BIONICS ENGINEERING
Corso di laurea magistrale
Piano di Studi
Curricula:
Neural Engineering
Primo anno
Bioinspired computational methods (12 cfu)
- The course aims to introduce the main concepts and techniques used in bioinspired computational methods. The course is divided in two modules “Neural and fuzzy computation” and “Biological data mining”. The first module intends to offer students the opportunity to learn the basic concepts and models of computational intelligence, to have a thorough understanding of the associated computational techniques, such as artificial neural networks, fuzzy systems and genetic algorithms, and to know how to apply them to a wide variety of application areas. The second module will focus on the basic aspects of biological data mining: data pre-processing, frequent pattern mining, classification, prediction, clustering and outlier detection.
- The course aims to introduce the main concepts and techniques used in bioinspired computational methods. The course is divided in two modules “Neural and fuzzy computation” and “Biological data mining”. The first module intends to offer students the opportunity to learn the basic concepts and models of computational intelligence, to have a thorough understanding of the associated computational techniques, such as artificial neural networks, fuzzy systems and genetic algorithms, and to know how to apply them to a wide variety of application areas. The second module will focus on the basic aspects of biological data mining: data pre-processing, frequent pattern mining, classification, prediction, clustering and outlier detection.
Statistical Signal Processing (6 cfu)
- The course will cover statistical signal processing methods, with application to bioengineering field. The students will become familiar with basic concepts of discrete representation of deterministic and random continuous-time signals, discrete-time random signal analysis, deterministic and random parameter estimation. Various estimation methods will be introduced and compared, such as the method of moments, the maximum likelihood and the linear and non-linear least squares methods. An introduction to Bayesian framework for random parameters and random signals estimation will be provided, with particular emphasis to the problem of linear smoothing, filtering, and prediction. Parametric auto-regressive moving average (ARMA) modeling and identification of discrete-time random signals will be also addressed. Advanced topics in parametric and non-parametric (adaptive and non-adaptive) methods for spectral estimation will be introduced, as well as some basic concepts of time-frequency analysis.
- The course will cover statistical signal processing methods, with application to bioengineering field. The students will become familiar with basic concepts of discrete representation of deterministic and random continuous-time signals, discrete-time random signal analysis, deterministic and random parameter estimation. Various estimation methods will be introduced and compared, such as the method of moments, the maximum likelihood and the linear and non-linear least squares methods. An introduction to Bayesian framework for random parameters and random signals estimation will be provided, with particular emphasis to the problem of linear smoothing, filtering, and prediction. Parametric auto-regressive moving average (ARMA) modeling and identification of discrete-time random signals will be also addressed. Advanced topics in parametric and non-parametric (adaptive and non-adaptive) methods for spectral estimation will be introduced, as well as some basic concepts of time-frequency analysis.
Applied brain science (12 cfu)
- This course is divided in two modules “Behavioral and Cognitive Neuroscience” and “Computational neuroscience”.
In the class “Behavioral and cognitive neuroscience”, the student will learn the following topics: introduction to cognitive and social neuroscience; introduction to neuronal functioning, brain metabolism and intrinsic brain activity; basic principles of brain imaging methodologies,
their uses for research and clinical purposes; introduction to the advanced methods for brain imaging analyses; the neurobiological correlates of human cognition and behavior; the mental representation of the external world; the functional neuroanatomy of perception and imagery;
introduction to consciousness and sleep; introduction to psycholinguistics; emotions and behavior; motor control and action representation, and their implications for the development of brain-computer interfaces.
The objectives of "Computational neuroscience" class include bio-inspired neural modelling, spiking and reservoir computing neural networks, advanced computational neural models for learning, architectures and learning methods for dynamical/recurrent neural networks for temporal data and the analysis of their properties, the role of computational neuroscience in
real-world applications (by case studies).
- This course is divided in two modules “Behavioral and Cognitive Neuroscience” and “Computational neuroscience”.
Methods and techniques of measurement and data analysis (6 cfu)
Analysis of bionic and robotic systems (12 cfu)
- In particular, the “Principles of Bionics and Biorobotics Engineering” module will introduce the wide and interdisciplinary field of bionics and related scientific areas, such as biorobotics, neuroengineering and bioengineering. Bionics aims at gathering specific knowledge through the analysis/modeling of living organisms/ecosystems and applies it to the development of newly inspired advanced devices. Bionics also focuses on artificial systems deeply connected to body tissues, and/or interacting with organisms. The application of bionics principles is nowadays widespread in many engineering sub-fields. During this course, the pillars of bionics and robotics are presented to allow the proper understanding of the whole loop from scientific insights to engineering innovation. In particular, the course focuses on the key principles of biological locomotion, swarm robotics, artificial organs, neurorobotics, neuroprostheses, neural interfaces with the nervous system, morphological computation, energy issues, principles of structural bionic design and main manufacturing techniques, as well as the fundamentals of robot mechanics.
At the end of the module “Principles of Bionics and Biorobotics Engineering”, the student:
' Will possess the main knowledge about bionic and robotics engineering principles;
' Will be able to orient him/herself in the scientific literature;
' Will be able to formulate innovative hypotheses and to devise new solutions on bionic design and bioinspired robotic paradigms.
