Telemedicine

Learning Outcomes

The course is introducing students in telemedicine systems and applications that improve the quality of life and provide remote electronic health services. The curriculum includes background knowledge in the areas of coding and processing of biomedical data, analyzes the design and implementation issues of telemedicine systems and discusses the next generation telemedicine systems, which include context awareness and computational intelligence as additional features. During the course case studies will be presented and there will be project assigned to students.

Students, upon successful completion of the course, will be able to:

A) Understand basic methodologies of design and development of telemedicine systems

B) Be familiar with the main techniques for the coding and processing of biomedical signals and data

C) Know the coding standards for medical information

D) Design telemedicine systems according to special requirements and the type of medical information exchanged

E) Evaluate telemedicine systems

Course Contents

  • Introduction to Telemedicine
  • Biomedical Data Coding and Compression
  • Biomedical Data Processing for Telemedicine Applications
  • Video Communication for Telemedicine Applications
  • Telemedicine Networks
  • Home Care Systems
  • Context Aware Telemedicine Systems
  • Wireless Telemedicine and Ambient Assisted Living
  • Wearable Systems
  • Clinical Applications of Telemedicine
  • Security in Telemedicine systems
  • Case Studies – Project Assignments

Recommended Readings

  • Medical Informatics, e-Health: Fundamentals and Applications (Health Informatics) Softcover reprint of the original 1st ed. 2014 Edition by Alain Venot (Editor), Anita Burgun (Editor), Catherine Quantin (Editor)
  • Telemedicine Handbook, Pompidou Alain, Apostolakis I, Α., Ferrer – Roca Olga, Sosa – Iudicissa Marcelo, Allaert Francois, Della Mea Vincenzo, Kastania Anastasia N.

Privacy Enhancing Technologies

Learning Outcomes

The purpose of the course is to highlight the concept of privacy, especially in relation to personal and / or sensitive data exchanged through open public networks, such as the Internet, in the context of various electronic services. Existing privacy enhancing technologies are introduced and special reference is made to the privacy problems faced by specific categories of applications. The proposed treatment mechanisms are also presented.

In this context, the learning outcomes of the course, after its successful completion, are that the students will be able:

  • to understand the basic concepts of privacy and personal data protection as well as how to recognize and analyze privacy requirements.
  • to know the basic privacy requirements that need to be taken into account when designing, and to be satisfied in the implementation, of an information system.
  • to analyse, evaluate and justify alternative technologies / mechanisms to protect privacy and meet the requirements.
  • to design systems that protect the privacy of its users

Course Contents

  • Definition of Privacy.
  • Legal Framework for the Protection of Personal Data.
  • Attacks on Privacy and Subjectivity of Impact in case of Privacy violation incidents.
  • Requirements for anonymity, unlinkability, undetectability and unobservability.
  • Pseudo-anonymity.
  • Identity Management.
  • Privacy Enhancing Technologies (Anonymizer, LPWA, Onion Routing, Crowds, MixNets, etc.).
  • Privacy protection in Ubiquitous Computing (RFIDs, Positioning Services), Internet Telephony, Health Information Systems, etc.
  • The Greek Framework for Digital Authentication and the Unique Citizen Identification Number for Electronic Services Offered by Government Bodies.
  • Privacy Economics

Recommended Readings

  • A. Acquisti, S. Gritzalis, C. Lambrinoudakis, S. De Capitani di Vimercati (Eds) (2008) Digital Privacy, Theory, Technology and Practices., Auerbach Publications.

Associated scientific Journals

  • IEEE Security and Privacy Magazine, IEEE
  • International Journal of Information Security, Springer
  • Computers and Security, Elsevier
  • Requirements Engineering, Springer
  • IEEE Transactions on Software Engineering, IEEE
  • Security and Communication Networks, Wiley

Multimedia Technology

Learning Outcomes

This course is the basic introductory course for the perception, representation and management of digital media through computational methods.

