bullet Modern Sensors, Transducers and Sensor Networks

        

  Title: Modern Sensors, Transducers and Sensor Networks (Book Series: Advances in Sensors: Reviews, Vol. 1)

  Editor: Sergey Y. Yurish

  Publisher: International Frequency Sensor Association (IFSA) Publishing

  Formats: printable pdf Acrobat (e-book) and paperback (print book), 422 pages

  Price: 162.95 EUR for e-book and 179.95 EUR (taxes and mail shipping cost are included) for print book in paperback

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  Pubdate: 15 May 2012

  ISBN: 978-84-615-9613-3

  e-ISBN: 978-84-615-9012-4

 

 

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 Book Description

 

Modern Sensors, Transducers and Sensor Networks - the first book from the Advances in Sensors: Reviews book Series started by the IFSA Publishing in 2012 contains dozen collected sensor related, advanced state-of-the-art reviews written by 31 internationaly recognized experts from academia and industry from 9 countries: Canada, Egypt, India, Malaysia, New Zealand, Spain, Taiwan, UK and USA: Elena Gaura, Sukumar Basu, Subhas C. Mukhopadhyay, Sergey Y. Yurish, Tom J. Kazmierski, and others.

 

Built upon the series Advances in Sensors: Reviews - a premier sensor review source, it presents an overview of highlights in the field. Coverage includes current developments in sensing nanomaterials, technologies, design, synthesis, modeling and applications of sensors, transducers and wireless sensor networks, signal detection and advanced signal processing, as well as new sensing principles and methods of measurements. This volume is divided into three main sections: physical sensors, chemical sensors and biosensors, and sensor networks including sensor technology, sensor market reviews and applications. Modern Sensors, Transducers and Sensor Networks comprises 12 Chapters. Each of chapter can be used independently and contains its own detailed list of references.

 

With this unique combination of information in each volume, the Advances in Sensors: Reviews book Series will be of value for scientists and engineers in industry and at universities, to sensors developers, distributors, and users.

 

Modern Sensors, Transducers and Sensor Networks is intended for anyone who wants to cover a comprehensive range of topics in the field of sensors paradigms and developments. It provides guidance for technology solution developers from academia, research institutions, and industry, providing them with a broader perspective of sensor science and industry.

 

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Contents:

 

Contributors

Preface

Chapter 1. Introduction

Sergey Y. Yurish, Spain


1.1. Some Data about Sensor Market
1.2. About this Book
 


Chapter 2. Resistive and Capacitive Based Sensing Technologies

Winncy Y. Du and Scott W. Yelich, USA


2.1. Introduction


2.2. Resistive Sensing Technologies
2.2.1. Principles
2.2.2. Design and Applications
2.2.2.1. Potentiometric Sensors
2.2.2.2. Photoresistive Sensors
2.2.2.3. Piezoresistive Sensors
2.2.2.4. Thermoresistive Sensors
2.2.2.5. Magnetoresistive Sensors
2.2.2.6. Resistance-Based Chemical Sensors (Chemoresistors)
2.2.2.7. Resistive Humidity Sensors (Hygristors)
2.2.2.8. Bioimpedance Sensors


2.3. Capacitive Sensing Technologies
2.3.1. Types of Capacitive Sensors
2.3.2. Design and Applications
2.3.2.1. Parallel Capacitor-based Sensors
2.3.2.2. Coaxial Capacitor-based Sensors
2.3.2.3. Spherical Capacitor-Based Sensors
2.3.2.4. Capacitive Sensor Arrays


2.4. Summary
References
 


Chapter 3. Automated Synthesis of MEMS Sensors

Chenxu Zhao and Tom J. Kazmierski, UK


Part 1: Layout Synthesis of MEMS Component with Distributed Mechanical Dynamics


3.1. Introduction
3.2. Genetic-based Synthesis of MEMS Sensors with Electronic Control Loop
3.2.1. Synthesis Initialization
3.2.1.1. MEMS Primitive Library
3.2.1.2. Electronic Control Loop
3.2.1.3. Parameter Initialization and Encoding
3.2.2. Genetic Approach to Synthesis


3.3. Synthesis Verification to Provide Appropriate Performance Metrics for the Synthesized MEMS Geometries


3.4. Conclusions

 


Part 2: Synthesis of a MEMS System with Associated Control Loop


3.5. Introduction


3.6. Genetic-based Synthesis of MEMS Accelerometer with Σ
D Control Loop
3.6.1. Synthesis Initialization
3.6.2. Genetic Synthesis of Electronic Control


