First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
PHYSICS OF DATA
Course unit
MANAGEMENT AND ANALYSIS OF PHYSICS DATASET (MOD. A)
SCP8082534, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
PHYSICS OF DATA
SC2443, Degree course structure A.Y. 2018/19, A.Y. 2019/20
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination MANAGEMENT AND ANALYSIS OF PHYSICS DATASET (MOD. A)
Website of the academic structure http://physicsofdata.scienze.unipd.it/2019/laurea_magistrale
Department of reference Department of Physics and Astronomy
Mandatory attendance No
Language of instruction English
Branch PADOVA

Lecturers
Teacher in charge GIANMARIA COLLAZUOL FIS/01

Integrated course for this unit
Course unit code Course unit name Teacher in charge
SCP8082533 MANAGEMENT AND ANALYSIS OF PHYSICS DATASET (C.I) DONATELLA LUCCHESI

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP9086805 PROGRAMMABLE HARDWARE DEVICES GIANMARIA COLLAZUOL IN2371
INP9086805 PROGRAMMABLE HARDWARE DEVICES GIANMARIA COLLAZUOL IN2371
INP9086805 PROGRAMMABLE HARDWARE DEVICES GIANMARIA COLLAZUOL IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses FIS/01 Experimental Physics 6.0

Course unit organization
Period Annual
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 30/09/2019
End of activities 20/06/2020
Show course schedule 2019/20 Reg.2018 course timetable

Syllabus

Common characteristics of the Integrated Course unit

Prerequisites: Elements of analysis and algebra.
General physics.
Statistics.
Basic programming elements.
Target skills and knowledge: Fundamental knowledge of Unix operating systems
Knowledge of distributed computing.
Knowledge of the management of big data on distributed architectures.
Ability to build a cluster with the available hardware.
Data management on the distributed cluster.
Analysis of data on distributed clusters.
Examination methods: Development of a project assigned at the end of the course. Presentation and discussion of the project, questions on the material presented in class.
Assessment criteria: Evaluation of the project delivered: accuracy, completeness and correctness of the work.
Presentation of the assignment: ability to synthesize information, completeness, correctness and accuracy in the presentation.
Evaluation of the answers: correctness, completeness and accuracy.

Specific characteristics of the Module

Course unit contents: PART I - Electronics for real-time data management systems

1) Data Sources
- signal generation in sensors/detectors
- early (analog) data processing (amplificaton, filtering, ...)
- digitization (A/D, ADC, TDC, ...)
- timing, sync and control signals distribution systems

2) Data Transport
- Data Transport Architectures
- Physical layers for data streams
- Interconnections and buses

3) Real Time Data Processing
- Digital ports and logics
- Storage units - Memories
- Processing units - focusing on FPGA
- Parallel data streams

4) Real Time Data Filtering and System Control
- Trigger generation and distribution
- Transducers and System Control

PART II - Hands-on Laboratory of data management with FPGA

1) Introduction to FPGA and intro to the ARTY A7 board
2) FPGA Programming framework, Simulation and Test-Bench
3) Combinational Logic Circuits
4) Sequential Logic Circuits
5) Virtual Input Output and Integrated Logic Analyzer
6) Arithmetic Operations
- case study: DAC/ADC and FIR Filter
7) Finite State Machines
8) Memories
9) Buses and Protocols
- case study: SPI interface for accessing Flash memory
- case study: IPBUS - communication FPGA-PC via Ethernet interface

NOTE - Examples and Case studies will be chosen in various fields: from High Energy Physics to Astro-particle and Space Physics Systems on satellites; from Nuclear Imaging Medicine to Low-Latency Market Data Feed Processing; from Biomedical and Neuro Sciences to Gravitational Wave Physics.
Planned learning activities and teaching methods: - Frontal Lecturing (50%) + Hands-on Laboratory (50%)
- Case studies, Problem based learning
Additional notes about suggested reading: - Slides and lecture notes (provided via Moodle)
Textbooks (and optional supplementary readings)
  • Mark Zwolinski, Digital System Design with VHDL. --: Prentice Hall, 2004. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Working in group
  • Questioning
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab
  • Vivado

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