To profitably attend this module, no specialistic knowledge is required. However, consolidated competences of physics and mathematics are needed. In addition, even if not strictly needed, competences concern mechanics, signal processing, and electronics are desirable. The “Principles of Bionics and Biorobotics Engineering” module topics are the following:
' Historical evolution of bionics, related to robotics and bioengineering;
' Model organisms and biological locomotion principles in different media, and applications in robotics;
' Bionic energy management: comparison between organisms and robots;
' Fabrication technologies at different scales;
' Bioinspired structural design and advanced materials;
' Fundamentals of robot mechanics (schematic of the joints, homogeneous transformations, Jacobian, methods for kinematic and dynamic studies);
' Swarm robotics;
' Principles to design effective neural interfaces and neuroprostheses
' Ethical issues and legal considerations.
The “Modeling of multi-physics phenomena” module provides a basic, practical knowledge of computational mechanics of solids (linear/non-linear elasticity) and fluids (incompressible flows of Newtonian fluids). and computational electromagnetism at low frequencies, mainly associated to bioelectric phenomena and neural models. The first part of the course deals with the theory of the Finite Element Method and introduces numerical algorithms and practices for the solution of non-linear and time-dependent problems, which are common to all the aforementioned computational disciplines. Then, the numerical implementations of several physical models in a commercial FE software will be shown during hands-on sessions, along with applications to design problems involving sensors and bioinspired devices.
- In particular, the “Principles of Bionics and Biorobotics Engineering” module will introduce the wide and interdisciplinary field of bionics and related scientific areas, such as biorobotics, neuroengineering and bioengineering. Bionics aims at gathering specific knowledge through the analysis/modeling of living organisms/ecosystems and applies it to the development of newly inspired advanced devices. Bionics also focuses on artificial systems deeply connected to body tissues, and/or interacting with organisms. The application of bionics principles is nowadays widespread in many engineering sub-fields. During this course, the pillars of bionics and robotics are presented to allow the proper understanding of the whole loop from scientific insights to engineering innovation. In particular, the course focuses on the key principles of biological locomotion, swarm robotics, artificial organs, neurorobotics, neuroprostheses, neural interfaces with the nervous system, morphological computation, energy issues, principles of structural bionic design and main manufacturing techniques, as well as the fundamentals of robot mechanics.
12 cfu a scelta nel gruppo Free choice
- List of classes that the student chooses freely. These classes will be automatically approved by the board of the Master Degree Course
Electronics for Bionics Engineering (6 cfu)
- The student who successfully completes the course will be able to demonstrate a solid knowledge of the main issues related to the design of sensor based electronic systems for bionics engineering. He or she will acquire the ability to analyse and design the building blocks of an analogue front-end for the acquisition, conditioning and conversion of biological sensor data, and will master the design methodologies adopted for instrumentation amplifiers, passive and active filters, analogue-to-digital and digital-to-analogue converters. He or she will then familiarise with standard digital interfaces utilised to transfer digitalised sensor data to an embedded microcontroller or microprocessor. The student will deepen his or her learning on the conditioning and digitalization chain of biological sensor data by familiarising with standard digital interfaces utilised to transfer digitalised sensor data to an embedded microcontroller or microprocessor. Finally, he or she will be exposed to state-of-the-art design methodologies adopted for tightly energy-/power-constrained electronic systems in wearable and implantable devices, and will have the opportunity to consolidate his or her learning by working with advanced EDA tools.
- The student who successfully completes the course will be able to demonstrate a solid knowledge of the main issues related to the design of sensor based electronic systems for bionics engineering. He or she will acquire the ability to analyse and design the building blocks of an analogue front-end for the acquisition, conditioning and conversion of biological sensor data, and will master the design methodologies adopted for instrumentation amplifiers, passive and active filters, analogue-to-digital and digital-to-analogue converters. He or she will then familiarise with standard digital interfaces utilised to transfer digitalised sensor data to an embedded microcontroller or microprocessor. The student will deepen his or her learning on the conditioning and digitalization chain of biological sensor data by familiarising with standard digital interfaces utilised to transfer digitalised sensor data to an embedded microcontroller or microprocessor. Finally, he or she will be exposed to state-of-the-art design methodologies adopted for tightly energy-/power-constrained electronic systems in wearable and implantable devices, and will have the opportunity to consolidate his or her learning by working with advanced EDA tools.
Neuromorphic engineering (6 cfu)
- The course will explore computational and physical models that emulate the neural dynamics of biological neurons of peripheral and central nervous system. A particular focus will be dedicated to real-time implementation of spiking artefacts that could be integrated in neurophysiological studies and in closed loop hybrid-bionic systems to restore missing sensorimotor functions.
- The course will explore computational and physical models that emulate the neural dynamics of biological neurons of peripheral and central nervous system. A particular focus will be dedicated to real-time implementation of spiking artefacts that could be integrated in neurophysiological studies and in closed loop hybrid-bionic systems to restore missing sensorimotor functions.
Robot programming frameworks and IoT platforms (6 cfu)
- The course aims at proving theoretical and practical competences in robotic programming frameworks and IoT platforms and it will provide knowledge in software design of autonomous robots and systems. Specific activities will be performed with ROS (Robotic Operating System) and YARP (Yet another robot platform) that will be implemented in simulated environments, also using dedicated SOM (System on Module) development boards. The course will also provide basic knowledge on brain-inspired controllers, based on machine learning approaches. Hands-on laboratory activities will be part of the course: students will be involved in the design and development of an IoT based environment, program the embedded system with sensors and cloud-based computing, design brain-inspired controllers and integrate them in robotic middleware (ROS and YARP).
