CODE 86960 ACADEMIC YEAR 2016/2017 CREDITS 5 cfu anno 2 INGEGNERIA ELETTRONICA 8732 (LM-29) - SCIENTIFIC DISCIPLINARY SECTOR ING-INF/03 LANGUAGE Italiano TEACHING LOCATION SEMESTER 1° Semester 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. 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 TEACHERS AND EXAM BOARD CARLO REGAZZONI 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) SILVANA DELLEPIANE LUCIO MARCENARO LESSONS LESSONS START 1st semester 2016 - September 19th 2016 Class schedule 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 in discussing how proposed techniques can be used in the context of an application selected by the student Exam schedule Data appello Orario Luogo Degree type Note 15/06/2017 09:30 GENOVA Scritto + Orale 06/07/2017 09:30 GENOVA Scritto + Orale 06/07/2017 09:30 GENOVA Scritto + Orale 14/09/2017 09:30 GENOVA Scritto + Orale 14/09/2017 09:30 GENOVA Scritto + Orale