Generate the digital AWGN signal n[k] (sampled n(t)) by generating zero mean
Gaussian random Variables independently (separately) for each k MATLAB function random.
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.
Deployment of Server
1.install Tomcat(superior than j2sdk1.4.2 and Tomcat 5.0),and setup the envionment Variables of JAVA_HOME
2.decompress file assignment2.war, and copy the directory to the directory called webapps in localhost machine.
3.start Tomcat
4.access to the application via http://<hostname>:8080/assignment2
IDE開發環境，可以掛載SDCC
The BASIC IDE is a new, RAD (Rapid Application Development) IDE (Integrated Development Environment) for the RapidQ programming language. The IDE currently has rich project options, a form designer (similar to Delphi s), and code editor. The BASIC IDE is being coded in Borland® Delphi® . We are currently using Delphi 6, but you should be able to use Delphi 3 or later (Delphi 7 included).
Some of the BASIC IDEs features include:
Form Designer with support for all of RapidQ s components Flexible Code Editor Project Management Written in OO (Object Oriented) Delphi. Some future items that we are working on are:
Code Tip Code Completion CVS Integration Code Editor Macros Point-and-Click access to subroutines, functions, and Variables
The main features of the considered identification problem are that there is no an a priori separation of the Variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
This text shows how to analyze programs without its source code, using a debugger and a disassembler, and covers hacking methods including virtual functions, local and global Variables, branching, loops, objects and their hierarchy, and more.
PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those Variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative Variables is the reliability of the regression coefficients: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a matrix of random Variables.The predictive power of PLS models built on the retained Variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
getip1.cpp
//
// This program reports the IP address for each adapter in your machine.
// To compile from command-line type:
//
// cl getip1.cpp wsock32.lib
//
// Make sure your INCLUDE and LIB environment Variables are set up properly
// you can run vcvars32.bat
The BNL toolbox is a set of Matlab functions for defining and estimating the
parameters of a Bayesian network for discrete Variables in which the conditional
probability tables are specified by logistic regression models. Logistic regression can be
used to incorporate restrictions on the conditional probabilities and to account for the
effect of covariates. Nominal Variables are modeled with multinomial logistic regression,
whereas the category probabilities of ordered Variables are modeled through a cumulative
or adjacent-categories response function. Variables can be observed, partially observed,
or hidden.
