COGNITIVE DATA FUSION

COGNITIVE DATA FUSION

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iten
Code
86960
ACADEMIC YEAR
2020/2021
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

  Knowledge on methods and techniques for acquisition, joint  representaion and processing of proprioreceptive and exteroreceptive  multisensorial signals in cognitive dynamic agents (e.g. semi autonomous&autonomous vehicles like drones, cars, robots) cognitive radios,   etc.) 

- Knowledge on methods and techniques for Multisensor Data Fusion: coupled hierarchical processing of multisensorial signals. Machine learning for data driven driven experience based learning of Dynamic Generative Fusion models from sequences of multdimensional sensorial data.

- Knowledge on Machine Learning methods and techniques based on Cognitive Dynamic Systems theory for Situation awareness and Self awareness in artificial cognitive agents 

- Knowledge and capabilities on case studies: design of Self Awareness frameowrk for autonomous systems (dataset on cars robots and drones, cognitive radios) 

- Knowledge and capabilities to use and apply: multisensorial signal processing tools and algorithms for acquisition,  experience driven machine learning techniques for estimation of Generative multisensorial Bayesian  hierarchical models. Bayesian Inference on learned Generative Models for dynamic state estimation, prediction and anomaly detection  of interaction between agent and its contextual environment situation .

PREREQUISITES

Probability theory, Random Processes, Signal theory 

Teaching methods

Lectures and lab exercises Project based learning

SYLLABUS/CONTENT

  • Cognitive Telecommunications Systems: an introduction 
  • Signal Processing and Cognitive Systems: Bio inspired models
  • Acquisition, representation and inference in Cognitive Dynamic systems 
  • Data fusion architectural models 
  • Data fusion levels and techniques 
    • Temporal and Spatial alignment
    • State estimation (Kalman filter,  Particle Filter, Switching models, Hierarchical filters)
    • Situation Awareness /Self awareness and Threat Assessment 
  • Probabilistic Graphical Models and Dynamic Bayesian Networks 
    • Attractors and Bayesian inference
    • DBNs as experiences models 
      • ​Haykin model 
      • Damasio models
      • Friston model 
  • Machine learning models for interaction modeling: 
    • Unsupervised and supervised clustering of big data
      • self Organizing Maps, Growing Neural Gas, Gaussian Processes, Dirichlet model
    • Mapping of learned models onto DBNs
    • Incremental learning of multiple models based on agent experiences abnormal situations

Applying knowledge and understanding in lab 

  • Basic language and tools inytroduction (matlab, C++, datasets used) I
  • Case studies: autonomous car, lego robots, drones and simulators. 
  • Applied Experiments using programming techniques and tools 
    • Filtering methods on data from dataset;
    • Single agent proprioreceptive and exteroceptive models 
    • Self awareness coupled interaction models.

Making Judgements:

  • Interactive and Cognitive Systems project oriented techniques
    • Case study identification
    • Interaction system goal identification  (entities, service, evaluation performances)
    • State of the art description
    • Project design: architectural and technique level
    • Slide presentation
  • Small team collaborative project definition; project management
  • Individuating Emerging techniques in Cognitive Telecommunications domain

Learning and communications skills:

  • Bibliographic search on scientific data bases (e.g. IEEEE Explore)

Conference style oral slide presentation 

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

1

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

Project/Assigned dataset processing Report submission  plus oral project discussion

Project can be done either

- producing results using tools introduced in lessons and labs. A data set will be assigned describing a set of multisensorial signals acquired by an agent during a simulated or real experience. A report will have to be produced describing selected tools to allow agent to obtain a generative self awareness model allowing it anomaly detection on future experiences  

- considering a student selected application involving a agent and a CDS and producing a poster to discuss how course techniques can be applied on it 

Oral will consist in presenting and discussing slides related to the project by highlighting relationships and theoretical aspects of methods introduced in the course 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

 

Exam schedule

Date Time Location Type Notes
10/06/2021 10:15 GENOVA Orale
24/06/2021 10:15 GENOVA Orale
15/07/2021 10:15 GENOVA Orale
29/07/2021 10:15 GENOVA Orale
26/08/2021 10:15 GENOVA Orale
16/09/2021 10:15 GENOVA Orale

FURTHER INFORMATION

Dataset will be assigned at least two weeks before exam and report will have to be presented on Monday before exam date (usually on Thursday)  Oral admission will be communicated a day before oral exam.