Contributors

Preface

Chapter 1. Advances in Intelligent Force Transducers
 

1.1. Introduction and Terminology
1.2. Intelligent Design of Force Transducers
1.3. Electrical Methods for Intelligent Force Measurement
1.3.1. Electromagnetic Force Compensation Weighing Cells
1.3.2. Smart Force Transducer with Vibrating Wire
1.3.3. Classical and Differential Piezoelectric Force Transducers
1.3.4. Electro-optical Catheter
1.4. Intelligent Force Measurement Channels
1.4.1. Signal Conditioners
1.4.2. Digital Displays
1.5. Intelligent Force Sensing Applications
1.5.1. Intelligent Robots
1.5.2. Wireless Force Sensing
1.5.3. Virtual Instrumentation – TensoDentar
1.6. Conclusions

Acknowledgements
 

References
 

Chapter 2. Intelligent Slip Displacement Sensors  in Robotics
 

2.1. Introduction
2.2.Methods and Sensors for Detection of Slip Displacement Signals 
in Intelligent Robotics
2.2.1. Analysis of the Main Methods for Slip Displacement Signals Detection
2.2.2. Main Requirements for Real-Time Detection of Slip Displacement Signals
2.2.3. The Trends in Slip Sensors Design and Their Modern Modifications
2.2.4. Advances in the Development of Self-Clamping Grippers of Intelligent Robots
2.3. Slip Displacement Sensors with Magnetic Sensitive Components
2.3.1. Mathematic Models of SDS with Magnetic Sensitive Roller
2.3.2. The Simulation Results for Magnetic SDS Based on Sensitive Rod’s Deviation
2.4. Slip Displacement Sensors with Capacitive Sensing Components
2.4.1. The Analysis of Modern Capacitive Slip Displacement Sensors
2.4.2. Fuzzy-logic Approach for the Identification of the Slip Displacement Direction
2.5.Computerized System for Intelligent Robot’s Control Based on Tactile
and Slip Displacement Sensors
2.6. Conclusions
 

References
 

Chapter 3. Oscillating Wave Sensors Based on Symmetrical
Metal-cladding Waveguide

3.1. Symmetrical Metal-cladding Waveguide
3.1.1. Free-space Coupling Technology
3.1.2. Ultrahigh Order Mode
3.2. Goos-Hänchen Shift
3.2.1. Theoretical Description of the Goos-Hänchen Shift
3.2.2. Enhancement of the GH Shift
3.2.2.1. Surface Plasmon Resonance
3.2.2.2. Prism-waveguide Coupling System
3.2.2.3. Symmetrical Metal Cladding Waveguide
3.3. Analysis on the Sensitivity
3.3.1. Definition of the Sensitivity
3.3.2. Physical Meaning of the Sensing Efficiency
3.4. Oscillating Wave Sensors
3.4.1. Displacement Sensor
3.4.2. Angular Displacement Sensor
3.4.3. Wavelength Sensor
3.4.4. Aqueous Solution Concentration Sensor
3.4.5. Trace Chromium (VI) Sensor
3.4.6. Trace Glyphosate Sensor
3.5. Summary and Outlook

Acknowledgements
 

References
 

Chapter 4. Garnet-like Solid State Electrolyte Li₆BaLa₂Ta₂O₁₂ Based Potentiometric CO₂ Gas Sensor

4.1. Introduction
4.2. Experimental
4.2.1. Fabrication of Sensor Devices
4.2.2. Evaluation of Sensing Properties
4.3. Results and Discussion
4.3.1. Sensing Characteristics
4.3.2. Characterization of Electrolyte and Auxiliary Layer
4.3.3. CO2 Sensing Properties
4.4. Conclusions
 

Acknowledgment

Reference
 

Chapter 5. The Characteristics of Residual Tree-ring CO₂ and H₂O Chronologies  for Conifer Species

5.1. Introduction
5.2. Materials and Techniques
5.2.1. A description of the Discs under Study
5.2.2. The Experimental Procedure and Data Processing Techniques
5.3. Results
5.3.1. An Analysis of the Desorbed CO2 Carbon Isotope Composition (δ13C)
5.3.2. The Total Pressure Variations in the Larch Disc Tree Rings
5.3.3. The Special Features Inherent in the Behavior of Tree Ring H₂O
5.3.4. The Cyclic Components of the Chronologies
5.3.5. The Association of Chronologies and Meteorological Parameters
5.3.6. The СО2 Variations in Discs Brought from Different Sites
5.4. Conclusions

Acknowledgements

References
 

Chapter 6. Graphene: A Unique Constructional Material for Electroanalytical Applications

6.1. Graphene
6.2. Basic Structure and Properties of Graphene
6.3. Basic Identification of Graphene and its Hybrid Materials
6.4. Decoration of Graphene with Different Materials e.g. Metal Nanoparticles,
Organic Compounds, Conducting Polymers etc.
6.5. Application of Graphene and its Hybrid Materials in Sensors and  

