COGNITIVE DATA FUSION

COGNITIVE DATA FUSION

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iten
Code
86960
ACADEMIC YEAR
2019/2020
CREDITS
5 credits during the 2nd year of 8732 Electronic Engineering (LM-29) GENOVA
SCIENTIFIC DISCIPLINARY SECTOR
ING-INF/03
LANGUAGE
Italian (English on demand)
TEACHING LOCATION
GENOVA (Electronic Engineering)
semester
1° Semester
Teaching materials

AIMS AND CONTENT

LEARNING OUTCOMES

- To introduce theory and techniques for architectural design of context-aware telecommunications systems able to provide informative services according to a cognitive paradigm

- To provide a common framework to identify and to describe methodologies and techniques for perception, representation and analysis of contextual multisensorial physical (radio, video, audio, etc.) and virtual signals (e.g.network-based context data))

- To provide a common framework to identify and to describe methodologies and techniques for integrating multisensorial contextual data by using Data Fusion paradigms and techniques

- To provide a common framework for defining behavioral artificial models for context based, adaptive and personalized  decision steps used by cognitive system to address and react with respect to different contextual working situations.

- To show examples and applications of specific techniques within cognitive telecommunication systems by means of description of two main case studies: cognitive radio and multisensor/multimodal cognitive human-machine interfaces in smart spaces. 

AIMS AND LEARNING OUTCOMES

 To introduce theory and techniques for architectural design of context-aware telecommunications systems able to provide informative services according to a cognitive paradigm

- To provide a common framework to identify and to describe methodologies and techniques for perception, representation and analysis of contextual multisensorial physical (radio, video, audio, etc.) and virtual signals (e.g.network-based context data))

- To provide a common framework to identify and to describe methodologies and techniques for integrating multisensorial contextual data by using Data Fusion paradigms and techniques

- To provide a common framework for defining behavioral artificial models for context based, adaptive and personalized  decision steps used by cognitive system to address and react with respect to different contextual working situations.

- To show examples and applications of specific techniques within cognitive telecommunication systems by means of description of two main case studies: cognitive radio and multisensor/multimodal cognitive human-machine interfaces in smart spaces. 

Teaching methods

Lectures and lab exercises Project based learning

SYLLABUS/CONTENT

1) INTRODUCTION  General definitions and models for cognitive systems. Behavioral cognitive artificial models for context based, adaptive and personalized  decision The cognitive cycle model; perception, analysis, decision, action. Logical and bio-inspired cognitive system models. Cognitive Data fusion functional architectural the JDL model and its extensions. Haykin-Fuster Cognitive Dynamic Systems. The Probabilistic Graphical Model based Data fusion architecture

 2) Data Fusion methodologies and techniques for integrating multisensorial contextual data

Acquisition  and representation of contextual data.. Contextual data hierarchical representation: presence, localization, behavior, situation, threat.  Methodologies and techniques for physical sensor signal processing: digital signal processing issues with radio, video, audio signals. Techniques and algorithms for acquisition and analysis of contextual data. Bayesian Data Fusion processing techniques: alignment, data association, state estimation, abnormality detection   Probabilistic Graphical Models: Dynamic Bayesian Networks (DBN)  and Markov Random Fields. Representation and inference. Factorization and Belief propagation. Bayesian State estimation techniques: Kalman filter, Extended Kalman Filter, Unscented Kalman Filter, Particle Filtering.  Processing algorithms and PGM representation   Situation assessment: Interaction Model Representation using coupled DBNs. State, superstate and event based interaction representation. Methods for dimensionality reduction and classification: elf Organizing Map, Neural Gas. Threat assessment by incremental evaluation of  distance between Prediction from Update in a Bayesian node.  Kllback Leiber. Distributed decision theory.

 3) Case studies

1) design of  cognitive Data fusion systems with application to health, surveillance, smart environments, robotsic Lego applications.

RECOMMENDED READING/BIBLIOGRAPHY

 Basic: Class notes written  by the lecturer and made available through Internet 

- A. R. Damasio, Looking for Spinoza: Joy, Sorrow, and the Feeling Brain, 1st ed. Orlando: Harcourt, 2003. [Online]. Available:http://lccn.loc.gov/2002011347
- S. Haykin, Cognitive Dynamic Systems: Perception-action Cycle, Radar and Radio, ser. Cognitive Dynamic Systems: Perception–action Cycle, Radar, and Radio. Cambridge University Press, 2012.

- P. R. Lewis, M. Platzner, B. Rinner, J. Torresen, and X. Yao, Eds., Selfaware Computing Systems: An Engineering Approach. Springer, 2016.

- K. J. Friston, B. Sengupta, and G. Auletta, “Cognitive dynamics: From attractors to active inference,” Proceedings of the IEEE, vol. 102, no. 4, pp. 427–445, 2014. [Online]. Available:
https://doi.org/10.1109/JPROC.2014.2306251

- S. Haykin and J. M. Fuster, “On cognitive dynamic systems: Cognitive neuroscience and engineering learning from each other,” Proceedings of the IEEE, vol. 102, no. 3, pp. 608–628, 2014.

 

TEACHERS AND EXAM BOARD

Ricevimento: Students can ask appointments for clarifications, explanations on course subjects by sending e-mail at Carlo.Regazzoni@unige.it   

Exam Board

CARLO REGAZZONI (President)

LUCIO MARCENARO

SILVANA DELLEPIANE

LESSONS

Teaching methods

Lectures and lab exercises Project based learning

LESSONS START

1st semester 2016 - September 19th 2016

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

Vedi anche:

COGNITIVE DATA FUSION

EXAMS

Exam description

Oral Examination (70/100%)

Assigned Project  evaluation (30%)

Assessment methods

Oral will consist of project driven slide presentation. Questions will rely on selected techniques chosen from ones presented in the course for the application case as well as on state of the art analysis approach followed

Project will consist of either provding code to analyzse a sequence of sensorial data coming from a daatset or n discussing how proposed techniques can be used in the context of an application selected by the student