Available PhD Courses for 2014phd.dibris.unige.it/biorob/media/pdf/Advanced Neurophysiology.pdf · Understanding how intelligence works and how it can be emulated in machines is an

PhD Program in Bioengineering and Robotics - 2014

Available PhD Courses for 2014 Artificial Cognitive Systems .............................................................................................................................. 3

Regularization Methods for High Dimensional Machine Learning ................................................................. 5

Introductory design of mechatronic systems .................................................................................................. 6

Advanced EEG analyses .................................................................................................................................... 8

Research oriented structural and functional medical imaging ..................................................................... 11

Data Analysis in R ............................................................................................................................................ 13

iCub programming .......................................................................................................................................... 15

Advanced Neurophysiology ............................................................................................................................ 16

Introduction to non linear control theory ..................................................................................................... 17

Introduction to linear systems ....................................................................................................................... 19

Psychophysical methods ................................................................................................................................. 20

Neurophysiology of the motor systems ......................................................................................................... 21

Design of Experiments .................................................................................................................................... 22

C++ programming techniques ........................................................................................................................ 23

Tissue Engineering: Cells, Biomaterials and Bioreactors .............................................................................. 24

Modeling neuronal structures: from single neurons to large-scale networks ............................................. 26

Public Speaking and Effective Communication Skills .................................................................................... 27

Introduction to Python programming ............................................................................................................ 29

Advanced microscopy methods ..................................................................................................................... 30

Non-linear excitation microscopy: from theory to tissue imaging ............................................................... 31

Bio-imaging at the single molecule level. ...................................................................................................... 32

Photo-physical mechanisms and dynamic investigations in super-resolution microscopy ......................... 33

Characterization of Polymeric Materials ....................................................................................................... 35

Nano-plasmonic devices: an introduction ..................................................................................................... 37

Nano-plasmonic devices: from fabrication to applications .......................................................................... 39

Laser-matter interactions: from fundamentals to applications .................................................................... 40

Virtual Prototyping Design ............................................................................................................................. 41

List of suggested Schools ................................................................................................................................ 43


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Points of reference:

@IIT :Anastasia Bruzzone - [email protected] – Courses at pages 2 - 15

@UNIGE: Valentina Resaz – [email protected] – Courses at pages 16 – 21

@IIT: Manuela Salvatori – [email protected] - Courses at pages 22 - 32

Students are requested to enroll for the courses by sending an email to the following address:

[email protected] before march 31th

Starting April 2014, courses material will be available on AULAWEB

http://dottorati.aulaweb.unige.it/ DIBRIS PhD programs

mailto:[email protected]




http://dottorati.aulaweb.unige.it/


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Artificial Cognitive Systems

Course at a Glance

This course provides a comprehensive introduction to the emerging field of artificial cognitive systems. Inspired by artificial intelligence, developmental psychology, and cognitive neuroscience, the aim is to build systems that can act on their own to achieve goals: perceiving their environment, anticipating the need to act, learning from experience, and adapting to changing circumstances.

Instructors

Prof. David Vernon

Informatics Research Centre,

University of Skövde,

Sweden

[email protected]

Credits: 5

Synopsis

We develop a working definition of cognitive systems, one that strikes a balance between being broad enough to do service to the many views that people have on cognition and deep enough to help in the formulation of theories and models. We then survey the different paradigms of cognitive science to establish the full scope of the subject. We follow this with a discussion of cognitive architectures before tackling the key issues: autonomy, embodiment, learning & development, memory & prospection, knowledge & representation, and social cognition.

Syllabus

The course will be given over five days. There will be two 2-hour classes each day, plus

homework (4-5 hours)

1. The nature of cognition: models, definitions, autonomy, Marr’s levels of abstraction.

2. Paradigms of cognitive science: cognitivism and artificial intelligence, emergent systems,

connectionism, dynamical systems, enaction.

3. Cognitive architectures: cognitivist, emergent, and hybrid architectures, desirable

characteristics, example cognitive architectures.

4. Autonomy: robotic, biological, behavioural, & constitutive autonomy, homeostasis,

allostasis, self-organization and emergence, autopoiesis, self-maintenance, continuous

reciprocal causation, autonomic systems.

5. Embodiment: the three hypotheses, the mutual dependence of perception and action, off-line

embodied cognition, situated, embedded, grounded, extended, and distributed cognition.

6. Development and learning: motives, imitation, supervised, unsupervised, and reinforcement

learning, phylogeny and ontogeny, developmental psychology.

7. Memory and prospection: short-term, long-term, declarative, procedural, semantic, episodic,

symbolic, sub-symbolic, modal, amodal, & associative memory, internal simulation,

prospection, mental imagery, functional imagination, forgetting.

8. Knowledge and representation: memory and knowledge, representation, anti-representation,

sharing knowledge, radical constructivism, symbol grounding, co-joint representation,

Theory of Event Coding, learning from demonstration.

9. Social cognition: interaction, intentionality, theory of mind, instrumental helping,

collaboration, joint action, shared intention, shared goals, joint attention, development and

interaction dynamics.



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10. Review and discussion.

There will be a final examination decided by the instructor.

Reading List

Vernon, D. Artificial Cognitive Systems – A Primer, MIT Press (in press).

Venue

Istituto Italiano di Tecnologia, Via Morego 30, Bolzaneto, Genova

Course dates

20-24 October 2014


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Regularization Methods for High Dimensional Machine Learning Instructors Francesca Odone ([email protected]) Lorenzo Rosasco ([email protected]) DIBRIS - Department of Informatics, Bioengineering, Robotics, Systems Engineering

Credits: 5

Synopsis Understanding how intelligence works and how it can be emulated in machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, its principles, and computational implementations are at the very core of this endeavor. Only recently we have been able, for the first time, to develop artificial intelligence systems that can solve complex tasks considered out of reach for decades. Modern cameras can recognize faces, and smart phones recognize people voice; car provided with cameras can detect pedestrians and ATM machines automatically read checks. In most cases at the root of these success stories there are machine learning algorithms, that is, softwares that are trained rather than programmed to solve a task. In this course, we focus on the fundamental approach to machine learning based on regularization. We discuss key concepts and techniques that allow to treat in a unified way a huge class of diverse approaches, while providing the tools to design new ones. Starting from classical notions of smoothness, shrinkage and margin, we cover state of the art techniques based on the concepts of geometry (e.g. manifold learning), sparsity, low rank, that allow to design algorithms for supervised learning, feature selection, structured prediction, multitask learning. Practical applications will be discussed. Syllabus Organization: 20 hours course including practical laboratory sessions. Exam: Final project or Wikipedia-style article. Program summary:

Introduction to Machine Learning

Kernels, Dictionaries and Regularization

Regularization Networks and Support Vector Machines

Spectral methods for supervised learning

Sparsity-based learning

Multiple kernel learning

Manifold regularization

Multi-task learning

Applications to high dimensional problems

Venue

TBD

Course dates 7-11 July 2014




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Introductory design of mechatronic systems

Course at a Glance

The course “Mechanical design for robotics” will introduce the most salient topics regarding the

design of robotic devices. The course cover the basics of engineering design, the main types of

components and their working principles of components, as well as actuators and sensors normally

integrated in robotic devices.

Instructors

Alberto Parmiggiani - [email protected]

Credits: 5

Synopsis This short course is intended to present the classical methods for solving design problems. After

that the course will cover actuators, sensors, mechanical components, and production technologies.

The classes will also comprise practical exercitation sessions. Given the relatively short duration of

the course it will not be possible to cover in details several important topics (e.g. pneumatic and

hydraulic actuation, composite materials). Nevertheless the course will provide essential notions on

the hardware aspects of mechatronic/robotic devices.

