New Optimization Tool greatly simplifies solving of optimization tasks
The Optimization Toolbox extends the MATLAB technical computing environment with tools and widely used algorithms for standard and large-scale optimization. These algorithms solve constrained and unconstrained continuous and discrete problems. The toolbox includes functions for linear programming, quadratic programming, nonlinear optimization, nonlinear least squares, nonlinear equations, multi-objective optimization, and binary integer programming.

The New Optimization Tool GUI
With the latest release of Optimization Toolbox 3.1, users can now solve optimization problems easily via the new Optimization Tool (optimtool) graphical user interface. By using the optimtool GUI, users can:

1. Interactively select a suitable solver and define optimization tasks
2. Set and inspect optimization options and their default values
3. Run problems and visualize results
4. Import and export problem definitions, algorithm options, and results between the MATLAB workspace and the Optimization Tool
5. Automatically generate M-code to capture, automate, and recreate your problem
6. Access built-in help

Figure 1: The Optimization Tool GUI

For more information about Optimization Toolbox 3.1 please visits the following URL:
http://www.mathworks.com/products/optimization/

To learn more about the Optimization Tool GUI and how it facilitates solving of optimization tasks, please visit the following URL:
http://www.mathworks.com/access/helpdesk/help/toolbox/optim/ug/bqt8msp.html

The MathWorks adds Support for Signal Integrity Engineering to RF Toolbox
In RF Toolbox 2, The MathWorks have incorporated new functions that enable signal integrity engineers to design, model, analyze, and visualize networks of radio frequency (RF) components commonly found in high-speed digital electronics. Now engineers can better model the impedance differences and reflection effects compromising signal distortion that occur with high-speed semiconductor devices connected to backplanes and printed circuit boards. By combining the new modeling capabilities of RF Toolbox with the power of Model-Based Design in MATLAB® and Simulink®, engineers can significantly reduce the time required to develop I/O circuitry for these devices used throughout the aerospace, defense, communications, and automotive industries.

RF Toolbox from The MathWorks now enables signal integrity engineers to design, model, analyze, and visualize networks of radio frequency components.

RF Toolbox eliminates the need for manually building transmission line models from measured data to test I/O circuit designs. Instead, engineers can quickly model transmission lines as rational functions, a type of behavioral model that is faster, more accurate, and provides greater insight into transmission line characteristics than traditional alternatives like inverse fast Fourier transforms (IFFTs).

The added capabilities in RF Toolbox complement the product's existing support for designing, modeling, and analyzing networks of RF components in wireless communications and radar projects. Applying the same workflow, the new version helps engineers design for signal integrity by letting them use network parameters to specify RF filters, transmission lines, amplifiers, and mixers, either directly or by their physical properties. Network parameters can be generated from within MATLAB or read in from external data. When data describing the response of the backplane is imported into RF Toolbox, it generates a rational function model that can be exported as a test environment either into Simulink or directly into a Verilog-A-compatible circuit simulator from an electronic design automation (EDA) vendor. RF Toolbox also provides Smith® charts and rectangular and polar plots for visualizing data.

For more information about RF Toolbox, please visit the following URL: http://www.mathworks.com/products/rftoolbox/

To learn more about RF Toolbox through the following online recorded webinar, please visit the following URL:
How RF Toolbox can be used for Signal Integrity Engineering

To view the demo, click here.

Debugging MATLAB M-files from the MATLAB Command Prompt
Part 2 - Moving from Workspace to Workspace
This section is intended for anyone writing codes in MATLB who would like to learn how to use MATLAB's tools to find and eliminate bugs within their programs.

What Debugging Tools Are Available at the MATLAB Command Prompt?
This section describes how to make use of functions to debug programs from the MATLAB command prompt. There are altogether seven topics and in the e-newsletter issue, we will look at the second topic. The rest of the topics will be covered in subsequent e-newsletter issues.

