Unit MEASUREMENT DATA PROCESSING
- Course
- Electronic engineering for the internet-of-things
- Study-unit Code
- 70A00106
- Curriculum
- Elettronica per l'internet of things
- Teacher
- Paolo Carbone
- Teachers
-
- Paolo Carbone
- Hours
- 72 ore - Paolo Carbone
- CFU
- 9
- Course Regulation
- Coorte 2021
- Offered
- 2022/23
- Learning activities
- Caratterizzante
- Area
- Ingegneria elettronica
- Academic discipline
- ING-INF/07
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- Procedures, methods and systems for processing measurement data in uncertain conditions and to extract meaningful parameters for the users.
- Reference texts
- Class handouts.
S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory - Prentice Hall, 1993
Louis Scharf, Statistical Signal Processing, Pearson, 1991 - Educational objectives
- This class has the goal of transferring knowledge and competence for the definition and usage of estimation techniques for processing uncertain measurement data. At the end of this class, students will be able to:
- select the best estimator for solving practical estimation problems
- realize a measurement system using such estimator in the solution of typical problems in the electronic engineering area - Prerequisites
- None
- Teaching methods
- Frontal lectures practical in-class sessions, exercises to be solve autonomously, design of practical systems
- Other information
- Information about available services for people with disabilities and/or with learning disabilities, see:
http://www.unipg.it/disabilita-e-dsa - Learning verification modality
- The final grade is based on the following:
- Final (50%)
- Project (team score, 50%)
The final exam will be a written test. The project grade is assigned to the group as a whole, after oral discussion of the design outcomes. - Extended program
- Introductory material and goal of this class. Application examples of estimation techniques applied to measurement data in the area of Internet-of-Things.
The Monte Carlo method. Minimum variance estimators. Cramer-rao limit. BLUE estimators. Maximum likelihood estimators. Least squares estimators. Method of moments. Bayesian estimators. Kalman filter.
To transfer knowledge and competences, groups of students will realize a measuring instrument.
Lectures on project management and systems engineering will be provided to sustain the project design process.