3.7. Synthesis Experiments
3.7.1. Experiment Land 2(maximum SNR)
3.7.2. Experiment 3 (Maximum Static Sensitivity of Sensing Element)
3.7.3. Experiment 4 (Minimum Area of Mechanical Sensing Element)


3.8. Conclusion
 


Chapter 4. Sensors for Food Inspections

Mohd Syaifudin Bin Abdul Rahman, Subhas C. Mukhopadhyay, Pak Lam Yu,

Michael J. Haji-Sheikh and Cheng-Hsin Chuang, New Zealand, Malaysia and Taiwan


4.1. Introduction
4.1.1. Seafood Poisoning (Marine Biotoxins)
4.1.2. Food Poisoning (Endotoxin)


4.2. Existing Method of Domoic Acid and Pathogens Detection
4.2.1. Domoic Acid Detection
4.2.2. Pathogens Detection (Endotoxin)


4.3. Development of Novel Planar Interdigital Sensor
4.3.1. Introduction to Planar Interdigital Sensors
4.3.2. Analytical Analysis and Modeling
4.3.2.1. Calculation of Capacitance using Circuit Analysis
4.3.2.2. Modeling using COMSOL Multiphysics
4.3.3. Sensor Design and Fabrication
4.3.3.1. Design and Fabrication Process
4.3.3.2. Conventional Interdigital Sensors
4.3.3.3. Novel Planar Interdigital Sensors


4.4. Experimental Results and Discussions
4.4.1. Characterization of Sensors only without Material under Test (MUT)
4.4.1.1. Characterization of FR4 Sensors
4.4.1.2. Characterization of Alumina Sensors
4.4.1.3. Characterization of Glass Sensors
4.4.2. Characterization of sensors with ionic solution, Sodium Chloride (NaCl)
4.4.3. Experiments with Peptides Related to Domoic Acid
4.4.4. Experiments with LPS O111:B4


4.5. Conclusion and Future Work
 


Chapter 5. Concept of Force Sensing and Measurements: from Past to Present

Ebtisam H. Hasan, Egypt


5.1. Introduction


5.2. Type of Forces
5.2.1. Force Concept
5.2.2. Force Models
5.2.2.1. Electromagnetic Force
5.2.2.2. Nuclear Force
5.2.2.3. Non-fundamental Forces


5.3. Force-measurement System
5.3.1. Force Transducers
5.3.1.1. Strain Gauge Load Cells
5.3.1.2. Piezoelectric Crystal
5.3.1.3. Measuring Force through Pressure
5.3.1.4. Other Types of Force Measuring System
5.3.2. Selection Criteria for Force Transducers
5.3.2.1. Capacity Selection
5.3.2.2. Accuracy
5.3.2.3. Environmental Protection
5.3.3. Development of the Load Cell Design Technology, Force Application Systems and New Force Measurement Techniques
5.3.3.1. Load Cells
5.3.3.2. Force Application Systems
5.3.3.3. Techniques


5.4. Summary
 


Chapter 6. Gas Sensors Based on Inorganic Materials

K. R. Nemade, India


6.1. Introduction


6.2. Experimental Methodology
6.2.1. DC Measurements
6.2.2. Work Function Change Measurements


6.3. Sensing Mechanism


6.4. Relation between Resistance and Sensitivity


6.5. Factors Affecting Sensitivity
6.5.1. Doping
6.5.2. Surface Area of Grain
6.5.3. Working Temperature
6.5.4. Dielectric Constant


6.6. Selectivity of Metal Oxide Gas Sensors


6.7. Stability of Metal Oxide Gas Sensors


6.8. Metal Oxides for Gas Sensors
6.8.1. Hydrogen Gas Sensors
6.8.2. Oxygen Gas Sensor
6.8.3. Nitrogen Oxides Gas Sensor
6.8.4. Carbon Monoxide Gas Sensor


6.9. Conclusions
 


Chapter 7. Microcantilever-based Sensors for Biological and Chemical Sensing Applications

Qing Zhu, USA


7.1. Introduction


7.2. Detection Schemes
7.2.1. Dynamic Sensing Method
7.2.2. Scaling Law Based on Mass Loading Model
7.2.3. Adsorption Induced Surface Stress
7.2.4. Static Sensing Method


7.3. Applications of Microcantilever Sensors in Biological and Chemical Detections
7.3.1. Silicon-based Microcantilever Sensor
7.3.2. Piezoresistive Microcantilever Sensor
7.3.3. Capacitive Microcantilever Sensor
7.3.4. Magnetostrictive Microcantilever Sensor
7.3.5. Piezoelectric Microcantilever Sensor


