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Getting Started with Signal Processing Toolbox

Tutorials

Getting Started with Signal Analyzer App

Visualize, measure, analyze, and compare signals in both the time domain and the frequency domain.

Representing Signals

Use vectors or matrices to represent signals.

Waveform Generation: Time Vectors and Sinusoids

Generate a vector representing a time base. Create a sample signal consisting of two sinusoids.

Filtering Data With Signal Processing Toolbox Software

Design and implement a filter using command-line functions or an interactive app.

Common Applications

Import or Generate Data

Supported File Formats for Import and Export

Table of file formats that MATLAB® can read and write, and recommended functions

Create Uniform and Nonuniform Time Vectors

Create time vectors to use as independent variables in computations involving time series.

Preprocess Signals

Remove Trends from Data

Take out irrelevant overall patterns that impede data analysis.

Remove the 60 Hz Hum from a Signal

Filter out 60 Hz oscillations that often corrupt measurements.

Remove Spikes from a Signal

Use median filtering to eliminate unwanted transients from data.

Process a Signal with Missing Samples

Signals often have missing samples. To provide estimates for these values, use resampling.

Reconstruct a Signal from Irregularly Sampled Data

Resample and interpolate data measured at irregular intervals.

Align Signals with Different Start Times

Synchronize data collected by different sensors at different instants.

Find a Signal in a Measurement

Determine if a signal matches a segment of a noisy longer stream of data.

Find Patterns and Extract Features

Find Peaks in Data

Locate the local maxima in a set of data and determine if those peaks occur periodically.

Find Periodicity Using Autocorrelation

Verify the presence of cycles in a noisy signal, and determine their durations.

Extract Features of a Clock Signal

Determine how often and how sharply a bilevel signal turns on and off.

Find Periodicity in a Categorical Time Series

Perform spectral analysis of data whose values are not inherently numerical.

Design, Analyze, and Apply Digital Filters

Compensate for the Delay Introduced by an FIR Filter

Use indexing to counteract the time shifts introduced by filtering.

Compensate for the Delay Introduced by an IIR Filter

Remove delays and distortion introduced by filtering, when it is critical to keep phase information intact.

Take Derivatives of a Signal

Use a differentiator filter to differentiate a signal without amplifying the noise.

Perform Spectral Analysis

Find Periodicity Using Frequency Analysis

Spectral analysis helps characterize oscillatory behavior in data and measure the different cycles.

Detect a Distorted Signal in Noise

Use frequency analysis to characterize a signal embedded in noise.

Measure the Power of a Signal

Estimate the width of the frequency band that contains most of the power of a signal. For distorted signals, determine the power stored in the fundamental and the harmonics.

Compare the Frequency Content of Two Signals

Identify similarity between signals in the frequency domain.

Detect Periodicity in a Signal with Missing Samples

Use the Lomb-Scargle periodogram to study the periodicity of an irregularly sampled signal.

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