.1
 Release 11 New Features      

Toolboxes and Blocksets

Almost all of the toolboxes and blocksets were updated for release with MATLAB 5.2. For many of these toolboxes and blocksets, the updates simply involved fixing software problems and taking more advantage of MATLAB 5 features.

These toolboxes and blocksets were updated for 5.2. The toolboxes and blocksets with significant updates are highlighted with an asterisk and are discussed in more detail in the rest of this chapter (in alphabetical order).

Power System Blockset 1.0

The Power System Blockset is a new blockset introduced with MATLAB 5.2.

The Power System Blockset is a modern design tool that allows scientists and engineers to build models rapidly and easily that simulate power systems. The blockset uses the Simulink environment, allowing a model to be built using simple click-and-drag procedures. Not only can you draw the circuit topology rapidly, but the analysis of the circuit can include its interactions with mechanical, thermal, control, and other disciplines. This is possible because the electrical portions of the simulation interact with Simulink's extensive modeling library. Because Simulink uses MATLAB as the computational engine, MATLAB's toolboxes can also be used by the designer.

Power System Blockset libraries contain models of typical power equipment such as transformers, lines, machines, and power electronics. Their validity is based on the experience of the Power Systems Testing Laboratory of Hydro-Quebec, a large North American utility located in Canada.

See the Power System Blockset User's Guide for information about using this blockset.

Communications Toolbox 1.3

Note Much of the new functionality of the Communications Toolbox 1.3 requires Simulink 2.2. However, even if you use the Communications Toolbox without Simulink, upgrading to Version 1.3 will let you take advantage of a number of other software quality improvements in the toolbox.

The Communications Toolbox 1.3 added 22 new Simulink function blocks and 12 new example block diagrams.

The new function blocks are:

These new blocks expand the functionality of the Communications Toolbox so that it now provides:

The Communications Toolbox 1.3 also builds on recent MATLAB and Simulink enhancements. These minor changes to the Communications Toolbox are primarily in the area of graphical scopes such as the Error Rate Meter, Eye-Pattern and Scatter plots, and the Trellis plot in the Convolutional Decode block.

This release of the Communications Toolbox also includes changes made to ensure integration with the Real-Time Workshop 2.2. If you are using Real-Time Workshop with the Communications Toolbox 1.3, you need Real-Time Workshop 2.2. Specifically, a few parameter definitions in the Communications Toolbox have been changed for use with C-coded S-functions in Real-Time Workshop.

See the Communications Toolbox 1.3 New Features Guide, available in printed form and online (PDF), for more details on these new features.

Control System Toolbox 4.1

The Control System Toolbox 4.1 provided two main enhancements:

The Root Locus Design GUI is an interactive design tool that you can use to:

The Root Locus Design GUI is documented in Chapter 6 of the Control System Toolbox User's Guide.

The Simulink LTI Viewer is similar to the Control Systems Toolbox LTI Viewer. The Simulink LTI Viewer is used to analyze portions of a Simulink model. Its features include:

The Simulink LTI Viewer is documented in Chapter 4 of the Control System Toolbox User's Guide.

Two additional enhancements are:

DSP Blockset 2.2

DSP Blockset 2.2 introduced a number of new features and improvements. There are over 30 new and enhanced blocks, a filter design wizard, support for data frames, and expanded support of vector and matrix inputs. This section outlines the new additions and provides pointers to the complete feature descriptions in the DSP Blockset User's Guide. See Chapter 1 of the online User's Guide for an overview of the blockset's contents.

Also see the DSP Blockset readme file for a summary of the new additions. To view the readme file, type

at the MATLAB command line.

Data Frames

The DSP Blockset added support for data frames, vectors whose elements represent consecutive time samples from a single signal. Framed data is a common format in real-time systems, where the data acquisition hardware often operates most efficiently by accumulating a large number of signal samples at a high rate, and then propagating these samples to the real-time system as a block, or frame, of data. Data frames can also be constructed through the usual DSP Blockset buffering operations (using the Buffer and Complex Buffer blocks, for example).

Version 2.2 includes two new blocks designed to operate specifically on framed data. They are frame-oriented counterparts to the FIR Rate Conversion and Multichannel IIR Filter blocks and are distinguished by the word "Frame" in the block name:

Use these blocks to directly filter or resample framed data in its native format without the computational expense of unbuffering. Other blocks that operate on framed data include the FFT, DCT, and cepstrum blocks in the Transforms library.

