function about = about(action)
% This function contains the 'More on' information  for Toolboxes

%   Yama Habibzai
%   Copyright (c) 1984-98 by The MathWorks, Inc. All Rights Reserved
%   $Revision: 1.14 $
%   $Date: 1998/12/31 21:12:32 $
 
info =' ';
if strcmp(action, 'communications');

   info = ...
        {'                                               '
  'The Communication Toolbox is a collection of computation'
  'functions and simulation blocks for research, development,'
  'system design, analysis, and simulation in the'
  'communications area. The Toolbox is designed to be'
  'suitable for both experts and beginners in the communications'
  'field. The Toolbox contains ready-to-use functions and blocks'
  'which you can easily modify to implement your own scheme,'
  'methods, and algorithms. The Communications Toolbox is'
  'designed for use with MATLAB and Simulink.'
  ' '
  'The Communications Toolbox includes:'
  ' '
  '-Simulink Blocks'
  '-MATLAB functions'
  ' '
  'You can use the Toolbox directly from MATLAB workspace.'
  'You can use the Simulink environment to construct a '
  'simulation block diagram for your communications system.'
  'For convenience, the Communications Toolbox provides online'
  'interactive examples that use many of the functions found'
  'in this Toolbox.'
  ' '
  'Available Functions:'
  ' '
  'This Toolbox supports a variety of functions in the'
  'communications area. These functions include:'
  ' '
  '-Data source'
  '-Source coding/decoding'
  '-Error-control coding'
  '-Modulation/demodulation'
  '-Transmission/reception filters'
  '-Transmitting channel'
  '-Multiple Access'
  '-Synchronization'
  '-Utilities'};
 
%-----------------------------------
elseif strcmp(action, 'stateflow');

info = {'Stateflow is a major product addition to the Simulink'
'modeling and simulation environment. A graphical tool for'
'designing complex control and supervisory logic systems,'
'Stateflow gives you the ability to model and simulate the'
'behavior of complex reactive, event-based systems based on'
'finite state machine theory.  This capability adds to Simulink'
'the ability to combine both continuous and event-driven'
'elements of a system in a single, closed-loop simulation,'
'making the combination a unique product offering.'
' '
'Stateflow represents an evolution from finite state machine'
'theory by adding several major improvements, including'
'hierarchy, parallelism, junctions, and history.  These'
'changes enable Stateflow to make practical use of finite'
'state machine theory with realistic application to control'
'systems.  '
' '
'A major benefit of Stateflow is its seamless point-and-click'
'interface to Simulink.  The control behavior that Stateflow'
'models provides an ideal complement to the algorithmic'
'behavior modeled in Simulink. In Simulink, you develop your'
'model of continuous-and discrete-time dynamic systems using'
'its graphical, block diagram environment. Then,  you'
'drag-and-drop the blocks that represent Stateflow diagrams'
'directly into your Simulink model to add event-driven behavior'
'to Simulink simulations.'
' '
'Applications for Stateflow include developing the control'
'logic for electronic and mechanical systems found in'
'automobiles, aircraft, telecommunications systems, computer'
'peripherals, office automation equipment, and medical'
'instrumentation.'
' '
'Stateflow works with the latest versions of Simulink and'
'Real-Time Workshop the automatic code generator'
'for Simulink to offer an integrated environment for'
'modeling, simulating, and prototyping real-time embedded'
'systems applications.'
' '
'Stateflow provides automatic C code generation through the'
'optional Stateflow Coder. C code generated by Stateflow Coder'
'can be used independently or integrated with code from'
'Real-Time Workshop. Thus, Simulink, Stateflow, and Real-Time'
'Workshop are integrated from the design and modeling phase'
'through the code generation stage. The generated code can be'
'executed for rapid prototyping, hardware-in-the-loop'
'testing, or for standalone simulations.'};

%----------------------------------
elseif strcmp(action, 'sfcoder');

 info = ...
    {'Stateflow Coder is a product for use with Stateflow to'
     'generate code for Real-Time Workshop and Stateflow standalone'
     'targets. Stateflow Coder has these additional features:'
     ' '
     '-Generated code is immediately very readable including fully'
     '  commented sections.'
     ' '
     '-All data object are typed which ensures more efficient'
     '  generated code.'
     ' '
     '-Implementation of flow diagram notation is highly optimized.'
     ' '
     '-Function inlining is used for optimizing event broadcasts.'
     ' '
     '-Stateflow Coder takes advantage of any directed event'
     '  broadcasting and thus ensures minimal overhead during that'
     '  event broadcast.'};
 
%-----------------------------------
elseif strcmp(action, 'control');

   info = ...
        {'                                               '
         ' The Control System Toolbox is a collection    '
         ' of functions for the modeling, analysis, and  '
         ' design of automatic control systems.          '
         ' Automatic control appears in an enormous      '
         ' number of areas, and its span grows with each '
         ' year as sensors and computers get cheaper.    '
         '                                               '
         ' MATLAB by itself includes a rich collection of'
         ' functions that are immediately useful to the  '
         ' control engineer, and when you add the        '
         ' Control System Toolbox, you end up with an    '
         ' extremely powerful platform that puts the best'
         ' tools in classical and modern design at your  '
         ' fingertips.                                   '};

%-----------------------------------
elseif strcmp(action, 'dsp');

   info = ...
  {'The DSP Blockset is a collection of block libraries'
  'for use with the Simulink dynamic system simulation'
  'environment. These libraries are designed specifically'
  'for digital signal processing applications, and include'
  'operations such as complex arithmetic, transcendental'
  'and statistical operations, convolution, and Fourier'
  'transforms.'
  ' '
  'Key Features:'
  ' '
  'The DSP Blockset extends the Simulink environment'
  'to make signal processing tasks easier and more'
  'intuitive. Some important features are:'
  ' '
  '-Vector-oriented environment'
  '-Complex vector support'
  '-Statistical functions'
  ' '
  'You access the blockset''s vector-oriented'
  'environment through a Buffer block (either a real'
  'data or complex data version). The Buffer block'
  'transforms a stream of discrete time values into'
  'a Simulink vector. This approach lets you manipulate'
  'sections of signal data as single units, effectively'
  'turning a "serial" frame of data points into a "parallel"'
  ' data buffer, or vector. '
  ' '
  'The buffering operation is the cornerstone of the DSP'
  'Blockset. It provides the means for a variety of signal'
  'processing block operations such as fast Fourier'
  'transforms. '
  ' '
  'The DSP Blockset includes a library of complex vector'
  'support blocks. These blocks create complex vectors and'
  'support complex arithmetic, facilitating frequency-domain'
  'analysis of data. The complex library directly supports'
  'operations like complex conjugation, magnitude, and'
  'phase angle. '
  ' '
  'Use the blockset''s library of statistical function blocks'
  'for fundamental statistical analysis. These blocks calculate'
  'the mean, minimum and maximum value, standard deviation,'
  'and root-mean-square of an input vector. The Statistics'
  'library also includes running blocks that "track" these'
  'values over time.'
  ' '
  'You can use the components of the DSP Blockset in the same'
  'way that you would use any other Simulink blocks, combining'
  'them with blocks from other libraries to create sophisticated'
  'systems. You can also use the Real-Time Workshop product'
  'to generate C code for models containing these blocks.'};
  
