
The sample rate is 0.1s, so the actual speed target is "rc"=r/10.
The "r" and "rc" are integer, and the range of "r" is from 50 to 100.
Keep rc=r/10!!!
The array "speed1" and "speed2" are the control result, in 0.1s and 1s.
The length of "speed1" is 2400, and "speed2" is 240.
The "pw" and "nw" are the Parameters of PWM.
The test will last 4 min.
NIST Net – A Linux-based Network Emulation Tool, It is a raw IP packet filter with many controllable channel Parameters such as packet loss ratio, jitter, bandwidth variation, delay, and network buffer size. To simulate different network environments
For build this project you can use ant (www.apache.org). Before build project rename
file build.properties.pattern in build.properties and set specific for your machine
Parameters, then start build.bat (for Windows platform) or build.sh (for Linux)
in root project folder. After compile process all binary files will be copy into
build forlder.
For additional information please visit web site http://www.m-g.ru/corba
To subscribe on news about MT_DORB send e-mail with subject subscribe to corba@m-g.ru
We recommend that you place the MTDORB_UCUtils.dll ( or MTDORB_UCUtils.so for Linux)
in the Windows\System directory (or Windows\System32 for WinNT and Win2K and
/lib for Linux).
Main MTDORB author: Oleg V. Safonov <safonov@m-g.ru>
Visual tracking is one of the key components for robots
to accomplish a given task in a dynamic environment,
especially when independently moving objects are included.
This paper proposes an extension of Adaptive
Visual Servoing (hereafter, AVS) for unknown moving
object tracking. The method utilizes binocular stereo
vision, but does not need the knowledge of camera Parameters.
Only one assumption is that the system
need stationary references in the both images by which
the system can predict the motion of unknown moving
objects. The basic ideas how we extended the AVS
method such that it can track unknown moving objects
are given and formalized into a new AVS system. The
experimental results with proposed control architecture
are shown and a discussion is given.
Versatile visual servoing without knowledge of true jacobian.pdf cobian matrix estimator.
The Jacobian matrix estimator does not need a priori
knowledge of the kinematic structure and Parameters
of the robot system, such as camera and link Parameters.
The proposed visual servoing control scheme ensures
the convergence of the image-features to desired
trajectories, by using the estimated Jacobian matrix,
which is proved by the Lyapunov stability theory. To
show the effectiveness of the proposed scheme, simulation
and experimental results are presented.
The package includes 3 Matlab-interfaces to the c-code:
1. inference.m
An interface to the full inference package, includes several methods for
approximate inference: Loopy Belief Propagation, Generalized Belief
Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods
(Metropolis, Gibbs, Wolff, Swendsen-Wang).
Use "help inference" from Matlab to see all options for usage.
2. gbp_preprocess.m and gbp.m
These 2 interfaces split Generalized Belief Propagation into the pre-process
stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use
only one of them, or changing some Parameters in between.
Use "help gbp_preprocess" and "help gbp" from Matlab.
3. simulatedAnnealing.m
An interface to the simulated-annealing c-code. This code uses Metropolis
sampling method, the same one used for inference.
Use "help simulatedAnnealing" from Matlab.
A Matlab toolbox for exact linear time-invariant system identification is presented. The emphasis is on the variety of possible ways to implement the mappings from data to Parameters of the data generating system. The considered system representations are input/state/output, difference equation, and left matrix fraction.
KEYWORDS: subspace identification, deterministic subspace identification, balanced model reduction, approximate system identification, MPUM.
