This example shows how to visualize and measure signals in the time and frequency domain in MATLAB® using a time scope and spectrum analyzer.
Create a sine wave with a frequency of 100 Hz sampled at 1000 Hz. Generate five seconds of the 100 Hz sine wave with additive N(0,0.0025) white noise in one-second intervals. Send the signal to a time scope and spectrum analyzer for display and measurement.
SampPerFrame = 1000; Fs = 1000; SW = dsp.SineWave('Frequency',100,... 'SampleRate',Fs,... 'SamplesPerFrame',SampPerFrame); TS = timescope('SampleRate',Fs,... 'TimeSpanSource','property',... 'TimeSpan',0.1,... 'YLimits',[-2, 2],... 'ShowGrid',true); SA = spectrumAnalyzer('SampleRate',Fs,... 'Method','welch','AveragingMethod','exponential'); tic; while toc < 10 sigData = SW() + 0.05*randn(SampPerFrame,1); SA(sigData); TS(sigData); end release(TS)
release(SA)
Using the time scope, you can make a number of time-domain signal measurements.
The following measurements are available:
Cursor Measurements - Puts screen cursors on all scope displays.
Signal Statistics - Displays maximum, minimum, peak-to-peak difference, mean, median, RMS values of a selected signal, and the times at which the maximum and minimum occur.
Bilevel Measurements - Displays information about a selected signal's transitions, aberrations, and cycles.
Peak Finder - Displays maxima and the times at which they occur.
You can enable and disable these measurements from the Measurements tab.
To illustrate the use of measurements in the time scope, simulate an ECG signal. Use the ecg
function to generate 2700 samples of the signal. Use a Savitzky-Golay filter to smooth the signal and periodically extend the data to obtain approximately 11 periods.
x = 3.5*ecg(2700).'; y = repmat(sgolayfilt(x,0,21),[1 13]); sigData = y((1:30000) + round(2700*rand(1))).';
Display the signal in the time scope and use the Peak Finder and Data Cursor measurements. Assume a sample rate of 4 kHz.
TS_ECG = timescope('SampleRate',4000,... 'TimeSpanSource','Auto',... 'ShowGrid', true); TS_ECG(sigData); TS_ECG.YLimits = [-4, 4];
Peak Measurements
Enable Peak Measurements from the Measurements tab by clicking the corresponding toolstrip button. The Peaks pane appears at the bottom of the time scope window. For the Num Peaks property, enter 8
and press Enter. In the Peaks pane, the time scope displays a list of 8 peak amplitude values and the times at which they occur.
There is a constant time difference of 0.675 seconds between each heartbeat. Therefore, the heart rate of the ECG signal is given by the following equation:
60sec/min0.675sec/beat=88.89beats/min(bpm)
Cursor Measurements
Enable Cursor Measurements from the Measurements tab by clicking the corresponding toolstrip button. The cursors appear on the time scope with a box showing the change in time and value between the two cursors. You can drag the cursors and use them to measure the time between events in the waveform. As you drag a cursor, the time an value at the cursor appears. This figure shows how to use cursors to measure the time interval between peaks in the ECG waveform. The ΔT measurement in the cursor box demonstrates that the time interval between the two peaks is 0.675 seconds corresponding to a heart rate of 1.482 Hz or 88.9 beats/min.
Signal Statistics and Bilevel Measurements
You can also select Signal Statistics and various bilevel measurements from the Measurements tab. Signal Statistics can be used to determine the signal's minimum and maximum values as well as other metrics like the peak-to-peak, mean, median, and RMS values. Bilevel measurements can be used to determine information about rising and falling transitions, transition aberrations, overshoot and undershoot information, settling time, pulse width, and duty cycle. To read more about these measurements, see Configure Time Scope MATLAB Object.
This section explains how to make frequency domain measurements with the spectrum analyzer.
The spectrum analyzer provides the following measurements:
Cursor Measurements - places cursors on the spectrum display.
Peak Finder - displays maxima and the frequencies at which they occur.
Channel Measurements - displays occupied bandwidth and ACPR channel measurements.