  %----------------------------------
elseif strcmp(action, 'powersys');
 
 info = ...
    {'The Power System Blockset provides the ability to '
    'model and simulate electrical circuits and power   '
    'systems within Simulink.                           '
    '                                                   '};

  %-----------------------------------

elseif strcmp(action, 'financial');

   info = ...
  {'MATLAB and the Financial Toolbox provide a complete integrated'
  'computing environment for financial analysis and engineering.'
  'The Financial Toolbox has everything you need to perform mathematical'
  'and statistical analysis of financial data and display the results with'
  'presentation-quality graphics. You can quickly ask, visualize, and'
  'answer complicated questions. '
  ' '
  'In traditional or spreadsheet programming you must deal with all sorts'
  'of housekeeping details: declaring, data typing, sizing, etc. MATLAB'
  'does all that for you. You just write expressions the way you think of'
  'problems. And there''s no need to switch tools, convert files, or'
  'rewrite applications.'};
  
  %-----------------------------------
elseif strcmp(action, 'fixpoint');

   info = ...
  {'The Fixed-Point Blockset for use with MATLAB and Simulink,'
  'consists of basic fixed-point Simulink building blocks,'
  'which are used to design and simulate dynamic systems using'
  'fixed-point arithmetic.'
  ' '
  'With this blockset, you can:'
  ' '
  '-Use fixed-point arithmetic to develop and simulate'
  '  fixed-point Simulink models.'
  ' '
  '-Change the fixed-point data types, while the model is'
  '  simulating, allowing you to see the effects immediately'
  '  of different word sizes, binary point locations, etc.'
  ' '
  '-Generate fixed-point model code ready for execution on'
  '  a floating-point processor. This allows you to emulate the'
  '  effects of fixed-point arithmetic in a floating-point'
  '  rapid prototyping system.'
  ' '
  '-Generate fixed-point model code ready for execution on a'
  '  fixed-point processor. '
  ' '
  '-Modify and/or add new fixed-point blocks. Source code'
  '  is provided for all fixed-point blocks.'
  ' '
  'The purpose of this blockset is to bridge the gap between'
  'designing a dynamic system (e.g., a control system or'
  'digital filter) and implementing it on fixed-point digital'
  'hardware. Those who have tried implementing a control system'
  'on fixed-point digital hardware know how difficult and time'
  'consuming this process can be.'
  ' '
  'This blockset provides tools that aid in development and testing'
  'of fixed-point dynamic systems. You directly design dynamic'
  'system models in Simulink, which are ready for implementation'
  'on fixed-point hardware.'
  ' '
  'The development cycle, using MATLAB, Simulink, the'
  'Fixed-Point Blockset, and the Real-Time Workshop, is generally:'
  ' '
  '1. Model the system (plant or signal source) within Simulink'
  '   using standard floating-point blocks.  Typically, the model '
  '   will contain nonlinear elements.  '
  ' '
  'Modeling the system may be aided by the Real-Time Workshop '
  'in conjunction with hardware (e.g., A/D\022s). For example, '
  'you can use the Real-Time Workshop to generate a frequency '
  'response and gather the results, which can be analyzed in MATLAB. '
  ' '
  '2. Design and simulate a fixed-point dynamic system (e.g., a '
  '   control system or digital filter) in Simulink that meets the '
  '   design performance and other constraints. '
  ' '
  '3. Generate fixed-point C code using the Real-Time Workshop for '
  '   the model and execute it on a rapid prototyping hardware. '
  ' '
  '4. Analyze the results and go back to (1) if needed. '
  ' '
  '5. Now that the design requirements have been met, you can, '
  '   optionally, modify code for production using the model '
  '   and the generated code as a specification. Possible '
  '   optimizations might include removing the extra element of'
  '   the wide vectors connecting the blocks and using assembly'
  '   language where appropriate.'
  ' '
  'In steps 1 and 2, there is a significant amount of freedom to'
  'select different solutions. Generally, the model is fine tuned'
  'based upon feedback from the results of the current implementation'
  '(step 4). There is no one specific modeling approach. For example,'
  'models may be obtained from first principles (e.g., equations of'
  'motion or physics) or a frequency response (sine sweep). There are'
  'many controllers which meet the same frequency-domain and/or'
  'time-domain specifications. In addition for each controller there'
  'are an infinite number of realizations.'
  ' '
  'This blockset helps expedite the design cycle, by allowing you'
  'to simulate the effects of various fixed-point controller/digital'
    'filter structures. After your system meets its required design'
  'constraints, you can verify the real-time execution by using'
  'the Real-Time Workshop in a rapid prototyping environment'};
  
  %-----------------------------------
%%%%% Third party products
elseif strcmp(action, 'connections')

info = ...
{'With the MATLAB environment, we strive to combine the best-of-class'
'tools with an open-system architecture that enables you to customize'
'and extend the environment. More than 100 products and services listed'
'on the MathWorks Web page are a natural result of this philosophy.'
' '
'To learn more about being listed in the partners directory, or to'
'request a copy of the Connections Directory, contact The MathWorks'
'by telephone: (508)647 -7000; fax:(508) 647-7101; or by'
'email: connections@mathworks.com'};   

 %-----------------------------------

%%%% Frequency Domain System Identification
elseif strcmp(action, 'freqid');

