bullet  Advances in Measurements and Instrumentation: Reviews

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  Title: Advances in Measurements and Instrumentation: Reviews

  Editor: Sergey Y. Yurish

  Publisher: International Frequency Sensor Association (IFSA) Publishing, S. L.

  Formats: hardcover (print book) and printable pdf Acrobat (e-book), 298 pages

  Price: 95.00 EUR for print book in hardcover

  Delivery time for print book: 7-17 days. Please contact us for priority (5-9 days), ground (3-8 days) and express (2-3 days) delivery options by e-mail

  Pubdate: 15 December 2018

  ISBN: 978-84-09-07321-4

  e-ISBN: 978-84-09-07322-1


  Creative Commons License




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 Book Description


 'Advances in Measurements and Instrumentstion: Reviews', Vol. 1 Book Series is covering some aspects related to metrology, sensors, measuring systems and sensor instrumentation as well as related modeling and mathematical tools for measurements in quality control and other applications. The book volume contains seven chapters written by nine contributors from academia and industry from 6 countries: Algeria, Canada, China, Germany, Slovak Republic and United Kingdom. The book will be a valuable tool for those who involved in research and development of various measuring instruments and systems.







1. Generalized Polynomial Comparative Calibration: Parameter Estimation and Applications

1.1. Introduction
1.2. Measurement Procedure
1.3. Calibration Model
1.3.1. Polynomial Calibration Function
1.3.2. Linearized Calibration Model
1.4. Estimation of the Model Parameters
1.4.1. Best Linear Unbiased Estimator of the Calibration Model Parameters
1.4.2. Algorithm to Estimate the Calibration Model Parameters
1.5. State-of-Knowledge Distribution  about the Calibration Parameters
1.6. Measuring with the Calibrated Device
1.7. Example: Calibration of the Industrial Platinum Resistance Thermometer
1.8. Conclusions



Appendix 1.A

1.A.1. Measurement Uncertainty and the State-of-Knowledge Distributions
1.A.2. GUM Uncertainty Framework (GUF)
1.A.3. Monte Carlo Method (MCM)
1.A.4. Characteristic Functions Approach (CFA)


2. Fundamental Principles of Spectral Methods Related to Discrete Data

2.1. Introduction
2.2. Mathematical Concepts
2.2.1. Signal Definition
2.2.2. The Fundamental Idea of Frequency Analysis
2.2.3. Integral Transforms
2.3. Background and Subtleties of Spectral Methods with Discrete Data
2.3.1. Derivation of the Discrete Fourier-Transform
2.3.2. The Origin of fs/2
2.3.3. Derivation of the Analytic Signal and the Hilbert Transform
2.4. Application Examples and Hints
2.4.1. The Essence of Band Limitation and the Nyquist Condition
2.4.2. Center a Discrete Spectrum
2.4.3. The Analytic Signal
2.4.4. Calculating the Envelope
2.4.5. Convolution
2.4.6. The Sampling Theorem and Filtering
2.4.7. Non-Stationary Processes Spatially Dependent Spectral Analysis
2.4.8. Fragmented and Irregularly Sampled Data
2.5. Conclusion



3. Mathematical Tools for Measurements. Application for Quality Control Based Material Testing and Characterization

3.1. Quality Assurance and Conformity Declaration
3.2. Overview of Mathematical Tools in Measurement
3.2.1. Structure and Model of Measurement Processes
3.2.2. General Formalism
3.2.3. Extension to Dynamic Measurement System
3.2.4. Model Based Neural Network Model
3.2.5. Sensitivity Analysis Sensitivity Based Derivative Form 134 Sensitivity Based Modeland Monte Carlo Simulation
3.3. Application for Quality Control Based Material Testing and Characterization
3.3.1. Testing of Welding Quality Hardness model Uncertainty Evaluation Based Sensitivity Analysis
3.3.2. Results
3.4. Conclusion