The course material focuses on the introduction of the students to the basic concepts and algorithms for the representation, processing and interaction with digital audiovisual media. Moreover, the course material refers to the description of the correlation between computational techniques and human perception in media environments.

The course seeks to make students understand the ways with which is possible the development and management of media sources in coomputational systems.

With the successful completion of the course the student will be capable of:

  • understanding the basic and important features of the computational representation, processing and interaction with digital audiovisual media.
  • knowing the major features of the tools and development methods of digital audiovisual environments and applications.

Course Contents

  • Definition and classification of multimedia technologies.
  • Audio and visual perception.
  • Audio processing.
  • Image and video processing.
  • Design and development of multimedia systems.

Data Processing Techniques

Learning Outcomes

The objective of this course is to familiarize students with: (a) learning access methods for large data volumes for various data formats, as well as scalable writing, (b) efficient data storage and retrieval with appropriate indexing techniques, (c) the design and implementation of data processing algorithms aiming at the development of efficient applications that manage data.

Upon successful completion of the course, the students will be in position:

  • to develop data-centric applications with emphasis in efficiency and scalability
  • to use the most appropriate indexing methods for a given problem
  • to evaluate and improve the parts of data processing algorithms that incur high computational load
  • to apply the most suitable data processing techniques that match with data under analysis and for a given query workload
  • to develop efficient data processing algorithms

Course Contents

  • Operation of disk and main memory, serial and random access, cost and efficiency, data locality on disk and main-memory, direct and indirect access, main-memory data structures (arrays, priority queues, hashing)
  • Data access techniques for structured, semi-structured and unstructured data: relational DBs, XML, RDF, text documents, web pages, web APIs, social networks.
  • One-dimensional data and indexing, B-tree, variations (B+tree, B*tree), range queries, inverted indexes.
  • Spatial data, spatial data types, spatial queries, approximation in representation, distance measures, extensions for multidimensional data.
  • Spatial indexing techniques, grid file, spatial indexes (R-tree, QuadTree), space-filling curves (Hilbert, Z-order)
  • Similarity search, k-nearest neighbor search, branch-and-bound algorithms, locality sensitive hashing (LSH), approximate k-nearest neighbor.
  • Top-k search: algorithms based on pre-processing, online algorithms, Fagin’s algorithm, index-based algorithms.
  • Join queries, spatial joins, top-k joins.
  • Spatio-textual data, query types, indexing methods, processing algorithms.

Recommended Readings

  • Ramakrishnan R. & Gehrke J. (2002): Database Management Systems (3rd Edition), McGraw Hill.
  • N.Mamoulis (2011): Spatial Data Management, Synthesis Lectures on Data Management, Morgan & Claypool.

Optimization Techniques

Learning Outcomes

The course pertains to the modeling and solving of operational research problems via linear programming, integer programming and related optimization models. In this context, the theoretical foundations of these optimization models are developed; solving algorithms are presented, for global optimization (e.g., the Simplex method, Branch-and-Bound), as much as the design and analysis of heuristic methods, inclusively of local search and approximation algorithms.

Upon successful completion of the course, the students will be in position:

  • to develop the formal/abstract mathematical representation of an operational optimization problem, given its description in natural language along with the problem’s parameters and input data.
  • to choose appropriate solving methods for a given mathematical model of an operational optimization problem.
  • to program a mathematical optimization model in an appropriate programming language, while using relevant software for solving the model.
  • to assess and evaluate the solution to a mathematical optimization model, along with the performance of the chosen solving method.
  • to discriminate between computationally tractable and hard mathematical models for operational research problems.

Course Contents

  • Modeling Problems through Linear Programming.
  • Linear Programming Theory, Duality.
  • The Simplex Algorithm.
  • Integer Linear Programming, Branch and Bound Method.
  • Transportation and Assignment Problems.
  • Network Optimization (paths, trees, flows, matchings, cuts).
  • Computationally Hard Optimization Problems.
  • Introduction to Approximation Algorithms.
  • Local Search Methods.