   info = ...
{'Modern system identification methods heavily use matrix'
'calculations, usually based on complex numbers.  Therefore,'
'algorithms for system identification can be very effectively'
'implemented in MATLAB. Its interactive environment and the'
'graphics possibilities offer an easy to use and flexible tool'
'for specific applications and for further development of'
'the implemented methods. This toolbox is a collection of'
'frequency domain system identification procedures, covering'
'the whole identification process from excitation signal'
'design, through data preprocessing, parameter estimation,'
'graphics presentation of the results, to model verification.'
' '
'Identification means determining models of physical systems from'
'noisy measured data. Since the modeling of nature is the basis'
'of our understanding of the world, identification methods have'
'applications in virtually every field of sciences, especially'
'technical ones.'
' '
'One of the most often used models for dynamic physical systems is '
'an ordinary linear differential equation with constant coefficients.'
'This model appears in the same mathematical form in very different'
'fields. Thus, the common properties of these equations, and the'
'measurement and estimation procedures of their coefficients can'
'be treated independently of the dimensions of the physical'
' quantities. This is done in the frame of linear system theory.'
' '
'Linear dynamic systems have two equivalent descriptions: in the time '
'domain (differential equations), and in the frequency domain (transfer '
'functions in the fdomain or in the sdomain). Also the discrete-time '
'descriptions exhibit this duality: difference equations are equivalent '
'to the z\026domain transfer functions. '
' '
'Time domain and frequency domain have different advantages and '
'disadvantages, they are in several respects complementary to each '
'other. Engineers often prefer frequency domain descriptions, because '
'of the following reasons: '
' '
'-While the solution of a differential equation needs convolution in '
'  the time domain, this convolution is substituted by simple '
'  multiplication in the frequency domain. This often makes it '
'  possible to explain system behavior in a visual way. '
' '
'-It is often possible to decompose signals/noises into different '
'  frequency bands. Signal-to-noise ratios can often be improved '
'  in the frequency domain by choosing appropriate bands, and an '
'  occasional dc offset can also be easily removed. Moreover, the '
'  energy (power) of periodic signals is concentrated to '
'  discrete points in the frequency domain. Thus, frequency domain '
'  is very selective with respect to periodic components. '
' '
'-By selecting the Fourier coefficients of the appropriate '
'  frequency band only, significant data reduction can be achieved. '
' '
'-It is often easier to calculate accurately a good model of a '
'  (continuous-domain) physical system in the sdomain, using a digital '
'  computer, than in the time domain. Continuous-time linear systems '
'  can be modeled without systematic errors by difference equations, '
'  if the excitation is piecewise constant (Ljung, 1987). If this '
'  assumption is not met, modeling errors are introduced, resulting '
'  in a limited accuracy of the results. '
' '
'-The attainable dynamic range is usually significantly larger in '
'  the frequency domain than in the time domain. '
' '
'-Slight nonlinearities are easier detected and measured by frequency '
'  domain methods. '
' '
'-FFT is a very powerful tool for fast time domain to frequency domain '
'  conversion, and thus, e. g., the evaluation of a correlation function '
'  is much quicker via the frequency domain, than directly in the time '
'  domain. '
' '
'On the other hand, time domain has also important advantages: '
' '
'-It is very natural to deal with time domain signals (although '
'  after some practice, even the two-sided frequency axis becomes very '
'  visual, too). '
' '
'-Recursive methods often provide on-line calculation possibilities. '
' '
'-Time varying systems are easier modeled in the time domain. '
' '
'-The transient behavior of systems can be directly measured in '
'  the time domain. '
' '
'-The transient behavior of systems can be directly measured in '
'  the time domain. '
' '
'-Time domain digital methods are not very sensitive to the type of '
'  the signal, while frequency domain methods suffer from leakage effects '
'  when non-periodic signals are processed. '
' '
'-Certain nonlinearities (clipping, slew rate etc.) are easier detected '
'  in the time domain. '
' '
'Although the two descriptions are basically equivalent to each'
'other, the formulation of the identification problem leads to very'
'different methods in the two domains. Thus, time domain identification'
'methods and frequency domain identification methods form two distinct'
'groups. '
' '
'Time domain methods are covered by several books, and a comprehensive'
'System Identification Toolbox is available for MATLAB. However,'
'frequency domain parametric methods are usually not treated, and these'
'are not incorporated in the System Identification Toolbox. '
' '
'This Frequency Domain System Identification Toolbox has been'
'written to fill this gap. It is based mostly on the book of Schoukens'
'and Pintelon (1991), and covers the whole identification procedure'
'from excitation signal design through data preprocessing and system'
'parameter estimation to model validation. It is also possible to'
'convert identified parameters to the System Identification Toolbox'
'and vice versa. '
' '
'The Tutorial of this manual contains the essentials for the use'
'of these methods, thus the toolbox can be used in its own right.'
'However, for a more profound understanding of the methods, the book'
'of Schoukens and Pintelon is highly recommended. '
' '
'MATLAB proved to be an ideal frame for all these methods, because'
'most algorithms are based on complex vector/matrix calculations'
'and array manipulations, and since interactive graphical checking'
'of the results is often essential. MATLAB also provides an'
'easy-to-use environment for the embedding of the prepared routines'
'into a dedicated program, written for use in a given field.'
'Thus, the use of the toolbox may be twofold: it can be used as'
'a tool for the design and evaluation of experiments, and also'
'as a frame to check ideas before writing a lengthy special-purpose'
'measurement and data processing program.'
' '
'The demonstrations accompanying the toolbox are not merely'
'illustrations to the use and the power of the functions, but also'
'work with the measured data used in the examples of Schoukens'
'and Pintelon.  Thus, the toolbox is an ideal supplement to the'
'book.'};
 
%-----------------------------------
elseif strcmp(action, 'fuzzy');

   info = ...
{'Fuzzy logic is all about the relative importance of precision:'
'How important is it to be exactly right when a rough answer'
'will do? '
' '
'The Fuzzy Logic Toolbox for use with MATLAB is a tool '
'for solving problems with fuzzy logic. Fuzzy logic is a '
'fascinating area of research because it does a good job of'
'trading off between significance and precision\027something'
'that humans have been managing for a very long time. '
' '
'Fuzzy logic sometimes appears exotic or intimidating to those '
'unfamiliar with it, but once you become acquainted with it, it '
'seems almost surprising that no one attempted it sooner. In '
'this sense fuzzy logic is both old and new because, although '
'the modern and methodical science of fuzzy logic is still young, '
'the concepts of fuzzy logic reach right down to our bones. '
' '
'What Is Fuzzy Logic? '
' '
'Fuzzy logic is a convenient way to map an input space to '
'an output space. This is the starting point for everything '
'else, and the great emphasis here is on the word '
'\023convenient.\024  '
' '
'What do I mean by mapping input space to output space? '
'Here are a few examples: You tell me how good your service '
'was at a restaurant, and I\022ll tell you what the tip '
'should be. You tell me how hot you want the water, and '
'I\022ll adjust the faucet valve to the right setting. You '
'tell me how far away the subject of your photograph is, and '
'I\022ll focus the lens for you. You tell me how fast '
'the car is going and how hard the motor is working, '
'and I\022ll shift the gears for you. '
' '
'It\022s all just a matter of mapping inputs to the '
'appropriate outputs. Between the input and the output we\022ll '
'put a black box that does the work. What could go in the '
'black box? Any number of things: fuzzy systems, linear '
'systems, expert systems, neural networks, differential equations, '
'interpolated multi-dimensional lookup tables, or monkeys with '
'typewriters just to name a few of the possible options. '
'Clearly the list could go on and on. '
' '
'Of the dozens of ways to make the black box work, it turns '
'out that fuzzy is often the very best way.  Why should that '
'be? As Lotfi Zadeh, who is considered to be the father '
'of fuzzy logic, once remarked:\023In almost every case you '
'can build the same product without fuzzy logic, but fuzzy '
'is faster and cheaper.\024 '
' '
'What Can the Fuzzy Logic Toolbox Do? '
' '
'The Fuzzy Logic Toolbox allows you to do several things, '
'but the most important thing it lets you do is create and '
'edit fuzzy inference systems. You can create these systems '
'by hand, using graphical tools or command-line functions, '
'or you can generate them automatically using either '
'clustering or adaptive neuro-fuzzy techniques.  '
' '
'If you have access to Simulink, the simulation tool that '
'runs alongside MATLAB, you can easily test your fuzzy '
'system in a block diagram simulation environment. If you '
'have Real-Time Workshop\031capabilities available, you can '
'generate realtime or non-realtime code from the Simulink'
'environment.  '
' '
'The toolbox also lets you run your own standalone C programs '
'directly, without the need for Simulink.  This is made '
'possible by a standalone Fuzzy Inference Engine that reads '
'the fuzzy systems saved from a MATLAB session (the standalone '
'code, unlike that generated by the Real-Time Workshop, does '
'not run in real time). You can customize the standalone engine '
'to build fuzzy inference into your own code. All provided '
'code is ANSI compliant.  Because of the integrated nature of '
'MATLAB\022s environment, you can create your own tools to '
'customize the Fuzzy Logic Toolbox or harness it with another '
'toolbox, such as the Control System, Neural Network, or '
'Optimization Toolbox, to mention only a few of the possibilities'};


