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Course
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Credits
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Scientific Disciplinary Sector Code
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Contact Hours
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Exercise Hours
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Laboratory Hours
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Personal Study Hours
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Type of Activity
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Language
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20810224 -
MACROEONOMICS
(objectives)
This Course gives students a thorough understanding of macroeconomics by taking a unified view of the subject, allowing connections to be made between the short, medium and long run. By using this unified structure we will analyse the macroeconomic effects of the last financial crisis.
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D'AGOSTINO GIORGIO
( syllabus)
First part:
i. The goods market, ii. financial markets, iii. the IS-LM model, iv. the labour market, v. the Phillips Curve, vi. the natural rate of unemployment, vii. the Inflation rate, viii. from the short to the medium run.
Second part:
i. The facts of growth, ii. saving, capital accumulation, and output, iii. the role of public spending in the long-run, iv.openness in goods and financial markets, v. should policy makers me restrained?, vi. fiscal Policy, vii. monetary policy.
( reference books)
Macroeconomia. Una prospettiva europea 2016, di Olivier J. Blanchard , Alessia Amighini, Francesco Giavazzi. Il Mulino.
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9
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SECS-P/02
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90
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-
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-
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-
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Related or supplementary learning activities
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ITA |
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20810207 -
CONTROL MEASURES AND TECHNOLOGIES
(objectives)
Present the main aspects of the measures and technologies to build modern control systems based on transductors, data extraction and data processing.
To present, in particular, processing of sensory data, estimation techniques for auto and cross-correlation, test signal generation, FFT based harmonic response estimation, as well as the techniques and components at the basis of the actuators of electric engines.
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LA GALA FRANCESCO
( syllabus)
1. GENERALITÀ SULL’ARCHITETTURA TECNOLOGICA DI SISTEMI DI CONTROLLO.
2. PROBLEMI REALIZZATIVI DEGLI SCHEMI DI CONTROLLO LEGATI A LIMITI TECNOLOGICI:
• SATURAZIONI
• RUMORE, ALIASING
• RISOLUZIONE DI TRASDUTTORI ED ATTUATORI
3. CARATTERISTICHE E POTENZIALITÀ DEI MODERNI MICROCONTROLLORI E DSP IN
RELAZIONE ALL’IMPIEGO NEI SISTEMI DI CONTROLLO
• ARCHITETTURA DI UN MICROCONTROLLORE
• PERIFERICHE DI UN MICROCONTOLLORE
• SOLUZIONI IMPLEMENTATIVE
4. INTERFACCIAMENTO DEI PRINCIPALI SENSORI PER L’AUTOMAZIONE:
• POSIZIONE: POTENZIOMETRO, RESOLVER, ENCODER,LVDT, EFFETTO HALL..
• VELOCITÀ: DINAMO, RESOLVER,ENCODER.
• SENSORI INERZIALI “MEMS” ACCELEROMETRI E GIROSCOPI
• CORRENTE ELETTRICA: SENSORI DI CORRENTE
5. CRITERI DI PROGETTO PER LA REALIZZAZIONE DI UNA CATENA DI MISURA PER IL RAMO
DI CONTROREAZIONE.
• SCELTA DEI COMPONENTI
• IL PROBLEMA DELLE ALIMENTAZIONI ELETTRICHE
• RIDUZIONE DEI RUMORI IN CONTOREAZIONE
• CENNI SUL PROGETTO DI SCHEDE ELETTRONICHE
6. ESEMPI DI SISTEMI DI ACQUISIZIONE E CONDIZIONAMENTO TRAMITE L’ANALISI DI
CIRCUITI REALI.
