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.
Making a cheap 1M SPI Rom Emulator
8 second to copy from parallel to SPI
re-Program STM Serial Flash M25P10 by reading 29010 parallel ROM
Running on standard 8051 32 I/O, a TTL 7407 as bus switch.
Total programming time is about 8 seconds including Erase, Program
WinCC provides the possibility of making the runtime environment dynamic using the Visual Basic Script. It is possible use VBS to program global actions and procedures as well as to dynamize and trigger graphic objects in runtime.
# This resource site for "Grid Computing: Making the Global Infrastructure a Reality " edited by Fran Berman, Geoffrey Fox and Tony Hey. This is a book (over 1000 pages) published March 2003 by Wiley and (for those papers not published elsewhere) a special issue of Concurrency and Computation: Practice and Experience
The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants.
The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks Optimization Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France).
The version 2.0 (February 2005) handles sparse matrices and contains an example
A Web Tutorial on Discrete Features of Bayes Decision Theory
This applet allows for the calculation of the decision boundary given a three dimensional feature vector. Specifically, by stipulating the variables such as the priors, and the conditional likelihoods of each feature with respect to each class, the changing decision boundary will be displayed.