Syllabus

Total of 14 hours (2 hours per class).

lecture 1

o Course introduction

o The design process (design problem definition, setting specifications, concept

exploration, concept generation, concept selection, artifact architectures)

lecture 2

o Robotic systems architectures

o Bearings

o Bearing selection exercises

lecture 3

o Sensors

o Power transmission (transmission basics, speed reducers, geared systems)

lecture 4

o Power transmission (harmonic drives, cycloidal gears, belts and timing belts)

o Power transmission selection exercises

lecture 5

o Electric actuators

o Motor selection exercises

lecture 6

o Production technologies

o Large assemblies

o Part coding and warehouse management

o Engineering materials

lecture 7

o Rapid prototyping



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The course program might undergo minor variations.

The students will be required to carry out homework assignments. The estimated weekly workload

for this course is around 8 hours. There will be a final examination decided by the instructor.

Prerequisites

Basic knowledge of physics and mechanics .

Reading list

• Universal principles of design. Rockport Publishers, 2003.

• Design, the creation of artifacts in society. Available on-line:

http://opim.wharton.upenn.edu/~ulrich/designbook.html

Additional references

• Mechanical engineering design. Joseph Edward Shigley, Charles R. Mischke. McGraw-

Hill.

• Engineering materials. Properties and selection. Kenneth G. Budinski and Micheal K.

Budinski. Pearson, Prentice Hall.

• Product design and development. Karl Ulrich and Steven Eppinger. McGraw-Hill.

Venue


Course dates

March 20th, 24th, 27th, 31th, April 3rd, 7th and 10th (+ April 14th or 18th for test or rescheduling)

http://opim.wharton.upenn.edu/~ulrich/designbook.html


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Advanced EEG analyses Course at a Glance

The present course will introduce the student to the most advanced technique to process the EEG

signal and infer over the cortical areas that create it. The course will consists on a first part based on

electrode analysis and a second part on distributed sources analysis. Analysis will be performed in

both the time and time-frequency domain and will be performed within the Matlab and R

environments, using a semi-automatic analysis framework developed in the RBCS department.

Instructors

Alberto Inuggi, PhD ([email protected] )

Claudio Campus, PhD ([email protected] )

Credits: 5

Synopsis

EEG is a well consolidated technique to explore the cortical activity in a non-invasive way. Its main

advantage has always been the possibility to monitor brain activity with a millisecond temporal

resolution but, since electrodes were placed on the scalp, information over the brain structures that

created the observed changes in the signal could not be inferred. With the introduction of high

density recordings (from 64 up to 256 electrodes) and proper models defining the electrical

potential propagation within the brain, a new approach called source analysis emerged. In the

present course we will start with describing the basic and advanced steps to remove the many

artifacts usually present in the EEG data. Special attention will be given to the ICA approach with

the EEGLAB software. Then the classical approach based on electrodes analysis will be explored,

covering both the time and time-frequency analyses, investigating single electrodes or cluster of

them, following changes sample-by-sample or averaging activity in time windows. The second part

of the course will focus on distributed source analysis. We will describe the rationale, model, the

importance of having a realistic volume conductor, the different post-processing approaches, the

limits and the possible applications. Source analysis will be performed with the Brainstorm

software, while statistical analysis with both Brainstorm and SPM. A comparison between EEG

source analysis and fMRI technique will be done, describing also the rationale of integrating the

two informations to increase the spatio-temporal resolution of the investigation. All the analysis will

be performed both with the proper software and with the RBCS EEG Tools, a set of Matlab scripts

developed by the two authors to automatize the analyses. The course will finish with a practical day

where the two tutors will assist students practicing with the EEG Tools.

Software used: EEGLAB, Brainstorm, R, SPM, Matlab

Syllabus

total of 15 hours

Class 1 (2h) EEG signal origin and spatial-temporal-spectral characteristics. Data recording,

preprocessing (referencing, filtering and epoching) and artefact removal through

independent analysis as implemented in EEGLAB. Introduction to RBCS’s EEG Tools

analysis framework. Teacher Alberto Inuggi and Claudio Campus..

Class 2 (2h) Electrode analysis of ERP. Peak analysis, clustering electrodes and averaging

time interval. Subject and group level analysis. Statistical analysis in EEGLAB and R

Teacher Claudio Campus.




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Class 3 (2h) Spectral analysis of ERSP. Peak analysis, clustering electrodes and averaging

time interval. Subject and group level analysis. Statistical analysis in EEGLAB and R.

Teacher Claudio Campus.

Class 4 (2h) Introduction to EEG source analysis. Theory, forward model and inverse

problem resolution. Differences between dipoles and distributed source analysis. Alternative

models, post processing approaches. Teacher Alberto Inuggi.

Class 5 (2h) Source analysis in Brainstorm. Teacher Alberto Inuggi.

Class 6 (2h) Statistical analysis in SPM. Comparison between EEG, fMRI and TMS tools.

Teacher Alberto Inuggi.

Class 7 (3h). Practical day on the RBCS’s EEG Tools analysis framework. Teacher Alberto

Inuggi and Claudio Campus.

There will be a final examination decided by the instructors.

Prerequisites

Good knowledge of Matlab environment and syntax.

Reading List

Allena M, Campus C, Morrone E, De Carli F, Garbarino S, Manfredi C, Sebastiano DR, Ferrillo F (2009). Periodic limb movements both in non-REM and REM sleep: relationships between cerebral and autonomic activities. Clin Neurophysiol., 120:1282-90. doi: 10.1016/j.clinph.2009.04.021. Epub 2009 Jun 7.

Babiloni F, Babiloni C, Carducci F, Romani GL, Rossini PM, Angelone LM, Cincotti F(2003). Multimodal integration of high-resolution EEG and functional magnetic resonance imaging data: a simulation study. Neuroimage 19, 1-15.

Campus C, Brayda L, De Carli F, Chellali R, Famà F, Bruzzo C, Lucagrossi L, Rodriguez G (2012). Tactile exploration of virtual objects for blind and sighted people: the role of beta 1 EEG band in sensory substitution and supramodal mental mapping. J Neurophysiol., 107:2713-29. doi: 10.1152/jn.00624.2011. Epub 2012 Feb 15.

Fuchs M, Wagner M, Kohler T, Wischmann HA (1999). Linear and nonlinear current density reconstructions. Adv Neurol 16, 267-295.

Inuggi A, Filippi M, Chieffo R, Agosta F, Rocca MA, González-Rosa JJ, Cursi M, Comi G, Leocani L (2010). Motor area localization using fMRI-constrained cortical current density reconstruction of movement-related cortical potentials, a comparison with fMRI and TMS mapping. Brain Res. 1308:68-78.

Inuggi A, Amato N, Magnani G, González-Rosa JJ, Chieffo R, Comi G, Leocani L (2011). Cortical control of unilateral simple movement in healthy aging. Neurobiol Aging 32, 524-538.

Makeig S, Debener S, Onton J, Delorme A (2004). Mining event-related brain dynamics.Trends Cogn Sci., 8:204-10.

Nunez PL, Srinivasan R. Electric Fields of the Brain: The Neurophysics of EEG. Oxford, UK: Oxford University Press, 2006.

http://www.ncbi.nlm.nih.gov/pubmed/19853590




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Pfurtscheller G, Lopes da Silva FH (1999). Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110: 1842–1857.

Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM, “Brainstorm: A User-Friendly

Application for MEG/EEG Analysis,” Computational Intelligence and Neuroscience, vol.

2011, Article ID 879716, 13 pages, 2011. doi:10.1155/2011/879716

Venue


Course dates

March/April 2014


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Research oriented structural and functional medical imaging

Course at a Glance

The present course will review the past and present main medical imaging technologies, giving emphasis to

the physical basis of image formation and the specific feature of each imaging method. Additionally, it will

illustrate the most advanced techniques used in research to extract in-vivo information over functional and

structural organization of human brain.