Recommended Readings

  • F. S. Hillier, G. J. Lieberman. Introduction to Operations Research. McGraw-Hill Higher Education, 2004.
  • J. Kleinberg, E. Tardos. Algorithm Design. Pearson, 2013.

Design and Optimization of Networks

Learning Outcomes

The course presents principles and methodologies on the design, evaluation and optimization of networks and services, complementing the basic knowledge of architecture, protocols and functions of communication networks.

Upon successful completion of the course, the students will be in position to:

  • follow and utilize the approach of top-down network design, that is most commonly encountered on medium to large scale networking projects
  • understand and evaluate alternative design options at every stage of data networks design, (e.g. requirement and specification definition, logical and physical design, selection of appropriate technologies and protocols, addressing and naming of network devices, implementation, testing and optimization)
  • select and propose proper architectures, network technologies, protocols and politics, depending on the design, upgrade and/or optimization of the network at hand
  • implement, control and readjust solutions on new or redesign existing network projects
  • run and operate routing protocols simulation software and packet sniffing software

Course Contents

  • Introduction to the design and performance evaluation of networks and services.
  • Modelling and topological design of communication networks.
  • Modelling of network services traffic and work load.
  • Top-down network design under service requirements and various constraints.
  • Selection of most appropriate link, network and transport layer protocols.
  • Selection of most appropriate network architecture and network devices.
  • Network optimization techniques and algorithms, network reliability.
  • Performance measures.
  • Quality of service assurance.
  • Theoretical exercises and network design projects.

Recommended Readings

  1. Spiros Arsenis, “Network Design and Implementation”, Kleidarithmos Publications.
  2. Priscilla Oppenheimer, “Top-Down Network Design”, 2nd Edition, Cisco Press.
  3. James D. McCabe, “Network Analysis, Architecture and Design”, 2nd Edition, Morgan Kaufmann Publishers Inc.
  4. Thomas Robertazzi, “Planning Telecommunication Networks”, IEEE Press.

Mobile Communication Systems

Learning Outcomes

The course provides the basic principles of cellular mobile communication systems. It also provides the methodologies of analysis and design of these systems. By concluding the course, students are able to

  • analyze and design basic mobile communication systems by emphasizing in physical layer techniques
  • recognize, describe and distinguish the characteristics of several type of cells, communication channels and multiple access techniques
  • analyze and design systems with different requirements of telecommunication traffic and quality links
  • compute the thresholds of link performance,
  • compare alternative implementation plans and evaluate the total performance of digital systems

The lab sessions aim to provide a deeper understanding of physical phenomena of propagation in the wireless channel and the simulation of cellular systems.

Course Contents

Initially, basic concepts of Mobile Communications Radiosystems are provided (cell types, communication channel types, basic cellular system operations). Basic Network Access Techniques (Multiple Access Techniques, Random Access Techniques) are discussed. Also, reference is made to the evolution of Wireless Communication Systems (1st, 2nd, 3rd, and 4th generation cellular systems, Wireless Telephony Systems, Paging Systems, WLANs, WPANs, PMRs). Students are introduced to the concept of cells  and frequency reuse (elements from regular hexagon geometry, cellular systems design). Then the basic concepts of telecommunication traffic analysis and systems performance is provided (elements of Queuing Theory, Erlang B model, Erlang C model, spectral performance of cellular systems). In the following the main wireless propagation mechanisms are presented (multipath propagation, Doppler fading and shift, propagation loss, shadowing, coverage area definition, radio channel capacity limits). Interference types (co-channel interference and noise, neighboring channel interference) as well as handover and performance techniques (categorization of handover techniques, advantages and disadvantages of techniques, stable performance, dynamic performance, elastic performance) are discussed and compared. Techniques for improving spectral efficiency (sectoring, cell splitting) are then analyzed. Finally, elements and techniques of physical layer design (modulation and coding techniques, co-channel interference mitigation techniques) are presented and a presentation of standardized Mobile Communications Systems (GSM, GPRS, 3G and 4G) is presented.