- The course aims at proving theoretical and practical competences in robotic programming frameworks and IoT platforms and it will provide knowledge in software design of autonomous robots and systems. Specific activities will be performed with ROS (Robotic Operating System) and YARP (Yet another robot platform) that will be implemented in simulated environments, also using dedicated SOM (System on Module) development boards. The course will also provide basic knowledge on brain-inspired controllers, based on machine learning approaches. Hands-on laboratory activities will be part of the course: students will be involved in the design and development of an IoT based environment, program the embedded system with sensors and cloud-based computing, design brain-inspired controllers and integrate them in robotic middleware (ROS and YARP).
Artificial intelligent systems for human identification (6 cfu)
- This course provides fundamentals about techniques to verify or recognize the identity of a living person based on the analysis of biological/physiological traits and/or behavioural features.
The topics of the course will be:
- Biometrics overview (history of biometrics, applications);
- Recognition, identification and verification;
- Privacy, security and ethics;
- Overview of image processing;
- Physiological biometric systems: fingerprint recognition, face recognition, iris recognition, retina recognition, hand recognition, vein patterns;
- Behavioral biometric systems: keystroke dynamics, signature recognition, voice recognition, gait recognition;
- Multi-modal biometric systems;
- Biometric applications.
- This course provides fundamentals about techniques to verify or recognize the identity of a living person based on the analysis of biological/physiological traits and/or behavioural features.
The topics of the course will be:
- Biometrics overview (history of biometrics, applications);
- Recognition, identification and verification;
- Privacy, security and ethics;
- Overview of image processing;
- Physiological biometric systems: fingerprint recognition, face recognition, iris recognition, retina recognition, hand recognition, vein patterns;
- Behavioral biometric systems: keystroke dynamics, signature recognition, voice recognition, gait recognition;
- Multi-modal biometric systems;
- Biometric applications.
Advanced materials for bionics (6 cfu)
- The aim of the course is to provide a solid background in Materials Science and Engineering as relevant for Bionics Engineering. Together with basic understanding of the various classes of materials, their structure, composition, structural and functional properties, the students will learn about more advanced concepts and recent developments of modern Materials Science, lying at the interface between chemistry, physics, engineering and biology.
The course is structured in three sections.
In the first section (1-Basic Materials Science & Engineering) basic and traditional topics of Materials Science will be covered/refreshed to provide a common basis among students with different background knowledge. Topics include: nomenclature and classification of materials, basic physical and chemical knowledge of solid matter, structure/properties correlation in materials, hierarchical structuring, phase transitions, fundamentals of mechanical behaviour, viscoelasticity, basic characterization tools. Various classes of engineering relevant materials will be reviewed: metals, ceramics, polymers, composites.
In the second section (2 – Advanced Materials Concepts) more advanced concepts will be introduced, regarding: biocompatibility of Materials, complex behaviour of soft matter, nanotechnology and nanostructures, biological materials, bioinspired materials, stimuli-responsive materials, electro-active polymers, advanced investigation and fabrication techniques for micro- and nano-structures.
In the third section (3 Technology & Bionics Applications) relevant examples of applications of materials in Bionics will be reviewed, including: advanced functional surfaces and interfaces, structural materials for bionics, materials for bioelectronics, materials for sensors & actuators in robotics.
- The aim of the course is to provide a solid background in Materials Science and Engineering as relevant for Bionics Engineering. Together with basic understanding of the various classes of materials, their structure, composition, structural and functional properties, the students will learn about more advanced concepts and recent developments of modern Materials Science, lying at the interface between chemistry, physics, engineering and biology.
The course is structured in three sections.
In the first section (1-Basic Materials Science & Engineering) basic and traditional topics of Materials Science will be covered/refreshed to provide a common basis among students with different background knowledge. Topics include: nomenclature and classification of materials, basic physical and chemical knowledge of solid matter, structure/properties correlation in materials, hierarchical structuring, phase transitions, fundamentals of mechanical behaviour, viscoelasticity, basic characterization tools. Various classes of engineering relevant materials will be reviewed: metals, ceramics, polymers, composites.
In the second section (2 – Advanced Materials Concepts) more advanced concepts will be introduced, regarding: biocompatibility of Materials, complex behaviour of soft matter, nanotechnology and nanostructures, biological materials, bioinspired materials, stimuli-responsive materials, electro-active polymers, advanced investigation and fabrication techniques for micro- and nano-structures.
In the third section (3 Technology & Bionics Applications) relevant examples of applications of materials in Bionics will be reviewed, including: advanced functional surfaces and interfaces, structural materials for bionics, materials for bioelectronics, materials for sensors & actuators in robotics.
Probability and Biostatistics (6 cfu)
- The student will learn the basics of probability theory and statistical inference and will be able to apply appropriate methodologies for applications in biology and, in general, for the study of life sciences. In addition to the planning, collection and analysis of experimental data, the following topics will be discussed: theory of probability, discrete and continuous random variables, descriptive statistics of statistical samples, multivariate random variables, sums of random variables and associated limit theorems, interval estimation, inferential statistics and hypothesis testing, non-Gaussianity and chi-square tests, inference through non-parametric methods, regression analysis, and evaluation of diagnostic tests: sensitivity, specificity, and ROC curves.
- The student will learn the basics of probability theory and statistical inference and will be able to apply appropriate methodologies for applications in biology and, in general, for the study of life sciences. In addition to the planning, collection and analysis of experimental data, the following topics will be discussed: theory of probability, discrete and continuous random variables, descriptive statistics of statistical samples, multivariate random variables, sums of random variables and associated limit theorems, interval estimation, inferential statistics and hypothesis testing, non-Gaussianity and chi-square tests, inference through non-parametric methods, regression analysis, and evaluation of diagnostic tests: sensitivity, specificity, and ROC curves.
