- Presentation of the syllabus with support of laboratorial classes, problem solving and programming examples and challenges.
- Students are encouraged to work alone and in groups
To understand the global architecture of data acquisition systems
- To identify and characterize the different tasks and functions of a data acquisition system
- To identify the physical and technical limitations of data acquisition systems and the associated instrumentation
- Ability to use and specify data acquisition systems
- Knowledge of basic and intermediate concepts of sensor elements and data communication networks
- Use data analysis tools for data processing (Matlab) in the scope of data acquisition systems.
1. Data Acquisition Systems
Characterization and Performance Evaluation
Definitions, characteristics and terminology
Main types of sensors
Calibration and compensation
3. Signal Conditioning and Multiplexing
Instrumentation amplifiers and galvanic insulation
Intrinsic noise (fundamental ): thermal and quantum 1 / f
Modes of extrinsic noise coupling
Methods for noise elimination
5. Analog-to- Digital Conversion
Periodic sampling of signals
Nyquist sampling theorem
Differential non-linearity errors
Signal to Noise Ratio
Auto-correlation and cross-correlation
Main types of ADCs
6. Analysis and filtering
Properties of analog and digital signals
Analysis of the frequency spectrum
Continuous evaluation: 100.0%
- Apresentações e apontamentos (v2.1) disponibilizados aos alunos
Alguns livros de referência:
- Data Acquisition Fundamentals, National Instruments, 2002.
- Edward E. Lee, Pravin Varaiya, Structure and Interpretation of Signals and Systems, Addison Wesley, 2003.
- John G. Webster, ed., Medical Instrumentation: Application and Design, 3/e, John Wiley & Sons, 1997.
- John Bently, Principles of Measurement, Prentice Hall, 2004.
- Gilbert Held, Understanding Data Communication, 7/e, Addison Wesley, 2002.
- Eva Pärt-Enander, Anders Sjöberg, Matlab 5 Handbook, Addison Wesley Longman, 1999