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Signal Processing with Simulink
Signal Processing and Communications
Signal Processing with Simulink
Learn how to develop Simulink block diagrams for signal processing applications
Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!
Course
Highlights
This is a 2-day fundamental course for signal processing engineers who are new to system and algorithm modeling and design in Simulink. Through basic modeling techniques and tools, it shows how to develop Simulink block diagrams.
Who Should
Attend
Engineers, researchers who are working with signal processing applications using Simulink.
Course
Prerequisites
MATLAB Fundamentals and Simulink for System and Algorithm Modeling.
Course
Benefits
Upon the completion of the course, the participants will be able to:
- Use Simulink interface
- Model single-channel and multi-channel discrete dynamic systems
- Implement sample-based and frame processing
- Model mixed-signal (hybrid) systems
- Perform spectral analysis with Simulink
- Integrate filter designs into Simulink
- Model multirate systems
- Incorporate external code
- Develop custom blocks and libraries
Partners
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
What is Simulink?
Objective: Get an introduction to Simulink.
- What is Simulink?
- Benefits of using Simulink
- Simulink add-ons
- A look at a Simulink model
Creating and Simulating a Model
Objective: Explore the Simulink interface and block libraries. Build a simple model and analyze the simulation results.
- Creating and editing a Simulink model
- Defining system inputs and outputs
- Simulating the model and analyzing results
Modeling Discrete Dynamic Systems
Objective: Model discrete dynamic systems, and visualize frame-based signals and multichannel signals using a scope.
- Modeling a discrete system with basic blocks
- Finding sample times of block outputs
- Using frames in your model
- Using buffers
- Frames vs. multichannel signals
- Viewing frame-based signals
- Behavior of delay blocks with frame-based signals
- Multichannel frame-based signals
Spectral Analysis
Objective: Perform spectral analysis in the Simulink environment, and use spectrum computation in an algorithm.
- Performing spectral analysis with the Spectrum Scope block
- Choosing spectral analysis parameters
- Analyzing power spectrum of a motor noise
- Building a spectral classifier of speech
- Determining the frequency response of discrete system
Designing and Applying Filters
Objective: Incorporate filters in a model, and explore different ways filters can be designed and implemented in a Simulink model.
- Designing filters in Simulink
- Converting filters to fixed point
Multirate Systems
Objective: Model multirate systems. Resample data and explore multirate filter blocks.
- Modeling multirate systems
- Exploring blocks for multirate signal processing
- Resampling oversampled data
- Designing and implementing anti-imaging and anti-aliasing filters
- Using multirate filter blocks
- Case study: Converting professional audio to CD format
- Converting the design to fixed point
Incorporating External Code
Objective: Import or incorporate custom or external MATLAB and C code into a Simulink model.
- Working with custom and external code considerations
- Incorporating MATLAB code and C code with the MATLAB Function block
Subsystems and libraries
Objective: Create custom blocks in Simulink, apply masks, and develop custom libraries.
- Creating subsystems
- Understanding virtual and atomic subsystems
- Using a subsystem as a model component
- Masking subsystems
- Creating custom block libraries
- Working with and modifying library blocks
- Adding custom libraries to the Simulink Library Browser
- Creating configurable subsystems
Combining Models into Diagrams
Objective: Explore model integration, an important topic for large-scale projects in which several developers are developing different portions of a large system.
- Exploring model referencing and subsystems
- Setting up a model reference
- Setting up model reference arguments
- Exploring model reference simulation modes
- Viewing signals in referenced models
- Browsing the model reference dependency graph
Automating Modeling Tasks
Objective: Control and run Simulink models from the MATLAB command line.
- Automating test runs
- Checking and modifying parameter settings
- Finding blocks with specific parameter values
- Constructing and modifying block diagrams