This book introduces the basic analysis methods in signal processing, principles of various sensors and the concept of measurement system. To make students better understand and apply the theories, the book includes many MATLAB examples, such as the generation of standard signals and the spectrum analysis of audio signals in the signal processing part and Arduino examples as well, such as temperature measuring and ultrasonic ranging to show the applications of sensors. Readers can not only learn the fundamental theories but also get many opportunities to apply the theories to perform measurement tasks.
Author(s): Lingsong He, Bo Feng
Publisher: Springer
Year: 2022
Language: English
Pages: 378
City: Singapore
Introduction
Definition of Measurement Technology
Basic Concepts of Measurement Technology
Constitution of Measurement Systems
Engineering Applications of Measurement Technology
Industrial Automation
State Monitoring of Industrial Equipment
Product Quality Inspection
Smart Buildings, Smart Homes and Smart Offices
Measurement Technology in Smartphones
Other Applications
Developing Trends of Measurement Technology
Miniaturization, Integration and Intelligence of Sensors
High Precision, High Speed, Large Range
Network-Based Measurement and Automatic Measurement
Intelligent Processing of Measurement Signal
Main Manufacturers of Sensor and Measurement Instrument
Exercise
Contents
1 Waveform Analysis of Signals
1.1 Classification of Signals
1.1.1 Deterministic and Non-Deterministic Signal
1.1.2 Energy Signal and Power Signal
1.1.3 Other Classifications of Signals
1.2 Sampling Theorem
1.2.1 A/D Conversion
1.2.2 Sampling Error
1.2.3 Nyquist–Shannon Sampling Theorem
1.2.4 Frequency Aliasing in Sampling
1.2.5 Anti-Aliasing Filtering Before A/D Conversion
1.2.6 Quantization Error
1.2.7 Technical Index of A/D Converter
1.2.8 D/A Conversion
1.3 Standard Functions in Signal Analysis
1.3.1 Unit Impulse Function (Δ Function)
1.3.2 Unit Step Function
1.3.3 Unit Ramp Function
1.3.4 Complex Exponential Function
1.3.5 Sine Function
1.3.6 Sinc Function
1.3.7 White Noise
1.4 Generation of Standard Functions
1.5 Waveform Analysis of Signals
2 Frequency Domain Analysis
2.1 Concept of Frequency Domain Analysis
2.1.1 Advantages of Frequency Domain Analysis
2.1.2 Objectives of Frequency Domain Analysis
2.2 Fourier Series Representation of Periodic Signals
2.2.1 Orthogonal Decomposition of Vectors
2.2.2 Orthogonal Function
2.2.3 Orthogonality of Trigonometric Functions
2.2.4 Fourier Series in Trigonometric Function Form
2.2.5 Spectrum of Periodic Signals
2.2.6 Synthesis of Periodic Signals
2.2.7 Walsh Orthogonal Function Set
2.2.8 Fourier Series in Complex Form
2.2.9 Gibbs Phenomenon
2.2.10 Parseval’s Theorem
2.3 Fourier Transform of Aperiodic Signals
2.3.1 Fourier Integral
2.3.2 Spectrum of Aperiodic Signal
2.4 Fourier Transform of Typical Signals
2.5 Properties of Fourier Transform
2.6 Fourier Transform for Discrete-Time Signals
2.6.1 Sampling in Time and Frequency Domain
2.6.2 Discrete Fourier Series (DFS)
2.6.3 Discrete Fourier Transform (DFT)
2.6.4 Fast Fourier Transform (FFT)
2.6.5 Applications of FFT Algorithm
2.7 Error Analysis in FFT
2.7.1 Signal Truncation
2.7.2 Effect of Spectral Leakage
2.7.3 Fence Effect
2.7.4 Window Functions
2.8 Applications of Frequency Domain Analysis
3 Amplitude Domain Analysis
3.1 Probability Density Function and Histogram
3.2 Probability Distribution Function
3.2.1 Cumulative Distribution of Discrete Random Variables
3.2.2 Probability Distribution of Continuous Random Variables
3.3 Engineering Applications of Amplitude Domain Analysis
3.3.1 Machine Fault Diagnosis
3.3.2 Histogram Analysis of Photo Quality