%-----------------------------------
elseif strcmp(action, 'hosa');

   info = ...
       {'                                               '
'There is much more information in a stochastic non-Gaussian '
'or deterministic signal than is conveyed by its '
'autocorrelation or power spectrum. Higher-order spectra, '
'which are defined in terms of the '
'higher-order moments or acumulants of a signal, contain '
'this additional information. The  Higher-Order '
'Spectral Analysis (HOSA) Toolbox provides comprehensive '
'higher-order spectral analysis capabilities for signal '
'processing applications. The toolbox is an excellent resource '
'for the advanced researcher, the practicing engineer, as well '
'as the novice student who wishes to learn about concepts '
'and algorithms in statistical signal processing.  '
' '
'This is a collection of M-files which implement a variety '
'of advanced signal processing algorithms for spectral estimation, '
'polyspectral estimation, and computation of time-frequency '
'distributions, with applications such as parametric and '
'non-parametric blind system identification, time delay '
'estimation, harmonic retrieval, direction of arrival '
'estimation, parameter estimation of Volterra (non-linear) '
'models, and adaptive linear prediction.  Other potential '
'applications include acoustics, biomedicine, econometrics, '
'exploration seismology, non-destructive testing, '
'oceanography, plasma physics, radar, sonar, speech etc.  '
' '
'The Tutorial has numerous examples which reinforce the '
'theory and demonstrate how to use the toolbox functions. All '
'of the data files used by these examples are included in '
'the distribution diskette, and are described in the Section '
'on `Data files'' starting on page . We encourage you to try '
'out the examples yourself.  Additional examples may be '
'found in the demo, which can be invoked via hosademo. '};

%-----------------------------------
elseif strcmp(action, 'image');

   info = ...
        {'                                                   '
         ' The Image Processing Toolbox provides a           '
         ' powerful and flexible environment for image       '
         ' processing and analysis.  It represents the       '
         ' state of the art in image and 2-dimensional       '
         ' signal processing for a wide range of             '
         ' applications.  Its comprehensive library of       '
         ' functions is an open system that may be easily    '
         ' tailored to fit specific application requirements.'
         '                                                   '
         ' The MATLAB environment is ideally suited to       '
         ' image processing.  In particular, MATLAB''s        '
         ' matrix-oriented language is well suited for       '
         ' manipulating images, which are nothing more       '
         ' than a visual rendering of matrices.  The result  '
         ' is an incomparably easy to use, economical        '
         ' way of expressing image processing                '
         ' operations.                                       '};

%-----------------------------------
elseif strcmp(action, 'lmi');

   info = ...
       {'                                               '
'The LMI Control Toolbox, authored by leading researchers:'
'Pascal Gahinet, Arkadi Nemirovski, and Alan Laub, allows'
'one to efficiently solve Linear Matrix Inequalities'
'(LMIs). LMIs are special convex optimization problems'
'that arise in many disciplines including control,'
'identification, filtering, structural design, graph'
'theory, interpolation, and linear algebra.'
' '
'Until recently, these problems could not be efficiently'
'solved. Due to breakthroughs instigated by Nesterov'
'and Nemirovski (one of the toolbox authors), it is now'
'possible to solve LMI problems with a new set of'
'Interior-Point methods that are significantly faster'
'and require fewer iterations to converge. These methods'
'are the basis of the LMI Control Toolbox.'
' '
'Control engineers have found that LMIs are convenient'
'mathematical tools to express and combine a variety'
'of design specifications including tracking performance,'
'disturbance rejection, LQG performance, robustness'
'to model uncertainty, and pole placement objectives.'
'The LMI Control Toolbox includes additional tools for'
'the analysis and design of control systems using LMI'
'techniques. The LMI framework allows robustness analysis'
'of quadratic stability (time-varying linear systems), or'
'the Popov criterion (systems with nonlinear components),'
'which previously had been considered too complex. The LMI'
'framework also makes it possible to consider'
'multi-objective controller synthesis with H-infinity,'
'LQG, and pole placement objectives, as well as design'
'of robust gain-scheduling controllers.'};


%-----------------------------------
elseif strcmp(action, 'compiler');

   info = ...
        {'                                               '
         ' The MATLAB Compiler takes MATLAB M-files as   '
         ' input and generates C source code as output.  '
         ' The MATLAB Compiler can generate two different'
         ' categories of C source code:                  '
         '                                               '
         '-C source code for building MEX-files.          '
         '                                               '
         '-C source code for combining with external modules'
         ' to form standalone applications.    '
         ' Standalone external applications run independent'
         ' of MATLAB and can run even if MATLAB  '
         ' is not installed on the system.               '};
 
%-----------------------------------
%%%%% C Math Library 
elseif strcmp(action, 'cmath');

   info = ...
       {'                                               '
'The MATLAB C Math Library makes the mathematical core'
'of MATLAB available to application programmers. The'
'library is a collection of approximately 300 mathematical'
'routines written in C. Programs written in any'
'language capable of calling C functions can call these'
'routines to perform mathematical computations. '
' '
'The MATLAB C Math Library is used in 2 situations:'
' '
'-C code generated by the MATLAB Compiler and then run outside of MATLAB'
' '
'To generate C code versions of your MATLAB M-files,'
'and be able to run them independent of MATLAB, you use the'
'MATLAB compiler to generate the code, and the C Math Library to'
'make the math function available to the generated code.' 
' '
'-User written C functions which call the MATLAB C Math Library' 
' '
'The MATLAB C Math Library contains core MATLAB language math'
'algorithms. However, you do not need to know MATLAB or own a copy of'
'MATLAB in order to use the MATLAB C Math Library. If you have'
'purchased the MATLAB C Math Library, then the only'
'additional software you need is an ANSI C compiler.'}; 