Biosensors
6.5.1. Detection of Pesticide
6.5.2. Detection of Hemoglobin
6.5.3. Detection of Heavy Metal Ions
6.5.4. Detection of Hydrogen Peroxide
6.5.5. Detection of Glucose
6.5.6. Detection of Organic Pollutants/Pathogens
6.5.7. Sustainability and Uniqueness of Graphene
 

References
 

Chapter 7. Gold Nanoparticle Based Colorimetric Sensors for Dopamine
 

7.1. Dopamine
7.2. Colorimetric Detection
7.3. Metal Nanoparticle–based Colorimetric Sensors
7.3.1. Gold Nanoparticles (AuNPs)
7.3.2. Methods for the Synthesis of Gold Nanoparticles
7.3.2.1. Seed–growth Method
7.3.2.2. In situ Synthesis
7.4. Gold Nanoparticle–based Colorimetric Detection of Dopamine
7.5. Conclusions
 

Acknowledgements
 

References
 

Chapter 8. Bio Implant ECG Sensor with Continuous Arrhythmia Monitoring and Auto Diagnosis

8.1. Introduction
8.2. System Design Concepts
8.2.1. Cardiac ECG Measurement and Electrodes
8.2.2. Telemetry Methods
8.2.3. Wireless Power for Biomedical Implants
8.2.4. Circuit Design
8.2.5. Packaging
8.3. Experimental Results
8.3.1. Self-sealing Airtightness Testing
8.3.2. Thermal Testing
8.3.3. Insertion Experiment Using Animal Model
8.4. Summary
 

Acknowledgement
 

References
 

Chapter 9. Fano-Resonance Plasmonic Biosensors

9.1. Introduction
9.2. Basic Concepts of Plasmonic Biosensors
9.2.1. SPR and LSPR
9.2.2. Plasmonic Biosensing Principle
9.2.3. Performance Evaluation of Plasmonic Biosensors
9.3. The Fano-resonance
9.4. Design Method of Fano Resonance Plasmonic Biosensor
9.5. Fano-resonance Plasmonic Biosensors
9.5.1. Dolmen-type Biosensor
9.5.2. Nanohole Array Sensor
9.5.3. Slit-groove Nanostructure Sensor
9.6. Conclusions
 

Acknowledgements
 

References

Chapter 10. Linearization of Sensor Signal in FPGA: A Multichannel Approach for High Speed Real Time Applications

10.1. Introduction
10.2. Theory of Linearization and Experimental Setup
10.3. FPGA Implementation
10.3.1. Piecewise Linearization (PWL)
10.3.1.1. FPGA Implementation of PWL
10.3.1.2. Data Representation
10.3.1.3. Results of PWL
10.3.2. Linearization by Interpolation (LI)
10.3.2.1. FPGA Implementation of LI
10.3.2.2. Results of LI
10.3.3. Look up Table (LUT) Based Linearization
10.3.3.1. FPGA Implementation of LUT Based Linearization
10.3.3.2. Data Representation
10.3.3.3. Results of LUT Based Linearization
10.3.4. ANN Based Linearization
10.3.4.1. FPGA Implementation of a Neuron
10.3.4.2. Implementation of Activation Function in FPGA
10.3.4.3. Results of FPGA Implementation of ANN for Linearization
10.4. Conclusion
 

Acknowledgements
 

References

Chapter 11. Low Value Capacitance Measurements for Capacitive Sensors: A Review

11.1. Introduction
11.2. Methodology
11.2.1. Measuring Capacitance Using Double Differential Principle
11.2.2. Capacitance Measurement with High Resolution and High Linearity
11.2.3. Measuring Capacitance Based on RC Phase Delay
11.2.4. Micro Controller Interface for Low value Capacitance Sensors
11.2.5. Measuring Capacitance Using Oscillator
11.2.6. Measuring Capacitance Based on Phase Angle
11.2.7. Capacitance to Frequency Converter Suitable for Sensor Applications Using Telemetry
11.2.8. Universal Capacitive Sensors and Transducers Interface (USTI)
11.2.9. An Integrated Interface Circuit with a Capacitance-to-voltage Converter
as Front-end for Grounded Capacitive Sensors
11.2.10. A 16-channel Capacitance-to-period Converter for Capacitive Sensor Applications
11.2.11. A CMOS Integrated Capacitance-to-Frequency Converter with Digital Compensation Circuit Designed for Sensor Interface Applications
11.3. Summary
 

References

Chapter 12. Design and Validation of Unimorph Piezoelectric Energy Harvesters

12.1. Introduction
12.2. Parametric Study and Effect of Beam Material on a Unimorm Energy Harvester
12.2.1. Mathematic Concept of Mechanical Energy Conversion
12.2.2. Experimental and Modelling of Unimorph Energy Harvester with Tip Mass
12.3. A Unimorph Energy Harvester with Cantilever Arrays
12.3.1. Modeling Results