Instructors

Franco Bertora and Alberto Inuggi ([email protected], [email protected] )

Credits: 4 Synopsis

Medical Imaging was born, one could say by accident, in 1895 when Roentgen, while experimenting with

the peculiar radiation he had just discovered, asked his wife to place the left hand over a photographic

plate. Few years later radiography was going full steam ahead, soon to establish itself as an important new

tool for the medical profession. Relatively little progress followed until about 1970, when the

cost/performance ratio of electronics and computing equipment made digital imaging possible. As a result,

almost at the same time, echography, computed tomography and nuclear medicine blossomed and then

melted: radiology gave place to medical imaging.

Around mid/end of 80’s two further steps were done with the discoveriy of the BOLD effect and the

development of the Diffusion MRI technique. With the former the scanner could be programmed to obtain

non-invasive maps of functional brain activity, with the latter it became possible to assess the path and the

integrity of the white-matter bundles that connect the different brain areas.

The goal of the course is to give a broad perspective of the main medical imaging technologies available

today. The first part of the course will give emphasis to the physical basis of image formation and the

specific feature of each imaging method. The second part will concentrate on the most used technique in

clinical and research context with the clear aim to enable each student to easily read and understand a

neuroimaging paper. Special attentions will be given to those non-invasive techniques able to estimate the

functional and structural properties of human brain. Among the former, we will focus on functional MRI,

introducing the independent component analysis to extract the cortical networks present at rest and the

methods to assess task-related cortical activation. Among structural imaging, we will introduce the voxel

based morphometry (VBM) and the cortical thickness to assess the status of gray matter and two post-

processing approaches of the diffusion tensor imaging, the tracto-based spatial statistic (TBSS) and the

tractograpy, used to assess the integrity of the white matter fibers bundles. Finally, a comparison between

fMRI and EEG methods to reconstruct cortical activity will be shown.

Syllabus

Total of 12 hours in 6 classes of 2 hours each.

X-Ray and Computed Tomography. Teacher: Franco Bertora

Ultrasound. Teacher: Franco Bertora



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Magnetic Resonance Imaging. Teacher: Franco Bertora

Special imaging modalities (BOLD effect, Diffusion Tensor Imaging, Near Infrared Spectroscopy, Elastography, Thermometry). Teacher: Franco Bertora

Functional imaging in research. Teacher: Alberto Inuggi

Structural imaging in research. Multimodal integration with EEG. Teacher: Alberto Inuggi

There will be a final examination decided by the instructors. Reading List

Ashburner J, Friston KJ (2000). Voxel-based morphometry—the methods. NeuroImage, 11, 805–821

Beckmann, C.F., DeLuca, M., Devlin, J.T. and Smith, S.M. (2005). Investigations into resting-state

connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci, 255, 1001-

1013.

Conturo TE, Lori NF, Cull T, et al (1999). "Tracking neuronal fiber pathways in the living human

brain". PNAS 96 (18): 10422–10427

Ogawa S, Lee TM, Kay AR, Tank DW (1990). Brain magnetic resonance imaging with contrast

dependent on blood oxygenation. Proc Natl Acad Sci U S A. 87:9868-9872.

Venue


Course dates

May 2014

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC17904

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC17904


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Data Analysis in R

Course at a Glance

This course will provide an introduction to reproducible data analysis with R (see Syllabus).

Instructor

Gabriel Baud-Bovy ([email protected] )

Credits: 5

Synopsis

This course aims at giving to the student a methodology to analyze experimental results, from how

to organize data to the writing of a report. It includes:

an introduction to R

an introduction to reproducible research with R

examples of statistical analysis with R

During this course, the student will have to analyze his own data and is expected to read before each

course the material that will be made available on this page. The final grade will consist in the

evaluation of a report demonstrating familiarity with the concepts and methods presented in the

course.

As an editor, the instructor will use Notepad++ (together with NppToR) on a Windows Machines

but the student might use other ones (e.g., R studio, EMACS+ESS, Lyx, TexWork). The course will

use Mardown as typesetting language. For those desirous to work with Latex and/or generate pdf,

you will need to install also MikeTex.

Syllabus

Total of 15 hours

Class 1: Case study, Reproducible research

Class 2: R fundamental

Class 3: Exploratory data analysis and graphical methods in R

Class 4: Basic statistics

Class 5: To be determined

There will be a final examination decided by the instructor.

Prerequisites

The course assumes some familiarity with programming concepts and data structures

(MATLAB, C/C++, Java or any other programming language). Contact the instructor if you

have never programmed anything.

Install R on your laptop.

Bring a data set that you want to analyze.

Reading List

Baud-Bovy (2014) Notes on reproducible research with R

R Core Development Team “An introduction to R".

References

Baud-Bovy (2014) Notes on reproducible research with R. Draft

R Core Development Team “An introduction to R".

Onlines (optional)



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The R Project website: http://cran.r-project.org/ (to download R, R packages knitr and R documentation)

pandoc: http://johnmacfarlane.net/pandoc/index.html

Notepad++: http://notepad-plus-plus.org/

NppToR: http://sourceforge.net/projects/npptor/

MikeTex: http://miktex.org/

R studio: see http://www.rstudio.com/.

knitr website: http://yihui.name/knitr/

References books (optional)

Yihui Xie Dynamic (2013) Documents with R and knitr. CRC Press.

Christopher Gandrud (2014) Reproducible Research with R and RStudio. CRC Press

Peter Dalgaard. Introductory Statistics with R.

Robert Kabacoff . R in Action.

Brian S. Everitt, Torsten Hothorn. A Handbook of Statistical Analyses Using R.

Norman Matloff. The Art of R Programming: A Tour of Statistical Software Design .

Venue


Course dates

March/April 2014

http://miktex.org/

http://www.rstudio.com/

http://yihui.name/knitr/


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iCub programming

Course at a Glance

The course will describe how to write software on the iCub humanoid robot.

Instructor

Lorenzo Natale ([email protected] )

Credits: 4

Synopsis

The iCub is a 53 degrees of freedom humanoid robot designed for research on embodied cognitive

systems. To maximize the usability of the platform and its impact on the scientific community in

developing the iCub we have adopted an open source strategy: hardware and software have been

released with open licenses (FDL and GPL). A large community of developers has grown around

the several iCub (about 20) that have been realized to date and the use of the software middleware

tools and simulators. An important component of the iCub is the software architecture. This has

been designed to realize an infrastructure that supports collaboration between researchers by

facilitating integration of software modules developed independently. The design has focused on:

usability, code reuse and support for realizing complex behaviors as integration of simpler modules.

The software is composed of two layers: 1) the YARP middleware and 2) the iCub modules and

applications. The goal of the course is to describe how to write software on the iCub. We will

provide a general understanding of the software architecture, a description of the main YARP

functionalities and the robot API. Finally we will see how to write iCub modules and integrate them

in the iCub build system.

Syllabus Total of 10 hours in 5 classes of 2 hours each.


Prerequisites

Throughout the course we will use C++ so a basic (but not advanced) understanding of the language

is required.

Reading List

http://eris.liralab.it/yarpdoc/namespaces.html

Towards Long-Lived Robot Genes

Venue

TBD.

Course dates

June/Julyl 2014


http://eris.liralab.it/yarpdoc/namespaces.html

http://eris.liralab.it/yarp/media/humantech07towards.pdf


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Advanced Neurophysiology

Course at a Glance

The Advanced Neurophysiology course is a series of lessons dedicated to an in-depth analysis of a few selected topics of Neurophysiology. In particular there will be lessons held by Prof. Becchio, Prof. Burr, Prof. Fadiga, Prof. Morasso, Prof. Morrone and Prof. Pozzo.