Neural prostheses (12 cfu)
- The course on “Neural prostheses” is composed of two modules: “Neural interfaces and bioelectronic medicine” and “Neural tissue engineering”.
During the course on “Neural interfaces and bioelectronic medicine” the students will acquire the basic principles underlying the design and development of implantable neural interfaces for different parts of the nervous system They will also develop a broad view on i) existing neuroprosthetic systems to restore motor functions, ii) neural stimulation therapies for motor disorders, and iii) novel solutions based on the stimulation of the autonomic nervous system, and will be able to identify current limitations and challenges for future applications. Finally, the students will learn the conceptual and practical bases for the development of a novel neuroprosthesis (group project).
The course “Neural tissue engineering” will introduce the students to the methods, protocols and engineering tools for mimicking both the central nervous system (CNS) and peripheral nervous system (PNS) at the microscale. Specifically, after a brief introduction on the physiopathology of human nervous system and its buiding blocks (i.e. neurons and glia cells), the course will focus on biomaterials and biofabrication techniques, traditional and advanced in vitro systems as well as computational methods for modeling, monitoring and characterizing neural structure and function.
- The course on “Neural prostheses” is composed of two modules: “Neural interfaces and bioelectronic medicine” and “Neural tissue engineering”.
Bionic senses (6 cfu)
- The course “Bionic senses” refers to engineering artificial sensing and perceptual systems through biological principles to implement neuro-prostheses to restore lost functions, for human augmentation and bioinspired perceptional machines. The basis of the needed methodology and technology will be given tutorially. The course will introduce the physiology of senses and psychophysics and will introduce engineering solutions for bionics touch, vision and hearing.
- The course “Bionic senses” refers to engineering artificial sensing and perceptual systems through biological principles to implement neuro-prostheses to restore lost functions, for human augmentation and bioinspired perceptional machines. The basis of the needed methodology and technology will be given tutorially. The course will introduce the physiology of senses and psychophysics and will introduce engineering solutions for bionics touch, vision and hearing.
Integrative cerebral function and image processing (12 cfu)
- The course is divided in two modules:
Integrative cerebral functions – All cognitive and emotional functions are the by-product of the activity of anatomo-functional distributed and, at the same time, integrated networks. The didactic module entitled "Integrative cerebral functions" will address the following main topics: 1) Node and rich-clubs in the human connectome; 2) Sleep, mentation and dreaming; 3) Biological bases of consciousness; 4) Theory of mind and mirror neuron system; 4) Empathy in the emotional context; 5) Stress in the context of body and mind integration
Advanced image processing - This module will cover advanced image processing methods that can be applied to biomedical images of the brain. In particular, the methods used to study structural and functional connectivity, as well as brain metabolism, will be deeply covered. The students will be trained to process images acquired using different neuroimaging techniques, as those based on MRI, PET and NIRS. The course will also introduce the main approaches for the integration of biomedical images and electrophysiological recordings.
- The course is divided in two modules:
Lab Training (3 cfu)
- This activity will consist of Lab training that the student will perform in dedicated facilities and laboratories, with the aim to increase his/her experience in laboratory practice.
- This activity will consist of Lab training that the student will perform in dedicated facilities and laboratories, with the aim to increase his/her experience in laboratory practice.
Final examination (15 cfu)
- The final examination involves the preparation of a report led to a design or research activity, and in its presentation and discussion.
- The final examination involves the preparation of a report led to a design or research activity, and in its presentation and discussion.
Interactive Systems and Affective Computing (Interactive systems module) (12 cfu)
- The course is composed of two modules “Interactive Systems” and “Affective computing”.
The module of “Affective computing” aims at showing how computational technology can be used to understand and interpret human emotions. Specifically, modelling of human emotional expression will be addressed, including software and hardware solutions to acquire, communicate, and express affective information. Understanding how emotions can be experienced can be also of help to quantify correlated patterns of central and autonomic nervous activity in order to investigate on mood and consciousness disorders.
For the module of “Interactive Systems”, at the end of the course the student will have learned how:
• organize a design and development team for a complex hardware/software system;
• study the market, find information on existing systems and identify a strategic positioning for the software product;
• define the project requirements and organize the project roadmap
• work as a team on product development
• test, validate, document and release the system using the appropriate DEVOPS platforms
- The course is composed of two modules “Interactive Systems” and “Affective computing”.
Statistical Signal Processing (6 cfu)
- The course will cover statistical signal processing methods, with application to bioengineering field. The students will become familiar with basic concepts of discrete representation of deterministic and random continuous-time signals, discrete-time random signal analysis, deterministic and random parameter estimation. Various estimation methods will be introduced and compared, such as the method of moments, the maximum likelihood and the linear and non-linear least squares methods. An introduction to Bayesian framework for random parameters and random signals estimation will be provided, with particular emphasis to the problem of linear smoothing, filtering, and prediction. Parametric auto-regressive moving average (ARMA) modeling and identification of discrete-time random signals will be also addressed. Advanced topics in parametric and non-parametric (adaptive and non-adaptive) methods for spectral estimation will be introduced, as well as some basic concepts of time-frequency analysis.
