Signal Preprocessing Techniques

Learn and apply preprocessing techniques to improve signal quality. You will learn to fill missing data, replace outliers, remove trends, and remove noise in signals. After each preprocessing step, you will analyze changes in the signal’s frequency spectrum to ensure that key signal information is preserved. You will also evaluate preprocessing results using signal quality metrics.

Course modules

Introduction

Familiarize yourself with the course.

Lessons:

Course Overview

Learning Outcomes and Prerequisites

Common Issues in Signals

Course Example: Aircraft Vibration Signals

Identify Issues in a Signal

Fill Missing Data and Replace Outliers in Signals

Learn to handle missing samples and outliers in signals using suitable gap-filling methods and smoothing techniques.

Lessons:

Missing Data in Signals

Fill Large and Small Gaps in a Signal

Handle Outliers in Signals

Overview of Signal Smoothing

Replace Outliers in a Signal

Detrend Signals

Use detrending techniques to remove linear trends while preserving the true signal characteristics.

Lessons:

Baseline Trends in Signals

Remove a Linear Trend from a Signal

Denoise Signals

Identify noise in signals and use denoising techniques to clean and improve signal quality.

Lessons:

Signal Noise Types and Denoising Methods

Remove Periodic Noise from a Signal

Remove White Noise from a Signal

Automate Signal Preprocessing

Generate a MATLAB function from the Signal Analyzer app to automate signal preprocessing operations.

Lessons:

Use a Generated MATLAB Function to Preprocess Signals

Evaluate Signal Quality

Evaluate the overall quality of the preprocessed signal using the signal quality metrics.

Lessons:

Types of Signal Quality Metrics

Evaluate the Quality of a Preprocessed Signal

Conclusion

Learn next steps and give feedback on the course.

Lessons:

Summary

Additional Resources

Survey

Signal Preprocessing Techniques

Course modules

Introduction

Fill Missing Data and Replace Outliers in Signals

Detrend Signals

Denoise Signals

Automate Signal Preprocessing

Evaluate Signal Quality

Conclusion

Language

Hands-on exercises with automated feedback
Access to MATLAB through your web browser
Shareable progress report and course certificate

Feature Extraction Techniques for Signals

Signal Processing Onramp

Signal Preprocessing Techniques

Course modules

Introduction

Fill Missing Data and Replace Outliers in Signals

Detrend Signals

Denoise Signals

Automate Signal Preprocessing

Evaluate Signal Quality

Conclusion

Language

Hands-on exercises with automated feedbackAccess to MATLAB through your web browserShareable progress report and course certificate

Feature Extraction Techniques for Signals

Signal Processing Onramp

Hands-on exercises with automated feedback
Access to MATLAB through your web browser
Shareable progress report and course certificate