In addition, extra content (in evdoxos.ds.unipi.gr) like articles, audiovisual lectures and Internet addresses, as well as exercises for student’s practice are posted electronically. Case studies, exemplary problems and methods for solving them are presented.

Recommended Readings

  • “Mobile Communications Systems”, Book code in www.eudoxus.gr: 33154041, Edition: 2nd edition/2013, Authors: Kanatas Athanasios, Pantos Georgios, Costantinou Filippos, ISBN: 978-960-491-086-1, Publisher: A.Papasotiriou & Sia (1st Book)
  • “Antennas and propagation for wireless communication systems”, Book code in www.eudoxus.gr: 59386401, Edition: 1st edition/2016, Authors: S. R. Saunders, A. Aragon-Zavala, Scient. Edit.: Dimosthenis Vougioukas, ISBN: 978-960-546-737-1, Publisher: Pedio S.A. (2nd Book)

Associated scientific Journals

  • ΙΕΕΕ Transactions on Vehicular Technology
  • ΙΕΕΕ Transactions on Wireless Communications
  • ΙΕΕΕ Transactions on Antennas & Propagation
  • ΙΕΕΕ Journal on Selected Areas in Communications
  • IEEE Communications Magazine

e-Learning Systems

Learning Outcomes

Upon successful completion of the course the students will be able:

  • to know and understand the key concepts of digital teaching and learning
  • to analyse, assess, select and justify pedagogically appropriate e-learning methods and tools for digital teaching and learning innovations.
  • to design and create pedagogically grounded online courses.

Course Contents

  • Online Teaching and Learning: Theoretical Underpinnings
  • Educational Design for Online Teaching and Learning
  • An hierarchical Open Access to Online Education framework: Elements (Open Educational Resources, Learning Activities and Lesson Plans, Online Courses, Digital Learning Spaces). Tools and Key Roles (Online Education Instructional Designers, e-Tutors, e-Learning Systems Administrators, Managers)
  • Open Educational Resources: Learning Objects, Educational Metadata, Repositories of Learning Objects. Case Studies: the National Repositories of Learning Objects
  • Learning Activities and Lesson Plans: Authoring Tools for Learning Activities and Lesson Plans, Repositories of Learning Activities and Lesson Plans. Case Studies: the National Repositories of Learning Activities and Lesson Plans
  • Design, Development and Delivery of Online Courses: Methodology for Designing Online Courses. Authoring Tools for Developing Online Courses. Course Management Systems. Case Study: Open edX, MOODLE
  • Digital Learning Spaces: 3D Virtual Classrooms and Laboratories

Recommended Readings

  • Textbook in Greek (provided for free)
  • Additional Open Access Educational Resources available through the course management system

Ιntelligent Agents and Multiagent Systems

Learning Outcomes

Upon successful completion of this course, students should be able to know principles, paradigmatic architectures and methods for developing single agent and multi agent systems, have a critical and informed view of strengths and limitations regarding agents and multi agent systems, towards designing and delivering such systems.

Specifically, students know and acquire the abilities to develop:

  • Architectures of single and multi-agent systems
  • Methods for agents coordination, collaboration and competition in specific settings and paradigmatic environments and problems
  • Agents’ communication methods and protocols

Via the critical view of agents technology and experience in developing such systems.

Course Contents

  • Agents: Principles, architectures and application examples
  • Deliberation vs Reaction: Architectures
  • Mental attitudes, states and their representation
  • Multi-agent Systems: Interactions and dependencies
  • Multiagent organizations and communication
  • Cooperation and collaboration
  • Agents communication

Recommended Readings

  • Michael Wooldridge, Introduction to MultiAgent Systems, 2008.
  • Yoav Shoham, Kevin Leyton-Brown Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2009.
  • Gerhard Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, 2000.
  • John Miller Scott Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity), Princeton University Press, 2007.
  • David Easley, Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press 2010.

Associated scientific Journals

  • Autonomous Agents and Multi-Agent Systems, Springer, ISSN: 1387-2532
  • IEEE Distributed Systems, ISSN: 1541-4922