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This MATLAB function returns the time differences between the mid-reference level instants of the initial and final transitions of each positive-polarity pulse in the input bilevel waveform.
- Signal Generation and Visualization - MATLAB & Simulink Example - MathWorks
An optional width parameter specifies a fractional multiple...
- Measurement of Pulse and Transition Characteristics - MATLAB & Simulink ...
Measure Pulse Width and Duty Cycle. Width is the duration...
- Pulse and Transition Metrics - MATLAB & Simulink - MathWorks
Analyze pulses and transitions and compute metrics such as...
- Measure Signals - MATLAB & Simulink - MathWorks United Kingdom
In the Signal Analyzer app, you can measure data,...
- Signal Processing with MATLAB | Self-Paced Online Courses - MATLAB ...
Use established signal processing methods for sampling,...
- Signal Generation and Visualization - MATLAB & Simulink Example - MathWorks
An optional width parameter specifies a fractional multiple of 2 π at which the signal's maximum occurs. The square function generates a square wave with a period of 2 π . An optional parameter specifies duty cycle, the percent of the period for which the signal is positive.
Measure Pulse Width and Duty Cycle. Width is the duration between the mid-reference level crossings of the first and second transitions of each pulse. Use pulsewidth with no output argument to plot highlighted pulse widths. Specify a positive polarity.
Use the following MATLAB code to use the fft function to perform Fourier transform on the generated rectangular pulse signal and visualize the magnitude of the rectangular pulse signal in frequency domain. y=fftshift(fft(x)); % apply Fourier transform and move zero frequency component to the center . N=length(y); % measure frequency range .
Analyze pulses and transitions and compute metrics such as rise time, fall time, slew rate, overshoot, undershoot, pulse width, and duty cycle.
In the Signal Analyzer app, you can measure data, statistics, and peaks for a plotted signal. On the Measurements tab, calculate the values for the minimum, maximum, mean, median, peak-to-peak amplitude, root-mean-square value, and peaks of each signal.
Use established signal processing methods for sampling, spectral analysis, and filtering in MATLAB®. Learn to use different techniques to get accurate, informative results from your signals. Generate different types of sampled signals.