Topics
1.	Setting, Clearing, and Querying Breakpoints
2.	Moving from Workspace to Workspace
3.	Executing Your Code Using the DBSTEP Function
4.	Displaying Status Messages Periodically
5.	Using the TRY/CATCH Block to Capture Errors
6.	Using the ERROR Function with the LASTERR and RETHROW Functions
7.	The WHICH Function

Moving from Workspace to Workspace
Once you have entered debug mode (by reaching a line with a breakpoint or a KEYBOARD statement on it or satisfying a trap condition set using DBSTOP) you can use the DBUP and DBDOWN functions to move up and down the calling stack. This allows you to trace an error back to the calling M-file if it involves incorrect data being passed from a calling function's workspace to the workspace of the function where the error occurs.

This is not an issue with script files, as they use the workspace of their caller (or the base workspace if called from the command line or another script.) To display the entire calling stack, use the DBSTACK function.

As an example, the testdbstack.m file, which is used to illustrate the feature, is shown as follow:

In MATLAB command prompt, type:

Then run it by typing:

Once you do this, you will see the following error message and the prompt will change to the debug mode prompt:

The Editor/Debugger will open (if it is not already open) to line 13. Verify that it stopped on line 13 by typing:

This is the calling stack. First MATLAB started testdbstack and executed it up to line 8. Line 8 contains a call to a subfunction, mytestfun, so that subfunction call is added to the stack. The error occurred on line 13 of that function, which the stack reflects.

Check what value was passed to mytestfun by moving up the stack 1 level using

and verifying that it moved up with

The green arrow in the Editor/Debugger will move from line 13 of the file to line 8, to show the position of the last command to be executed. Now, verify that the variable MyInput referred to on line 8 has the expected value (or confirm that the unexpected value of MyInput is the cause of the problem) by typing

at the MATLAB command prompt. Since MyInput is passing an unexpected value to the function mytestfun, you have narrowed down the cause of the error message. Now, check how MyInput was computed and discover the problem - an unexpected minus sign.

For more information on the DBSTACK, DBUP and DBDOWN functions, please visit the following links:

DBSTACK:
http://www.mathworks.com/access/helpdesk/help/techdoc/ref/dbstack.html

DBUP:
http://www.mathworks.com/access/helpdesk/help/techdoc/ref/dbup.html

DBDOWN:
http://www.mathworks.com/access/helpdesk/help/techdoc/ref/dbdown.html

How to input data using the mouse?
Have you intend your executing program to receive user data input, not just through the keyboard but also using the mouse interactively on a plot figure?

MATLAB has a function to enable user input using the mouse.

The ginput function enables you to use the mouse or the arrow keys to select points to plot. ginput returns the coordinates of the pointer's position.
Code below does a basic line plot through the points selected with the mouse:
axis([0 10 0 10]) x=0; y=0; while ~isempty(x)
[x1,y1] = ginput(1); plot([x x1],[y y1],'r.-'); hold on x=x1; y=y1;
end

The example below illustrates the use of ginput with the spline function to create a curve by interpolating in two dimensions:
http://www.mathworks.com/access/helpdesk/help/techdoc/creating_plots/f10-21736.html

Learn and do more with MATLAB & Simulink

'Applying Control Design with MATLAB, SIMULINK, Real-Time Workshop and Stateflow' Training Course

The 4-day course includes hands-on exercises with the Control System Toolbox, Stateflow, Real-time Workshop, and shows how to linearize a model and develop control laws using a variety of design methodologies. Comprehensive control design case studies demonstrate effective techniques for improving efficiency in the use of MATLAB and SIMULINK for modeling and simulation. Participants will be guided on the steps needed to design a controller suitable for hardware implementation and will have the opportunity to design their own control laws to control the magnetic levitation device.

'Applying Finite State Machine Modeling with Stateflow' Training Course

This one-day course provides an understanding of how to use Stateflow to model finite-state machine theory and supervisory logic. The course discusses how to interact with Simulink, and graphically build flow diagrams and functions. Code generation and sending data out of Stateflow are briefly mentioned in this course as well.

In-house or customized training is also available on request, please contact Activemedia at 6742-8173 for details. Other relevant training courses provided by Activemedia include:

- Comprehensive MATLAB
- Comprehensive SIMULINK
- Applying Signal Processing with MATLAB
- Applying Image Processing Techniques with MATLAB and SIMULINK
- Applying Neural Network with MATLAB

DEQX Improves Speaker Sound Quality with MATLAB