Data Acquisition and Signal Processing for Smart Sensors

299 Pages·2002·53.6 MB·English

by  Nikolay V. Kirianaki

DATA ACQUISITION AND
SIGNAL PROCESSING
FOR SMART SENSORS
This Page Intentionally Left Blank
DATA ACQUISITION AND
SIGNAL PROCESSING
FOR SMART SENSORS
Nikolay V. Kirianaki and Sergey Y. Yurish
International Frequency Sensor Association, Lviv, Ukraine
Nestor 0. Shpak
Institute of Computer Technologies, Lviv, Ukraine
Vadim P. Deynega
State University Lviv Polytechnic, Ukraine
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Librnrg of Congress CaIaloging-in-Publirolion Data
.. .
Data acquisition and signal processing for sman senson / Nikolay V. Kirianaki let al.1.
p. cm.
Includes bibliographical references and index.
ISBN 0-470-84317-9 calk. paper)
1. Detcctoa. 2. Miercpmcessors. 3. Signal processing. 4. Automatic data collection
systems. I. Kirianaki. Nikolai Vladimirovich.
B&k Ubmry Cataloguing in Publimlion hta
A catalogue record for this book is available fmm the British Library
ISBN 0 470 84317 9
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CONTENTS
Preface
List of Abbreviations and Symbols xiii
Intmduction
1 Smart Sensors for Electrical and Non-Electrical, Physical and
Chemical Variables: Tendencies and Perspectives
1. I Temperature IC and Smart Sensors
1.2 Pressure IC and Smart Sensors and Accelerometers
1.3 Rotation Speed Sensors
1.4 Intelligent Opto Sensors
1.5 Humidity Frequency Output Sensors
1.6 Chemical and Gas Smart Sensors
Summary
2 Converters for Different Variables to Frequency-Time
Parameters of the Electric Signal
2.1 Voltage-to-Frequency Converters (VFCs)
2.2 Capacitance-to-Period (or Duty-Cycle) Converters
Summary
3 Data Acquisition Methods for Multichannel Sensor
Systems
3.1 Data Acquisition Method with Time-Division Channelling
3.2 Data Acquisition Method with Space-Division Channelling
3.3 Smart Sensor Architectures and Data Acquisition
3.4 Main Errors of Multichannel Data-Acquisition Systems
3.5 Data Transmission and Error Protection
3.5.1 Essence of quasi-ternary coding
3.5.2 Coding algorithm and examples
3.5.3 Quasi-ternary code deccding
Summary
vi CONTENTS
4 Methods of Frequency-to-Code Conversion
4.1 Standard Direct Counting Method (Frequency Measurement)
4.2 Indirect Counting Method (Period Measurement)
4.3 Combined Counting Method
4.4 Method for Frequency-to-Code Conversion Based on Discrete
Fourier Transformation
4.5 Methods for Phase-Shift-to-Code Conversion
Summary
5 Advanced and Self-Adapting Methods of Frequency-to-Code
Conversion
5.1 Ratiometric Counting Method
5.2 Reciprocal Counting Method
5.3 Mfr Counting Method
5.4 Constant Elapsed Time (CET)M ethod
5.5 Single- and Double-Buffered Methods
5.6 DMA Transfer Method
5.7 Method of Dependent Count
5.7.1 Method of conversion for absolute values
5.7.2 Methods of conversion for relative values
5.7.3 Methods of conversion for frequency deviation
5.7.4 Universal method of dependent count
5.7.5 Example of realization
5.7.6 Metrological characteristics and capabilities
5.7.7 Absolute quantization emr A,
5.7.8 Relative quantization error 8,
5.7.9 Dynamic range
5.7.10 Accuracy of frequency-to-rode wnverIers based
on MDC
5.7.1 1 Calculation emr
5.7.12 Quantization error (error of method)
5.7.13 Reference frequency e m
5.7.14 Trigger ermr
5.7.15 Simulation results
5.7.16 Examples
5.8 Method with Non-Redundant Reference Frequency
5.9 Comparison of Methods
5.10 Advanced Method for Phase-Shift-to-Code Conversion
Summary
6 Signal Pmcessing in Quasi-Digital Smart Sensors
6.1 Main Operations in Signal Processing
6.1.1 Adding and subtraction
6.1.2 Multiplication and division
6.1.3 Frequency signal unification
6.1.4 Derivation and integration
CONTENTS
6.2 Weight Functions, Reducing Quantization Error
Summary
7 Digital Output Smart Sensors with Software-Controlled
Performances and Functional Capabilities
7.1 Program-Oriented Conversion Methods Based on Ratiometric
Counting Technique
7.2 Design Methodology for Program-Oriented Conversion Methods
