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1 Presentation Title By Author 2014 The MathWorks, 1

2 Practical Signal Processing Techniques with MATLAB 实用信号处理技术 John Zhao ( 赵志宏 ) Technical Marketing Manager 2014 The MathWorks, 2

3 Agenda Signal Processing and Its Applications Practical Signal Processing Examples Signal Generation Signal Measurement Signal Smoothing Signal Similarity Signal Processing Workflow Summary 3

4 Signals are Everywhere 4

5 Why Do Signals Need Processing? Convert the physical real world to information Eliminate noise, distortion, fading, echoes, interference, etc. Optimize resource utilization Improve system performance Capacity and speed of mobile networks Range and accuracy of RADAR 5

6 What is Signal Processing? Mathematical algorithms that Extract information from data and the physical world Enable communications and many other electronic systems Fundamental concepts Analog-to-digital (A/D) and Digital-to-analog (D/A) conversion Spectral analysis Estimate frequency components in a signal Key buzzword: FFT (Fast Fourier Transform) Filtering Enhance or remove specific elements of a signal Sound Sensor Analog Signal Digital Signal Spectral Analysis A/D Digital Signal Information 6

7 Example: Internet of Things (IoT) Send/store vibration data Please contact technical support your pump is likely to fail soon Live information on mobile app Pump vibration monitor 95% of pumps fail soon after the vibration reading climbs above 1.35 Source: iaqualink by Zodiac Analysis of pump failure data 7

8 What s in Signal Processing Toolbox? Signal generation, visualization and measurement Signal transforms Digital filter design, analysis, and implementation Statistical signal measurements and data windowing functions Power Spectral Density estimation algorithms 8

9 Example: Generate commonly used waveforms Periodic (Square, Sawtooth) Aperiodic (Chirp) Specialty Functions Pulstran function Sinc function 9

10 Signal Identification What is the signal telling you? How do I tell signal apart from noise? Are there similarities between these signals? Is this signal periodical? 10

11 Example : Measure Sunspot Activity Sunspots are temporary phenomenon that appear as dark spots Two methods to determine the cycle of the sunspot activity Time Series Analysis Frequency Analysis 11

12 Example: Align Signals With Different Start Time 12

13 Level (Volts) Count Level (Volts) Measurement Functions 3 Signal Signal Statistics Min/Max, Mean, Median, RMS PeakToPeak, PeakToRMS Pulse Measurements State Levels Rise/Fall Time, Overshoot/Undershoot, Settling Time Pulse Width/Period/Frequency, Duty Cycle Channel power Bandpower, ENBW Distortion Measurements SFDR, SINAD, SNR*, THD, TOI Samples Histogram of signal levels (100 bins) Level (Volts) pulse width signal mid cross upper boundary upper state lower boundary mid reference upper boundary lower state lower boundary Time (seconds) 6 8 x

14 Examples: Clock Signal Measurement State levels Pulse period Pulse duty ratio Overshoot and undershoot RMS 14

15 Signal Smoothing Why smoothing? Better visual and graphics Removing interference information Types of smoothing Removing trend from a signal Remove spikes Removing noises Recovering missing samples 15

16 Filter Design in Signal Processing Toolbox Filter Types lowpass, highpass, bandpass, bandstop, Hilbert, differentiator, pulse-shaping, and arbitrary magnitude Design Methods FIR: Parks-McClellan and Kaiser window IIR: Butterworth, Chebyshev Type I and Type II, and elliptic Analyses Magnitude response, phase response, and group delay Impulse response and step response Pole-zero plot 16

17 Example: Analyze and Filter an EKG signal Load EKG Signal De-trend the EKG signal Low Pass Filtering Delay Compensation Visualize Signal Processing Algorithms 17

18 Example: Removing Spikes 18

19 Example: Recovering Missing Sample 19

20 What We have Seen De-trending an EKG signal Visualize in power content of signal Design and apply a lowpass IIR filter Compensate the delay introduced by the filter Removing spikes from a signal Recover missing data in a signal 20

21 Signal Processing Workflow Access Files Explore & Discover Signal Analysis & Visualization Share Reporting and Documentation Software Algorithm Development Outputs for Design Code & Applications Instruments/Devices Application Development Deployment Automate 21

22 Summary With MATLAB and Signal Processing Toolbox, you can perform Signal Generation,Visualization and Measurement Signal transforms Analog and Digital filter design, analysis, and implementation Statistical signal measurements and data windowing functions Power Spectral Density estimation algorithms 22

Signal Processing Toolbox

Signal Processing Toolbox Perform signal processing, analysis, and algorithm development Signal Processing Toolbox provides industry-standard algorithms for analog and digital signal processing (DSP).