Instructors

Cristina Becchio, David Burr, Pietro Morasso, Concetta Morrone, Thierry Pozzo

Credits: 4

Syllabus Each lesson will last about two hours and will be held by one of the instructors. Duration of the course:

10 hours.

Venue



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Introduction to non linear control theory

Course at a Glance

The course aims at introducing methods for analysis and control of nonlinear systems. Particular attention

is paid to time domain analysis techniques, such as Lyapunov stability and feedback linearization.

Applications to robotic manipulators is also presented. The course is co-organized by the Robotics, Brain

and Cognitive Science Department, Istituto Italiano di Tecnologia (IIT).

Instructors

Daniele Pucci (RBCS) [email protected],Francesco Nori (RBCS) [email protected]

Credits: 5 Synopsis

Over the last three decades, nonlinear control techniques have experienced a tremendous boost, which led

to several applications in aviation, robotics, chemistry, and industrial processes. In fact, advances in

modeling and computer-aided design systems allow us to simulate complex and highly nonlinear processes,

the robust control of which may require more than simple linear controllers. This course introduces control

techniques for nonlinear ordinary differential equations powered by control variables. Emphasis is given on

cases where linear methods are not sufficient for ensuring large domains of stability and robustness.

Applications to robotic manipulators is also presented with simulations performed in the MATLAB

environment, and on the real platform iCub.

Syllabus

Duration of the course: 12 -14 hours

From linear to nonlinear: recap of linear systems and peculiar phenomena in nonlinear

systems.

Fundamental properties: existence and uniqueness of solutions to differential equations.

Fundamentals of stability. Interconnected systems.

Lyapunov stability 1: autonomous systems. Definitions of Lyapunov functions, la Salle

principle. Stability of nonlinear systems from linearization.

Lyapunov stability 2: nonautonomous systems. Linear time-varying systems. Converse

theorem. Barbalat lemma and its applications.

Feedback control: stabilization via linearization, integral control, and gain scheduling.

Feedback linearization: theory and applications to robotic manipulators.

Nonlinear design tools: backstepping, adaptive control with applications to robotic

manipulators.

Advanced stability analysis: center manifold theorem, region of attraction, stability of

periodic solutions.

There will be a final examination decided by the instructors. Prerequisites

Multivariable calculus, linear differential equations, control of linear systems.




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Reading List

1) Nonlinear systems,

H. K. Khalil

2) A mathematical introduction to robotic manipulation,

R.M. Murray, A. Li, S. S. Sastry

3) Nonlinear control systems,

A. Isidori

Venue


Course dates

November 2014


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Introduction to linear systems Course at a Glance

An introduction to linear systems for students who do not have a bachelor’s degree in electrical

engineering. The course privileges breadth over depth and may be useful to those with general interests in

linear systems analysis, control systems, and/or signal processing.

Instructors

Franco Bertora - [email protected]

Credits: 3 Synopsis

Topics include signal representations, linearity, time-variance, convolution, Fourier series and transforms

filtering, in one and two dimensions. Coverage includes both continuous and discrete-time systems.

Practical applications in filter design, data analysis, and image processing are introduced.

Syllabus

total of 9 hours + final exam

Class 1

• Systems and linearity

• LTI systems solution

• Filters

Class 2

• Discrete Systems

• The Design of Digital Filters

Class 3

• Interpolation and Decimation

• Introduction to Random Signals

• Power Spectral Density Estimation

• Adaptive Filtering

There will be a final examination decided by the instructors. Prerequisites

Some knowledge of elementary calculus.

Venue




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Psychophysical methods

Course at a Glance

The course Psychophysics Methods will briefly review the principal methods used in psychophysics to measure sensory thresholds and perceptions in general. The course is organized by the RBCS group at the Italian Institute of Technology.

Instructors

Gabriel Baud Bovy ([email protected])

Monica Gori ([email protected])

Credits: 4

Synopsis

Psychophysics investigates the relationship between stimuli in the physical domain and sensations or perceptions in the psychological domain. It provides a corpus of well-established methods to study and formulate models of perception. The course will start with a review of the history of psychophysics and of the principal results obtained in this field. Then, the course will present some psychophysical concepts (e.g., the concepts of sensory threshold and psychological scale) and describe classic and modern psychophysical methods to measure them. The students will have the opportunity to make simple psychophysical experiments in class to test their understanding of the methods.

Syllabus

total of 12 hours - each class is 2 hours.

class 1 (C1) History of psychophysics and concept of threshold. Teacher Monica Gori

class 2 (C2) Methods of threshold measurement (Method of constant stimuli, Methods of

limits, Methods of adjustment). Teacher Monica Gori

class 3 (C3) Methods of threshold measurement (Adaptative Methods). Teacher Monica

Gori

class 4 (C4) Signal Detection Theory. Teacher Gabriel Baud Bovy

class 5 (C5) Unidimensional scaling methods. Teacher Gabriel Baud Bovy

class 6 (C6) Item Response Theory. Teacher Gabriel Baud Bovy


Reading List

Psychophysics the fundamentals, George A. Gescheider

Venue





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Neurophysiology of the motor systems

Course at a Glance

The course Neurophysiology of the motor systems will review the main functional characteristics and the anatomical and physiological bases of the motor systems. The course will also cover recent advances in selected topics. See the synopsis and the syllabus for more details. The course is organized by the RBCS group at the Italian Institute of Technology.

Instructors

Alessandro D’Ausilio ([email protected])

Credits: 3

Synopsis

From birth, we interact with the world through our senses and movements. How the brain process and transform sensory signals and organize the motor output is a major research question in Experimental Psychology and Neuroscience. The goal of the course is to present the motor systems from the anatomical, physiological and functional points of view. A particular focus will be on how physical stimuli are transduced into sensory signals by our peripheral sensory apparatus, as well as how the motor hierarchy organizes complex behavior.

Syllabus

total of 8 hours - each class is 2 hours

class 1 (C1) Motor system I.

class 2 (C2) Motor system II.

class 3 (C3) Motor system III.

class 4 (C4) Probing the motor system with Transcranial Magnetic Stimulation.

There will be a final examination chosen by the instructor.

Reading List

Kandel, Eric R.; Schwartz, James Harris; Jessell, Thomas M. (2000) [1981], Principles of

Neural Science (Fourth ed.), New York: McGraw-Hill

Venue




22

Design of Experiments

Course at a Glance

The course aims at presenting the statistical theory of design of experiments. The basic question is

at which settings to observe a system in order to estimate, or at least identify, some specific model

parameters. Emphasis will be given to the study of optimal designs, where optimality is refers to

specific statistical properties of the estimates, and to a recently developed algebraic encoding of

designs supported on polynomials.

Instructors

Eva Riccomagno

Department of Mathematics (DIMA)

Via Dodecaneso 35, Office 938

e-mail: [email protected]

tel: 010-3536938

Credits: 5

Syllabus

The course develops in about 20h. Lectures will be in English

General concepts, controlling for bias and variation, interplay between design and analysis.

Factorial designs and aliasing.

Basics on computational commutative algebra, polynomial representations of factorial designs.

Optimal designs for linear regression models.

Optimality criteria.

Algorithms for design construction.

Space filling designs.

Adaptive and sequential designs.

The examination will consist in a seminar on a topic and material to be decided with the lecturer.

Venue

TBD

Reading List

D.R. Cox and N. Reid (2000), The theory of the design of experiment, Chapman & Hall/CRC.



23

C++ programming techniques

Course at a Glance

An overview of the Object Oriented Programming concepts using C++ language.