- The course will cover statistical signal processing methods, with application to bioengineering field. The students will become familiar with basic concepts of discrete representation of deterministic and random continuous-time signals, discrete-time random signal analysis, deterministic and random parameter estimation. Various estimation methods will be introduced and compared, such as the method of moments, the maximum likelihood and the linear and non-linear least squares methods. An introduction to Bayesian framework for random parameters and random signals estimation will be provided, with particular emphasis to the problem of linear smoothing, filtering, and prediction. Parametric auto-regressive moving average (ARMA) modeling and identification of discrete-time random signals will be also addressed. Advanced topics in parametric and non-parametric (adaptive and non-adaptive) methods for spectral estimation will be introduced, as well as some basic concepts of time-frequency analysis.
Methods and techniques of measurement and data analysis (6 cfu)
Analysis of bionic and robotic systems (12 cfu)
- In particular, the “Principles of Bionics and Biorobotics Engineering” module will introduce the wide and interdisciplinary field of bionics and related scientific areas, such as biorobotics, neuroengineering and bioengineering. Bionics aims at gathering specific knowledge through the analysis/modeling of living organisms/ecosystems and applies it to the development of newly inspired advanced devices. Bionics also focuses on artificial systems deeply connected to body tissues, and/or interacting with organisms. The application of bionics principles is nowadays widespread in many engineering sub-fields. During this course, the pillars of bionics and robotics are presented to allow the proper understanding of the whole loop from scientific insights to engineering innovation. In particular, the course focuses on the key principles of biological locomotion, swarm robotics, artificial organs, neurorobotics, neuroprostheses, neural interfaces with the nervous system, morphological computation, energy issues, principles of structural bionic design and main manufacturing techniques, as well as the fundamentals of robot mechanics.
At the end of the module “Principles of Bionics and Biorobotics Engineering”, the student:
' Will possess the main knowledge about bionic and robotics engineering principles;
' Will be able to orient him/herself in the scientific literature;
' Will be able to formulate innovative hypotheses and to devise new solutions on bionic design and bioinspired robotic paradigms.
To profitably attend this module, no specialistic knowledge is required. However, consolidated competences of physics and mathematics are needed. In addition, even if not strictly needed, competences concern mechanics, signal processing, and electronics are desirable. The “Principles of Bionics and Biorobotics Engineering” module topics are the following:
' Historical evolution of bionics, related to robotics and bioengineering;
' Model organisms and biological locomotion principles in different media, and applications in robotics;
' Bionic energy management: comparison between organisms and robots;
' Fabrication technologies at different scales;
' Bioinspired structural design and advanced materials;
' Fundamentals of robot mechanics (schematic of the joints, homogeneous transformations, Jacobian, methods for kinematic and dynamic studies);
' Swarm robotics;
' Principles to design effective neural interfaces and neuroprostheses
' Ethical issues and legal considerations.
The “Modeling of multi-physics phenomena” module provides a basic, practical knowledge of computational mechanics of solids (linear/non-linear elasticity) and fluids (incompressible flows of Newtonian fluids). and computational electromagnetism at low frequencies, mainly associated to bioelectric phenomena and neural models. The first part of the course deals with the theory of the Finite Element Method and introduces numerical algorithms and practices for the solution of non-linear and time-dependent problems, which are common to all the aforementioned computational disciplines. Then, the numerical implementations of several physical models in a commercial FE software will be shown during hands-on sessions, along with applications to design problems involving sensors and bioinspired devices.
- In particular, the “Principles of Bionics and Biorobotics Engineering” module will introduce the wide and interdisciplinary field of bionics and related scientific areas, such as biorobotics, neuroengineering and bioengineering. Bionics aims at gathering specific knowledge through the analysis/modeling of living organisms/ecosystems and applies it to the development of newly inspired advanced devices. Bionics also focuses on artificial systems deeply connected to body tissues, and/or interacting with organisms. The application of bionics principles is nowadays widespread in many engineering sub-fields. During this course, the pillars of bionics and robotics are presented to allow the proper understanding of the whole loop from scientific insights to engineering innovation. In particular, the course focuses on the key principles of biological locomotion, swarm robotics, artificial organs, neurorobotics, neuroprostheses, neural interfaces with the nervous system, morphological computation, energy issues, principles of structural bionic design and main manufacturing techniques, as well as the fundamentals of robot mechanics.
Bioinspired and soft robotics (12 cfu)
- “Mechanics of smart materials and structures” aims at providing a working knowledge of modeling tools to analyze and understand the mechanical response of smart materials and structures. These include the variational formulation of equilibrium and stability problems, up to the foundations of the Finite Element Method (FEM). The course will enable the student to formulate and solve models for the equilibrium response of structures in the large deformation regime. The student will be introduced to advanced modeling tools and their applications in bioinspired and soft robotics. Advanced design principles (based e.g., on morphing structures, deployable structures, multi-stable devices) will be discussed through the analysis of their theoretical basis and the solution of practical problems.
“Soft robotics technologies” aims at providing an advanced knowledge on soft robotics and soft mechatronics technologies. The course will enable the student to implement a comparative analysis for the choice of the most suitable technologies for specific engineering problems. Different transduction principles will be analysed from the basic physics to their exploitation as sensors or actuators. The student will be introduced to advanced design principles (e.g. bioinspiration and morphological computation) and tools (e.g. FEM) also through hands-on lab activities.