%-----------------------------------
% C++ Math Library

elseif strcmp(action, 'cplus');

info ={'With the C++ Math Library, you can incorporate powerful'
'MATLAB numeric routines into programs written in C++. The MATLAB'
'C++ Math Library is designed for building standalone mathematical'
'applications that require the power of MATLAB functions in a'
'compiled C++ based environement.'};

%-----------------------------------
%%% Model Predictive Toolbox
elseif strcmp(action, 'modelpredict');

   info = ...
       {'                                               '
'The Model Predictive Control Toolbox is a collection of '
'functions (commands) developed for the analysis and '
'design of model predictive control (MPC) systems. Model '
'predictive control was conceived in the 1970s primarily '
'by industry. Its popularity steadily increased '
'throughout the 19080s. At present, there is little doubt '
'that it is the most widely used multivariable control '
'algorithm in the chemical process industries and in '
'other areas. While MPC is suitable for almost any kind '
'of problem, it displays its main strength when applied to '
'problems with  '
' '
'-A large number of manipulated and controlled variables '
' '
'-Contrains imposed on both the manipulated and controlled variables '
' '
'-Changing control objectives and/or equipment (sensor/actuator) failure '
' '
'-Time delays '
' '
'The routines included in the MPC Toolbox fall into two '
'basic categories: routines which use a step response model '
'description and routines which use a state-space model '
'description. In addition, simple identification tools are '
'provided for identifying step response models from plant '
'data. Finally, there are also various conversion routines '
'which convert between differnt model formats and analysis '
'routines which can aid in determining the stabilitey of '
'the unconstrained system, etc.  '
' '
'All MPC Toolbox commands have a build-in usage display. '
'Any compand called with no input arguments results in a '
'brief description of the command line. For example, '
'typing mpccon at the command line gives the following: '
' '
'usage: Kmpc = mpccon(model, ywt, uwt, M, P) '
' '
' '};

%-----------------------------------
elseif strcmp(action, 'nag');

   info = ...
      {'                                               '
'The NAG Foundation Toolbox product enables MATLAB users '
'to access the numerical functionality of the NAG '
'Foundation Library providing increased scope for '
'application development and problem solving from within '
'the familiar MATLAB environment.'
' '
'The Nag Foundation Toolbox adds valuable function to MATLAB'
'such as:'
' '
'-Solving boundary-value problems'
'-Performing adaptive one-dimensional and multi-dimensional quadrature'
'-Fitting to curves and surfaces'
'-Solving linear equations using LAPACK algorithms'
' '};


%-----------------------------------
elseif strcmp(action, 'nnet');

   info = ...
       {'                                               '
         ' The Neural Network Toolbox is a collection      '
         ' of MATLAB functions for designing and           '
         ' simulating neural networks.                     '
         '                                                 '
         ' Neural networks, originally inspired by their   '
         ' biological namesakes, are composed of many      '
         ' simple intercommunicating elements, or          '
         ' neurons, working in parallel to solve a problem.'
         '                                                 '
         ' What makes them exciting is the fact that       '
         ' once a network has been set up, it can learn    '
         ' in a self-organizing way that seems to mimic    '
         ' simple biological nervous systems. Because      '
         ' neural networks can be trained to respond in    '
         ' parallel to the inputs presented to them, they  '
         ' often are much faster than more conventional    '
         ' methods.                                        '};
 
%-----------------------------------
elseif strcmp(action, 'ncd');

   info = ...
       {'                                               '
         ' The Nonlinear Control Design (NCD) Toolbox      '
         ' offers a new approach to computer-aided         '
         ' control system design and system identification.'
         ' NCD lets you automatically tune parameters in   '
         ' nonlinear Simulink block diagrams.              '
         '                                                 '
         ' The toolbox provides a graphical user interface '
         ' for specifying time domain constraints, tunable '
         ' variables and plant uncertainty.  With a press  '
         ' of a button, the Simulink system, time domain   '
         ' constraints, tunable variables and uncertainty  '
         ' are massaged into a constrained optimization    '
         ' problem.  The optimization performs successive  '
         ' simulations on the Simulink system and changes  '
         ' the tunable variables in an attempt to better   '
         ' meet the time response specifications.          '};


%-----------------------------------
elseif strcmp(action, 'optim');

   info = ...
       {'                                               '
         ' The Optimization Toolbox contains many         '
         ' commands for the optimization of general linear'
         ' and nonlinear functions. Optimization is a very'
         ' broad topic; its purpose is to find the best   '
         ' possible solution to a given problem (which may'
         ' also include a number of limiting constraints).'
         '                                                '
         ' Graphically, an optimization problem can be    '
         ' visualized as trying to find the lowest (or    '
         ' highest) point in a complex, highly contoured  '
         ' landscape. An optimization algorithm can thus  '
         ' be likened to an explorer wandering through    '
         ' valleys and across plains in search of the     '
         ' topological extremes.                          '};

%-----------------------------------
elseif strcmp(action, 'pde');

   info = ...
       {'                                               '
'The Partial Differential Equation (PDE) Toolbox provides'
'a powerful and flexible environment for the study and'
'solution of partial differential equations in two space'
'dimensions and time. The equations are discretized by the'
'Finite Element Method (FEM). The objectives of the'
'PDE toolbox are to provide you with tools that:'
' '
'-Define a PDE problem, i.e. define 2-D regions,'
'  boundary conditions, and PDE coefficients. '
' '
'-Numercially solve the PDE problem, i.e. , generate'
'  unstructured meshes, discretize the equations,'
'  and produce an approximation to the solution. '
''
'-Visualize the results. '
' '
'Can I use the PDE Toolbox?'
' '
'The PDE Toolbox is designed for both beginners and'
'advanced users. '
' '
'The minimal requirements is that you can formulate'
'a PDE problem on paper (draw the domain, write the'
'bondary conditions, and the PDE)'
' '
'The Toolbox contains a self-contained graphical'
'environment for PDE solving. For common applications'
'you can use the  specific phusical terms rather than'
'abstract coefficients. The PDE graphical interface'
'requires no knowledge of the mathematics behind the'
'PDE, the numerical schemes, or MATLAB. '
' '
'Advanced applications are also possible by downloading'
'the domain geometry, bounday conditions and mesh'
'description of the MTLAB workspace. From the command'
'line (or M-file) you can call functions from the'
'toolbox to do the hard work, e.g. generate meshes,'
'discretize your problem, perform interpolation,'
'plot data on unstructured grids, etc., while you'
'retain full control over the gloabl numerical'
'algorithm.'};

