Friday, 11 April 2014

FP7 TREND

trend logo

The IHU as a CI at FP7 NoE TREND

The FP7 Network of Excellence TREND (Towards Real Energy Efficient Network Design) is a European project in the area of energy efficient networking and Green ICT.

The International Hellenic University has been officially accepted as a Collaborating Institute (CI) of TREND among other partners such as:

  • Politecnico di Milano, Italy
  • Deutsche Telekom Laboratories, Germany
  • Università di Roma La Sapienza, Italy
  • Fondazione Ugo Bordoni, Italy
  • Technische Universitat Dresden, Germany
  • Institute IMDEA Networks, Spain
  • ICAR-CNR (CNR Institute for High Performance Computing and Networking), Italy

 

The TREND Consortium is composed of the following partners:

  • POLITECNICO DI TORINO (Italy)
  • ALCATEL - LUCENT BELL LABS FRANCE (France)
  • HUAWEI TECHNOLOGIES DUESSELDORF GmbH (Germany)
  • TELEFONICA INVESTIGACION Y DESARROLLO SA (Italy)
  • FRANCE TELECOM SA (France)
  • FASTWEB SPA (Spain)
  • UNIVERSIDAD CARLOS III DE MADRID (Spain)
  • INTERDISCIPLINARY INSTITUTE FOR BROADBAND TECHNOLOGY (Belgium)
  • TECHNISCHE UNIVERSITAT BERLIN (Germany)
  • ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE (Switzerland)
  • CONSORZIO NAZIONALE INTERUNIVERSITARIO PER LE TELECOMUNICAZIONI (Italy)
  • PANEPISTIMIO THESSALIAS - UNIVERSITY OF THESSALY (Greece)


The IHU research team works on issues related to energy efficient networking, energy efficient planning and management of cellular networks, green datacenters, smartgrids/ smartbuildings. More information can be found at http://www.fp7-trend.eu/.

 

Friday, 11 April 2014

Virtual Labs

This project was concerned with the preparation of teaching materials to assist in the teaching procedure of graduate programs in ICT and Energy Systems, School of Technology. The proposed project was necessary to supply several software platforms, which provided essential infrastructure for the teaching and research activities for the School of Technology.

The proposed Virtual Labs implemented a number of experiments using a graphical user interface on several key topics for the Master courses. The delivered software has provided an interactive opportunity for students to experiment and deepen their knowledge and understanding of the presented material. The delivered software must be scalable and expandable to cater for the future research and teaching needs. The following virtual labs are currently under implementation:

For more details please visit http://rad.ihu.edu.gr/index.php?id=10

 

Wireless Telecommunication and Sensor Networks

Research Assistant: Mr. Christos Liaskos
Academic Assistant: Dr. George Koutitas
Courses covered: Mobile Communications & Sensor Networks

Under this virtual laboratory, several interactive exercises through Graphical User Interfaces (GUIs) will be implemented, through which the students will be able to practice some of the theoretical aspects taught in class. The aim is to introduce the student to common industrial problems and allow an in depth analysis and practice of the theory. This virtual laboratory will cover the following topics

 

Mobile Communication Networks

1. Virtual antenna simulation with applications. Empirical propagation models. Coverage estimations.

  • Antenna radiation pattern computation and visualization
  • Empirical propagation models
  • Coverage predictions
  • Antenna positioning effects
  • Point to point predictions
  • Predictions over measurement lines
  • Predictions over a plane
  • Link budget analysis

wtsn1

 

2. Narrowband and Wideband channel characteristics

  • Rayleigh Channels
  • Rice Channels
  • Statistical generation of wireless channels
  • First order fading statistics
  • Second order fading statistics
  • Narrowband channel modeling
  • Wideband channel modeling

wtsn2

3. Virtual drive test

  • Virtual drive test in a microcells urban environment
  • Virtual drive test in macrocell hilly environment

wtsn3

4. Applications for WLAN and Single Frequency Networks

  • Network Planning for an indoor WLAN system
  • Network Planning for a Single Frequency Network in a rural environment

 

wtsn4

Sensor Networks

1. Sensor battery lifetime simulations.

2. Spatial distribution of sensors over different area types. Network formation, routing.

3. Simplified simulations of communication protocols.

wtsn5

 

Multimedia Systems in Telecommunication, Medicine and Remote Sensing applications

Research Assistant: Dr. Dimitrios Alexiadis
Academic Assistant: Dr. Nikolaos Mitianoudis
Courses covered:

Multimedia Content Management

Medical Imaging

GIS and Remote Sensing

 

Under this virtual laboratory, several interactive exercises through Graphical User Interfaces (GUIs) will be implemented, through which the students will be able to practice some of the theoretical aspects taught in class. This virtual laboratory will cover the following topics:

 

1. Audio-Image-Video Encoding and Compression. More specifically, the students will interact with:

 

• The concept of transform coding, i.e. coding in transform domain, in which the energy-information tends to be concentrated in a few major coefficients.

