MATLAB & Simulink


Signal Processing and Feature Extraction for Data Analytics with MATLAB


The data sets that are produced by sensors are very large. After loading the data into MATLAB, preprocessing and feature extraction steps are required to reduce the data and transform it into a format that can be presented to data analytic and machine learning algorithms. Typically, the relevant features for this time-based data can be found in the time and frequency domains using traditional signal processing techniques such as spectral analysis and filtering.

With MATLAB, engineers and data scientists have immediate access to prebuilt functions, extensive toolboxes, and specialized apps for signal analysis and classification.

MATLAB lets you:

  • Import a signal into the Signal Analyzer App, perform spectral analysis to view the signal in the frequency domain and use a spectrogram to visualize the signal in the joint time-frequency plane.

  • Perform resampling to ensure common time base across signals, work with nonuniformly sampled data, find gaps in data and remove or fill, use wavelet denoising, use envelope spectrum to perform fault analysis, locate outlier value in data and replace with acceptable data.

  • Use basic statistics to generate features, use spectral analysis to detect periodicity embedded inside a signal, locate peaks, use the Classification Learner App to train all available classifiers.


Upcoming Program

Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner

Course Highlights

This one-day course shows how to use MATLAB®, Signal Processing Toolbox™ and Wavelet Toolbox™ to preprocess time-based signals and extract key features in the time and frequency domains. This course is intended for data scientists and engineers analyzing signals (time series) for data analytics applications.

Topics include:

  • Creating, importing, and visualizing signals

  • Preprocessing to improve data quality, including filling data gaps, resampling, smoothing, aligning signals, finding and removing outliers, and handling non-uniformly sampled signals

  • Extracting features in the time and frequency domains, including finding signals from patterns, finding change points, locating peaks, and identifying trends

Course Objectives

Upon the completion of the course, the participants will gain skills to:

  • Import, visualize and browse signals to gain insights

  • Preprocess signals to improve data sets quality

  • Extract features from signals

Who Should Attend

This course is intended for mathematician or statistician or engineer with some statistics domain expertise but little signal processing or machine learning expertise that needs to apply machine learning to time-based data.


Comprehensive MATLAB” or equivalent experience in using MATLAB 

Course Outline

Day 1 of 1

Explore and Analyze Signals (Time Series) in MATLAB

Objectives: Learn to easily import and visualize multiple signals or time series data sets to gain insights into the features and trends in the data.

  • Import, visualize, and browse signals to gain insights

  • Make measurements on signals

  • Compare multiple signals in the time and frequency domain

  • Perform interactive spectral analysis

  • Extract regions of interest for focused analysis

  • Recreate analysis with auto-generated MATLAB scripts


Preprocess Signals to Improve Data Set Quality


Objectives: Learn techniques to clean signal sets with operations such as resampling, removing outliers, and filling gaps.

  • Perform resampling to ensure common time base across signals

  • Work with non-uniformly sampled data

  • Find gaps in data and remove or fill gaps

  • Remove noise and unwanted frequency content

  • Perform wavelet denoising

  • Use the envelope spectrum to perform fault analysis

  • Locate outlier values in data and replace them with acceptable data

  • Locate signal changepoints and use boundaries to automatically create signal segments


Extract Features from Signals


Objectives: Apply different techniques in time and frequency domains to extract features. Become familiar with the spectral analysis tools in MATLAB and explore ways to bring out features for multiple signals.

  • Locate peaks

  • Locate desired signals from patterns in the time and spectral domains

  • Use spectral analysis to extract features from signals

  • Perform classification using supervised learning

  • Use the Classification Learner app to interactively train and evaluate neural networks

**Group Discount is available for registration of 3 delegates and above.  Kindly check with our Training Consultants for more details.