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THAT'S
MODEL-BASED DESIGN!
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| TECHNICAL
UPDATES |
Products Updates
|
|
Check
out the NEW ENHANCED features in VIP Blockset 2!
Video
and Image Processing Blockset 2.0 is useful for engineers
who are developing innovative and intelligent video and image
processing systems. It expands an extensive library of video
and image processing blocks by adding new functionality for
target tracking, region of interest processing, blob analysis,
optical flow, projective transformations, pyramiding and color
processing.
The enhancements
include:
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New
blocks for Optical Flow, Gaussian Pyramiding, Projective
Transform, Block Matching, Peak |
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Signal-to-Noise
Ratio (PSNR), and De-interlacing. |
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Region
of interest (ROI) processing added to Mean, Standard Deviation,
Variance and Projective |
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Transform
blocks. |
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New
features in the following blocks: Blob Analysis, MPlay,
and Color Conversion. |
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Figure
1: Tracking cars model uses an optical flow estimation
technique to estimate the motion vectors in each
frame of the video sequence. The counter in the
upper left corner of the Results window tracks the
number of cars in the region of interest. |
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For
more information about Video and Image Processing
Blockset 2.0:
http://www.mathworks.com/products/viprocessing/ |
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New
product demos of Video and Image Processing Blockset
include: Cars and people tracking using background estimation
and optical flow estimation; traffic warning sign recognition;
and cell counting using morphological operators and
blob analysis.
To
learn more about Video and Image Processing Blockset
through online demos, please visit the following URL:
http://www.mathworks.com/products/viprocessing/demos.html
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TMW
releases the NEW SimEvents 1.0.
For
engineers designing packet-based communications networks,
distributed control, manufacturing, mission planning,
supervisory control and logistics systems who need to
model both event-driven server or queue systems and
time-driven dynamics, SimEvents provides a discrete
event simulation domain within the Simulink environment.
Unlike stand-alone discrete event simulators, SimEvents
provides a single environment for hybrid time- and event-driven
modeling.
Key features include
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Enables
entity-based, discrete-event simulation. |
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Libraries
of queues, servers, switches and gates. |
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Generators
for entities, events and signals. |
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Supports
hybrid simulation of models that contain both event-based
and time-based execution components. |
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Automatically
collects common statistics, such as delay and throughput.
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.gif) |
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Figure
2: A SimEvents model of shared access communications and
a plot of entity delays showing two simultaneous arrival
events. |
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For more
information about SimEvents 1.0:
http://www.mathworks.com/products/simevents/
To learn
more about SimEvents through online demos, please visit the
following URL:
http://www.mathworks.com/products/simevents/demos.html
|
| |
Tips
and Techniques
|
| How
to Speed up Model Simulation & Manage Large Models? |
| |
|
| 1. |
What
is Model Referencing? |
| |
|
|
The
new Simulink 6 (R14) allows a Simulink model to include
blocks that refer to other models. You create references
to other models by creating instances of these models
using the 'Model' block. This feature works by generating
code for the referenced models. This generated code is
used during simulation of the parent model, thus reducing
simulation time. |
|
| |
|
| 2. |
Benefits
of model referencing |
| |
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|
|
Inclusion
by reference |
| |
You
can reference a model multiple times in another
model without having to make redundant copies and
multiple models can reference the same model. |
| |
|
|
Incremental
loading
|
| |
The
referenced model is not loaded until it is needed,
speeding up model loading. |
| |
|
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Incremental
code generation
|
| |
If
there is no change to the referenced model, no code
generation occurs when models that reference them
are simulated or compiled. |
| |
|
|
| 3. |
How
to do Model Referencing? |
|
|
|
| |
Method
1:
|
| |
Create
an instance of the Model block in the parent model.
Enter the name of the referenced model in the parameter
dialog box's Model name field. |
| |
|
| |
|
| |
|
| |
Method
2: |
| |
You
can use Simulink.SubSystem.convertToModelReference
to convert an atomic subsystem to model reference.
It does this by creating a model, copying the
contents of the subsystem into the model, and
reconfiguring the root level Inport and Outport
blocks and configuration parameters of the new
model.
|
| |
|
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 |
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|
| 4. |
Model
Dependency Graph |
| |
|
| |
To
browse a graph of the models that a model references
directly or indirectly, select Tools > Model
Reference Graph from the Model Editor's menu bar.
A MATLAB figure window appears that displays a browsable
graph of the models that the top model references. |
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Technical
Applications
|
|
|
Discover
the Tracking People Capabilities in VIP Blockset 2.
Using
Video & Image processing blockset and Simulink, users
can now build simple image processing application using the
advance functions that are available in the library. The Simulink
model shown below is a demonstration of the people tracking
application that is build using mainly the Video & Image
processing blocks.
|
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| This
model is consists of four stages, the first stage is to
input an .avi file using "From Multimedia File"
block. Then, the Background Estimator subsystem will use
the first few frames of the video to estimate the background
image. |
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|
|
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|
In
the People Tracker subsystem, the estimated background
image will be used to subtract each video frame to produce
foreground images. In the final stage, a bounding box
will be defined around each person identified using
the Blob Analysis block, and cyan rectangles will be
drawn around the people in the video stream using the
"Draw Shapes" block, as seen in the final
image.