Instructors Fabio Solari and Manuela Chessa

Dept. of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS)

Via Opera Pia 11A, 3rd

floor


e-mail: manuela,[email protected]

tel: 010-3532059

tel: 010-3532289

Credits: 5

Synopsis

This course focuses on the use of C++ for large software projects and for the implementation of

high performance object-oriented modules. In particular, programming techniques to tackle the

issues of reusability, robustness and efficiency are considered.

Syllabus

The course develops in about 8/10 hours in the classroom, and 8/10 hours of practical work

(homework).

Basic Facilities: The C and C++ languages: pointers, arrays, and structures. Functions.

Namespaces and exceptions.

Abstraction Mechanisms: Classes and objects. Operator overloading. Class hierarchies.

Polymorphism. Templates.

Case studies: Containers and algorithms. Iterators. OpenGL.

The examination consists in the development (with discussion) of a specific software module.

Reading List

[1] B. Stroustrup. The C++ Programming Language. Addison-Wesley.

[2] B. Eckel. Thinking in C++. Prentice Hall.

(http://www.mindview.net/Books/TICPP/ThinkingInCPP2e.html)

Venue

DIBRIS, Via Opera Pia 13.

Course dates

October-November 2014



http://www.mindview.net/Books/TICPP/ThinkingInCPP2e.html


24

Tissue Engineering: Cells, Biomaterials and Bioreactors

Course at a Glance

Basic concepts for generating engineered bone/cartilage grafts for clinical and/or research

applications

Instructors Silvia Scaglione

Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni (IEIIT),

Consiglio Nazionale delle Ricerche (CNR)

Via De Marini, 6, 16° floor


tel: 010-6475206

Credits: 5

Synopsis

Tissue Engineering is an emerging multidisciplinary field involving biology, medicine, and

engineering aimed to improve the health and quality of life for millions of people worldwide by

restoring, maintaining, or enhancing tissue and organ function. Tissue engineering research includes

the following areas: (i) Biomaterials: including novel biomaterials designed to direct the

organization, growth, and differentiation of cells in the process of forming functional tissue by

providing both physical and chemical cues. (ii) Cells: including enabling methodologies for the

proliferation and differentiation of cells, acquiring the appropriate source of cells such as

autologous cells, allogeneic cells, xenogeneic cells, stem cells, genetically engineered cells, and

immunological manipulation. (iii) Biomolecules: including growth factors, differentiation factors,

angiogenic factors. (iv) Engineering Design Aspects: including 2D cell expansion, 3D tissue

growth, bioreactors. (v) Biomechanical Aspects of Design: including properties of native tissues,

identification of minimum properties required of engineered tissues, mechanical signals regulating

engineered tissues, and efficacy and safety of engineered tissues. (vi) Informatics to support tissue

engineering: gene and protein sequencing, gene expression analysis, protein expression and

interaction analysis, quantitative tissue analysis, in silico tissue and cell modeling.

Syllabus

The course develops in about 15/20 hours in the classroom.

Cell-Based Therapies for TE: methodologies for isolation, differentiation, selection of adult

progenitors/stem cells.

Biomaterials for TE: design of intelligent biomaterials; study or the proper macro-micro-

nano-structures, chemical compositions, biomechanical properties; cell-biomaterials

interfaces, bioactivation of surfaces.

Bioreactor systems for TE: perfusing bioreactor systems, biomechanical stimulating

bioreactors, fluido-dynamic stimulating bioreactors.

Pre-clical/Clinical models: in vivo case studies, implant of cell-biomaterials constructs,

animal models.

The examination consists in a journal club or a brief research project proposal.

Reading list

[1] Jeffrey A. Hubbell 1995 “Biomaterials in Tissue Engineering” Nature Biotechnology 13, 565 -

576

[2] Ivan Martin, David Wendt and Michael Heberer 2004 “The role of bioreactors in tissue

engineering” Trends in Biotechnology 22(2): 80-86

[3] Paolo Bianco & Pamela Gehron Robey 2001 “Stem cells in tissue engineering”Nature 414, 118-

121


http://www.sciencedirect.com/science/journal/01677799

http://www.sciencedirect.com/science/journal/01677799


25

Venue

TBD

Course date

March-June 2014


26

Modeling neuronal structures: from single neurons to large-scale networks

Course at a Glance

Theoretical models of neurons and neuronal networks

Instructors Paolo Massobrio

Department of Informatics Bioengineering, Robotics, System Engineering (DIBRIS)

Via Opera Pia 11A, 2nd

floor


tel: 010-3532761

Credits: 4

Synopsis

Computational neuroscience is an approach to understanding the information content of neural

signals by modeling the nervous system at many different structural scales, including the

biophysical, the circuit, and the systems level. These models are implemented on computers

translating equations theoretically derived and describing the neuronal system behavior. In this

course, the basis to model the electrophysiological activity (namely action potentials, post-synaptic

potentials, bursts of action potentials) of neurons and networks of neurons will be provided. The

most important and widely used models will be presented and quantitatively analyzed.

Syllabus

The course develops in about 10 hours in the classroom.

Neuron models: Hodgkin Huxley model; modeling the signal propagation (Cable theory);

Modeling the neuron morphology (Cullheim method, Rall rule); Complexity reduction

(Morris-Lecar and FitzHugh-Nagumo models)

Synapse models: Exponential synapses, NMDA synapses, Spike-timing dependent plasticity

Network models: abstracted models (IF, LIF, Izhikevich), spiking rate models, interplay

between network connectivity and network dynamics

The course will be activated with 5 students at least. Please, contact the teacher within the end of

July to attend the course. The examination consists in a journal club. Papers will be given at the end

of the course and are strictly related to the topics presented in the theoretical lessons.

Reading list

[1] Methods in Neuronal Modeling, Koch and Segev, MIT press, 1999.

[2] Spiking Neuron Models, Gerstner and Kistler, Cambridge press, 2002.

[3] Dynamical systems in neuroscience,. Izhikevich, MIT press, 2007.

[4] Computational Modeling Methods for Neuroscientists, De Schutter, MIT press, 2010.

Venue

DIBRIS, Via Opera Pia 13.

Course dates

October-November 2014



27

Public Speaking and Effective Communication Skills

Course at a Glance

Basic communication skill knowledge. Understanding and optimizing your own

communication style. Both theoretical basis and tips instruction to speaking effectively in

public.

Instructors Giovanna Morgavi

Istituto di Elettronica e Ingegneria dell’Informazione e delle Telecomunicazioni (IEIIT)

Consiglio Nazionale delle Ricerche (CNR)

Via De Marini 6, 16149 Genova


tel.: +39 010 6475203

Credits: 5

Synopsis

Professionals in many jobs are expected to be able to speak in public or make professional oral

presentations in a formal context. Such exposure is necessary in order for them to deliver

themselves confidently and competently in their daily professional lives.. This course is intended to

provide tips and experience in preparation and delivery of speeches within a public setting, taking

into account the nature of the audience, the cultural context and the available equipments.

Syllabus

The course develops 24 hours organised in 8 hours weekly

Basics of the Communication Process

Communication Presupposition

o The Map Is Not The Territory

o You Cannot Not Communicate

o The Meaning of Your Communication is The Response You Get

The communication codes (verbal code, non-verbal code and paraverbal code)

The sensory filters

Re- learn your own communication style

Mirroring and empathy

The Feedback

Public Speaking

The design of the speech

How to keep the public attention

The choice of instruments

o The technological tools ( video, slides , transparencies, etc. )

o The speaker’s language :

verbal language

body language

The Feedback

The course content may include traditional lectures in the classroom and experiential

exercises to help participants to know and improve their communication skills. It will be done

only if there will be at least 6 attendants.

As examination each student will prepare a speech on a topic and material to be decided with the

lecturer. Attendance to all seminars is part of the course.



