MATLAB | How to Average a Signal Using Simulink @MATLAB | Uploaded July 2024 | Updated October 2024, 1 week ago.
Watch this detailed introduction to signal averaging using Simulink. Averaging is the one of the most commonly applied signal processing algorithms. There are many forms of averaging and each method has its own advantages. You’ll learn about running averages, sliding window or FIR-based averages, and exponential or IIR-based averages and when you would use one method versus another. Signal averaging serves one of two purposes, either to reduce noise or to better estimate it.
You’ll learn how these averaging algorithms work at the detailed block diagram level as applied to streaming time series data. The focus is on signal processing applications but these same averaging techniques apply to other domains as well, e.g. financial or stock market data.
You can download the R2024a examples used in this video here: github.com/kschutz68/AVERAGING_1.git
Learn more:
- Moving Average: bit.ly/3W1Qi3y
- dsp.MovingAverage: bit.ly/3L3oANp
- Spectrum Analyzer: bit.ly/3XG32xS
Chapters:
0:00 Introduce Different Types of Signal Averaging
2:10 Walk Through of a Running Average Implementation
3:46 Test Running Average Model
5:38 Brute-Force Moving Average Implementation
8:55 Efficient Vectorized Delay-Line based Moving Average Implementation
10:10 FIR-based Moving Average Implementation
10:30 CIC-based Moving Average Implementation
11:30 Moving Average from Sliding Window in DSP System Toolbox
12:10 Using the Scope Legend to turn traces on and off
12:34 Walk-through of the Fixed-Coefficient Exponential Averaging Approach
14:22 Walk-through of the Dynamic-Coefficient Exponential Averaging Approach
16:15 Exponential Averaging block Implementation from DSP System Toolbox
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Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Watch this detailed introduction to signal averaging using Simulink. Averaging is the one of the most commonly applied signal processing algorithms. There are many forms of averaging and each method has its own advantages. You’ll learn about running averages, sliding window or FIR-based averages, and exponential or IIR-based averages and when you would use one method versus another. Signal averaging serves one of two purposes, either to reduce noise or to better estimate it.
You’ll learn how these averaging algorithms work at the detailed block diagram level as applied to streaming time series data. The focus is on signal processing applications but these same averaging techniques apply to other domains as well, e.g. financial or stock market data.
You can download the R2024a examples used in this video here: github.com/kschutz68/AVERAGING_1.git
Learn more:
- Moving Average: bit.ly/3W1Qi3y
- dsp.MovingAverage: bit.ly/3L3oANp
- Spectrum Analyzer: bit.ly/3XG32xS
Chapters:
0:00 Introduce Different Types of Signal Averaging
2:10 Walk Through of a Running Average Implementation
3:46 Test Running Average Model
5:38 Brute-Force Moving Average Implementation
8:55 Efficient Vectorized Delay-Line based Moving Average Implementation
10:10 FIR-based Moving Average Implementation
10:30 CIC-based Moving Average Implementation
11:30 Moving Average from Sliding Window in DSP System Toolbox
12:10 Using the Scope Legend to turn traces on and off
12:34 Walk-through of the Fixed-Coefficient Exponential Averaging Approach
14:22 Walk-through of the Dynamic-Coefficient Exponential Averaging Approach
16:15 Exponential Averaging block Implementation from DSP System Toolbox
--------------------------------------------------------------------------------------------------------
Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