7.4. Conclusions
 


Chapter 8. Nanomaterials and Chemical Sensors

Sukumar Basu and Palash Kumar Basu, India


8.1. Nanomaterials
8.1.1. Properties of Nanomaterials
8.1.1.1. Quantum Tunneling
8.1.1.2. Quantum Confinement
8.1.1.3. Random Molecular Motion
8.1.1.4. Surface and Reactivity
8.1.1.5. Mechanical Properties
8.1.2. Nanomaterials Used for Chemical Sensors


8.2. Chemical Sensors
8.2.1. Advantages of Chemical Sensors
8.2.2. Applications
8.2.2.1. Direct Sensor
8.2.2.2. Indirect Sensor
8.2.3. Gas Sensors
8.2.3.1. Metal Oxide Based Solid-state Resistive Gas Sensor
8.2.3.2. Principle of Gas Sensing
8.2.4. Electrochemical Sensors
8.2.4.1. Principle
8.2.4.2. Applications of Electrochemical Sensors


8.3. Carbon Nano Tube Chemical Sensors
8.4. Summary & Concluding Remarks
 


Chapter 9. Advance in Biosensors and Biochips

Sarmishtha Ghoshal, Debasis Mitra, Sudip Roy, Dwijesh Dutta Majumder, India


9.1. Introduction


9.2. Nanobiosensors


9.3. Types of Biosensors
9.3.1. Quantum dot Biosensors
9.3.2. Porous Silicon Biosensors
9.3.3. Silicon Nanoparticle Sensors


9.4. Biochip or Lab-on-a-Chip
9.4.1. Classification of Biochips
9.4.1.1. Lab-on-a-chip
9.4.1.2. Implantable Biochips
9.4.2. Integration of Sensors


9.5. Optical Detection Techniques


9.6. Conclusion
 


Chapter 10. Distributed Information Extraction from Large-scale Wireless Sensor Networks

Elena Gaura, James Brusey, John Halloran, Tessa Daniel, UK


10.1. Introduction


10.2. Agent Based Approaches to Information Extraction
10.2.1. Agent-based Approaches and Architectures
10.2.1.1. Mobile Agent-based Distributed Sensor Networks (ADSN)
10.2.1.2. Autonomic Wireless Sensor Networks (AWSN)
10.2.1.3. Mobile Agent-based Wireless Sensor Networks (MAWSN)
10.2.1.4. Multi-Agent Systems
10.2.2. Agent-based Middleware
10.2.2.1. Mate
10.2.2.2. Agilla
10.2.2.3. Impala
10.2.2.4. SensorWare
10.2.2.5. TinyLIME
10.2.3. Remarks


10.3. Query-based and Macroprogramming Approaches
10.3.1. Query-based Information Extraction
10.3.1.1. COUGAR
10.3.1.2. Sensor Information Networking Architecture (SINA)
10.3.1.3. Data Service Middleware (DSWare)
10.3.1.4. Framework in Java for Operators on Remote Data Streams (Fjords)
10.3.1.5. TinyDB
10.3.1.6. Active Query Forwarding (ACQUIRE)
10.3.2. Macroprogramming Approaches
10.3.2.1. Node-level Abstractions
10.3.2.2. Semantic Streams
10.3.2.3. The Regiment Macroprogramming System
10.3.2.4. Kairos
10.3.2.5. Knowledge-Representation for Sentient Computing
10.3.3. Remarks


10.4. Towards a Hybrid Approach
10.4.1. Introduction
10.4.2. Information Extraction in Monitoring Applications
10.4.2.1. Habitat and Environmental Monitoring
10.4.2.2. Agricultural Monitoring
10.4.2.3. Structural Health Monitoring
10.4.2.4. A Motivating Scenario
10.4.3. Requirements for a Higher Level Information Extraction System
10.4.3.1. Catering for Complex Queries
10.4.3.2. Catering for In-Network Complex Query Processing
10.4.4. WSN Topologies, Routing Protocols and Architectures
10.4.4.1. WSN Topologies
10.4.4.2. Routing Protocols for WSNs
10.4.4.3. Query Processing Architectures
10.4.4.4. Example Systems
10.4.4.5. In-Network Processing Techniques for WSNs
10.4.5. A Distributed Complex Query Processor
10.4.5.1. The Network Model
10.4.5.2. A Distributed Complex Query Processing Architecture
10.4.5.3. Acoustic Monitoring Using Region-based Querying
10.4.5.4. Region Based Query Resolution
10.4.5.5. Efficiency of Region-based Querying
10.4.5.6. Effectiveness of Region-based Querying