7. ATTUATORI ELETTRICI:
• PILOTAGGIO DI MOTORI IN CC TRAMITE AMPLIFICATORI LINEARI E SWITCHING
• AZIONAMENTI PER MOTORI BRUSHLESS
• AZIONAMENTI PER MOTORI STEPPER
• ATTUATORI LINEARI
• ATTENUAZIONE DEL RUMORE ELETTRICO E MECCANICO GENERATO DAI MOTORI
8. ATTUATORI IDRAULICI :
• CENNI SUL FUNZIONAMENTO FUNZIONAMENTO DELLE SERVOVALVOLE
• DIMENSIONAMENTO DI MASSIMA DI UN ATTUATORE OLEODINAMICO/PNEUMATI
( reference books)
HANDOUTS PROVIDED BY THE TEACHER
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6
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ING-INF/04
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54
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-
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-
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-
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Core compulsory activities
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ITA |
Optional group:
I ANNO AUTOMAZIONE DEI SISTEMI COMPLESSI -UNO A SCELTA - (show)
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6
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20802073 -
Optimization of Public Services
(objectives)
This course gives a compendium of techniques, methods and solution
approaches to support the decision making process in the public sector. The
course is based on case studies concerning the design and the management of
public services and it is focused on the development of optimization models
and solution algorithms. Ethical and political issues, typical of the public
sector, are also addressed
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D'ARIANO ANDREA
( syllabus)
Course programme
1. Introduction to Multi-Criteria Decision Making for Public Service Management Problems
2. Optimization in Project Planning and Scheduling
3. Disjunctive Programming: Scheduling and Routing Models, (Meta)Exact /Heuristic Algorithms
4. Reservations Systems and Interval Scheduling
5. Timetabling with Operator or Tooling Constraints
6. Scheduling and Timetabling in Sport Tournaments
7. Scheduling Network Television Programs
8. Transportation Problems: Tanker/Aircraft/Train Coordination, Scheduling and Routing
9. Decision Support Systems for Real-Time Dispatching of Operations
10. Workforce Scheduling: Days-Off Scheduling, Shift Scheduling, Cyclic Staffing
11. Airline Crew Scheduling
12. Discrete Location Problems
13. Water and Air Quality Management
14. Health Care Delivery
( reference books)
Michael L. Pinedo (Author) “Planning and Scheduling in Manufacturing and Services”, Springer Series in Operations Research, Edition 2005
S.M. Pollock, M.H. Rothkopf, A. Barnett (Editors), “Operations Research and the Public Sector”, Handbooks in Operations Research and Management Science, Volume 6, Edition 1994
Material given by the professor via the moodle page of the course, including lecture slides, scientific papers, optimization software and tutorials
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6
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MAT/09
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54
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-
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-
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-
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Related or supplementary learning activities
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ITA |
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Optional group:
I ANNO "DUE A SCELTA TRA "PER ENTRAMBI I CURRICULA - (show)
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12
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20810158 -
Model Identification and Data Analysis
(objectives)
Introduce the student to the fundamentals of system identification applied to sampled systems (ARX and ARMAX model, ordinary least squares, recursive least squares, bayesian filtering). Introduce the student to sensor fusion. To familiarize the student with the use of the MatLab identification toolbox
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PASCUCCI FEDERICA
( syllabus)
Dynamical models of stationary processes and prediction
- Physical laws in engineering and science
- Stochastic processes
- Models for filtering, prediction and control: Input-output models for time series and dynamical systems (AR, ARMA, ARX, ARMAX)
Identification
- Black-box identification (Least Squares and Maximum likelihood methods)
- Model complexity selection
- Cross-validation, FPE (Final Prediction Error), AIC (Akaike Information Criterion) or MDL (Minimum Description Length) techniques
- Recursive identification methods (RLS,ELS,RML). Adaptation via forgetting factor techniques
Bayesian filtering
- The state estimation problem. Filtering, prediction and smoothing.
- Kalman filter, steady-state filter Extended Kalman filter
- Unscented transformation, Unscented Kalman filter
- Grid-based filtering
- Particle filtering
Distributed filtering
- Information filter
- Extended Information filter
( reference books)
Sergio Bittanti, "Model Identification and Data Analysis", John Wiley and Sons Ltd
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6
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ING-INF/04
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54
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-
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-
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-
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Core compulsory activities
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ITA |
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20810206 -
SYSTEMS IOT FOR LARGE INFRASTRUCTURES
(objectives)
The objectives will be the study and comprehension of systems of system theory and the modeling of large infrastructures (distribution networks, telecommunication networks, transportation networks). Interdependencies among different infrastructures will be analyzed with the aim of evaluating the distributed risk and designing resilient systems. Service oriented architectures will be studied as well as distributed awareness systems.
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PANZIERI STEFANO
( syllabus)
Interdipendency and complexity within infrastructural systems and in emergency management. CISIApro 2.0. NIS directive. Control rooms. GDPR. Introduction to risk analysis in interdependent systems. MHR modelling. Complex networks.
Vulnerability of industrial control systems. Cyber attacks to ICS. Smart Behavioral Filter. Hands-on ICS.
Decision Support Systems. Building Automation Systems. Smart Cities. Introduction to IoT. IoT Database. Iot Cloud.