%-----------------------------------
elseif strcmp(action, 'qft');

   info = ...
        {'                                               '
'Quantitative Feedback Theory Toolbox is a practical '
'feedback systems design tool. The New QFT toolbox '
'solves robust control design problems using '
'classical frequency d main concepts.  '
' '
'The new Quantitative Feedback Theory (QFT) Toolbox '
'is the latest release in The MathWorks Partner '
'Series. It is a collection of M-files and a GUI '
'that provides practical tools devoted to designing '
'feedback systems. Written by Yossi Chait, Craig '
'Borghesani, and Oded Yaniv, this toolbox offers '
'a frequency domain-based design technique that '
'extends well-known, classical design approaches '
'to allow for an explicit consideration of plant '
'uncertainty and/or external disturbances and '
'simultaneous performance specifications.  '
' '
'The QFT Toolbox supports single-loop design problems '
'for wide classes of uncertainty: parametric, '
'non-parametric, or both, as well as frequency '
'response sets such as those obtained from physical '
'measurements.  The toolbox also provides '
'limited support for cascaded-loop and multiple-loop '
'design problems where familiarity with the '
'algorithms is required. '
' '
'QFT Background  '
' '
'The QFT design procedure involves two basic '
'steps: (1) computation of QFT bounds and '
'(2) loop-shaping (the controller and possibly a '
'pre-filter). The toolbox includes a convenient GUI '
'for an interactive tool that greatly simplifies '
'loop-shaping. '
' '
'A key consideration in any real feedback design '
'is making sure the control design can accomplish '
'performance specs in the presence of uncertainties. '
'In QFT, one of the main objectives is to design '
'a simple, low-order controller with low bandwidth. '
'Minimum bandwidth controllers are a natural '
'requirement in practice in order to avoid problems '
'with noise amplification, resonances, and unmodeled '
'high-frequency dynamics. In any real life design, '
'some iterations are inevitable, and QFT offers direct '
'insight into the tradeoffs between controller '
'complexity and performance specifications during '
'such iterations. '
' '
'Problem Solver  '
' '
'This toolbox is intended for solving problems such '
'as single-loop, cascaded-loop, sequentially closed '
'multi-loop, and decentralized systems. The GUI '
'greatly simplifies loop-shaping when dealing with '
'these systems.  '
' '
'The QFT Toolbox includes 15 examples. The first set '
'of examples provides a general introduction to '
'QFT and how it can be used for '
'designing robust systems with parametric or '
'non-parametric uncertainty.  '
' '
'Examples are also provided which address systems '
'with multiple outputs, in which feedback loops '
'are sequentially closed and loop-shaping '
'techniques are applied. Practical design methods '
'are illustrated for cases where significant '
'mechanical vibration is present.  Other examples '
'focus on systems with more than one output: the '
'cascaded-loop and multiple-loop design problem. '
'In addition, examples show how QFT can be applied '
'to the design of discrete-time controllers in the '
'presence of model uncertainty. Other examples '
'illustrate QFT application to practical systems '
'such as those having significant mechanical '
'vibration. Finally, several examples focus on '
'direct z-domain QFT design for robust performance'
'problems.'};


%-----------------------------------
elseif strcmp(action, 'rtw')

   info = ...
       {'                                               '
'The Simulink Real-Time Workshop is an automatic C'
'language code generation environment for Simulink.'
'It produces C code directly from Simulink graphical'
'models and automatically builds programs that can'
'be run in real-time in a variety of environments. '
' '
'With the Simulink Real-Time Workshop, you can run'
'your Simulink model in real time on a remote'
'processor. The Real-Time Workshop also enables'
'you ro run accelerated, stand alone simulations'
'on your host machine or on an external computer.'};


%-----------------------------------
elseif strcmp(action, 'robust')

   info = ...
        {'                                                 '
         ' The Robust Control Toolbox provides a           '
         ' specialized set of tools for the analysis and   '
         ' synthesis of control systems that are "robust"  '
         ' with respect to uncertainties that can arise in '
         ' the real world.                                 '
         '                                                 '
         ' Control system design using linear techniques   '
         ' is based on models that approximate the         '
         ' dynamic behavior of a physical system. But      '
         ' deviations between the model and the real       '
         ' world often exist. In addition, system dynamics '
         ' change over time. Such scenarios require a      '
         ' controller capable of satisfactory performance  '
         ' despite uncertainties. In these situations, the '
         ' Robust Control Toolbox can be extremely         '
         ' useful.                                         '};


%-----------------------------------
elseif strcmp(action, 'signal');

   info = ...
        {'                                                '
         ' The Signal Processing Toolbox contains tools   '
         ' to help you do digital signal processing (DSP).'
         ' DSP is the process by which real-world         '
         ' phenomena such as sound and light are          '
         ' analyzed, manipulated, and created using a     '
         ' digital computer.                              '
         '                                                '
         ' Thus signal processing permits computers to    '
         ' interact with the world, and ultimately governs'
         ' the exchange of information from one kind of   '
         ' signal to another.                             '
         '                                                '
         ' The MATLAB environment is ideal for doing      '
         ' DSP because digital signals are MATLAB''s       '
         ' most natural data type.                        '};

%-----------------------------------
%  Info on Simulink for user who don't have Simulink installed
elseif strcmp(action, 'simulinknew');

info = ...
{' '
'  Simulink is a tool for modeling, analyzing, and simulating'
'  an extraordinary wide variety of physical and mathematical'
'  systems, including those with nonlinear elements and those'
'  which use continuous and discrete time.'
' '
'  An an extension of MATLAB, Simulink adds many features specific'
'  to dynamic systems while retaining all of MATLAB''s general'
'  purpose functionality.'
' '
'  Using Simulink, you model a system graphically, sidestepping'
'  much of the nuisance associacted with coventional programming.'
' '
'  Simulink has two phases of use: model definition and model'
'  analysis. A typical session starts by either defining a   '
'  a model or recalling a previously defined model, and then '
'  proceeds to analysis of that model. In practice, these two'
'  steps are often performed iteratively as the model        '
'  designer creates and modifies a model to achieve the      '
'  desired behavior.                                         '
'                                                          '
'  To facilitate model definition, Simulink adds a new class '
'  of windows called block diagram windows. In these windows '
'  models are created and edited principally by mouse driven '
'  commands. Part of mastering Simulink is to become        '
'  familiar with the manipulation of model components within '
'  these windows.                                            '
'                                                          '
'  After you define a model, you can analyze it either by     '
'  choosing options from the Simulink menus or by entering   '
'  commands in MATLAB''s command window. Built-in analysis    '
'  tools include various simulation algorithms, linmod, a    '
'  tool for extracting linear models of systems, and trim, a  '
'  tool for finding equilibrium points.                      '
'                                                          '
'  The progress of a simulation can be viewed while the      '
'  simulation is running, and the final results can be made  '
'  available in the MATLAB workspace when a simulation is    '
'  complete.                                                 '};