10.5. Conclusions
 


Chapter 11. Software Modeling Techniques for Wireless Sensor Networks

John Khalil Jacoub, Ramiro Liscano, Jeremy S. Bradbury, Canada


11.1. Introduction


11.2. Case Study
11.2.1. System Overview
11.2.2. Sensor Physical Layer
11.2.3. Routing Protocol
11.2.4. Sensor Software
11.2.5. Location of the Sensor Nodes


11.3. Overview of the Software Modeling Techniques for Sensor Networks
11.3.1. HL-SDL
11.3.2. Insense
11.3.2.1. Insense Model Elements
11.3.2.2. Insense Model for SensIV
11.3.3. Mathworks
11.3.3.1. Design Representation
11.3.3.2. System Analysis
11.3.3.3. Code Generation
11.3.3.4. Mathwork Model for SensIV
11.3.4. Model Driven Engineering Approach (MDEA)
11.3.4.1. WSN-DSL Model
11.3.4.2. UML-PIM Model
11.3.4.3. nesC Meta-model
11.3.5. Promela Model
11.3.5.1. Promela Model Elements
11.3.5.2. Promela Model and SensIV
11.3.6. SensorML
11.3.6.1. SensorML Elements
11.3.6.2. SensorML Model for SensIV
11.3.7. SystemC-AMS
11.3.7.1. SensIV Thermal Sensors Model
11.3.7.2. A/D Converter
11.3.7.3. Microprocessor
11.3.7.4. Transceiver
11.3.8. UM-RTCOM Model
11.3.8.1. System Components
11.3.8.2. Virtual Machines
11.3.8.3. Nodes Locations
11.3.8.4. UM-RTCOM Model for SensIV
11.3.9. eXtended Reactive Modules (XRM)
11.3.9.1. WSN Scalability
11.3.9.2. Node Locations
11.3.9.3. Package Delivery Probability
11.3.9.4. Power Consumptions
11.3.9.5. XRM Model for SensIV


11.4. Modeling At the Node and Sensor Level
11.4.1. Node Behavior
11.4.2. Modeling Sensors and Hardware


11.5. Modeling at the System Level
11.5.1. Network Behavior
11.5.2. Topology Modeling


11.6. Supporting Tools
11.6.1. Code Generation
11.6.2. Model Checking
11.6.3. Model Execution and Analysis


11.7. Related Work


11.8. Conclusion and Future Direction
 

 

Chapter 12. Multi-Sensor Wireless Network System for Hurricane Monitoring

Chelakara Subramanian, Gabriel Lapilli, Frederic Kreit, Jean- Paul Pinelli, Ivica Kostanic, USA


12.1. Introduction
12.1.1. The Wireless Sensors System
12.1.1.1. Remote Sensor Unit


12.2. Reliability of the Pressure Sensors
12.2.1. Comparison with Established References
12.2.1.1. Comparison with the National Weather Service (NWS) Pressure Measurements
12.2.1.2. Comparison with the MET3A
12.2.2. Wind Tunnel Tests
12.2.2.1. Wind Tunnel Test Description
12.2.2.2. Wind Tunnel Test Experimental Results
12.2.2.3. Wind Tunnel Analytical Study
12.2.2.4. Wind Tunnel Test CFD Study
12.2.2.5. Comparison between the Experimental, Numerical, and Analytical Results
12.2.3. Repeatability of the Measurements


12.3. Study of Sensor Shape Factor on Measured Pressure
12.3.1. Highway Test
12.3.1.1. Test Description
12.3.1.2. Data Analysis
12.3.1.3. CFD Model Comparison
12.3.1.4. Conclusions on Shape Factor Studies
12.3.2. University of Florida (UF) Hurricane Simulator Test
12.3.2.1. Test Description
12.3.2.2. Data Analysis
12.3.3. Flow Analysis
12.3.3.1. Pressure Autocorrelation
12.3.3.2. Pressure/pressure Cross-Correlation
12.3.3.3. Pressure Spectrum
12.3.3.4. Gust effects Velocity/Pressure Cross-correlations
12.3.4. CFD Simulation of UF Wind Tunnel Test
12.3.4.1. Geometrical Setup
12.3.4.2. Simulation Results


12.4. Vibration Study
12.4.1. Noise Source Analysis
12.4.2 Test Description
12.4.3. Results and Analysis


12.5. Conclusions

Index

 

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