Distortion Measurements - displays harmonic and intermodulation distortion measurements.
You can enable and disable these measurements from the spectrum analyzer toolstrip.
Distortion Measurements
To illustrate the use of measurements with the spectrum analyzer, create a 2.5 kHz sine wave sampled at 48 kHz with additive white Gaussian noise. Evaluate a high-order polynomial (9th degree) at each signal value to model non-linear distortion. Display the signal in a spectrum analyzer.
Fs = 48e3; SW = dsp.SineWave('Frequency',2500,... 'SampleRate',Fs,... 'SamplesPerFrame',SampPerFrame); SA_Distortion = spectrumAnalyzer('SampleRate',Fs,... 'Method','welch',... 'AveragingMethod','exponential',... 'PlotAsTwoSidedSpectrum',false); y = [1e-6 1e-9 1e-5 1e-9 1e-6 5e-8 0.5e-3 1e-6 1 3e-3]; tic; while toc < 5 x = SW() + 1e-8*randn(SampPerFrame,1); sigData = polyval(y, x); SA_Distortion(sigData); end release(SA_Distortion);
Enable the harmonic distortion measurements by selecting the Distortion button on the Measurements tab of the spectrum analyzer toolstrip. In the Distortion section, change the value for Num Harmonics to 9 and check the Label Harmonics checkbox. In the Harmonic Distortion panel at the bottom of the spectrum analyzer window, you see the value of the fundamental close to 2500 Hz and 8 harmonics as well as their SNR, SINAD, THD and SFDR values, which are referenced with respect to the fundamental output power.
Peak Finder
You can track time-varying spectral components by using the Peak Finder measurements. You can show and optionally label up to 100 peaks. To invoke the Peak Finder, select the Peak Finder button on the Measurements tab of the spectrum analyzer toolstrip.
To illustrate the use of Peak Finder, create a signal consisting of the sum of three sine waves with frequencies of 5, 15, and 25 kHz and amplitudes of 1, 0.1, and 0.01 respectively. The data is sampled at 100 kHz. Add N(0,10−8) white Gaussian noise to the sum of sine waves and display the one-sided power spectrum in the spectrum analyzer.
Fs = 100e3; SW1 = dsp.SineWave(1e0,5e3,0,... 'SampleRate',Fs,... 'SamplesPerFrame',SampPerFrame); SW2 = dsp.SineWave(1e-1,15e3,0,... 'SampleRate',Fs,... 'SamplesPerFrame',SampPerFrame); SW3 = dsp.SineWave(1e-2,25e3,0,... 'SampleRate',Fs,... 'SamplesPerFrame',SampPerFrame); SA_Peak = spectrumAnalyzer('SampleRate',Fs,... 'Method','welch',... 'AveragingMethod','exponential',... 'PlotAsTwoSidedSpectrum',false); tic; while toc < 10 sigData = SW1() + SW2() + SW3() + 1e-4*randn(SampPerFrame,1); SA_Peak(sigData); end release(SA_Peak);
Enable the Peak Finder to label the three sine wave frequencies. The frequency values and powers in dBm are displayed below the plot. You can increase or decrease the maximum number of peaks, specify a minimum peak distance, and change other settings in the Peaks section of the Measurements tab.
To learn more about the use of measurements with the spectrum analyzer, see the Spectrum Analyzer Measurements example.
Matlabsolutions.com provides guaranteed satisfaction with a
commitment to complete the work within time. Combined with our meticulous work ethics and extensive domain
experience, We are the ideal partner for all your homework/assignment needs. We pledge to provide 24*7 support
to dissolve all your academic doubts. We are composed of 300+ esteemed Matlab and other experts who have been
empanelled after extensive research and quality check.
Matlabsolutions.com provides undivided attention to each Matlab
assignment order with a methodical approach to solution. Our network span is not restricted to US, UK and Australia rather extends to countries like Singapore, Canada and UAE. Our Matlab assignment help services
include Image Processing Assignments, Electrical Engineering Assignments, Matlab homework help, Matlab Research Paper help, Matlab Simulink help. Get your work
done at the best price in industry.
