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Adaptive signal processing: foundations and applications.

Corso Ingegneria Elettrica ed Elettronica LM-29
Curriculum Electrical and Electronic Engineering
Orientamento Orientamento unico
Anno Accademico 2022/2023
Crediti 12
Settore Scientifico Disciplinare ING-IND/31
Anno Secondo anno
Unità temporale Secondo semestre
Ore aula 96
Attività formativa Attività formative affini ed integrative

Canale unico

Docente Cosimo IERACITANO
Obiettivi The course aims to give to the student the foundations of the treatment (or processing) of one-dimensional and multidimensional signals (temporal, spatial, frequency, time-frequency analysis and synthesis), including images from different sensors. The processing of the signals focuses on basic concepts of numerical analysis and processing, with the help of statistical and analytical-numerical techniques that can be then implemented on the computer. The fundamental objective of the course is the understanding and use of "tools" and standard tools at international level for the solution to the computer of complex problems mainly of a stochastic nature. In particular, the extensive use of MatLab, or equivalent codes, is suggested also through the presentation of specific exercises.

Specific training objectives:
Knowledge of the main deterministic and probabilistic models for electrophysiological signals.
Knowledge of the main algorithmic techniques for the treatment of biomedical signals.
Ability to use computer codes for the processing of digital signals.

Modality of examination of students:
The exam consists of an oral test that includes different aspects, focused on verifying the overall maturity of the candidate and ascertaining the achievement of specific objectives. The test aims to measure the critical skills developed by the student and the level of deepening of advanced knowledge of signal processing algorithms for environmental/biomedical applications. The oral test also consists in the public discussion of a course paper prepared by the student (or group of students) in consultation with the teacher, during which the communication skills acquired with reference to the presentation of research or projects developed during the course are ascertained. During the presentation, the candidate will also have to show the ability to work in a team on sector-specific applications.
Ability to structure a complex problem of modeling in terms of computational approaches.
Knowledge of international specific terminology for the topics covered.
Programma N.D.
Testi docente N.D.
Erogazione tradizionale No
Erogazione a distanza No
Frequenza obbligatoria No
Valutazione prova scritta No
Valutazione prova orale No
Valutazione test attitudinale No
Valutazione progetto No
Valutazione tirocinio No
Valutazione in itinere No
Prova pratica No
Docente FABIO LA FORESTA
Obiettivi The course aims to give to the student the foundations of the treatment (or processing) of one-dimensional and multidimensional signals (temporal, spatial, frequency, time-frequency analysis and synthesis), including images from different sensors. The processing of the signals focuses on basic concepts of numerical analysis and processing, with the help of statistical and analytical-numerical techniques that can be then implemented on the computer. The fundamental objective of the course is the understanding and use of "tools" and standard tools at international level for the solution to the computer of complex problems mainly of a stochastic nature. In particular, the extensive use of MatLab, or equivalent codes, is suggested also through the presentation of specific exercises.

Specific training objectives:
Knowledge of the main deterministic and probabilistic models for electrophysiological signals.
Knowledge of the main algorithmic techniques for the treatment of biomedical signals.
Ability to use computer codes for the processing of digital signals.

Modality of examination of students:
The exam consists of an oral test that includes different aspects, focused on verifying the overall maturity of the candidate and ascertaining the achievement of specific objectives. The test aims to measure the critical skills developed by the student and the level of deepening of advanced knowledge of signal processing algorithms for environmental/biomedical applications. The oral test also consists in the public discussion of a course paper prepared by the student (or group of students) in consultation with the teacher, during which the communication skills acquired with reference to the presentation of research or projects developed during the course are ascertained. During the presentation, the candidate will also have to show the ability to work in a team on sector-specific applications.
Ability to structure a complex problem of modeling in terms of computational approaches.
Knowledge of international specific terminology for the topics covered.
Programma BIOMEDICAL SIGNALS PROCESSING (3 ECTS)
Elements of electrophysiology, generation and the unique aspects of bio-signals, concepts of biomedical instrumentation, electronic systems for measuring and for the conditioning of bio-signals. Data acquisition and A/D conversion, acquisition interfaces, sensors for recording bio-signals, recording of bio-signals: the 12-leads ECG system, Jasper 10/20EEG system, Needle EMG and surface EMG, recording bio-signals using Biopac teaching System. Implementation of algorithms for multiresolution and multidimensional analysis of bio-signals, models for simulation of biological systems, numerical processing of bio-signals, design and implementation of circuits for processing bio-signals, laboratory exercises.
Testi docente Principe, N. R. Euliano, W. C. Lefebvre, “Neural and Adaptive Systems: Fundamental
through Simulations”, J. Wiley & Sons.
Bishop C.M., “Pattern Recognition and Machine Learning”, Oxford University Press.
Hyvarinen A., J. Karhunen, E. Oja, “Independent Component Analysis”, J. Wiley & Sons.
M. Akay, “Time Frequency and Wavelets in Biomedical Signal Processing”, Wiley-IEEE Press.
Erogazione tradizionale
Erogazione a distanza
Frequenza obbligatoria No
Valutazione prova scritta No
Valutazione prova orale No
Valutazione test attitudinale No
Valutazione progetto No
Valutazione tirocinio No
Valutazione in itinere No
Prova pratica No
Docente FRANCESCO CARLO MORABITO
Obiettivi The course aims to give to the student the foundations of the treatment (or processing) of one-dimensional and multidimensional signals (temporal, spatial, frequency, time-frequency analysis and synthesis), including images from different sensors. The processing of the signals focuses on basic concepts of numerical analysis and processing, with the help of statistical and analytical-numerical techniques that can be then implemented on the computer. The fundamental objective of the course is the understanding and use of "tools" and standard tools at international level for the solution to the computer of complex problems mainly of a stochastic nature. In particular, the extensive use of MatLab, or equivalent codes, is suggested also through the presentation of specific exercises.

Specific training objectives:
Knowledge of the main deterministic and probabilistic models for electrophysiological signals.
Knowledge of the main algorithmic techniques for the treatment of biomedical signals.
Ability to use computer codes for the processing of digital signals.

Modality of examination of students:
The exam consists of an oral test that includes different aspects, focused on verifying the overall maturity of the candidate and ascertaining the achievement of specific objectives. The test aims to measure the critical skills developed by the student and the level of deepening of advanced knowledge of signal processing algorithms for environmental/biomedical applications. The oral test also consists in the public discussion of a course paper prepared by the student (or group of students) in consultation with the teacher, during which the communication skills acquired with reference to the presentation of research or projects developed during the course are ascertained. During the presentation, the candidate will also have to show the ability to work in a team on sector-specific applications.
Ability to structure a complex problem of modeling in terms of computational approaches.
Knowledge of international specific terminology for the topics covered.
Programma N.D.
Testi docente N.D.
Erogazione tradizionale No
Erogazione a distanza No
Frequenza obbligatoria No
Valutazione prova scritta No
Valutazione prova orale No
Valutazione test attitudinale No
Valutazione progetto No
Valutazione tirocinio No
Valutazione in itinere No
Prova pratica No

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