|SCIENTIFIC DISCIPLINARY SECTOR||ING-INF/03|
|LANGUAGE||Italian (English on demand)|
- 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.
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 .
Probability theory, Random Processes, Signal theory
Lectures and lab exercises Project based learning
Applying knowledge and understanding in lab
Learning and communications skills:
Conference style oral slide presentation
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:
- 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.
Office hours: Students can ask appointments for clarifications, explanations on course subjects by sending e-mail at Carlo.Regazzoni@unige.it
CARLO REGAZZONI (President)
Oral Examination (70/100%)
Assigned Project evaluation (30%)
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
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.