Computes BER v EbNo curve for convolutional encoding / soft decision
Viterbi decoding scheme assuming BPSK.
Brute force Monte Carlo approach is unsatisfactory (takes too long)
to find the BER curve.
The computation uses a quasi-analytic (QA) technique that relies on the
estimation (approximate one) of the information-bits Weight Enumerating
Function (WEF) using
A simulation of the convolutional encoder. Once the WEF is estimated, the analytic formula for the BER is used.
In just 24 lessons of one hour or less, you will be able to build dynamic Web sites using JavaServer Pages. Using a straightforward, step-by-step approach, each lesson builds on the previous ones, enabling you to learn the essentials of JavaServer Pages 2.0 from the ground up. The book includes Apache Tomcat, Sun s reference implementation of JSP, so you can start developing applications immediately.
Huo Chess by Spiros (Spyridon) Kakos (http://www.kakos.com.gr) is a micro chess program in CLI C++ v8.0 that attempts to be smaller in size than the Commodore-era Microchess. The goal is to create the smallest chess program that exists. More versions are to come in the future.
* $Id: housekeeper.c,v 1.12 2003/08/15 08:45:46 weiym Exp $
*
*little game housekeeper, also named sokoban.
*written by Song Lixin(zjujoe@263.net) 2001.3.6
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.
This diskette (version 1.0) contains demonstration programs and source codes in MATLAB (v.5.2) for algorithms listed in the textbook Global Positioning Systems, Inertial Navigation, and Integration, by M. S. Grewal, Lawrence Weill, and A. P. Andrews, published by John Wiley and Sons, 2000.
Contents: MATLAB (Version 5.2) Demonstrations & Scripts
Chapter4
ephemeris.m calculates the GPS satellite position in ECEF coordinates from its ephemeris parameters.
Chapter5
Klobuchar_fix.m calculates the ionospheric delay.
Chapter6 (shows the quaternion utilities)
Top module name : SHIFTER (File name : SHIFTER.v)
2. Input pins: SHIFT [3:0], IN [15:0], SIGN, RIGHT.
3. Output pins: OUT [15:0].
4. Input signals generated from test pattern are latched in one cycle and are
synchronized at clock rising edge.
5. The SHIFT signal describes the shift number. The shift range is 0 to 15.
6. When the signal RIGHT is high, it shifts input data to right. On the other hand, it
shifts input data to left.
7. When the signal SIGN is high, the input data is a signed number and it shifts with
sign extension. However, the input data is an unsigned number if the signal SIGN
is low.
8. You can only use following gates in Table I and need to include the delay
information (Tplh, Tphl) in your design.
Top module name : SHIFTER (File name : SHIFTER.v)
2. Input pins: SHIFT [3:0], IN [15:0], SIGN, RIGHT.
3. Output pins: OUT [15:0].
4. Input signals generated from test pattern are latched in one cycle and are
synchronized at clock rising edge.
5. The SHIFT signal describes the shift number. The shift range is 0 to 15.
6. When the signal RIGHT is high, it shifts input data to right. On the other hand, it
shifts input data to left.
7. When the signal SIGN is high, the input data is a signed number and it shifts with
sign extension. However, the input data is an unsigned number if the signal SIGN
is low.
8. You can only use following gates in Table I and need to include the delay
information (Tplh, Tphl) in your design.
This GUI can be used by entering nu at the MATLAB command prompt. The user can either select a function (f(x)) of their choice or a statistical distribution probability distribution function to plot over a user defined range. The function s integral can be evaluated over a user defined range by using: The composite trapezium, simpsons and gauss-legendre rules. This is useful for calculating accurate probabilities that one might see in statistical tables.
