With this function you can construct your weekly calendar of lessons, which is customized on the basis of the courses that you intend to follow. Warning: the personal schedule does not replace the presentation of the study plan! It's an informal tool that can help you better manage the organization of class attendance before the study plan presentation. After the study plan presentation we recommend you to use the Lecture timetable service in your Online Services.
To create your customized schedule follow these instructions:
- Click on the "Enable" link to proceed. You will be asked your surname and first name in order to determine your alphabetic grouping.
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To add or remove courses from your personal schedule, use the small icons which are found next to the courses:
addition of the course
removal of the course
selection of the section of the Laboratory of Architecture (Note: the effective area in which the teaching will be carried out will be determined after the presentation of the Study Plans)
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The sidebar on the left displays the number of lessons included in schedule.
There are also these commands:
View the schedule: allows the viewing of the weekly synoptic schedule
Delete the schedule: cancels the selections made
When you have finished the entry, you can print the calendar you have made.
Semester (Sem) | 1 | First Semester | 2 | Second Semester | A | Annual course | Educational activities | A | Basic activities | B | Identifying activities | Language |  | Course completely offered in italian |  | Course completely offered in english | -- | Not available | Innovative teaching |  | The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching. These CFUs include:
- Subject taught jointly with companies or organizations
- Blended Learning & Flipped Classroom
- Massive Open Online Courses (MOOC)
- Soft Skills
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Academic Year
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2017/2018
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School
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School of Industrial and Information Engineering
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Name
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(Bachelor of Science degree)(ord. 270) - MI (363) Biomedical Engineering
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Track
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BND - Non Diversificato
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Programme Year
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2
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ID Code
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085856
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Course Title
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BASICS OF STATISTICS AND BIOMEDICAL SIGNALS
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Course Type
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Integrated Course
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Credits (CFU / ECTS)
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10.0
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Semester
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Second Semester
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Course Description
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The aim of this course is to provide the background and the basic methodologies for biomedical data and signal processing. Examples and applications will provide descriptions according to their principal characteristics, their generation models and the basis of processing procedures.
BIOMEDICAL STATISTICS - Aim: acquainting the student with basic knowledge about Statistics, necessary for biomedical engineering applications. Subjects of the course: data analysis (exploratory methods; data summaries; histograms and boxplots); probability (Bayes' Theorem; random variables; moments; correlation and independence; more common probability distributions; Law of Large Numbers and Central Limit Theorem); statistical inference (parameter estimation; confidence intervals; hypothesis testing); evaluation of classifiers (sensitivity, specificity, ROC graphs); linear regression models. Use of a statistical SW package for data analysis.
BIOMEDICAL SIGNALS - Introduction to biomedical signals. Biomedical signals in time domain. Periodicity, stationarity. Ergodic processes. Signal to noise ratio SNR. Acquisition, sampling, A/D conversion. Autocorrelation and cross-correlation functions. Frequency analysis: Fourier series and transform, discrete Fourier transform, Fast Fourier transfrom. z-transform. Digital filters FIR and IIR: design methods and applications. Wavelet detection and classification. SNR improvement: averaging. Spectral analysis: energy and power spectra. Periodogram. Time/frequency resolution. Parametric models. AR, ARX. Parametric spectral estimation. Optimal least variance filtering: Wiener and Widrow filters. Introduction to biomedical images.
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Scientific-Disciplinary Sector (SSD)
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Educational activities
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SSD Code
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SSD Description
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CFU
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A
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MAT/06
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PROBABILITY AND STATISTICS
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5.0
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B
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ING-INF/06
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ELECTRONIC AND INFORMATICS BIOENGINEERING
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5.0
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