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

The course is divided in two parts. Lectures in frontal teaching modality presented together with slides will aim at describing the theroretical concepts and the techniques. Such lectires will cover 40 hours and can be recorded and made available on those channels recommended by Univeristy of Genova. The second part is done within a laboratory carried on by an expert of the field and will involve application of programs in Matlab framework that correspond to theories and techniqeus shon at lectures. Students will be required to present a report at the end of each lab experience. 10 Lab experiences are planned and will help students to be prepared to present the final report to be discussed during the exam that will show application and discussion of techniques abnd results over a dataset assigned.. 

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.