 

• The concept of Scalar and Vector Quantization, which is the essential part of “lossy” encoders.

 

• Entropy coding (encoding Huffman, arithmetic coding, etc.), which is the last subsystem of modern codecs, which does not result in loss of information (lossless).

 

• Audio Coding : the compression standard MP3 (MPEG Audio Layer III). Attention will be paid mainly on the lossy psychoacoustic masking, based on the limitations of the human hearing system.

 

• Still Image Coding: the compression formats JPEG and JPEG-2000 will be visualized with special emphasis on the 2-D Discrete Cosine Transform (DCT) and the 2-D Discrete Wavelet Transform (DWT).

 

• Compression of moving pictures: The encoding standards MPEG1-MPEG2 and MPEG-4 will be presented. In the case of MPEG1-MPEG2, apart from a single frame coding (intra-frame coding), we will be focusing on motion estimation (motion vectors) between successive frames. In the case of MPEG-4, the focus will be on the separation of moving objects, the encoding of shape and motion of which is the basic idea of the model.

 

mult_sys1

 

 
 

2. Streaming audio and video through communication channels are susceptible to noise and errors. This study will study the impact of transmission errors and packet loss on the quality of decoded signals and the bit-error rate. Forward Error Correction / Detection techniques that are 'hiding' the error in the decoder.

 

mult_sys2

 

 
 

3. Equalization - Noise Reduction - Registration - fusion of medical images. More specifically:

 

• Image Equalization: histogram equalization and local adaptive methods for image enhancement.

 

• Image Noise Reduction: Noise Reduction methods with simple filters (moving average, middle) and shrinkage methods using 2-D wavelet 2-D directional local bases (curvelets, contourlets) or 2D self-trained bases using Independent Component Analysis.

 

• Image Registration and Fusion: The concept of registration and fusion of images acquired from different modality sensors.

 

 

4. Fusion Applications on remote sensing images.

 

With the help of the corresponding interactive graphical display (GUI), students can experiment with the parameters and observe the effect of these on the performance of remote sensing imaging fusion algorithms (pansharpening).

Environmental monitoring & Energy Efficiency labs

Research Assistant:

Dr. Ioannis Antoniadis

Academic Assistant:

Dr. Z. Ziaka

Courses covered:

Energy Efficiency and Savings

Information Systems for Efficient Energy Management

Environmental Monitoring

Information Systems for Public Safety and Security

 

In this virtual laboratory, students of the relevant classes will be able to practice some of the theoretical aspects taught, through interactive demo tools that employ Graphical User Interfaces. Two distinct tools will be developed:

1. Energy Performanceof Buildings: A tool that will produce the dynamic description of the energy behaviour of the building as a function of several parameters involving the materials used during construction, operational characteristics, microclimatic parameters and various domestic energy production systems, conventional or alternative.

 

2. Simulation of an environmental monitoring ICT system for forest fire detection: It will provide a realistic simulation of the operation of a forest fire detection information system based on a Wireless Sensor Network under the Zigbee protocol.

  

In more detail:

 

Energy Performanceof Buildings

 In the first step, the user will be able, through a GUI, to select parameters such as number of inhabitants, building type/use, heat system and fuel type used (diesel, gas, solar, biomass, etc.), building location and orientation. In the second step, energy flows will be calculated and an energy production/consumption time footprint of the building will be produced depending on the time step selected by the user.
 

Simulation of an environmental monitoring ICT system for forest fire prevention

The user will be able to select parameters such as type of terrain (clutter info), prevalent wind direction and intensity, temperature, humidity, number of wireless sensors. By considering a realistic wind field for the chosen terrain and meteorological parameters, the simulation system will output the 2D dynamical field of the Fire Weather Index (FWI). According to the value of FWI at sensor locations, the system will recommend the optimal data sampling frequency from the given sensor so as to optimize battery energy consumption.
 

The user will also be able to simulate the spreading of forest fire by selecting ignition point(s). In this case, the system will produce and depict the simulated output of each of the available sensors as well as its status (e.g., operational/non-operational, data transmission rate, etc.).