28

Reading list

Bandler, R. & Grinder, J. The Structure of Magic: a book about language and therapy. Palo

Alto: Science and Behavior Books, 1975,

Dilts, R., Grinder, J., Delozier , J., and Bandler, R., The Study of the Structure of Subjective

Experience, Cupertino, CA: Meta Publications., 1980.

Paul Watzlawick, J. H. Beavin, D. D. Jackson Pragmatics of Human Communication,NY

Norton 1967

Scardovelli, M., - Feedback e cambiamento, Borla, 1998

On line presentations

- http://sixminutes.dlugan.com/

- http://www.slideshare.net/

Venue

IEIIT- CNR via De Marini 6, 16149 Genova Sampierdarena

Course date

October - December 2014

http://sixminutes.dlugan.com/

http://www.slideshare.net/colorstime/parlare-in-pubblico-1797918


29

Introduction to Python programming

Course at a Glance

A hands-on introduction to Python, and its idioms, focusing in how to develop correct, elegant and

maintainable software

Instructors

Giovanni Lagorio, [email protected]

Credits: 4

Location

DIBRIS – Sede di Valle Puggia

Via Dodecaneso, 35 - 16146 Genova

Synopsis

This intensive course presents the Python programming language, from the ground-up, covering

procedural, object-oriented and functional features.

The goal is to provide a framework for understanding how to use Python constructs for developing

correct, elegant and maintainable software.

Live demonstrations and practice sessions complement lectures, allowing the students to understand

how theoretical aspects fit together and how to use effectively the tools at their disposal.

Syllabus

The course consists in 12h of lessons (including both lectures and practice sessions).

Addressed topics:

Simple data types: booleans, numbers, strings

Collections: sequence, set and map types

Control structures and functions

Modules

Object-oriented programming

File handling

Debugging, testing and profiling

GUI Programming

Examination: each student has to develop a small project (program or library)

Prerequisites

Having some basic programming experience (in any language). Each student should have his/her

own computer to solve the proposed programming exercises.

Reading List

Practical Programming (2nd ed.): An Introduction to Computer Science using Python

Programming in Python 3: A Complete Introduction to the Python Language

Course dates

April-May 2014


30

Advanced microscopy methods

Course at a Glance

Advanced microscopy methods based on scanning probe and optical microscopy towards life

sciences, materials science and biomedical engineering applications.

Instructors Alberto Diaspro

([email protected]) Nanophysics, Istituto Italiano di Tecnologia

Tel: +39-010.71.781.503

Credits: 3

Synopsis

This course covers the aspects related to advanced microscopy methods based on scanning probe

(SPM) and optical microscopy (OM) towards life sciences, materials science and biomedical

engineering. Electron microscopy is able to provide excellent spatial resolution presenting as main

drawback the preparation of the sample. Using SPM and OM one can get important information at

the nanoscale level of resolution including time. Time related to long term experiments and to

nanoscale time changes. Staring from a comparison of microscopy imaging methods including pros

and cons, theoretical and architectural aspects will be discussed considering different applications

from biology to nano materials, from medicine to nano chemistry.

Syllabus


· Spatial and Temporal resolution in image formation (spatial and frequency domain)

· Comparison of microscopy methods based on electron, force and photons interactions.

· Theoretical and experimental layouts towards super resolution.

. Scanning probe microscopy, Optical super resolution microscopy (including label free methods

and FRET).

. Choice of the method towards applications: a critical overview


Reading list

- Notes by the Instructor

- A.Diaspro, Nanoscopy Multidimensional Optical Fluorescence Microscopy, Springer (2011)

- A.Diaspro, New world microscopies, IEEE EMB Magazine (1996)

Venue

IIT - Via Morego, 30 16163 Genova

Course date

May-June 2014



31

Non-linear excitation microscopy: from theory to tissue imaging

Course at a Glance

Multi-photon and second harmonic generation imaging and its application in various tissues

imaging,

Instructors Paolo Bianchini ([email protected])

Nanophysics, Istituto Italiano di Tecnologia

Tel: +39-010.71.781.724

Credits: 3

Synopsis

This course covers the theory of multiphoton excitation fluorescence microscopy (MPEFM) and of

second and third harmonics generation (SHG-THG) microscopy. We will zoom into the basics of

the MPE, SHG and THG phenomena and how they can be exploited in an optical microscope.

Nonlinear light-matter interactions will be explained in an intuitive yet thorough fashion, followed

by a discussion on light propagation and tight focusing. Finally, an overview will be presented of

recent non-linear imaging applications in biology and nanoscience, and a critical look into the

future will be provided.

Syllabus


Two-photon and multi-photon excitation microscopy

Second harmonic genration microscopy

Application in tissue imaging: brain, tumors, collagen, myosin, starch.


Reading list

- Diaspro, A., Chirico, G. & Collini, M. (2005). Two-photon fluorescence excitation and

related techniques in biological microscopy. Quarterly Reviews of Biophysics 38, 97–166.

- Bianchini, P. & Diaspro, A. (2008). Three-dimensional (3D) backward and forward second

harmonic generation (SHG) microscopy of biological tissues. Journal of Biophotonics 1, 443–

450.

- Brown, E., Mckee, T., Ditomaso, E., Pluen, A., Seed, B., Boucher, Y. & Jain, R. K. (2003).

Dynamic imaging of collagen and its modulation in tumors in vivo using second-harmonic

generation. Nature Medicine 9, 796–801.

- Chen, X., Nadiarynkh, O., Plotnikov, S. & Campagnola, P. J. (2012). Second harmonic

generation microscopy for quantitative analysis of collagen fibrillar structure. Nature

protocols 7, 654–669.

Venue


Course date

May-June 2014



32

Bio-imaging at the single molecule level. Course at a Glance

Single molecule localization techniques and their application to biological systems: from the

cellular level to live tissue imaging.

Instructors Francesca Cella Zanacchi ([email protected]) Nanophysics, Istituto Italiano di Tecnologia

Tel: +39-010.71.781.289

Credits: 3

Synopsis

This course covers the theory behind single molecule localization (SML) and single molecule

tracking (spt) techniques. Particular attention will be addressed to the theoretical framework behind

the localization process and to the practical experimental aspects related to single molecule

detection. We will also focus our attention on the improvements in single molecule localization

provided by selective plane illumination microscopy (SPIM) and two photo excitation

(2PE). Critical aspects in single molecule localization and tracking will be discussed, with

particular attention to the role of scattering effects on the localization accuracy and artifacts induced

in the tracking process. An overview of the application of SML techniques to the study of biological

systems, both at the cellular level and towards live tissue super-resolution imaging will be provided.

Syllabus


· Single molecule localization and tracking techniques (theory and experimental protocols)

· Single molecule localization in thick samples by selective plane illumination microscopy and

two photon photo-activation

· Critical aspects and biological application of single molecule techniques


Reading list

E. Betzig et al. (2006). "Imaging Intracellular Fluorescent Proteins at Nanometer Resolution".

Science 313 (5793)

Live-cell 3D super-resolution imaging in thick biological samples. Francesca Cella Zanacchi et

al. Nature Methods 12/2011

High-density mapping of single-molecule trajectories with photoactivated localization microscopy

Suliana Manley et al. Nature Methods 5(2), 2008

Venue


Course date

May-June 2014



33

Photo-physical mechanisms and dynamic investigations in super-resolution microscopy

Course at a Glance

Fluorescence super-resolution microscopy: photo-physical mechanisms underlying and combination

with fluorescence dynamic investigations

Instructors Giuseppe Vicidomini ([email protected]),

Nanophysics, Istituto Italiano di Tecnologia

Tel: +39 010 71781976

Synopsis

Introduced at the beginning of the 20th

-century the fluorescent probes had truly revolutionized

optical microscopy. The high sensitivity and specificity of the fluorescent probes in combination

with optical microscopy allow understanding life at the molecular level. After one century

fluorescent probes become again the major players of a new revolution in optical microscopy. Until

not very long ago, it was widely accepted that due to the diffraction the optical microscopes cannot

visualize details much finer than about half the wavelength of light. The photo-physical

mechanisms of the fluorescent probes, in particular the possibility to drive the probes in different

states, set up the basis for overcoming the limiting role of diffraction. This breakthrough has led to

readily applicable and widely accessible fluorescence microscopes with nanoscale spatial resolution

(super-resolution).

