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

  Delivery time for print book: 7-17 days dependent on country of destination. Please contact us for priority (5-9 days), ground (3-8 days) and express (3-5 days) delivery options by e-mail

  Pubdate: 15 May 2012

  ISBN: 978-84-615-9613-3

  e-ISBN: 978-84-615-9012-4



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Modern Sensors, Transducers and Sensor Networks book's cover


 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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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 Potentiometric Sensors Photoresistive Sensors Piezoresistive Sensors Thermoresistive Sensors Magnetoresistive Sensors Resistance-Based Chemical Sensors (Chemoresistors) Resistive Humidity Sensors (Hygristors) Bioimpedance Sensors

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

2.4. Summary

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 MEMS Primitive Library Electronic Control Loop 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 Calculation of Capacitance using Circuit Analysis Modeling using COMSOL Multiphysics
4.3.3. Sensor Design and Fabrication Design and Fabrication Process Conventional Interdigital Sensors Novel Planar Interdigital Sensors

4.4. Experimental Results and Discussions
4.4.1. Characterization of Sensors only without Material under Test (MUT) Characterization of FR4 Sensors Characterization of Alumina Sensors 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 Electromagnetic Force Nuclear Force Non-fundamental Forces

5.3. Force-measurement System
5.3.1. Force Transducers Strain Gauge Load Cells Piezoelectric Crystal Measuring Force through Pressure Other Types of Force Measuring System
5.3.2. Selection Criteria for Force Transducers Capacity Selection Accuracy Environmental Protection
5.3.3. Development of the Load Cell Design Technology, Force Application Systems and New Force Measurement Techniques Load Cells Force Application Systems 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 Quantum Tunneling Quantum Confinement Random Molecular Motion Surface and Reactivity 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 Direct Sensor Indirect Sensor
8.2.3. Gas Sensors Metal Oxide Based Solid-state Resistive Gas Sensor Principle of Gas Sensing
8.2.4. Electrochemical Sensors Principle 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 Lab-on-a-chip 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 Mobile Agent-based Distributed Sensor Networks (ADSN) Autonomic Wireless Sensor Networks (AWSN) Mobile Agent-based Wireless Sensor Networks (MAWSN) Multi-Agent Systems
10.2.2. Agent-based Middleware Mate Agilla Impala SensorWare TinyLIME
10.2.3. Remarks

10.3. Query-based and Macroprogramming Approaches
10.3.1. Query-based Information Extraction COUGAR Sensor Information Networking Architecture (SINA) Data Service Middleware (DSWare) Framework in Java for Operators on Remote Data Streams (Fjords) TinyDB Active Query Forwarding (ACQUIRE)
10.3.2. Macroprogramming Approaches Node-level Abstractions Semantic Streams The Regiment Macroprogramming System Kairos 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 Habitat and Environmental Monitoring Agricultural Monitoring Structural Health Monitoring A Motivating Scenario
10.4.3. Requirements for a Higher Level Information Extraction System Catering for Complex Queries Catering for In-Network Complex Query Processing
10.4.4. WSN Topologies, Routing Protocols and Architectures WSN Topologies Routing Protocols for WSNs Query Processing Architectures Example Systems In-Network Processing Techniques for WSNs
10.4.5. A Distributed Complex Query Processor The Network Model A Distributed Complex Query Processing Architecture Acoustic Monitoring Using Region-based Querying Region Based Query Resolution Efficiency of Region-based Querying 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 Insense Model Elements Insense Model for SensIV
11.3.3. Mathworks Design Representation System Analysis Code Generation Mathwork Model for SensIV
11.3.4. Model Driven Engineering Approach (MDEA) WSN-DSL Model UML-PIM Model nesC Meta-model
11.3.5. Promela Model Promela Model Elements Promela Model and SensIV
11.3.6. SensorML SensorML Elements SensorML Model for SensIV
11.3.7. SystemC-AMS SensIV Thermal Sensors Model A/D Converter Microprocessor Transceiver
11.3.8. UM-RTCOM Model System Components Virtual Machines Nodes Locations UM-RTCOM Model for SensIV
11.3.9. eXtended Reactive Modules (XRM) WSN Scalability Node Locations Package Delivery Probability Power Consumptions 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 Remote Sensor Unit

12.2. Reliability of the Pressure Sensors
12.2.1. Comparison with Established References Comparison with the National Weather Service (NWS) Pressure Measurements Comparison with the MET3A
12.2.2. Wind Tunnel Tests Wind Tunnel Test Description Wind Tunnel Test Experimental Results Wind Tunnel Analytical Study Wind Tunnel Test CFD Study 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 Test Description Data Analysis CFD Model Comparison Conclusions on Shape Factor Studies
12.3.2. University of Florida (UF) Hurricane Simulator Test Test Description Data Analysis
12.3.3. Flow Analysis Pressure Autocorrelation Pressure/pressure Cross-Correlation Pressure Spectrum Gust effects Velocity/Pressure Cross-correlations
12.3.4. CFD Simulation of UF Wind Tunnel Test Geometrical Setup 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



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