OVERVIEW

Signal processing applications span an immense set of discipines that include communications, space explorations, and medicine just to name a few. In particular, digital signal processing deals with the representation, transformation, and manipulation of signals and the information therein.

AIMS AND CONTENT

LEARNING OUTCOMES

This course aims to provide the basic knowledge on the basic methodologies for signal manipulation, digital signal processing. Particular attention will be given to biomedical applications of each presented techinque. This to provide to the student a view of direct usage of theoretical knowledge 

AIMS AND LEARNING OUTCOMES

This course aims to provide theoretical instruments and practical examples for the treatment of digital signals and data with particular attention to the biomedical applications. The main objective of this teaching course is to give insights of the importance of signal processing techniques and of inferential statistics as fundamental instruments of the biomedical engineer. The application of those techniques will develop specific capabilites of the biomedical engineering student in the usage of those teoretical contecpts tailored to   problems specific of medical-biological contexts.

PREREQUISITES

It is required for the student to have basic understanding of the fundamental elements of system engineering (e.g., Laplace Fourier transforms) and basic descriptive statistics - e.g., hisotgrams, mean and variance

TEACHING METHODS

Frontal lessons with Matlab exercises that students will solve autonomously and deliver through aulaweb-platoform for their evaluaiton at the end of the semestrer.

SYLLABUS/CONTENT

Digital signal processing

• Discrete time signals : numerical representation of discerte time signals. Discerte time-fourier transforms. Sampling of continuous-time signals,
• Discerte time systems: linear constant-coefficient difference equations. Numerical convolution. Z-transform. FIR and IIR systems. Digital filter design. Structures of discrete-time systems. 

Data processing

• Descriptive statistics.
• Probability and probability estimation
• Hypothesis testing:
  • t-Student; Analisys of Variance; Type-I and Type-II errors; confidence intervals; Linear regression.

RECOMMENDED READING/BIBLIOGRAPHY

• Oppenheim A.V.,  Schafer R.W. Discrete-time signal processing. 3rd Edition. Pearson New International Edition.
• G. Filatrella, P. Romano Elaborazione statistica dei dati sperimentali, EDISES.
• T. Vargiolu, Elementi di probabilità e statistica, CLEUP.

TEACHERS AND EXAM BOARD

Exam Board

MARCO MASSIMO FATO (President)

VITTORIO SANGUINETI

GABRIELE ARNULFO (President Substitute)

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The final exam consists of a written tests with exercises and open-questions on the topic covered during frontal lessons. With a successful written exam (grade > 18), the student will undergo oral examination with a discussion of the written exam and the evaluation of the Matlab exercises.

ASSESSMENT METHODS

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• Knowledge of the basic and principal techniques for the treatment and manipulation of digital signals and data
• the capability to create Matlab modules that implement the above mentioned techinques.

Exam schedule