Book contents
- Electronic Sensor Design Principles
- Electronic Sensor Design Principles
- Copyright page
- Dedication
- Contents
- Preface
- Part I Fundamentals
- 1 Introduction
- 2 Sensor Modeling and Characterization
- 3 Sensor Design Optimization and Tradeoffs
- 4 Overview of Mathematical Tools
- 5 Compressive Sensing
- Part II Noise and Electronic Interfaces
- Part III Selected Topics on Physics of Transduction
- Part IV Problems and Solutions
- Index
- References
4 - Overview of Mathematical Tools
from Part I - Fundamentals
Published online by Cambridge University Press: 23 December 2021
Book contents
- Electronic Sensor Design Principles
- Electronic Sensor Design Principles
- Copyright page
- Dedication
- Contents
- Preface
- Part I Fundamentals
- 1 Introduction
- 2 Sensor Modeling and Characterization
- 3 Sensor Design Optimization and Tradeoffs
- 4 Overview of Mathematical Tools
- 5 Compressive Sensing
- Part II Noise and Electronic Interfaces
- Part III Selected Topics on Physics of Transduction
- Part IV Problems and Solutions
- Index
- References
Summary
This chapter gives a short summary of mathematical instruments required to model sensor systems in the presence of both deterministic and random processes. The concepts are organized in a compact overview for a more rapid consultation, emphasizing the convergences between different contexts.
Keywords
- Type
- Chapter
- Information
- Electronic Sensor Design Principles , pp. 189 - 235Publisher: Cambridge University PressPrint publication year: 2022
References
Further Reading
Carlson, A. B., Communication Systems: An Introduction to Signal and Noise in Electrical Communication. New York: McGraw-Hill, 1986.Google Scholar
Gardner, W. A., An Introduction to Random Processes with Application to Signal and Systems. New York: McGraw-Hill, 1990.Google Scholar
Oppenheim, A. V., and Schafer, R. W., Discrete-Time Signal Processing. Upper Saddle River, NJ: Pearson Education, 2011.Google Scholar
Oppenheim, A. V., and Willsky, A. S., Signals and Systems. Upper Saddle River, NJ: Pearson Education, 2013.Google Scholar
Papoulis, A., and Pillai, S. U., Probability, Random Variables, and Stochastic Processes. New York: McGraw-Hill, 2002.Google Scholar
Proakis, J. G., and Manolakis, D. G., Digital Signal Processing. Upper Saddle River, NJ: Pearson Prentice Hall, 2007.Google Scholar