 

em

Decision Support Systems & Knowledge Management

A. Decision Support Systems

Academic Assistant: Dr. Christos Berberidis
Research Assistants:

Dr. Fotios Kokkoras

Mr. Konstantinos Paraskevopoulos

Courses Covered: Decision Support Systems

 

The purpose of this virtual lab is to develop interactive training material on Decision Support Systems. The educational material will be developed in the form of training workshops/ experiments based on specialized software that will allow the learner:

 
  • understand in a practical manner the relevant theoretical knowledge through analytical solved problems that will highlight the current issues of theory
  • to check the degree of theory understanding, by solving problems and exercises
 

The above activities will be conducted using specialized software that will either be developed by the scientific team or free, open source popular software tools and platforms that will be evaluated and selected to be used.

In particular, the delivered software will provide an interactive opportunity for students to experiment and to deepen in a supervisory manner to topics that deal with. In addition, it will allow the teacher to create additional teaching material to meet possible future needs.

 

The thematic contents of the deliverable software to create training workshops are:

 

1. Simple decisions under certainty: Multicriteria Methods

Software and training workshops that will be developed will allow students to interact with the following multicriteria methods:

  • Weighted Average Sum (WAS),
  • ELECTRE methodology
  • Analytic Hierarchy Process (AHP)

 

DSS1

 

2. Simple Decisions under ignorance

Software and training workshops will be delivered for the following decision rules:

  • MaxiMax / MiniMin,
  • MiniMax / MaxiMin the Wald,
  • Hurwicz rule,
  • MiniMax Regret Savage rule

 

3. Simple Decisions under uncertainty

Will be delivered software and training workshops on:

  • Bayesian Networks
  • Decision Trees (Trees sequential decisions)
  • Game Theory

 

DSS2

 

4. Decision making through data analysis

Software and training workshops will cover:

  • Classification trees
  • Neural Networks
  • Genetic Algorithms
  • Clustering
  • Association Rules

 

DSS3

DSS4

 

B. Knowledge Management

Academic Assistant: Dr. Christos Berberidis
Research Assistants:

Mr. Efstratios Kontopoulos

Ms. Kalliopi Kravari

Courses Covered: Knowledge Management, Decision Support Systems (partial), Web Information Systems (partial)

 

This virtual laboratory involves a wide variety of task-oriented exercises grouped by escalating difficulty levels. Students will progressively proceed through the material with the help of popular free third-party software tools as well as rich Graphical User Interfaces (GUIs) that will be implemented for the purposes of the course.

The aim is to allow the students to get acquainted with modern (web) Knowledge Management (KM) technologies, practice the theoretical aspects taught in class and progressively develop a practical, real-life Web application (e.g. “Amazon-like” e-commerce web site).

 

DSS5

 

The following topics will be covered:

  • XML (Extensible Markup Language), which is a set of rules for encoding documents electronically. More specifically, students will practice with: (a) The XML Syntax and the three main XML building blocks, (b) DTD and XML Schema, (c) XML namespaces, (d) XML querying via XPATH and (e) XML processing via XSLT.
  • RDF (Resource Description Framework), a standard model for data interchange on the Web. More specifically, students will focus on: (a) The RDF triple-based model of statements, (b) RDF Schema (RDF/S), (c) RDF querying via SPARQL, and (d) RDF processing/parsing via Jena, a Java library for processing RDF and RDF/S documents.
  • OWL (Web Ontology Language), a family of knowledge representation languages for authoring ontologies. Users will get familiarized with: (a) The OWL syntax, which heavily builds on RDF and XML, (b) The three increasingly expressive OWL sublanguages (OWL Lite, OWL DL, OWL Full), (c) The basic notions of ontologies and ontology engineering, the main methodologies for manually and (semi)automatically constructing ontologies as well as reusing existing ontologies and the most widely-used software tools for these tasks.

DSS6

 

 

 

  • Logic & Inference, the study of reasoning and the process of drawing a conclusion by applying logical clues. Users will get familiarized with: (a) Deductive rules, (b) Production rules, (c) Monotonic rules, (d) Non-monotonic rules.

 

  • Knowledge and Ontology Engineering. Users will get familiarized with: (a) Knowledge-based systems, especially with their development cycle and architecture, (b) knowledge elicitation, (c) manually constructing ontologies, (d) re-using existing ontologies, (e) using relevant methodologies, semi-automatic methods and popular tools.
  • Knowledge search. Users will get familiarized with semantic search and knowledge portals by using popular SW Search Engines, such as Semantic Web Search Engine (SWSE), Sindise, Watson, Yahoo! Microsearch, Falcons, Swoogle, Semantic Web Search and Zitgist Search.

 

DSS7

 

 

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