( reference books)
Notes of the professor
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6
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ING-INF/04
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54
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-
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-
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-
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Core compulsory activities
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ITA |
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20801966 -
INDUSTRIAL PRODUCTION MANAGEMENT
(objectives)
This course is aimed at providing the basic methodological tools required for production planning and control in manufacturing systems. Specific methods used in make to stock, assemble to order, make to order, and engineering to order are analyzed, also discussing the differences between push and pull production systems. The course follows the traditional hierarchical approach including aggregate production and capacity planning, master production scheduling, materials and manufacturing resources requirements planning (MRP and CRP techniques), order release planning and job scheduling. furthermore, techniques for demand forecasting and implementation of just in time lean manufacturing systems are presented. The course also provides tools to estimate the performances of manufacturing systems, i.e. the links between work in process, throughput and cycle time, including variability effects and lot sizing decisions. finally, production planning decisions are put in perspective with strategic decisions, with capacity planning issues and with inventory management problems.
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CAPUTO ANTONIO CASIMIRO
( syllabus)
The industrial enterprise
Organization and scope of industrial enterprise. Classification of production systems. Analysis of production processes (process mapping and performance estimation). Little’s law. Impact of flow and process variability on main performance measures. Analysis of lot size effects on capacity, lead time and manufacturing cost. Lot sizing criteria in repetitive manufacturing.
Demand forecast
Analysis of demand variability components (random fluctuations, trends, seasonality). Classification of quantitative and qualitative forecasting methods. Linear regression causal models, time series methods (moving averages, exponential smoothing) and seasonal forecasting methods. Estimation of forecast error. Demand estimation for new products: market size and market penetration dynamics (Bass model).
Fundamentals of production planning and control
Analysis of P-Time and D-Time. Push and Pull production systems. Make to Stock, Assemble to Order, Make to Order ed Engineering to Order systems. The hierarchical production planning framework.
Aggregate planning
Alternatives to match production and demand. Trial and error aggregate planning methods (chase, level and mixed plans). LP models for the aggregate planning problem.
Master production scheduling
Criteria to disaggregate an aggregate plan and methods to develop a Master Production Schedule (MPS) based on items forecast and firm orders. Etsimation of Available to Promise capacity. Difference of MPS in MTS and ATO settings.
Requirements planning
MRPI and II methods. Capacity Requirements Planning. Lot sizing criteria for materials requirements planning. Limitations of MRP systems.
Operational planning and manufacturing execution
Final Assembly Schedule and operational plans. Criteria for job release and queues control. Heuristic rules for job scheduling and priority assignment. Production advancement and control systems.
Pull production systems
Kanban method and production leveling techniques. Methods for sequencing mixed model assembly lines. CONWIP. Comparison of push and pull systems.
Inventory management
Classification and scope of inventories. Relevant costs in inventory management. Management of dependent demand materials: economic order quantitym reorder cycle and reorder level policies. Service level and computation of safety stock. Benefits of safety stock pooling. Management of dependent demand items: lot by lot and dynamic lot sizing techniques. Newsboy model and single period order sizing. ABC classification and warehouse performance measures.
( reference books)
Lecture notes provided by instructor and uploaded on Moodle web site.
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6
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ING-INF/04
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54
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-
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-
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-
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Core compulsory activities
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ITA |
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Optional group:
I ANNO "UNO A SCELTA TRA" PER ENTRAMBI I CURRICULA - (show)
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6
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20810205 -
Digital entrepreneurship
(objectives)
Provide students with technical and methodological skills
necessary to conceive, develop and implement a digital business project.
The course will be divided into three parts. The first part aims to explain the reasons behind the success of digital companies (especially, but not only, startups) and digital innovation dynamics. The second part offers students the technical and methodological tools for the realization of a digital business project. The third part consists in the realization of a project and is characterized by a strongly experimental approach.
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MERIALDO PAOLO
( syllabus)
Part 1 (1CFU)
The success of digital enterprises
• From the invetion of the microprocessor to cloud computing
• Digital Enterpreses Business Models
• Life cycle of a digital enterprise
Part 2 (2CFU)
Design, build, improve a digital product
• Idea, team, fundings
• Lean Canvas
• User-centered design (UCD) and minimum viable product (MVP)
• Investors
Part 3 (3CFU)
Teamwork. Students can choose to join the dock3 training program or to develop their own idea by themselves.