This course will explain the photo-physical mechanism underlying the major super-resolution

microscopy techniques, with more emphasis on stimulated emission depletion (STED) microscopy.

We will highlight the importance of the time-component of the mechanisms to fully explore the

potential of STED microscopy. Further, we will show that the time-characteristic is fundamental for

combining STED microscopy with multi-scale fluorescence dynamic analysis, like fluorescence

lifetime and fluorescence correlation spectroscopy.

Syllabus

The course develops in about 9 hours.

Basic principles of fluorophore photophysics

Introduction to the major super-resolution microscopy techniques

Stimulated emission depletion microscopy: theory, experimental protocols, application,

limitation

Lifetime imaging in the contest of STED microscopy and in general

Fluorescence correlation spectroscopy in the contest of STED and in general


Reading list

Hell S.W., Microscopy and its focal switch, Nat. Methods, 6, 24-32 (2009)

Vicidomini G. et al., Sharper low-power STED nanoscopy by time gating, Nat. Methods, 8,

571-573 (2011)

Ringemann C. et al. Exploring single molecule dynamics with fluorescence nanoscopy.,

New J. Phys., 11:103054 (2009)

Venue



34

Course date

May-July 2014


35

Characterization of Polymeric Materials Course at a Glance

Basic concepts of chemical, physical and mechanical characterization techniques, with special focus

on polymeric materials.

Instructors Jose Alejandro Heredia-Guerrero

Luca Ceseracciu

Nanophysics Department, Istituto Italiano di Tecnologia

e-mail: [email protected]; [email protected]

tel: 010-71781276

Credits: 4

Synopsis

Polymers are ubiquitous materials in multiple fields of industry and research. Their extensive range

of properties facilitates their use in very diverse applications. In this PhD course, we show the main

techniques and methodologies in the characterization of their useful properties as materials. This

characterization includes the determination of the chemical composition and structure and the

potential reactivity as well as the determination of the polymeric structure and its implication on the

final properties. In this context, the size and the form of the macromolecules are key parameter.

Through a combination of mechanical characterization techniques it is possible to give a

comprehensive description of the deformation mechanisms of polymers, from elastic deformation to

plasticity and fracture, taking into account the influence of factors such as temperature, time and

scale. The mechanical parameters will include classical uni-axial stress-strain behavior, toughness

and fracture toughness, creep, dynamic behavior, hardness, with special focus on nanoindentation.

The objective of this course is to describe the experimental techniques used for this characterization.

The approach is very applied, starting from the theory for each technique and leading to practical

strategies to the test design and interpretation of results.

Syllabus The course develops in about 12 hours in the classroom.

Chemical characterization: main spectroscopy techniques, such us UV/VIS, infrared and Raman

spectroscopies and nuclear magnetic resonance;

Structural characterization: the order of the macromolecules in terms of amorphous and crystalline

domains will be depicted. Standard methods for the determination of the polymeric structures will

be described.

Determination of molecular weight of polymers: the number and weight average molecular weight

and the distribution of molecular weights will be explained and the usual methodologies for their

determination will be described.

Mechanical behavior of polymers: deformation mechanisms, uni-axial testing, hardness, toughness

measurements

Nanoindentation: fundamentals, analysis methods, practical applications

The examination consists in a written test.

Reading list [1] Physical properties of polymers handbook. James E. Mark. Springer. [2] Polymer Characterization: Physical Techniques, 2nd Edition. Dan Campbell, Richard A. Pethrick, Jim R. White. CRC Press.




36

[3] Nanoindentation, 2nd ed., A. C. Fischer-Cripps, Springer

Course date

May-June 2014


37

Nano-plasmonic devices: an introduction

Course at a Glance

Theoretical fundamentals on plasmonics; electromagnetic field on metallic nano-structures;

applications.

Instructors Remo Proietti Zaccaria

Istituto Italiano di Tecnologia (IIT)


tel: 01071781247

Credits: 3

Synopsis

Plasmonics is a branch of Physics extremely promising for its applications in electronics, chemistry,

computer science, solar energy harvesting and biology. In particular, it is dedicated to the

investigation of confined electromagnetic waves originating by the combination of free electrons

with the light source. Besides noble metals, artificial materials are at the base of Plasmonics. This

aspect strictly relates it to Nanotechnology, a growing science aiming to investigate both the

theoretical and fabrication aspects of devices with dimensions in the nanometer range.

In the present course will focus on a general introduction to plasmonics providing also the physical

[1] and mathematical tools to predict the electromagnetic behaviour of plasmonic nano-devices.

Among the many examples where plasmonics and nanotechnology are combined, we shall

introduce devices for light concentration [2], biological analysis [3] and light harvesting [4].

Syllabus


Fundamental concepts underneath plasmonics

Surface plasmon polaritons vs. localized plasmons

Key advantages of plasmonics

Plasmonics and its applications


Reading list

[1] Alessandro Alabastri, Salvatore Tuccio, Andrea Giugni, Andrea Toma, Carlo Liberale, Gobind

Das, Francesco De Angelis, Enzo Di Fabrizio and Remo Proietti Zaccaria,

“Molding of plasmonic resonances in metallic nanostructures: dependence of the non-linear

electric permittivity on system size and temperature”,

Materials 6, 4879-4910 (2013).

[2] Remo Proietti Zaccaria, Alessandro Alabastri, Francesco De Angelis, Gobind Das, Carlo

Liberale, Andrea Toma, Andrea Giugni, Luca Razzari, Mario Malerba, Hong Bo Sun, and Enzo Di

Fabrizio,

“Fully analytical description of adiabatic compression in dissipative polaritonic structures”,

Physical Review B 86, 035410 (2012).

[3] Manola Moretti, Remo Proietti Zaccaria, Emiliano Descrovi, Gobind Das, Marco Leoncini,

Carlo Liberale, Francesco De Angelis, and Enzo Di Fabrizio,

“Reflection-mode TERS on insulin amyloid fibrils with top-visual AFM probes”,

Plasmonics 8,25 (2013).

[4] Wenli Bai, Qiaoqiang Gan, Filbert Bartoli, Jing Zhang, Likang Cai, Yidong Huang, and

Guofeng Song,

“Design of plasmonic back structures for efficiency enhancement of thin-film Si solar cells",

Optics Letters 34, 3725 (2009).


38

Venue

IIT - Via Morego 30, 16163 Genova

Course date

June-Oct 2014


39

Nano-plasmonic devices: from fabrication to applications

Course at a Glance

Introduction to nanofabrication: top-down and bottom-up approaches for the realization of

plasmonic devices.

Instructors Andrea Toma

Istituto Italiano di Tecnologia (IIT)


tel: 01071781257

Credits: 3

Synopsis

The fabrication of complex plasmonic nanostructures integrated in innovative device architectures

represents a multidisciplinary key activity at the core of most research efforts in nanoscience and

technology. In particular, the possibility to promote giant field enhancement has gained increasing

attention over the last few years, enabling the detection of molecules in highly diluted liquids [1],

and/or the spectral signature collection of single/few molecules concentrated in nanovolumes [2].

