Training - Courses
SLBE-G: Signal Processing with Simulink
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This three-day course uses basic modeling techniques and tools to show how to develop Simulink® block diagrams for signal processing applications. Topics include:
- What is Simulink?
- Using the Simulink interface
- Modeling single-channel and multi-channel discrete dynamic systems
- Implementing sample-based and frame-based processing
- Modeling mixed-signal (hybrid) systems
- Developing custom blocks and libraries
- Modeling condition-based systems
- Performing spectral analysis with Simulink
- Integrating filter designs into Simulink
- Modeling multirate systems
- Incorporating external code
- Automating modeling tasks
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| Day 1 of 3 |
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| 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
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| 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
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| 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
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| Modeling Logical Constructs | Objective: Model logical expressions. See how zero-crossing detection is used in Simulink and model simple logic in Simulink using MATLAB code. - Modeling logical expressions
- Modeling conditional signal routing
- Understanding zero-crossing detection
- Modeling with the MATLAB Function block
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| From Algorithm to Model | Objective: Create a model from an algorithm specification. - Modeling from algorithmic specification
- Iterative algorithm development through modeling and simulation
- Verifying model against specified algorithm
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| Day 2 of 3 |
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| Mixed-Signal Models and Solvers | Objective: Model mixed-signal systems, and explore different solver types in Simulink. - What is a mixed-signal model?
- Modeling an ADC with aperture jitter and nonlinearity
- Understanding the Simulink solver
- Solving simple models
- Solving models with discrete and continuous states
- Solving models with multiple rates
- Fixed-step and variable-step solvers
- Choosing a continuous-state system solver
- Handling zero crossings
- Handling algebraic loops
- Case study: Modeling TI's ADS62P29 ADC
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| 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
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| Conditional Subsystems | Objective: Model systems with parts that are executed conditionally. - Conditionally executed subsystems
- Modeling condition-driven systems with enabled subsystems
- Modeling condition-driven systems with triggered subsystems
- An example using the AGC model
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| 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
- A spectral classifier of speech
- Determining frequency response of a discrete system
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| Day 3 of 3 |
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| Filter Design | 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
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| Multirate Systems | Objective: Model multirate systems. Resample data and explore multirate filter blocks. - Multirate systems
- 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
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| Incorporating External Code | Objective: Import or incorporate custom or external MATLAB and C code into a Simulink model. - Custom and external code considerations
- Incorporating MATLAB code with the MATLAB Function block
- Incorporating C code with S-Function Builder block
- Incorporating C code with Legacy Code Tool
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| Combining Models into Diagrams | Objective: Explore model integration, an important topic for large-scale projects, where several developers are developing different portions of a large system. - Model referencing and subsystems
- Setting up a model reference
- Setting up model reference arguments
- Model reference simulation modes
- Viewing signals in referenced models
- Browsing the model reference dependency graph
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| 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
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Prerequisites
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MATLAB Fundamentals and
Signal Processing with MATLAB
Course Length - 3
days
