Signal Processing with Simulink

This three-day course, targeted toward new users of Simulink^®, uses basic modeling techniques and tools to demonstrate 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

See detailed course outline

Prerequisites

MATLAB Fundamentals and basic knowledge of digital signal processing.

Day 1 of 3
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
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
From Algorithm to Model	Objective: Create a model from an algorithm specification. Modeling from algorithmic specifications Iterative algorithm development through modeling and simulation Verifying models against specified algorithms

Day 2 of 3
Mixed-Signal Models	Objective: Model mixed-signal systems. What is a mixed-signal model? Modeling an ADC with aperture jitter and nonlinearity Case study: Modeling TI's ADS62P29 ADC
Simulink Solvers	Objective: Choose the right solver for a Simulink model. 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
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
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 Working with an example using the AGC model
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 a discrete system

Day 3 of 3
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
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

Guaranteed to Run

When you register for one of these courses, you can rely on the fact that it won't be canceled or rescheduled for any reason.

Course Schedule

Results 1 - 3 of 3

Dates	Location	Language	Price
06 Oct 2021- 08 Oct 2021	Virtual 9:00 am - 5:00 pm Japan Standard Time Register today - only 2 seat(s) remaining	Japanese	USD 1,950
26 Oct 2021- 28 Oct 2021	Virtual 9:00 am - 5:00 pm US Pacific Daylight Time	English	USD 1,950
07 Dec 2021- 09 Dec 2021	Virtual 9:00 am - 5:00 pm Central European Time	English	USD 1,950

Results 1 - 3 of 3

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