CRC Press, 2010. — 547 p. — ISBN 978-1-4200-8954-7.Explaining the underlying design methodologies of intelligent instrumentation, Intelligent Instrumentation: Principles and Applications provides a comprehensive and authoritative resource on the scientific foundations from which to coordinate and advance the field. Employing a textbook-like language, this book translates methodologies to more than 80 numerical examples, and provides applications in 14 case studies for a complete and working understanding of the material. Beginning with a brief introduction to the basic concepts of process, process parameters, sensors and transducers, and classification of transducers, the book describes the performance characteristics of instrumentation and measurement systems and discusses static and dynamic characteristics, various types of sensor signals, and the concepts of signal representations, various transforms, and their operations in both static and dynamic conditions. It describes smart sensors, cogent sensors, soft sensors, self-validating sensors, VLSI sensors, temperature-compensating sensors, microcontrollers and ANN-based sensors, and indirect measurement sensors. The author examines intelligent sensor signal conditioning such as calibration, linearization, and compensation, along with a wide variety of calibration and linearization techniques using circuits, analog-to-digital converters (ADCs), microcontrollers, ANNs, and software. The final chapters highlight ANN techniques for pattern classification, recognition, prognostic diagnosis, fault detection, linearization, and calibration as well as important interfacing protocols in the wireless networking platform.Preface. Background of Instrumentation. Introduction. Process. Process Parameters. Classical Sensors and Transducers. References. Further Readings. Sensor Performance Characteristics. Introduction. Static Characteristics. Dynamic Characteristics. Input–Output Impedances. Reference. Signals and System Dynamics. Introduction. Test Signals. Fourier, Laplace, and Z-Transform. Spectral Density and Correlation Function. Modifying and Modulating Inputs. Compensation Techniques. System Dynamics. Electronic Noise Cancellation. References. Intelligent Sensors. Introduction. Smart Sensors. Cogent Sensors. Soft or Virtual Sensors. Self-Adaptive Sensors. Self-Validating Sensors. VLSI Sensors. Temperature Compensating Intelligent Sensors. Indirect Sensing. Linearization, Calibration, and Compensation. Introduction. Analog Linearization of Positive Coefficient Resistive Sensors. Linearization of Negative Coefficient Resistive Sensors. Higher-Order Linearization Using MOS. Nonlinear ADC- and Amplifier-Based Linearization. Interpolation. Piecewise Linearization. Microcontroller-Based Linearization. Artificial Neural Network–Based Linearization. Nonlinear Adaptive Filter–Based Linearization. Sensor Calibration. Offset Compensation. Error and Drift Compensation. Lead Wire Compensation. References. Sensors with Artificial Intelligence. Introduction: Artificial Intelligence. Sensors with Artificial Intelligence. References. Intelligent Sensor Standards and Protocols. Introduction. IEEE 1451 Standard. Network Technologies. LonTalk. CEBUS Communication Protocol for Smart Home. J1850 Bus. MI Bus. Plug-n-Play Smart Sensor Protocol. References. Questions. Index.
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