- “Mechanics of smart materials and structures” aims at providing a working knowledge of modeling tools to analyze and understand the mechanical response of smart materials and structures. These include the variational formulation of equilibrium and stability problems, up to the foundations of the Finite Element Method (FEM). The course will enable the student to formulate and solve models for the equilibrium response of structures in the large deformation regime. The student will be introduced to advanced modeling tools and their applications in bioinspired and soft robotics. Advanced design principles (based e.g., on morphing structures, deployable structures, multi-stable devices) will be discussed through the analysis of their theoretical basis and the solution of practical problems.
Bioinspired computational methods (12 cfu)
- The course aims to introduce the main concepts and techniques used in bioinspired computational methods. The course is divided in two modules “Neural and fuzzy computation” and “Biological data mining”. The first module intends to offer students the opportunity to learn the basic concepts and models of computational intelligence, to have a thorough understanding of the associated computational techniques, such as artificial neural networks, fuzzy systems and genetic algorithms, and to know how to apply them to a wide variety of application areas. The second module will focus on the basic aspects of biological data mining: data pre-processing, frequent pattern mining, classification, prediction, clustering and outlier detection.
- The course aims to introduce the main concepts and techniques used in bioinspired computational methods. The course is divided in two modules “Neural and fuzzy computation” and “Biological data mining”. The first module intends to offer students the opportunity to learn the basic concepts and models of computational intelligence, to have a thorough understanding of the associated computational techniques, such as artificial neural networks, fuzzy systems and genetic algorithms, and to know how to apply them to a wide variety of application areas. The second module will focus on the basic aspects of biological data mining: data pre-processing, frequent pattern mining, classification, prediction, clustering and outlier detection.
12 cfu a scelta nel gruppo Free choice
- List of classes that the student chooses freely. These classes will be automatically approved by the board of the Master Degree Course
Electronics for Bionics Engineering (6 cfu)
- The student who successfully completes the course will be able to demonstrate a solid knowledge of the main issues related to the design of sensor based electronic systems for bionics engineering. He or she will acquire the ability to analyse and design the building blocks of an analogue front-end for the acquisition, conditioning and conversion of biological sensor data, and will master the design methodologies adopted for instrumentation amplifiers, passive and active filters, analogue-to-digital and digital-to-analogue converters. He or she will then familiarise with standard digital interfaces utilised to transfer digitalised sensor data to an embedded microcontroller or microprocessor. The student will deepen his or her learning on the conditioning and digitalization chain of biological sensor data by familiarising with standard digital interfaces utilised to transfer digitalised sensor data to an embedded microcontroller or microprocessor. Finally, he or she will be exposed to state-of-the-art design methodologies adopted for tightly energy-/power-constrained electronic systems in wearable and implantable devices, and will have the opportunity to consolidate his or her learning by working with advanced EDA tools.
- The student who successfully completes the course will be able to demonstrate a solid knowledge of the main issues related to the design of sensor based electronic systems for bionics engineering. He or she will acquire the ability to analyse and design the building blocks of an analogue front-end for the acquisition, conditioning and conversion of biological sensor data, and will master the design methodologies adopted for instrumentation amplifiers, passive and active filters, analogue-to-digital and digital-to-analogue converters. He or she will then familiarise with standard digital interfaces utilised to transfer digitalised sensor data to an embedded microcontroller or microprocessor. The student will deepen his or her learning on the conditioning and digitalization chain of biological sensor data by familiarising with standard digital interfaces utilised to transfer digitalised sensor data to an embedded microcontroller or microprocessor. Finally, he or she will be exposed to state-of-the-art design methodologies adopted for tightly energy-/power-constrained electronic systems in wearable and implantable devices, and will have the opportunity to consolidate his or her learning by working with advanced EDA tools.
Neuromorphic engineering (6 cfu)
- The course will explore computational and physical models that emulate the neural dynamics of biological neurons of peripheral and central nervous system. A particular focus will be dedicated to real-time implementation of spiking artefacts that could be integrated in neurophysiological studies and in closed loop hybrid-bionic systems to restore missing sensorimotor functions.
- The course will explore computational and physical models that emulate the neural dynamics of biological neurons of peripheral and central nervous system. A particular focus will be dedicated to real-time implementation of spiking artefacts that could be integrated in neurophysiological studies and in closed loop hybrid-bionic systems to restore missing sensorimotor functions.
Robot programming frameworks and IoT platforms (6 cfu)
- The course aims at proving theoretical and practical competences in robotic programming frameworks and IoT platforms and it will provide knowledge in software design of autonomous robots and systems. Specific activities will be performed with ROS (Robotic Operating System) and YARP (Yet another robot platform) that will be implemented in simulated environments, also using dedicated SOM (System on Module) development boards. The course will also provide basic knowledge on brain-inspired controllers, based on machine learning approaches. Hands-on laboratory activities will be part of the course: students will be involved in the design and development of an IoT based environment, program the embedded system with sensors and cloud-based computing, design brain-inspired controllers and integrate them in robotic middleware (ROS and YARP).
- The course aims at proving theoretical and practical competences in robotic programming frameworks and IoT platforms and it will provide knowledge in software design of autonomous robots and systems. Specific activities will be performed with ROS (Robotic Operating System) and YARP (Yet another robot platform) that will be implemented in simulated environments, also using dedicated SOM (System on Module) development boards. The course will also provide basic knowledge on brain-inspired controllers, based on machine learning approaches. Hands-on laboratory activities will be part of the course: students will be involved in the design and development of an IoT based environment, program the embedded system with sensors and cloud-based computing, design brain-inspired controllers and integrate them in robotic middleware (ROS and YARP).