elseif strcmp(action, 'simulink')

   info = ...
        {'                                                '
         'To run one of the Simulink Model examples: '
         '1) Open up the model'
         '2) Under the SIMULATION Menu select start'
         ' '
         'Simulink is a program for simulating dynamic systems. As an'
         'extension to MATLAB, Simulink adds many features specific '
         'to dynamic systems while retaining all of MATLAB''s        '
         'general functionality.                                    '
         '                                                          '
         'Simulink has the power to simulate some extraordinary'
         'complex systems, providing you with the results you need.'
         '                                             '
         'While the Simulink models you will see in this section'
         'are nowhere near the limits of what can be done, they'
         'do hint at the sort of sophistication you can expect.'
         '                                             '
         'The demos in this section call up only the Simulink model'
         'itself. It will be up to you to experiment with them,'
         'using the Simulink menu to start and stop the simulation'
         'and to close the window when you''re done.           '
         '      '
         'Simulink has two phases of use: model definition and model'
       'analysis. A typical session starts by either defining a   '
       'a model or recalling a previously defined model, and then '
       'proceeds to analysis of that model. In practice, these two'
       'steps are often performed iteratively as the model        '
       'designer creates and modifies a model to achieve the      '
       'desired behavior.                                         '
       '                                                          '
       'To facilitate model definition, Simulink adds a new class '
       'of windows called block diagram windows. In these windows '
       'models are created and edited principally by mouse-driven '
       'commands. Part of mastering Simulink is to become        '
       'familiar with the manipulation of model components within '
       'these windows.                                            '
       '                                                          '
       'After you define a model, you can analyze it either by     '
       'choosing options from the Simulink menus or by entering   '
       'commands in MATLAB''s command window. Built-in analysis    '
       'tools include various simulation algorithms, linmod, a    '
       'tool for extracting linear models of systems, and trim, a  '
       'tool for finding equilibrium points.                      '
       '                                                          '
       'The progress of a simulation can be viewed while the      '
       'simulation is running, and the final results can be made  '
       'available in the MATLAB workspace when a simulation is    '
       'complete.                                                 '};
%-----------------------------------
elseif strcmp(action, 'accelerator')

   info = ...
        {'                                                '
'The Simulink Accelerator increases the speed of a'
'simulation by generating C code for the Simulink '
'model, compiuling it as a MATLAB MEX-file, and'
'dynamically linking it in place of the original'
'internal model represntation. The Accelerator fully'
'automates the process of generating, compiling, and'
'linking the code. '
' '
'When the Accelerator is enabled and you start a '
'simulation, Simulink first looks for a MEX-file '
'whose name mathces that of the Simulink model. If '
'Simulink finds such a file, it determines if the '
'MEX-file mathces the current model.  '
' '
'If the MEX-file system is up to date (i.e. mathced '
'the current model), the simulation run by Simulink '
'is automatically replaced with the more efficient '
'MEX-file. '
' '
'If the MEX-file does not exist or is notup to date, '
'Simulink firest generates (or regenerates) the '
'MEX-file, and then runs the simulation using the '
'new version. All this happends transparently to the '
'user. '
' '
'You can accelerate all type sof Simulink systems, '
'realizing speed improvements for continuous, '
'discrtete-time, and hybird models as well as models '
'with multiple sampel rates.  '
' '
'The generated Ccode is highly optimized; for loops '
'are unrolled, zeros and ones are removed from '
'multiplication, and simulation run up to eight times '
'faster. '};


%-----------------------------------
elseif strcmp(action, 'mapping')

info ={'The simplest definition of a map is "a representation of'
       'geographic data". To the ancient Egyptians, this representation'
       'first took the form of lists of city names in the order they'
       'would be encountered when following a given road. '
       'Two-dimensional renditions, such as paper roadmaps, are more'
       'familiar to us today. '
       ' '
       'The Mapping Toolbox addresses electronic representations of'
       'geographics data. It allows the creation, use, and presentation'
       'of geographic data in a variety of map projections and for a'
       'variety of ends.'};




%-----------------------------------
elseif strcmp(action, 'spline')

   info = ...
        {'                                                '
         ' The Spline Toolbox provides a set of M-files     '
         ' for working with splines. Splines are useful     '
         ' because of their ability to approximate or       '
         ' "stand in" for other functions. They are flexible'
         ' mimics that can imitate the important features   '
         ' of other functions, while eliminating some of    '
         ' the nasty side-effects associated with other     '
         ' kinds of approximations (such as piecewise       '
         ' linear curves).                                  '
         '                                                  '
         ' Splines are therefore used to model functions    '
         ' of a more or less arbitrary nature. The Toolbox  '
         ' makes it possible to create and work with        '
         ' piecewise polynomial functions and curves in     '
         ' the convenient and interactive MATLAB            '
         ' environment.                                     '};


%-----------------------------------
elseif strcmp(action, 'stats') 

   info = ...
        {'                                                '
         ' The Statistics Toolbox is a collection of      '
         ' MATLAB functions for descriptive, inferential, '
         ' and graphical statistics, probability modeling,'
         ' and random number generation. The toolbox      '
         ' includes several interactive graphic           '
         ' environments for dynamic visualization of data,'
         ' functions, and probability distributions.      '
         '                                                '
         ' The Statistics Toolbox also includes datasets  '
         ' for examples discussed in the User''s Guide.    '
         ' The Tutorial and Reference sections explain    '
         ' each statistical concept and provide examples, '
         ' many of which are drawn from application areas '
         ' in science and engineering.                    '};

%-----------------------------------
elseif strcmp(action, 'symbolic')

   info = ...
        {'                                                '
         ' The Symbolic Math Toolbox integrates powerful   '
         ' symbolic and variable precision computing into  '
         ' the MATLAB environment. The toolbox is          '
         ' based on an embedded version of Maple V,        '
         ' which is widely recognized as the most efficient'
         ' and accurate symbolic computation software      '
         ' available.                                      '
         '                                                 '
         ' Symbolic computations are often an important,   '
         ' yet time-consuming, part of engineering         '
         ' projects or theoretical work in basic sciences. '
         ' Because the Symbolic Math Toolbox provides      '
         ' access to symbolic computation within the       '
         ' numeric MATLAB environment, you can             '
         ' choose the most appropriate tools for a job     '
         ' without compromising either speed or accuracy.  '
         '                                                 '};

%-----------------------------------
elseif strcmp(action, 'sysid')

   info = ...
        {'                                                '
         ' System identification is a way to find a        '
         ' mathematical model for a physical system        '
         ' (like an electric motor, or even a financial    '
         ' market) based only on a record of the inputs    '
         ' and outputs of that system.                     '
         '                                                 '
         ' By using the sophisticated system identification'
         ' routines available in the System Identification '
         ' Toolbox, you can move swiftly from data to      '
         ' mathematical model, avoiding the nuisance of    '
         ' slowly building a potentially inaccurate model  '
         ' by hand.                                        '};