The “hot spot” concept, induced by localized surface plasmon resonances (LSPR), will be

introduced as core idea behind the surface-enhanced infrared absorption (SEIRA) and the surface-

enhanced Raman spectroscopy (SERS) [3]. Within this context, we will pay attention to the state of

the art nanofabrication technologies, e.g. following top-down or bottom-up methods. In details, top-

down fabrication refers to approaches such as electron beam lithography (EBL) or focused ion

beam lithography (FIB) where focused electrons or ions are used to carve nanostructures into

macroscopically dimensioned materials. Alternatively, in the bottom-up approach, one begins to

assemble nanostructures from smaller units. Examples will include colloidal synthesis and

unfocused ion beam sputtering.

Syllabus


Nanofabrication technologies: top-down and bottom-up approaches for the realization of

next-generation devices.

Nano-plasmonic devices: design and realization of ultrasensitive biosensors.

SEIRA and SERS: employing plasmonic devices for the ultrasensitive detection both in the

visible and in the infrared range.


Reading list

[1] F. De Angelis, F. Gentile, F. Mecarini, G. Das, M. Moretti, P. Candeloro, M. L. Coluccio, G.

Cojoc, A. Accardo, C. Liberale, R. P. Zaccaria, G. Perozziello, L. Tirinato, A. Toma, G. Cuda, R.

Cingolani, E. Di Fabrizio, Breaking the diffusion limit with super-hydrophobic delivery of

molecules to plasmonic nanofocusing SERS structures, Nature Photon. 5 (2011) 682.

[2] K. Kneipp, Y. Wang, H. Kneipp, L. T. Perelman, I. Itzkan, R. R. Dasari, M. S. Feld, Single

Molecule Detection Using Surface-Enhanced Raman Scattering (SERS), Phys. Rev. Lett. 78

(1997) 1668. [3] R. Aroca, Surface-Enhanced Vibrational Spectroscopy, Wiley (2006).

Venue

IIT - Via Morego 30, 16163 Genova

Course date

June-October 2014


40

Laser-matter interactions: from fundamentals to applications

Course at a Glance

Physics of lasers, properties of laser radiation and basic fundamentals of light-matter interactions

with examples of key laser-based applications.

Instructors Martí Duocastella

Nanophysics Department, Italian Institute of Technology, Via Morego 30, Genova


Credits: 4

Synopsis

Lasers have become ubiquitous in our daily lives. From DVD players to bar code scanners, printers,

or even in medicine, laser-based applications are countless. Not to mention the pivotal role that

lasers are playing in science and in state of the art industrial processes. But what are the

characteristics of lasers that make them so broadly used?

In this brief course we will answer this question by describing the physics of laser systems and the

properties of laser radiation. We will also explain the basic interactions between laser light and

materials, and we will give examples of applications where lasers are the enabling technology.

This course is intended for PhD students who anticipate working with lasers. No background in

lasers is required. Emphasis is placed on the physical interpretation of lasers and on their

applications, with mathematics kept at a minimum.

Syllabus

The course develops in 12 hours in the classroom.

Fundamentals of lasers: lasers, laser radiation characteristics, laser types

Interaction of lasers with materials: absorption, dispersion, photophysical processes,

photochemical processes

Lasers at IIT: 3D microfabrication, nanoparticle generation, photolithography, surface

structuration, optical characterization

The examination consists in a brief research project proposal or in an oral presentation.

Reading list

[1] D. Bauerle, Laser Processing and Chemistry, Springer, 2011 ISBN: 978-3-642-17613-5

[2] S. Ezekiel, Understanding lasers and fiberoptics, MIT Open Course

http://ocw.mit.edu/resources/res-6-005-understanding-lasers-and-fiberoptics-spring-2008/

Venue

IIT - Italian Institute of Technology, Via Morego 30, Genova

Course date

May-July 2014

http://ocw.mit.edu/resources/res-6-005-understanding-lasers-and-fiberoptics-spring-2008/


41

Virtual Prototyping Design

Course at a Glance The aim of the Virtual Prototyping Design course is to give the basic knowledge about the Finite

Element Analysis (FEA) and Multi-Body Simulations (MBS). These computational techniques

predict the behaviour of physical systems: joined together permit to study the dynamics taking in

account the body flexibility, the control and optimisation. It will be introduced mainly applied to the

mechanical field, in particular to the robotics. The student gets 5 credits if he/she attends the entire

course and accomplishes the final project.

Instructors

Ferdinando Cannella ([email protected] )

Credits: 5

Synopsis Virtual Prototyping Design is the basic part of the Computer Aided Engineering (CAE) that in the

last decades involved more and more the R&D of the industries and the Research Centres. The

reason is that the physical models need more time and energies for being improved than virtual

ones. Moreover, running numerous simulations, these models permit to be optimized depending on

several parameters.

Thus the course will give an overview on the virtual prototyping design building the models with

the main software (MSC.Nastran, Ansys and MSC.Adams). In the second part of the course,

Multibody and Finite Element Analysis will be integrated in order to take the best advantage from

the virtual prototyping technique. Then the control (Matlab/Simulink) and the optimization will be

applied to the simulations.

Even if the training solutions concern the mechanical problems, it is designed to provide to

attendants with both the comprehensive and subject-specific knowledge; the students need to

effectively apply software tools to solve general problems: static, dynamic, linear, non-linear and

motion or multi-physics analysis. So the aim of the course is not only knowing the performances of

the software used to build the basic models, but it is also to be able to improve their skill by

themselves.

Syllabus

Total of 15 hours - each class is 3 hours.

class 1 (C1)

- Overview on Virtual Prototyping: Finite Element Analysis (FEA) and Multibody Simulation

(MBS)

- FEA using Ansys/Workbanch

class 2 (C2)

- FEA using Ansys/APDL

class 3 (C3)

- FEA using MSC.Nastran/Patran

class 4 (C4)

- MBS using MSC.ADAMS

- MBS + FEA

class 5 (C5)

- MBS using MSC.ADAMS + Control

- Model Validation – Optimisation



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3 hours will be added for the final examination.

Weekly homework will be assigned at the end of each lecture with an estimated average workload

of 3 hours per week. There will be a final examination decided by the instructor.

Prerequisites

Basic knowledge of classical physics and programming.

Reading List General references are:

- Klaus-Jurgen Bathe, Finite Element Procedures, Prentice-Hall of India, 2009

- Rajiv Rampalli, Gabriele Ferrarotti & Michael Hoffmann, Why Do Multi-Body

System Simulation?, NAFEMS Limited, 2011

- R.J.Del Vecchio, Design of Experiments, Hanser Understanding Books, 1997

Venue


Course dates April - May


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List of suggested Schools

Note that not all the schools take place every year

Annual School of Bioengineering School duration: 4 days CFs: 4 http://www.bioing.it/?q=node/35

IEEE-EMBS Summer School on Biomedical Signal Processing School duration: 1 week CFs: 5 http://www.eambes.org/events/ieee-embs-summer-school-on-biomedical-signal-processing

European Summer School of Neuroengineering School duration: 1 week CFs: 5 http://neuroengineering.it/

Advanced Course in Computational Neuroscience School duration: 4 weeks CFs: 15 http://fias.uni-frankfurt.de/de/accn/

The iCub Summer School School duration: 2 weeks CFs: 8 http://eris.liralab.it/summerschool/

School of Photonics 2014: “Seeing sharp and further with the optical microscope” School duration: 4 days CFs: 4 http://web.nano.cnr.it/scuolafotonica2014/

Others ….

http://www.neurodynamic.uottawa.ca/summer.html

http://www.bioing.it/?q=node/35

http://www.eambes.org/events/ieee-embs-summer-school-on-biomedical-signal-processing

http://neuroengineering.it/

http://fias.uni-frankfurt.de/de/accn/

http://eris.liralab.it/summerschool/

http://web.nano.cnr.it/scuolafotonica2014/

http://www.neurodynamic.uottawa.ca/summer.html