Acknowledgements

References

Chapter 13. Towards Tactical Military Software Defined Radio with the Assistance of Improved Data Gathering Tools

13.1. Introduction
13.2. Unmanned Aircraft Systems
13.3. Service Oriented Architecture
13.4. Military Communication Environment
13.5. Challenges of the Future Force Warrior
13.6. Software Defined Radio
13.7. Universal Software Peripheral Radio
13.8. Cognitive Radio
13.9. Graphic User Interface
13.10. A New Communication System
13.11. The set-up and Utilization of Sensor Element Munitions
13.12. On Airborne Sensors, SEMs and Communication
13.13. Comprehensive Targeting Process
13.14. Means to Analyze Collected Data
13.15. Discussion
13.16. Results
13.17. Conclusions
 

References

Chapter 14. A Survey on Wireless Sensor Networks Simulation Tools

and Testbeds

14.1. Introduction
14.2. WSNs Network Simulation Tools
14.2.1. SensorSim
14.2.2. TOSSIM
14.2.3. TOSSF
14.2.4. GloMoSim
14.2.5. Qualnet
14.2.6. OPNET
14.2.7. EmStar
14.2.8. SENS
14.2.9. J-Sim
14.2.10. Dingo
14.2.11. NS-2 and NS-3
14.2.12. Shawn
14.2.13. GTSNetS
14.2.14. CNET
14.2.15. TRMSim
14.3. Testbd as a Service
14.4. Discussion
14.5. Conclusion
 

References

Chapter 15. CWSN: A Graph-based Model for Collaborative Wireless Sensor Networks

15.1. Introduction
15.2. Related Works
15.3. The CWSN Model
15.3.1. CWSN Model Definitions
15.3.2. Main Properties Represented by the CWSN Model
15.3.3. Comparing the CWSN Model with Other Models for WSNs
15.3.4. Contributions of the CWSN Model
15.4. The CWSN Model Applied to Structural Health Monitoring
15.5. Conclusions
 

Acknowledgements

References

Chapter 16. Target Localization in Cooperative Wireless Sensor Networks Using Measurement Fusion

16.1. Introduction
16.2. Problem Formulation
16.2.1. Assumptions
16.3. Distributed Localization
16.3.1. The Proposed Distributed SOCP Algorithm
16.4. Complexity Analysis
16.5. Simulation Results
16.6. Conclusions

Acknowledgements

References

Chapter 17. Clustering Approach Based on the Redundancy in a Linear Sensor Network using a Token-based MAC Protocol

17.1. Introduction
17.2. State of Art
17.3. Hypothesis
17.4. The Mechanism of Redundancy
17.4.1. Definition of Redundancy
17.4.2. Some Examples of R-redundant Networks
17.4.2.1. Case of 1-redundant LSN
17.4.2.2. Case of 2-redundant LSN
17.4.2.3. Case of 3-redundant LSN
17.4.3. Impact of Redundancy on the Distance to the Sink
17.5. Estimation of the Distance between Token Holders
17.5.1. Case of a 1-redundant LSN
17.5.2. Case of a 2-redundant LSN
17.5.3. Case of a 3-redundant LSN
17.5.4. Generalization
17.6. Definition of a Logical Cluster
17.7. Impact of the Clustering on the Throughput
17.8. Conclusion

References

Chapter 18. Performance Study of Wireless Sensor Databases

18.1. Introduction
18.2. Sensor Database Approaches in WSN
18.2.1. Warehousing Approach
18.2.2. Distributed Approach
18.2.3. Abstract Database
18.2.3.1. Cougar
18.2.3.2. TinyDB
18.2.3.3. TikiriDB
18.2.3.4. MaD-WiSe
18.2.3.5. Corona
18.2.3.6. BBQ
18.3. Study of Temporal Aspects in Wireless Sensor Databases
18.3.1. Network Model
18.3.2. First Scenario: Data Collection with Remote Database
18.3.3. Second Scenario: Query Processing with WSN Abstract Database
18.4. Simulation Environment
18.4.1. Data Collection Scenario
18.4.2. Query Processing Scenario
18.4.3. Simulation Description
18.4.3.1. Data Collection Scenario
18.4.3.2. Query Processing Scenario
18.4.4. Simulation Results
18.4.4.1. Impact of Number of Nodes on Network Convergence Time
18.4.4.2. Impact of Number of Nodes on Data Collection Time
18.4.4.3. Impact of the MAC Layer Protocols
18.4.4.4. Impact of Choosing the Database on Average Response Time
18.4.4.5. Impact of the Nodes Positions and the Number of Hops on Data Collection Time
18.4.4.6. Impact of Network Topologies on Data Collection Time
18.5. Conclusions

References

Chapter 19. Review of RDC Soft Computing Techniques

19.1. Introduction
19.2. RDC Techniques
19.3. Conclusions

References

Index

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