4. Recent Advances in Water Cut Sensing Technology

4.1. Introduction
4.2. Water Cut Measurement Technology
4.2.1. Analytical Methods Sampling and Centrifugal Separation
4.2.2. Density Measurement Methods Differential Pressure Method Gamma Ray Densitometer Coriolis Densitometer
4.2.3. Infrared Method
4.2.4. Permittivity Measurement Methods Capacitance, Conductance & Impedance Principles Microwave Principles
4.3. Microwave Measurement Technology
4.3.1. Industrial Microwave Sensing Principles Transmission Sensors Reflection Sensors Resonator Sensors
4.3.2. Water Cut Estimation from Microwave Sensors
4.3.3. Advances in Microwave Sensing Technology
4.3.4. Research Challenges in Microwave Water Cut Metering Flow Regimes Periodic Calibration Oil Composition Scaling and Fouling Dielectric Mixture Models Emulsion Transition Zone Salinity Hydrate Inhibitors Sensitivity at Low Water Cut Heavy Oil Entrained Gas Other Factors
4.4. Summary



5. Modelling Methods for the Colored Noise of Inertial Sensors

5.1. Introduction
5.2. White Noise and Colored Noise
5.3. Shaping Filter
5.4. ARMA
5.4.1. Definition
5.4.2. Stationary, Invertible, and Expandable Stationary Invertible Expandable
5.4.3. ARMA Modeling Estimation of the AR Parameters Estimation of the MA Parameters and the Variance  of Noise Estimation of the AR and the MA Orders Modelling with the Observed Noise
5.5. Allan Variance
5.5.1. Methodology
5.5.2. Quantization Noise
5.5.3. Angular Random Walk
5.5.4. Bias Instability
5.5.5. Rate Random Walk
5.5.6. Drift Rate Ramp
5.5.7. First-Order Markov Process
5.5.8. Sinusoidal Noise
5.6. Conclusions



6. Open Channel Cross Section Design: Review of Recent Developments

6.1. Introduction
6.2. Historical Development
6.3. Conventional Sections (1894-2009)
6.3.1. Linear Family
6.3.2. Curved Family
6.3.3. Linear-Curved Family
6.4. New Inspiring Initiatives (2003-2009)
6.4.1. Inspiring Linear Sections
6.4.2. Inspiring Linear-Curved Sections
6.5. Following Developments (2010-2018)
6.5.1. Linear Family
6.5.2. Elliptic Family
6.5.3. Power-Law Family
6.6. Design Methods
6.6.1. Best Hydraulic Section
6.6.2. Most Economic Section
6.6.3. Probabilistic Methods
6.7. Design of Flexible Channels
6.7.1. Riprap, Cobble, and Gravel Linings
6.7.2. Grass-Lined Channels
6.8. Design Considerations
6.8.1. Normal and Critical Depths
6.8.2. Freeboard
6.8.3. Seepage Loss
6.8.4. Section with Smooth Top Corners
6.9. Concluding Remarks




7. Channel Flood Routing: Review  of Recent Hydrologic Muskingum Models

7.1. Introduction
7.2. Historical Perspective
7.3. Original Muskingum Models (1959-2013)
7.4. New Inspiring Initiatives (2013-2014)
7.4.1. Model with Variable Exponent Parameter
7.4.2. Model with 4 Constant Parameters
7.5. Following Muskingum Models (2014-Present)
7.5.1. Models with 5-7 Constant Parameters
7.5.2. Models with Discrete Variable Parameters
7.5.3. Models with Continuous Variable Parameters
7.5.4. Models with Lateral Flow
7.5.5. Models with Multiple Criteria
7.6. Routing Procedures
7.6.1. Modified Euler
7.6.2. Fourth-Order Runge-Kutta
7.7. Practical Considerations
7.7.1. Guidelines for Model Calibration
7.7.2. Continuous vs. Discontinuous Parameters
7.7.3. Guidelines for Selection of Model Type and Routing Procedure
7.7.4. Plagiarism and Ethical Issues
7.8. Concluding Remarks



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