7.2.1 Example
7.3 Adaptive FTM with Increased Speed
7.4 Error Analysis of PCM
7.4.1 Reference m r
7.4.2 Calculation error
7.4.3 E mo f Tm forming
7.5 Correction of PCM's Systematic Errors
7.6 Modified Method of Algorithm Merging for PCMs
Summary
8 Multichannel Intelligent and Virtual Sensor Systems
8.1 One-Channel Sensor Interfacing
8.2 Multichannel Sensor Interfacing
8.2.1 Smart rotation speed sensor
8.2.2 Encoder
8.2.3 Self-adaptive method f am tation speed measurements
8.2.4 Sensor interfacing
8.3 Multichannel Adaptive Sensor System with Space-Division
Channelling
8.4 Multichannel Sensor Systems with Time-Division Channelling
8.5 Multiparameters Sensors
8.6 Virtual Instrumentation for Smart Sensors
8.6.1 Set of the basic models for measuring insmments
8.7 Estimation of Uncertainty for Virtual Instruments
Summary
9 Smart Sensor Design at Software Level
9.1 Microcontroller Core for Smart Sensors
9.2 Low-Power Design Technique for Embedded Microcontrollers
9.2.1 lnsrmction selection and ordering
9.2.2 Code size and speed optimizations
9.2.3 Jump and call optimizations
9.2.4 Cycle optimization
9.2.5 Minimizing memory access cost
9.2.6 Exploiting low-power features of the hnrdware
9.2.7 Compiler optimization for low power
Summary
viii CONTENTS
10 Smart Sensor Buses and Interface Circuits
10.1 Sensor Buses and Network Protocols
10.2 Sensor Interface Circuits
10.2.1 Universal transducer interface (Un)
10.2.2 Time-todigital convener (TDC)
Summary
Future Directions
References
Appendix A What is on the Sensors Web Portal?
Glossary
Index
PREFACE
Smart sensors are of great interest in many fields of industry, control systems, biomed-
ical applications, etc. Most books about sensor instrumentation focus on the classical
approach to data acquisition, that is the information is in the amplitude of a voltage or a
current signal. Only a few book chapters, articles and papers consider data acquisition
from digital and quasi-digital sensors. Smart sensors and microsensors increasingly rely
on resonant phenomena and variable oscillators, where the information is embedded not
in the amplitude but in the frequency or time parameter of the output signal. As a mle,
the majority of scientific publications dedicated to smart sensors reflect only the tech-
nological achievements of microelectronics. However, modem advanced microsensor
technologies require novel advanced measuring techniques.
Because data acquisition and signal processing for smart sensors have not been
adequately covered in the literature before, this book aims to fdl a significant gap.
This book is based on 40 years of the authors' practical experience in the design and
creation of sensor instrumentation as well as the development of novel methods and
algorithms for frequency-timedomain measurement, conversion and signal processing.
Digital and quasi-digital (frequency, period, duty-cycle. time interval and pulse number
output) sensors are covered in this book.
Research results, described in this book, are relevant to the authors' international
research in the frame of different R&D projects and International Frequency Sensor
Association (IFSA) activity.
Who Should Read this Book?
This book is aimed at PhD students, engineers, scientists and researchers in both
academia and industry. It is especially suited for professionals working in the field
of measuring instruments and sensor instrumentation as well as anyone facing new
challenges in measuring. and those involved in the design and creation of new digital
smart physical or chemical sensors and sensor systems. It should also be useful for
students wishing to gain an insight into this rapidly expanding area. Our goal is to
provide the reader with enough background to understand the novel concepts, principles
and systems associated with data acquisition, signal processing and measurement so
that they can decide how to optimize their sensor systems in order to achieve the best
technical performances at low cost.