For
a detailed explanation of the model and the video for
the people tracking demo, please visit the link provided
below:
http://www.mathworks.com/products/demos/videoimage/PeopleTracking/viptrackpeople.html
|
| |
|
|
| EVENTS
& TRAINING |
|
Unleash the power of MATLAB & Simulink. |
All-in-One MATLAB and SIMULINK Training Package (5-Day) |
| This 5-day
training package provides the fundamentals of MATLAB and SIMULINK
and an introduction of selected toolboxes/blocksets under an
umbrella scheme to give participants a clearer picture on how
to integrate The Mathworks tools. Includes Signal Processing,
Image Processing, Control System, Communication and Model-based
Design for FPGA Implementation. |
|
Comprehensive MATLAB (3-Day) |
|
This
3-day hands-on training is especially designed for beginners
new to MATLAB. Beginning with an introduction to MATLAB, the
course will first provide participants with a working understanding
of MATLAB technical computing environment. Extensive examples
will be used to illustrate how to perform common tasks with
MATLAB on a step-by-step approach. The last part of the course
will delve deeper into sophisticated usage of MATLAB to create
more robust, flexible programs
|
|
|
Comprehensive SIMULINK (3-Day)
|
|
This 3-day
hands-on training will first provide the essential knowledge
required to build basic modeling techniques and tools to developing
SIMULINK block diagrams. Participants will be provided with
a working understanding of system and algorithm modeling and
design validation in SIMULINK.
The last part of the course shall cover effective system modeling
techniques to improve user's ability to model using SIMULINK
and also highlight which tools are most appropriate for certain
applications. Focuses on modeling effectively in SIMULINK
to increase simulation speed and to create readable, user-friendly
diagrams.
|
|
|
SIMULINK for Xilinx and DSP Design Flow (5-Day)
|
|
This is
a 5-day training package that provides system architects,
DSP designers, and FPGA designers a hands-on course covering
the basics of using SIMULINK and the Xilinx design flow for
implementing DSP functions. You will learn how to use Simulink
to perform system-level DSP design, approach the complexities
of high-performance DSP design and implement a design from
algorithm concept to hardware verification using Xilinx automatic
translation (System Generator) and implementation (ISE) tools.
|
|
Public Course Calendar Jan 06 to Mar 06 |
|
| Course
Title |
Jan
|
Feb
|
Mar
|
| |
|
|
|
| Singapore
|
|
|
|
|
All-in-One
MATLAB/SIMULINK Programming Productivity
|
|
16-17
27-28 |
01
|
|
Comprehensive
MATLAB
|
|
16-17
20 |
14-16
|
|
Comprehensive
SIMULINK
|
|
27-28 |
06
|
|
MATLAB
for Technical Computing
|
12-13 |
|
|
|
Advanced
MATLAB Programming Techniques
|
24 |
|
|
|
Advanced
SIMULINK Techniques for Effective System Modeling
|
|
|
16
|
|
Applying
Image Processing Techniques with MATLAB
|
|
|
30-31
|
|
Applying
Communication Design with SIMULINK
|
19-20 |
|
|
|
Applying
Control Design with MATLAB & SIMULINK
|
|
|
22-23
|
|
Applying
Signal Processing with MATLAB & SIMULINK
|
|
|
20-21
|
|
Applying
Neural Network with MATLAB
|
|
|
27-28
|
|
SIMULINK
for Xilinx and DSP Design Flow
|
|
|
02-03
08-10
|
| |
|
|
|
|
Malaysia
|
|
|
|
|
MATLAB
Fundamentals & Programming Techniques
|
04-05 |
|
28-29
|
|
Applying
Signal Processing with MATLAB & SIMULINK
|
|
|
08-09
|
|
Applying
Control Design with MATLAB & SIMULINK
|
25-26 |
|
|
|
Applying
Neural Network with MATLAB
|
|
|
22-23 |
|
Model-based
Design with SIMULINK
|
|
15-16 |
|
| |
|
|
|
|
Thailand
|
|
|
|
|
MATLAB
Fundamentals
|
19-20
|
|
|
|
Numerical
Computing using MATLAB
|
25
|
|
|
|
MATLAB
& SIMULINK for Students
|
28
|
|
|
|
Neural
Network Implementation in MATLAB
|
|
06-07
|
|
|
MATLAB
for Image Processing
|
|
16-17
|
|
|
Building
GUI with MATLAB
|
|
21-22
|
|
|
DSP
using MATLAB & SIMULINK
|
|
23-25
|
|
|
Wavelet
Analysis with MATLAB
|
|
|
3
|
|
|
| Visit
www.activemedia.com.sg or Contact us at: |
|
|
|