Artificial intelligent systems for human identification (6 cfu)
- This course provides fundamentals about techniques to verify or recognize the identity of a living person based on the analysis of biological/physiological traits and/or behavioural features.
The topics of the course will be:
- Biometrics overview (history of biometrics, applications);
- Recognition, identification and verification;
- Privacy, security and ethics;
- Overview of image processing;
- Physiological biometric systems: fingerprint recognition, face recognition, iris recognition, retina recognition, hand recognition, vein patterns;
- Behavioral biometric systems: keystroke dynamics, signature recognition, voice recognition, gait recognition;
- Multi-modal biometric systems;
- Biometric applications.
- This course provides fundamentals about techniques to verify or recognize the identity of a living person based on the analysis of biological/physiological traits and/or behavioural features.
The topics of the course will be:
- Biometrics overview (history of biometrics, applications);
- Recognition, identification and verification;
- Privacy, security and ethics;
- Overview of image processing;
- Physiological biometric systems: fingerprint recognition, face recognition, iris recognition, retina recognition, hand recognition, vein patterns;
- Behavioral biometric systems: keystroke dynamics, signature recognition, voice recognition, gait recognition;
- Multi-modal biometric systems;
- Biometric applications.
Advanced materials for bionics (6 cfu)
- The aim of the course is to provide a solid background in Materials Science and Engineering as relevant for Bionics Engineering. Together with basic understanding of the various classes of materials, their structure, composition, structural and functional properties, the students will learn about more advanced concepts and recent developments of modern Materials Science, lying at the interface between chemistry, physics, engineering and biology.
The course is structured in three sections.
In the first section (1-Basic Materials Science & Engineering) basic and traditional topics of Materials Science will be covered/refreshed to provide a common basis among students with different background knowledge. Topics include: nomenclature and classification of materials, basic physical and chemical knowledge of solid matter, structure/properties correlation in materials, hierarchical structuring, phase transitions, fundamentals of mechanical behaviour, viscoelasticity, basic characterization tools. Various classes of engineering relevant materials will be reviewed: metals, ceramics, polymers, composites.
In the second section (2 – Advanced Materials Concepts) more advanced concepts will be introduced, regarding: biocompatibility of Materials, complex behaviour of soft matter, nanotechnology and nanostructures, biological materials, bioinspired materials, stimuli-responsive materials, electro-active polymers, advanced investigation and fabrication techniques for micro- and nano-structures.
In the third section (3 Technology & Bionics Applications) relevant examples of applications of materials in Bionics will be reviewed, including: advanced functional surfaces and interfaces, structural materials for bionics, materials for bioelectronics, materials for sensors & actuators in robotics.
- The aim of the course is to provide a solid background in Materials Science and Engineering as relevant for Bionics Engineering. Together with basic understanding of the various classes of materials, their structure, composition, structural and functional properties, the students will learn about more advanced concepts and recent developments of modern Materials Science, lying at the interface between chemistry, physics, engineering and biology.
The course is structured in three sections.
In the first section (1-Basic Materials Science & Engineering) basic and traditional topics of Materials Science will be covered/refreshed to provide a common basis among students with different background knowledge. Topics include: nomenclature and classification of materials, basic physical and chemical knowledge of solid matter, structure/properties correlation in materials, hierarchical structuring, phase transitions, fundamentals of mechanical behaviour, viscoelasticity, basic characterization tools. Various classes of engineering relevant materials will be reviewed: metals, ceramics, polymers, composites.
In the second section (2 – Advanced Materials Concepts) more advanced concepts will be introduced, regarding: biocompatibility of Materials, complex behaviour of soft matter, nanotechnology and nanostructures, biological materials, bioinspired materials, stimuli-responsive materials, electro-active polymers, advanced investigation and fabrication techniques for micro- and nano-structures.
In the third section (3 Technology & Bionics Applications) relevant examples of applications of materials in Bionics will be reviewed, including: advanced functional surfaces and interfaces, structural materials for bionics, materials for bioelectronics, materials for sensors & actuators in robotics.
Probability and Biostatistics (6 cfu)
- The student will learn the basics of probability theory and statistical inference and will be able to apply appropriate methodologies for applications in biology and, in general, for the study of life sciences. In addition to the planning, collection and analysis of experimental data, the following topics will be discussed: theory of probability, discrete and continuous random variables, descriptive statistics of statistical samples, multivariate random variables, sums of random variables and associated limit theorems, interval estimation, inferential statistics and hypothesis testing, non-Gaussianity and chi-square tests, inference through non-parametric methods, regression analysis, and evaluation of diagnostic tests: sensitivity, specificity, and ROC curves.
- The student will learn the basics of probability theory and statistical inference and will be able to apply appropriate methodologies for applications in biology and, in general, for the study of life sciences. In addition to the planning, collection and analysis of experimental data, the following topics will be discussed: theory of probability, discrete and continuous random variables, descriptive statistics of statistical samples, multivariate random variables, sums of random variables and associated limit theorems, interval estimation, inferential statistics and hypothesis testing, non-Gaussianity and chi-square tests, inference through non-parametric methods, regression analysis, and evaluation of diagnostic tests: sensitivity, specificity, and ROC curves.
Wearable robotics (12 cfu)
- The course is composed of two modules: Prostheses and Exoskeletons. The overall goal is to introduce students to the main challenges to design wearable powered robots for movement assistance, rehabilitation, augmentation and/or functional replacement. Along with the analyses of the main components involved in the development of an effective human-robot interaction, students will be engaged in laboratory and hands-on activities with working devices. In particular: Within the module “Prostheses” students will be introduced to and will experiment the architecture and function of the microcontroller. Within the module “Exoskeletons” students will learn how to conceive, rapid-prototype and test multi-layered control architectures running on real-time targets endowed with FPGA processors.