Desktop Basics - MATLAB & Simulink
Array Indexing - MATLAB & Simulink
Workspace Variables - MATLAB & Simulink
Text and Characters - MATLAB & Simulink
Calling Functions - MATLAB & Simulink
2-D and 3-D Plots - MATLAB & Simulink
Programming and Scripts - MATLAB & Simulink
Help and Documentation - MATLAB & Simulink
Creating, Concatenating, and Expanding Matrices - MATLAB & Simulink
Removing Rows or Columns from a Matrix
Reshaping and Rearranging Arrays
Add Title and Axis Labels to Chart
Change Color Scheme Using a Colormap
How Surface Plot Data Relates to a Colormap
How Image Data Relates to a Colormap
Time-Domain Response Data and Plots
Time-Domain Responses of Discrete-Time Model
Time-Domain Responses of MIMO Model
Time-Domain Responses of Multiple Models
Introduction: PID Controller Design
Introduction: Root Locus Controller Design
Introduction: Frequency Domain Methods for Controller Design
DC Motor Speed: PID Controller Design
DC Motor Position: PID Controller Design
Cruise Control: PID Controller Design
Suspension: Root Locus Controller Design
Aircraft Pitch: Root Locus Controller Design
Inverted Pendulum: Root Locus Controller Design
Get Started with Deep Network Designer
Create Simple Image Classification Network Using Deep Network Designer
Build Networks with Deep Network Designer
Classify Image Using GoogLeNet
Classify Webcam Images Using Deep Learning
Transfer Learning with Deep Network Designer
Train Deep Learning Network to Classify New Images
Deep Learning Processor Customization and IP Generation
Prototype Deep Learning Networks on FPGA
Deep Learning Processor Architecture
Deep Learning INT8 Quantization
Quantization of Deep Neural Networks
Custom Processor Configuration Workflow
Estimate Performance of Deep Learning Network by Using Custom Processor Configuration
Preprocess Images for Deep Learning
Preprocess Volumes for Deep Learning
Transfer Learning Using AlexNet
Time Series Forecasting Using Deep Learning
Create Simple Sequence Classification Network Using Deep Network Designer
Train Classification Models in Classification Learner App
Train Regression Models in Regression Learner App
Explore the Random Number Generation UI
Logistic regression create generalized linear regression model - MATLAB fitglm 2
Support Vector Machines for Binary Classification
Support Vector Machines for Binary Classification 2
Support Vector Machines for Binary Classification 3
Support Vector Machines for Binary Classification 4
Support Vector Machines for Binary Classification 5
Assess Neural Network Classifier Performance
Discriminant Analysis Classification
Train Generalized Additive Model for Binary Classification
Train Generalized Additive Model for Binary Classification 2
Classification Using Nearest Neighbors
Classification Using Nearest Neighbors 2
Classification Using Nearest Neighbors 3
Classification Using Nearest Neighbors 4
Classification Using Nearest Neighbors 5
Gaussian Process Regression Models
Gaussian Process Regression Models 2
Understanding Support Vector Machine Regression
Extract Voices from Music Signal
Align Signals with Different Start Times
Find a Signal in a Measurement
Extract Features of a Clock Signal
Filtering Data With Signal Processing Toolbox Software
Find Periodicity Using Frequency Analysis
Find and Track Ridges Using Reassigned Spectrogram
Classify ECG Signals Using Long Short-Term Memory Networks
Waveform Segmentation Using Deep Learning
Label Signal Attributes, Regions of Interest, and Points
Introduction to Streaming Signal Processing in MATLAB
Filter Frames of a Noisy Sine Wave Signal in MATLAB
Filter Frames of a Noisy Sine Wave Signal in Simulink
Lowpass Filter Design in MATLAB
Tunable Lowpass Filtering of Noisy Input in Simulink
Signal Processing Acceleration Through Code Generation
Signal Visualization and Measurements in MATLAB
Estimate the Power Spectrum in MATLAB
Design of Decimators and Interpolators
Multirate Filtering in MATLAB and Simulink