In addition to these frame-based blocks, the data frame format is accepted by all blocks in the blockset that accept vector inputs. Be aware, however, that many blocks implicitly expect the elements of vector inputs to represent independent channels and not consecutive samples. Besides the FIR Rate Conversion and Multichannel IIR Filter blocks, others that expect non-frame data include the "running" blocks in the Statistics library, the variable delay blocks, and the filter design blocks. In general, if a block uses past inputs in generating the current output (and is not specifically designated as a frame-based block), then it considers the elements of a vector (or matrix) input to represent distinct channels, and not a frame of consecutive samples.

See "Working with Frames" in Chapter 3 of the User's Guide for a complete discussion of this data format.

Filter Realization Wizard

Another new element of the blockset is the Filter Realization Wizard, a GUI that allows you to construct filters easily with a a variety of different architectures. The GUI is shown below.


When you click the GUI's Build button with the particular settings shown above, the wizard constructs the specified moving average (MA) lattice architecture as a subsystem within a new model window.


You can then alter or optimize the filter to suit your own needs. Additional information about the Filter Realization Wizard can be found in the online Reference.

New and Enhanced Blocks

The table below lists the blocks added in Version 2.2. Among the most significant additions were variable delay blocks, discrete cosine transform and cepstrum blocks, linear prediction blocks (LPC, Levinson-Durbin), and new spectral estimation blocks.

Block Library
 Block Name
 Purpose
DSP Sources
 Complex Diagonal Matrix
 Generate a square, constant-diagonal complex matrix
DSP Sinks
Triggered Complex Matrix To Workspace
 Send a time sequence of complex matrices to the MATLAB workspace
Triggered Complex To Workspace
 Write the time sequence of a complex input to the MATLAB workspace
Triggered Matrix To Workspace
 Send a time sequence of matrices to the MATLAB workspace
Triggered To Workspace
 Write the time sequence of an input to the MATLAB workspace
Signal Operations
Complex Delay
 Delay a complex input by an integer number of sample periods
Complex Levinson-Durbin
 Apply Levinson-Durbin recursion to design an IIR filter with a prescribed autocorrelation sequence
Complex LPC
 Determine the coefficients of an FIR filter that predicts the next sequence value from past and present inputs
Levinson-Durbin
 Apply Levinson-Durbin recursion to design an IIR filter with a prescribed autocorrelation sequence
LPC
 Determine the coefficients of an FIR filter that predicts the next sequence value from past and present inputs
Variable Fractional Delay
 Delay an input by a fractional number of sample periods
Variable Integer Delay
 Delay an input by an integer number of sample periods
Transforms
Complex Cepstrum
 Compute the complex cepstrum of an input
DCT
 Compute the DCT of a complex vector input
IDCT
 Compute the complex-valued IDCT of a complex input
Real Cepstrum
 Compute the real cepstrum of an input
Real DCT
 Compute the DCT of a real vector input
Real IDCT
 Compute the IDCT of a real input
Buffers
Shift Register
 Convert a scalar time series into a vector time series with the same sample period (serial-to-parallel conversion)
Triggered Shift Register
 Convert a scalar time series into a vector time series with the same sample period (serial-to-parallel conversion)
Switches and Counters
N-Sample Enable w/Reset
Output 1s for a specified number of sample times
Sample and Hold
 Sample and hold an input signal
Vector Math
Autocorrelation
 Compute the autocorrelation of a real vector
Complex Autocorrelation
 Compute the autocorrelation of a complex vector
Complex
Complex Gain
 Multiply an input by a complex constant
Real to Complex
 Construct a complex output from a real input
Statistics
Histogram
 Compute the histogram (frequency distribution) of values in a vector input
Median
 Find the median value of a vector input
Running Histogram
 Track frequency distribution of values in a vector input over time
Sort
 Sort the elements in a vector by value
Filter Realizations
Filter Realization Wizard
 Build an IIR or FIR filter with a particular architecture
Multichannel IIR Filter (Frame)
 Apply an IIR filter to a multichannel input signal
Time Varying FIR Filter
 Apply a variable FIR filter to a multichannel input signal
Time Varying IIR Filter
 Apply a variable IIR filter to a multichannel input signal
Multirate Filters
 FIR Rate Conversion (Frame)
 Upsample, filter, and downsample a real input
Spectrum Analysis
Burg Method
 Compute a parametric estimate of the spectrum using the Burg method
Yule-Walker AR
 Compute a parametric estimate of the spectrum using the Yule-Walker AR method

In addition to the new blocks, several blocks were enhanced for Version 2.2, and are highlighted in the table below. The most important area of growth among the existing blocks is in the expanded support of vector and matrix inputs for buffering and unbuffering operations.