( reference books)
Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers
by Alexander Osterwalder , Yves Pigneur
The Four Steps to the Epiphany: Successful Strategies for Products that Win (English Edition)
by Steve Blank
The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses
by Eric Ries
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Coppola Augusto
( syllabus)
Part 1 (1CFU)
The success of digital enterprises
• From the invetion of the microprocessor to cloud computing
• Digital Enterpreses Business Models
• Life cycle of a digital enterprise
Part 2 (2CFU)
Design, build, improve a digital product
• Idea, team, fundings
• Lean Canvas
• User-centered design (UCD) and minimum viable product (MVP)
• Investors
Part 3 (3CFU)
Teamwork. Students can choose to join the dock3 training program or to develop their own idea by themselves.
( reference books)
Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers
by Alexander Osterwalder , Yves Pigneur
The Four Steps to the Epiphany: Successful Strategies for Products that Win (English Edition)
by Steve Blank
The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses
by Eric Ries
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6
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ING-INF/05
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54
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-
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-
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-
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Core compulsory activities
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ITA |
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20810211 -
Algorithms for big data
(objectives)
In many application contexts huge volumes of data are produced which are
used in the economic-financial, political, social and even institutional
fields. Often the data is stored in huge distributed clouds and is
sometimes generated according to a continuous flow, so large as to make
complete storage unfeasible. In many cases the data pertains to entities
in close relationship with each other and gives rise to massive networks
of connections. Familiar examples for such networks are biological and
social networks, distribution networks, and the Web graph. Furthermore,
the fact that the data is stored in systems managed by third parties
poses integrity problems, which have not been considered in the
classical IT literature in terms of both their type and scale.
This scenario poses unprecedented algorithmic challenges, which are
being considered by a vast audience of researchers. In the last decade,
this effort has produced many innovations on both the methodological and
technological level. This course aims at transferring to the students
some of the most important methodological tools originated from the
research on Big Data algorithms. These methodological tools are
presented within challenging application contexts.
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DI BATTISTA GIUSEPPE
( syllabus)
1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
2) Sub-linear algorithms
- Diameter approximation
- Property testing
3) Clustering
4) Algorithms and data structures for quantitative features analysis
- 1d-,2d-,3d-range queries
- Skyline (pareto frontier), near-neighbor search, voronoi diagrams
5) Dimensionality reduction
6) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
7) Distributed Hash Tables, Consistent Hashing
8) Integrity of large data sets, consistency in distributed systems, CAP/PACELC theorems and their impact on NoSQL databases
( reference books)
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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PATRIGNANI MAURIZIO
( syllabus)
1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
2) Sub-linear algorithms
- Diameter approximation
- Property testing
3) Clustering
4) Algorithms and data structures for quantitative features analysis
- 1d-,2d-,3d-range queries
- Skyline (pareto frontier), near-neighbor search, voronoi diagrams
5) Dimensionality reduction
6) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
7) Distributed Hash Tables, Consistent Hashing
8) Integrity of large data sets, consistency in distributed systems, CAP/PACELC theorems and their impact on NoSQL databases
( reference books)
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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PIZZONIA MAURIZIO
( syllabus)
1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
2) Sub-linear algorithms
- Diameter approximation
- Property testing
3) Clustering
4) Algorithms and data structures for quantitative features analysis
- 1d-,2d-,3d-range queries
- Skyline (pareto frontier), near-neighbor search, voronoi diagrams
5) Dimensionality reduction
6) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
7) Distributed Hash Tables, Consistent Hashing
8) Integrity of large data sets, consistency in distributed systems, CAP/PACELC theorems and their impact on NoSQL databases
( reference books)
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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FRATI FABRIZIO
( syllabus)
1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
2) Sub-linear algorithms
- Diameter approximation
- Property testing
3) Clustering
4) Algorithms and data structures for quantitative features analysis
- 1d-,2d-,3d-range queries
- Skyline (pareto frontier), near-neighbor search, voronoi diagrams
5) Dimensionality reduction
6) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
7) Distributed Hash Tables, Consistent Hashing
8) Integrity of large data sets, consistency in distributed systems, CAP/PACELC theorems and their impact on NoSQL databases
( reference books)
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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DA LOZZO GIORDANO
( syllabus)
1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Dimensionality reduction
-Johnson–Lindenstrauss lemma
Embedding metric spaces with low distortion
3) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees and range trees)
- nearest neighbour search, k-nearest neighbour search
- fractional cascading and simplex range search
4) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
5) NoSQL internals: Distributed Hash Tables, chord, consistent hashing
6) Scalable security: integrity of big data sets in the cloud, consistency and scalability issues with authenticated data structures, pipelining, blockchain scalability trilemma.
( reference books)
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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6
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ING-INF/05
|
54
|
-
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-
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-
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Core compulsory activities
|
ITA |
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Università Roma Tre