Preprocessing & Feature Extraction in Signal Processing ... · Scientists require signal processing...
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1© 2015 The MathWorks, Inc.
Preprocessing & Feature Extraction in
Signal Processing Applications
Rick Gentile
Product Manager
Signal Processing and Communications
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Signals and Data are Everywhere
temperature rotation
accelerationvibration
tilt
pressure
position
motionnoise
strain
phase
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Preprocess and Extract Features for Data Analysis
Data AnalysisConnect and AcquireSignal
Processing
Extract
FeaturesPreprocess
Challenge: Gain insights to improve data analysis
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Feature Extraction Techniques Help to Restore Arm Movement
Multichannel electrode implanted in the brain to
record brain signals
Wavelet techniques isolate frequency bands of
brain signals that govern movement
Wavelets help transform 3000 features per channel
into a single value
User Story: Battelle Neural Bypass Technology Restores
Movement to a Paralyzed Man’s Arm and Hand
Developed by Battelle Memorial Institute entirely in MATLAB and Wavelet Toolbox
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Real-World Signals are Challenging to Analyze
Large amounts of data– Wide data multiple streams, many sensors
– Tall data long signals
“Messy” time series– Noise
– Non-uniform sampling
– Lack of alignment between signals
– Missing data
– Data outliers
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Signal Processing for Engineers and Scientists
Signal Modeling,
Generation &
Preprocessing
Measurements &
Feature Extraction
Digital & Analog
Filter Design
Transforms &
Spectral AnalysisVibration Analysis
Is this a signal or just noise?
Are these signals related? How do I measure a delay between signals?
How do I compare signals? How do I align different signals?
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Support for Real-World Applications
Expanded focus over recent releases:
Scientists require signal processing techniques but may not be proficient in this area
GeologistScientist
Biologist
Mechanical Engineer
Oceanographer
Apps to work with data
Intuitive function names
Domain friendly defaults
Easy path to deeper analysis
Traditional users: Electrical Engineer with Signal Processing background
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Signal Preprocessing and Feature Extraction
Extract Features
PreprocessVisualize Transform
Signal Analyzer App
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Visualize
Extraction
Features
• Time and frequency
• Navigate, pan, &
zoom
• Compare multiple
signals
• Extract regions of
interest
Viewing and Exploring Signals with Signal Analyzer App
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Preprocess Messy Signals
Pre-processing for
sensor analytics
Non-uniformly sampled signals
Outliers & data gaps
Misaligned signals
Noise or unwanted frequency content
Preprocess
Visualize
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Resample Non-uniformly Sampled Signals
>>[y, Ty] = resample(x,nonUniformSig,desiredFs);
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What if Data is Missing?
>> [y, Ty] = resample(x,irregTx,desiredFs,'spline');
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Multiple Ways to Reconstruct Missing Data
>> x = y(1:3500);
>> x(2000:2600) = NaN;
>> y2 = fillgaps(x);
Resampling often best for
low frequency components
For large gaps in wideband
signals, autoregressive
modeling is more effective
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Synchronizing Signals from Multiple Sensors
Data collected asynchronously by multiple sensors may require alignment
»[x1,x3] = alignsignals(s1,s3);
»x2 = alignsignals(s2,s3);» dtw(s1,s2)
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Extract Features
Detect change points
Determine signal statistics
Find signal envelope
Extract
Features
Pre-processing for
sensor analytics
Find desired signal from patterns
Find peaks
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Finding Signals and Patterns of Interest
Similarity search for
finding repeat
occurrences
findsignal can be
used with time or
frequency data
Best match
in data
Signal we are
looking for
findsignal
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Searching the Spectral Content
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Finding a Signal of Interest
>>findsignal(PxxSignal,PxxMoan,'Normalization','power‘,'TimeAlignment','dtw',…
'Metric','symmkl','MaxNumSegments',3);
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Challenges of Time-Frequency Analysis
Fixed spectral windows can limit time-
frequency resolution
Features occurring at different scales
may be missed
Sinusoids may not be well localized in
frequency
May need a different class of functions to analyze real world signals
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Time-Frequency Analysis
Continuous
Wavelet Analysis
Discrete Wavelet
Analysis
Denoising and
CompressionFilter Banks
Transform
Extract
Features
Spectrogram
Fourier
Synchrosqueezed
Transform
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Localizing Unwanted Frequency Components
Wavelets used to localize & remove
unwanted spectral components
Wavelet
transform
Localize noise
frequency
Remove noise
Reconstruct
signal
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Summary
Real world signals are challenging
– MathWorks tools make preprocessing and feature extraction easy
MathWorks website includes many examples to get started with
– Data Analytics, Industrial, Automotive, Medical, Noise and Vibration, and
many others
Thank you for attending
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More Resources
https://www.mathworks.com/products/wavelet.html
Wavelet Tech Talks
– Series of 4 short videos on wavelet concepts including
MATLAB-based examples
https://www.mathworks.com/products/signal.html