Signal Preprocessing and Feature Extraction for Data Analytics with MATLAB

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. No prior knowledge on signal processing is needed for this course.

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 patterns in signals, finding change points, locating peaks, and identifying trends

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Prerequisites

MATLAB Fundamentals或同等经验erience using MATLAB

Day 1 of 1
Explore and Analyze Signals (Time Series) in MATLAB	Objective: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	Objective:Learn techniques to clean signal sets with operations such as resampling, removing outliers, and filling gaps. Perform resampling to ensure a 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	Objective: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 classification algorithms

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Dates	Location	Language	Price	Register
27 Sep 2022	Virtual 9:00 am - 5:00 pm China Standard Time	Chinese	USD 750

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