4 Correlation Analysis of Signals
4.1 Concept of Correlation Analysis
4.1.1 Correlation of Variables
4.1.2 Correlation of Signals
4.1.3 Cross-Correlation Function
4.1.4 Auto-Correlation Function
4.1.5 Convolution
4.1.6 Convolution Theorem
4.2 Properties of the Correlation Function
4.2.1 Properties of Auto-Correlation Function
4.2.2 Properties of Cross-Correlation Function
4.2.3 Convolution, Correlation and Fourier Transform
4.3 Calculation of Correlation
4.3.1 Numerical Calculation
4.3.2 FFT Method
4.4 Engineering Applications of Correlation Analysis
5 Time–Frequency Domain Analysis
5.1 Motivations of Time–Frequency Domain Analysis
5.1.1 Non-stationary Signals
5.1.2 Drawbacks of Global Analysis of Non-stationary Signals
5.1.3 Time–Frequency Domain Analysis
5.2 Short-Time Fourier Transform
5.2.1 Basic Principle of Short-Time Fourier Transform
5.2.2 Time–Frequency Resolution of Short-Time Fourier Transform
5.2.3 Time–Frequency Domain Decomposition and Synthesis
5.2.4 Discrete Short-Time Fourier Transform
5.2.5 Methods to Improve Estimation of Spectrogram
5.3 Wavelet Transform
5.3.1 Wavelet Transform and Short-Time Fourier Transform
5.3.2 Continuous Wavelet Transform
5.3.3 Applications of Wavelet Analysis
6 Digital Filters
6.1 Concept of Filtering
6.1.1 Ideal Filters
6.1.2 Practical Filter
6.1.3 Digital Filters
6.2 Filtering in Frequency Domain
6.3 Time Domain Filtering and Z-transform
6.3.1 Time Domain Filtering
6.3.2 Z-transform
6.3.3 Bilateral Z-transform
6.4 Finite Impulse Response (FIR) Filter
6.5 Infinite Impulse Response (IIR) Filter
6.6 Exercise
7 Principles of Sensors
7.1 Overview of Sensor Technology
7.1.1 Definition of Sensor
7.1.2 Composition of Sensors
7.1.3 Classifications of Sensors
7.2 Resistive Sensors
7.2.1 Potentiometer
7.2.2 Resistive Strain Gauge
7.2.3 Other Resistive Sensors
7.3 Inductive Sensors
7.3.1 Self-inductive Sensor
7.3.2 Eddy Current Sensor
7.3.3 Mutual-Inductive Sensor
7.4 Capacitive Sensors
7.5 Magnetoelectric Transducer
7.5.1 Moving Coil Type
7.5.2 Variable Reluctance Type
7.6 Piezoelectric Transducer
7.6.1 Piezoelectric Element
7.6.2 Ultrasonic Transducer
7.6.3 QCM Humidity Sensor
7.7 Hall Sensor
7.8 Photovoltaic Transducer
7.9 Image Sensors
7.10 Thermocouple
7.10.1 Thermoelectric Effects
7.10.2 Thermoelectric Laws
7.11 Fiber-Optic Sensor
7.11.1 Frequency/Wavelength Type
7.11.2 Intensity Type
7.12 Grating Sensor
7.13 Biosensor
7.13.1 Enzymatic Sensor
7.13.2 Microorganism Sensor
7.13.3 Immunosensor
7.14 Selection of Sensors
8 Signal Conditioning Techniques
8.1 Overview of Signal Conditioning
8.2 Analog Amplifiers and Operators
8.2.1 Operational Amplifier
8.2.2 Typical Amplification and Operation Circuits
8.3 Bridge Circuits
8.3.1 DC Bridge
8.3.2 AC Bridge
8.4 Analog Filters
8.4.1 Low-Pass Filter
8.4.2 High-Pass Filter
8.4.3 Band-Pass Filter
8.4.4 Band-Stop Filter
8.4.5 Comparison of Digital Filters and Analog Filters
8.5 Modulation and Demodulation
8.5.1 Amplitude Modulation
8.5.2 Frequency Modulation and Phase Modulation
9 Characteristics of Measurement System
9.1 Overview of Measurement System
9.2 Static Characteristics of Measurement Systems
9.3 Dynamic Characteristics of Measurement Systems
9.3.1 Transfer Function and Frequency Response Function
9.3.2 Linear Measurement System
9.4 Characteristics of Typical Linear Measurement Systems
9.4.1 Zero Order System
9.4.2 First Order System
9.4.3 Second Order System
10 Computerized Measurement System
10.1 Overview of Computerized Measurement System
10.2 Development of Measurement Instrument
10.3 Computerized Measurement Instrument
10.3.1 Virtual Instrument
10.3.2 Network Based Measurement Instrument
10.3.3 Internet of Things (IoT)