- The course is composed of two modules: Prostheses and Exoskeletons. The overall goal is to introduce students to the main challenges to design wearable powered robots for movement assistance, rehabilitation, augmentation and/or functional replacement. Along with the analyses of the main components involved in the development of an effective human-robot interaction, students will be engaged in laboratory and hands-on activities with working devices. In particular: Within the module “Prostheses” students will be introduced to and will experiment the architecture and function of the microcontroller. Within the module “Exoskeletons” students will learn how to conceive, rapid-prototype and test multi-layered control architectures running on real-time targets endowed with FPGA processors.
Advanced interventional and therapeutic technologies (12 cfu)
- The course “Advanced interventional and therapeutic technologies” is composed of two modules: “Robotics for minimally invasive and targeted therapy” and “Bionic organs and tissues”.
The overall course aims to provide students with knowledge and methodological tools typical of (i) robotics in the surgical/diagnostic domain, (ii) targeted therapies based on micro/nano smart materials, and (iii) mechatronics and bioengineering for bionic organs/tissues and regenerative purposes.
In particular, in the “Robotics for minimally invasive and targeted therapy” module, the students will gather knowledge and methodologies concerning the design of robots and intelligent instruments for surgery and interventional tasks, from the macro to the micro size, towards targeted therapy. At the end of the course, the students will be able to identify the most appropriate targeting/therapeutic solutions for different human body districts, at different scales and for different pathologies. Thanks to the analysis of the surgical robots available in the teacher’s Institute, the students will be able to relate the theoretical knowledge with real practical scenarios.
The module “Bionic organs and tissues” aims to teach students how to design and develop artificial and bioartificial technologies at different scales, bionic organs and tissues, smart materials for advanced therapy, organ function replacement, drug delivery and tissue regeneration. Principles of anatomy and physiology of different organs and tissues will be provided. Mechatronic/micro-mechatronic solutions, as well as “wet bioengineering” ones, alternative to or synergic with traditional medical/surgical procedures, will be also highlighted. Hands-on laboratory activities are part of this module: in particular, students will be involved in practical activities concerning cell cultures.
- The course “Advanced interventional and therapeutic technologies” is composed of two modules: “Robotics for minimally invasive and targeted therapy” and “Bionic organs and tissues”.
Design principles for bionic tissue engineering (6 cfu)
- This course will guide students in the design of cell and tissue-based systems for application to the development of in vitro models, artificial organs and delivery systems using technology based on 3D scaffolds, stem cells, organoids, and spheroids
Starting from a quantitative description of cell, tissue and organ assembly and of their requirements in terms of resources, microenvironmental conditions, cooperation and competition, the course covers fundamental design principles. It also provides a comprehensive overview of stem cell, organoid and on-chip technology as well as mathematical models of morphogenesis, self-assembly, growth and differentiation. Project work will include design and simulation of in vitro models.
Course contents: Basic principles of biochemistry and cell biology. Quantitative models of cell-material interaction, mechanobiology, cell assembly and 3D organisation. Allometric (power law) relationships in organisms. Design criteria for 3D tissue constructs based on resource supply and diffusion limitations. Stem cell technology, induced pluripotent stem cells, organoids (intestine, liver, brain). Fluidic bioreactors and organ and body on a chip design using computational methods.
- This course will guide students in the design of cell and tissue-based systems for application to the development of in vitro models, artificial organs and delivery systems using technology based on 3D scaffolds, stem cells, organoids, and spheroids
Rehabilitation and assistive technologies (12 cfu)
- The course on “Rehabilitation and assistive technologies” is composed of two modules: “Biomechanics of human motion” and “Robotic and data-driven rehabilitation”
The objectives of the module “Biomechanics of human motion” are to provide an introduction to the biomechanics of the human movements and then to understand the main role underlying the control of spatial multiple degree-of-freedom human motion. These objectives will be reached by means of both theoretical lessons and practical activities in a lab of human movement analysis, preparing the student to understanding the main concept of physical/motor rehabilitation and of bioinspired robotics.
The objectives of the module “Robotic and data-driven rehabilitation” is to provide elements to understand current trends in rehabilitation.
The course will sketch the scenario of the fourth industrial revolution and the digital transformation that is changing rehabilitation and healthcare with a particular focus on the evolution of robotics (rehabilitative, assistive, collaborative, social). Evidence-based studies in clinical rehabilitation, its assessment tools and data-driven methodologies will be analyzed. In this regard, the full track from machine learning methods to their applications in the field will be analyzed, towards the implementation of personalized medicine and rehabilomics.
- The course on “Rehabilitation and assistive technologies” is composed of two modules: “Biomechanics of human motion” and “Robotic and data-driven rehabilitation”
Final examination (15 cfu)
- The final examination involves the preparation of a report led to a design or research activity, and in its presentation and discussion.
- The final examination involves the preparation of a report led to a design or research activity, and in its presentation and discussion.
Lab Training (3 cfu)
- This activity will consist of Lab training that the student will perform in dedicated facilities and laboratories, with the aim to increase his/her experience in laboratory practice.
- This activity will consist of Lab training that the student will perform in dedicated facilities and laboratories, with the aim to increase his/her experience in laboratory practice.