Block Library
 Block Name
 Enhancement
DSP Sources
 Diagonal Matrix
 Allows specification of a nonconstant diagonal
DSP Sinks
Frequency Vector Scope
 Offers new menus, and window position memory
Time Vector Scope
 Offers new menus, and window position memory
Signal Operations
Complex Zero Pad
 Offers the option of truncating the input to the specified output vector length
Delay
 Accepts an initial condition
Zero Pad
 Offers the option of truncating the input to the specified output vector length
Buffers
Buffer
 Supports vector inputs, and accepts an initial condition
Complex Buffer
 Supports vector inputs, and accepts an initial condition
Complex Partial Unbuffer
 Supports matrix inputs
Complex Unbuffer
 Supports matrix inputs
Partial Unbuffer
 Supports matrix inputs
Unbuffer
 Supports matrix inputs
Switches and Counters
Commutator
 Supports matrix inputs
Distributor
 Supports vector inputs, and accepts an initial condition
Multirate Filters
 FIR Rate Conversion
 Supports matrix inputs

For Users Upgrading from Version 1.0a

The DSP Blockset 2.2 is completely compatible with Version 1.0a, but there are some limitations on mixing buffer blocks from the two versions, and you will need to recompile any custom blocks that use C-MEX S-functions so that they work with Simulink 2.2.

See "Upgrading to DSP Blockset 3.0 and Communications Toolbox 1.4" in Chapter 4 for more details about upgrading from Version 1.0a.

Financial Toolbox 1.1

The Financial Toolbox 1.1 supports detailed term structure analysis. In addition, this version provided new date functions, coupon date functions, portfolio allocation tools, and a new derivative pricing function. These new functions are summarized below.

For information about these functions, refer to the Financial Toolbox User's Guide.

Term Structure Functions

Function
 Description
disc2zero
 Zero rate curve from a discount curve.
fwd2zero
 Forward rate curve from a zero curve.
pyld2zero
 Par yield curve from a zero curve.
tbl2bond
 Conversion of TBills to TBond market convention.
termfit
 Demo function for smoothing rates with splines.
tr2bonds
 Conversion of Treasury data to bond input format.
zbtprice
 Bootstrap a zero curve from market bond prices.
zbtyield
 Bootstrap a zero curve from market bond yields.
zero2disc
 Discount factors from a zero curve.
zero2fwd
 Zero curve from a forward curve.
zero2pyld
 Zero curve from a par curve.

Derivatives Function

Function
 Description
blkprice
 Black's pricing model.

Portfolio Analysis Function

Function
 Description
ewcov
 Asset covariance estimation with exponential weighting.

Date Functions

Function
 Description
accrfrac
 Accrued interest coupon period fraction.
busdate
 Next or previous business day.
cfdates
 Cash flow dates of a security.
datefind
 Indices of date numbers in a matrix.
eomdate
 Last date of month.
fbusdate
 First business date of month.
holidays
 Holidays and nontrading days.
ibusday
 True for dates that are business days.
lbusday
 Last business date of the month.
lweekdate
 Date of last occurrence of weekday in month.
m2xdate
 MATLAB serial date number to Excel date number.
months
 Number of whole months between dates.
nweekdate
 Date of specific occurrence of weekday in month.
yeardays
 Number of days in year.
x2mdate
 Excel serial date number to MATLAB date number.