%-----------------------------------
%% Mu-Analysis and Synthesis

elseif strcmp(action, 'mu')

   info = ...
        {'                                                '
         ' The Mu-Analysis and Synthesis Toolbox is        '
         ' a specialized collection of M-files developed   '
         ' for the analysis and synthesis of robust        '
         ' control systems.                                '
         '                                                 '
         ' The overall objective of the functions contained'
         ' in the Toolbox is to design a robust control    '
         ' system in the presence of uncertainties. This   '
         ' Toolbox provides users with recent              '
         ' developments in control theory, namely          '
         ' H-infinity optimal control and Mu-analysis      '
         ' and synthesis techniques, in the MATLAB         '
         ' environment.                                    '};

%-----------------------------------
elseif strcmp(action, 'newforv5')

 info = ...
        {'                                                '
'The new release of MATLAB - MATLAB 5 - includes major new'
'language, graphics, data analysis, and user interface'
'development features. The following is a partial list of'
'the new features:'
' '
'Programming Environment'
' -M-file performance profiler'
' -Integrated M-file editor (PC, Mac)'
' -GUI-driven M-file debugger (PC, Mac)'
' -Object-oriented programming (including overloading)'
' -Workspace browser and search path browser/editor (PC, Mac)'
' -Handle Graphics (tm) property editor'
' -M-file to code conversion for code hiding'
' -On-line browser-based reference documention'
'    '
'GUI Development '
' -Interactive GUI builder (Guide)'
' -List boxes, including multi-item selection'
' -Modal and nonmodal dialogue and message boxes '
' -Multiline and in-line editable text' 
' -Stacking order stacking order control (e.g.  definable cursors)'
' -16-bit stereo sound (PC, Mac)'
' -Optionally grayed-out UI controls'
' -Blocking via waitfor (e.g. from callbacks)'
' -Platform and screen resolution independence'
' '
'Structures , Data Types, and Language Features'
' -Multidimensional (N-D) arrays'
' -User-definable data structures and structure arrays'
' -Mixed type data arrays (cell arrays)'
' -Variable length argument lists'
' -Function and operator overloading'
' -Primary function and subfunctions in one M-file'
' -switch/case statement'
' -Short-circuited if statements'
' -String arrays (2 bytes per character efficient storage)'
' -Integer bit manipulation functions'
' '
'Math and Data Analysis'
' -New ordinary differential equation solvers (ODE Suite)'
' -Sparse iterative methods, eigenvalues, and SVD'
' -Two-dimensional quadrature'
' -Multidimensional (N-D) interpolation'
' -Delaunay triangulation and display of irregularly-sampled data'
' -Efficient triangle-based interpolation (cubic, nearest, linear)'
' -Manipulation and analysis of  N-D arrays (e.g. reshape, convn, fftn)'
'  Time and date data support'
' '
'Graphics & Visualization'
' -Z-buffering for fast, accurate 3-D display (up to 100x speedup)'
' -True color (24-bit RGB) support'
' -Multiple light sources for colored surfaces and patch objects'
' -Camera angle and perspective control, including fly-throughs'
' -Flat, Gouraud, and Phong shading '
' -3-D aspect ratio control '
' -Vectorized patch object definition for 3-D modeling'
' -Support for irregularly-spaced data, including triangularized and gridded'
'  2-D and 3-D mesh plots'
' -Set and get for multiple objects and Handle Graphics attributes '
' -Efficient 8-bit image data import, display, and storage'
' -Additional image file formats supported (e.g. TIFF )'
' '
'Presentation Graphics'
' -2 x and y axes'
' -Plot legends'
' -Font control by text object'
' -Greek symbols, subscripts, and superscripts'
' -3-D pie charts, quiver, ribbon, and stem plots'
' -3-D and horizontal bar graphs'
' -Many more curve marker styles'
' '
'On-line Documentation'
' -Browser-based help facility (requires Netscape 2.0 or MS Explorer)'
' -Complete reference documentation in HTML and Acrobat formats'
' '};
%----------------------------------
elseif strcmp(action, 'wavelets')

   info = ...
{'The Wavelet Toolbox extends the MATLAB Technical '
'Computing Environment and provides a comprehensive '
'collection of routines for examining local, '
'multiscale, or nonstationary phenomena. Wavelet methods '
'offer additional insight and performance in any '
'application where Fourier techniques have been used. '
'The toolbox is useful in many signal and image '
'processing applications, including speech and audio '
'processing, communications, geophysics, finance, and '
'medicine. '
' '
'Features:  '
' '
'-Complete GUI and command line functionality for '
'  analysis, synthesis, de-noising, and compression '
'  of signals and images  '
' '
'-Continuous wavelet transform for multiscale signal '
'  analysis  '
' '
'-Analysis and synthesis of 1-D and 2-D discrete '
'  wavelet transform (DWT)  '
' '
'-Multiresolution decomposition and analysis of '
'  signals and images  '
' '
'-Wide selection of wavelet basis functions  '
' '
'-1-D and 2-D wavelet packet transforms  '
' '
'-Entropy-based wavelet packet tree pruning for '
'  "best-tree" and "best-level" analysis  '
' '
'-De-noising with soft and hard thresholding  '
' '
'* Highlights  '
' '
'Advanced Techniques. With wavelet analysis, you can '
'see and explore aspects of data that other signal '
'analysis techniques miss, such as trends, breakdown '
'points, discontinuities in higher derivatives, and '
'self similarity.  Wavelet analysis can often compress '
'or de-noise a signal without appreciable degradation, '
'even when you want to preserve both high-and '
'low-frequency components.  '
' '
'De-Noising and Compression. Routines for compression'
'and de-noising are provided for both wavelet and'
'wavelet packet techniques. The compression routines'
'extract the minimum number of wavelet coefficients that'
'represent the signal accurately, which is the first'
'stage of a complete compression system. '
' '
'Wavelet Families. The toolbox provides the following'
'wavelet families: biorthogonal, Daubechies, Haar,'
'Mexican hat, Meyer, Morlet, and Symlets. You can'
'easily add your own wavelet to the toolbox. '
' '
'Demos and Tutorial. An extensive user''s guide'
'introduces wavelet concepts and reinforces them '
'with numerous examples and a complete reference '
'section.  '
' '
'Book Included. Bundled with the Wavelet Toolbox is '
'the new book, Wavelets and Filter Banks, by Gilbert '
'Strang and Truong Nguyen. Many exercises in this '
'book are drawn from the toolbox. '
' '
'Toolbox Authors. The Wavelet Toolbox is written by '
'Michel Misiti, Yves Misiti, Georges Oppenheim and '
'Jean-Michel Poggi, who are all members of the '
'"Laboratoire de Math\151matiques" Orsay-Paris 11 '
'University France.  '};
end
about = info;
