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| 材料类型： | 互联网资源 |
|---|---|
| 文件类型： | 书, 互联网资源 |
| 所有的著者/提供者： |
Elena Gaura; Robert M Newman; Michael Kraft; Andrew Flewitt; Davies William de Lima Monteiro |
| ISBN: | 1860944930 9781860944932 |
| OCLC号码： | 73502017 |
| 描述： | xii, 539 p. : ill. ; 24 cm. |
| 内容： | Chapter 1 Markets and Applications 1 -- 1.1 Technology at Crossroads 1 -- 1.2 The Present -- MEMS in the News 2 -- 1.3 The Past -- Great Expectations 7 -- 1.4 The Future -- Maturity and Pervasive Applications 10 -- 1.5 Drivers for Progress 13 -- 1.6 Progress -- Device Improvement 15 -- 1.7 Progress -- Device Integration 18 -- 1.8 Smart MEMS -- The Research Agenda 23 -- Chapter 2 Microfabrication Technologies 31 -- 2.2 Passive Components 36 -- 2.3 Sensing Components 42 -- 2.4 Actuating Components 49 -- 2.5 Materials and Growth 55 -- 2.6 Fabrication Techniques 73 -- Chapter 3 Sensor Electronics 107 -- 3.2 Functions of a Sensor System 108 -- 3.3 Analogue and Digital Design Options 131 -- 3.4 Digital Signal Processing 137 -- 3.5 Interface Configurations for Different Transducer Types 143 -- 3.6 Integration 152 -- 3.7 Design for Power Awareness 158 -- Chapter 4 Sensor Signal Enhancement 173 -- 4.1 Errors in Sensor Systems and Measurement Quality (Non-linearity, Cross-sensitivity, Offset, Parameter Drift) 174 -- 4.2 Sensor Calibration and Compensation -- Techniques and Examples 187 -- 4.3 System Design Choices for Compensation -- Closed Loop Configurations and other Designs 225 -- 4.4 Summing up on Sensor Calibration and Compensation 226 -- Chapter 5 Case Study: Control Systems for Capacitive Inertial Sensors 233 -- 5.2 Open Loop Accelerometer 235 -- 5.3 Closed Loop Accelerometer 243 -- Chapter 6 Case Study: Adaptive Optics and Smart VLSI/MEMS Systems 273 -- 6.2 Adaptive Optics and MEMS Systems 274 -- 6.3 Operational Principles 276 -- 6.4 Device Implementation 283 -- 6.5 Closed-loop Adaptive Optical System 294 -- 6.6 Conclusions and Future Trends 299 -- Chapter 7 Artificial Intelligence Techniques for Microsensors Identification and Compensation 305 -- 7.1 Artificial Neural Networks: What They are and How They are Used for Microsensor Control and Identification 305 -- 7.2 Open Loop, Neural Transducer Prototype for Static/Low Frequency Applications 315 -- 7.3 Closed-loop Neural Network Controlled Accelerometer 331 -- 7.4 The Neural Network Non-linear Gain Controller 337 -- 7.5 Micromachined Sensor Identification Using Neural Networks 348 -- Chapter 8 Smart, Intelligent and Cogent MEMS Based Sensors 369 -- 8.2 Smart, Intelligent and Cogent Sensors -- What do the Terms Mean 370 -- 8.3 What and Where is the Added Value Brought by Intelligence? 379 -- 8.4 ANNs and MEMS 382 -- 8.5 AI for MEMS Intelligence 390 -- 8.6 'Cogent' Sensors -- Fault Detection Case Study 403 -- Chapter 9 Sensor Arrays and Networks 417 -- 9.1 Potential of Sensor Arrays 417 -- 9.2 Node Design 420 -- 9.3 An Architectural History of Sensor Arrays and Networks 425 -- 9.4 Systems Design Issues 440 -- 9.5 Network Technology and Topology 443 -- Chapter 10 Wireless and Ad Hoc Sensor Networks 465 -- 10.1 Sensor Network Applications 466 -- 10.2 System Designers' Role 472 -- 10.3 Design Assumptions for Ad hoc Networks 475 -- 10.4 Distributed System Design Philosophy 477 -- 10.5 Network Design Considerations 481 -- 10.6 Layered Model 485 -- 10.7 Sensor Network Operating Environments 488 -- 10.8 Application Services 498 -- 10.9 Proposed Sensor Support System Architecture 501 -- Chapter 11 Realising the Dream -- A Case Study 509 -- 11.2 The Mission 510 -- 11.3 Initial Rough Design 513 -- 11.4 Sensor Technology 518 -- 11.5 Deployment 523 -- 11.6 Operation, Control and Communication 525 -- 11.7 Querying the Array 526 -- 11.8 A Cogent Sensor 527 -- 11.9 A World of Applications 530. |
| 责任： | Elena Gaura & Robert Newman ; with contributions from Michael Kraft, Andrew Flewitt, Davies William de Lima Monteiro. |
| 更多信息： |
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