Demo of an Excel Link Portfolio Optimizer Tool

The following files provide a demo of an Excel Link portfolio optimizer tool:

Fuzzy Logic Toolbox 2.0

The Fuzzy Logic Toolbox 2.0 featured several improvements, including:

Graphical User Interface Enhancements

Fuzzy Logic Toolbox 2.0 added or enhanced several GUIs:

Fuzzy Algorithm Improvements

The following Fuzzy Logic algorithms have been added or enhanced:

FIS Represented As MATLAB Structures

The Fuzzy Inference System (FIS) is now represented as a MATLAB structure. A structure (instead of a flat matrix) is now the basic element in constructing a fuzzy logic system. This fundamental change in the way of representing the fuzzy logic system makes many details of working with the constructed system easier.

A Fuzzy Inference System that you created with a pre-2.0 version of the Fuzzy Logic Toolbox is still usable in 2.0, if you run the convertfis function on it. The convertfis function automatically converts pre-2.0 Fuzzy Inference Systems to work with Version 2.0.

More Dimensions Allowed for User-Defined Membership Functions

You can now use up to 16 parameters when you define your own customized membership functions.

Image Processing Toolbox 2.1

Interactive Pixel Value Display

The new function pixval installs in a figure an interactive display of the data values for whatever image pixel the cursor is currently over. You can also click and drag to display the Euclidean distance between two pixels.

Feature Measurement

The new function imfeature computes feature measurements, such as the center of mass and the bounding box, for regions in an image.

Inverse Radon Transform

The new function iradon uses the inverse Radon transform to reconstruct images from projection data. In addition, the toolbox has a new function, phantom, that generates test images for use with the Radon and inverse Radon transforms.

Canny Edge Detector

The edge function now supports the Canny edge detection method. This method is better at detecting weak edges and is less sensitive to noise than the other supported edge-detection methods.

Other Enhancements

Neural Network Toolbox 3.0

The Neural Network Toolbox 3.0 provided several important new features, including:

These features are summarized in more detail in the "What's New in 3.0" section of the updated Neural Network Toolbox User's Guide.

Signal Processing Toolbox 4.1

The Signal Processing Toolbox 4.1 introduces a number of improvements, including a new GUI for the Filter Designer. This section outlines the new additions and provides pointers to the complete feature descriptions in the online (PDF) Signal Processing Toolbox User's Guide. The Signal Processing Toolbox readme file also contains a short summary of this information.

To view the readme file, type at the MATLAB command line

Spectral Estimation

The MEM spectral estimation method (previously implemented by the pmem function) has been more accurately renamed the Yule-Walker AR method, and is now implemented by the pyulear function. The pmem function continues to work, but generates the following warning message:

In addition to this name change, the Burg method of spectral estimation has been added to the toolbox via the pburg function.

SPTool Graphical User Interface

Several areas of the SPTool interactive signal processing environment have been enhanced for Version 4.1. See Chapter 5 in the PDF version of the User's Guide for complete instructions on using the new features.

The Filter Designer interface has been revised for improved usability. A signal's spectrum can now be superimposed on any filter response, and a new Measurements panel displays the filter's characteristics as it is being designed.


The Filter Viewer is now capable of displaying multiple filter responses simultaneously, and also benefits from new rulers that can be used for fine measurement on all of the plot types.


The Spectrum Viewer offers two new spectral estimation methods, the fundamental FFT method, and the Burg method. Additionally, the MEM method has been renamed the Yule-Walker AR method. The MEM option has been retained in the Method pop-up menu for backwards compatibility, but will be removed in a future release. Please use Yule AR instead.

General Enhancements

The following enhancements and bug-fixes are also included in the 4.1 release.

Spline Toolbox 2.0

Multivariate Spline Support

All M-files for the construction of splines (in B-form or ppform) have been expanded to handle tensor-product splines in any number of variables. The same is true for most of the M-files that make use of splines. This means that it is now possible to interpolate, approximate, or smooth gridded data in any number of variables and then evaluate, plot, differentiate, or integrate the resulting multivariate spline.

User Interface Enhancements

In the same spirit of keeping the number of commands small (and of object-oriented programming), most of the form-specific commands (such as spval or ppbrk) have been replaced by generic commands (such as fnval or fnbrk). The forms themselves are now structures, but that should be irrelevant to the casual user.

Vector-Valued Spline Enhancements

Since splines in the toolbox can be vector-valued, it is now possible to handle certain surfaces as 3-vector-valued bivariate tensor-product splines.

Additional Enhancements

Further new features include:


[ Previous | Next ]