OVERVIEW

This course aims at provining to the Master student basic and advanced concepts on the design of methods and techniques for data driven self-awareness in autonomous artificial agents with respect to sensing and communications functionalities. Signal Processing, Wireless telecommunications, Data Fusion and Machine learning under a Bayesian pespective will be the key dimensions on which introduced concepts will be described. Laboratory application and agent design will integrate course theoretical activities  

AIMS AND CONTENT

LEARNING OUTCOMES

This subject introduces architectures of next-generation mobile communication systems based on software-defined and cognitive radio exploiting interaction between sensing and communications. The subject provides basic knowledge for co-design of sensing and communication, processing heterogeneous multisensory signals acquired by autonomous systems, and approaches for jammer and anomaly detection.

AIMS AND LEARNING OUTCOMES

-Basic and advanced knowledge on design of integrated susystems of  autonomous cognitive agents including sensing and mobile telecommunication functionalities 

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

- Knowledge on radio channel models including deterministic and statistical channel models; brief recall of digital modulation techniques.

- Knowledge on sensors used by autonomous agents for context sensing, including proprioreceptive and exteroreceptive sensors.   

- Knowledge on bayesian 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 Matlab examples useful to implement learned concepts.

- Knowledge and capabilities on lab defined case studies: design of integrated sensing and communication blocks for self aware autonomous systems (dataset on cars robots and drones, cognitive radios) 

PREREQUISITES

Probability theory, Random Processes, Signal and systems 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 lectures will cover 80%  hours and can be recorded and made available on those channels recommended by Univeristy of Genova. The second part  20% 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. 3-4 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

• The module can be associated with ONU 2023 Objectived for Sustinable Development Nr. 4,9 and 11
• Introduction to integrated sensing and communications in cognitive dynamic systems
• Wireless telecommunications systems
  • Recall to FDM and TDM.
  • Wireless free space channel model
  • Statistical radio channel Multipath channel model 
  • Time variant pulse response
  • Digital modulations: bandpass/baseband equivalent, random process, ASK, PSK, FSK
  • Lab experience on Matlab for channel model simulation and modulation
  • Software Defined Radio
    • SDR Architectures - RF frontends
• Sensors 
  • motivation for using them in 6G devivces, definition, principles
  • Lab experience in sensor data acquisition
• Represention and inference for cognitive cycle in ISAC using Bayesian networks
  • Bayesian networks:
  • • Hierarchical Dynamic bayesian networks
    • Belief propagation
    • conditional independence on subgraphs
    • factorization of joint probability
    • DBNs for multitarget tracking, interaction models.
    • Belief propagation in switching models
  •  Bayesian filter
    •  Kalman filter
    • Particle filter
    • Switching model filters: Markov Jump PF
    • Labs on Kalman, Particle and MJPF
  • Cognitive filters
    • Probabilistic Anomaly measurements for cognitive filters
    • incremental learning using generalized errors

RECOMMENDED READING/BIBLIOGRAPHY

Slides of all lectures written  by the lecturer will be made available

Books and research papers that can help the student to integrate concepts described during frontal activity are here provided and can be integrated during the year.  

- 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

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/11970/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy