Methods of acquisition and analog/digital conversion of continuous-time signals; pre-processing techniques; digital signal processing, in the frequency and time domain. Analysis in the time-frequency domain and techniques for extracting information from signals
"Teoria dei segnali" M. Luise, G. Vitetta - MC Graw-Hill
"Engineering Applications of Correlation and Spectral Analysis", Julius S. Bendat, Allan G. Piersol.
"Introduction to discrete-time signal and systems" R.I. Damper, Chapman & Hallms" R.I. Damper, Chapman & Hall
Learning Objectives
The principal aim of the course is to provide the knowledge and skills in the field of signal analysis acquired in the experimentation, measurement and testing on machines, structures and products and industrial processes. In particular, the course aims to provide knowledge on:
- difference between static and dynamic measurements;
- Analog/digital conversion;
- measuring chains for dynamic testing and measurement; Dynamic characterization of sensors and measurement chain;
- data pre processing;-data processing in the time, frequency and time-frequency domain;
Through laboratory activities , the course aims to increase the ability to apply the knowledge listed above, in particular:
- to acquire dynamic signals with different measurement chains;
- to manage the measurement chains and to process data whit dedicated software (Labview)
- to extract information from phisycal fenomena, machinery and structures, to understand how performance can be verified and systems can be improved, innovating the systems also through the development and improvement of measurement and control methods, in the field of mechanical engineering.
A further objective is to achieve an adequate preparation to achieve an adequate preparation to access the third level of university studies (attendance at second level master and doctoral schools), in order to further deepen knowledge and skills in the field of research.
Prerequisites
solid bases of physics, analysis. Knowledge of the basic methods of measurement and testing and data processing in the case of static systems
Teaching Methods
The course is dealt with through lectures which are followed, for each topic, by examples, applications and exercises in the laboratory, carried out in groups by the students under the supervision of the teacher.
Further information
The purpose of laboratory activities is to:
- provide the student with the ability to apply theoretical knowledge;
- give substance to the methods of analysis of the signal;
- understand and become familiar with the use of advanced instruments chain and devices and with the methods of conducting an experimentation.
Type of Assessment
On a voluntary basis, a measurement experience in the laboratory with the related report can be done, to be carried out normally in groups.
In this case the report will be discussed during a final oral examination, which is divided into the following parts:
1) presentation and discussion of the eventual report (10-15 minutes)
2) standard oral exam (30-40 minutes ) with two "" theoretical "questions taken from the program and an application question on a practical measurement case.
Course program
Signal and their classifications;
Sensors and measurement chains;
Time and frequency domain ;
Signal processing, analogue to digital conversion, time samplig and amplitude quantisation;
Time sampling and amplitude quantization errors. Convolution, Dirac delta function, sampling theorem, aliasing, leakage.
Error minimizing techiques, windowing, zero padding;
Laboratory activities: analog and digital oscilloscope e digitale, I/O devices- quantization errors, aliasing, leakage.
Characteristics of A/D converters and multiplexer.
Analysis of deterministic signals, best fitting techniques, normal equations, singular value decomposition.
Analysis of random signals in the time domain, ensemble averages, time averages.
Statistical parameters for the random signal in the time domain.
Analysis of random signal in the frequency domain, spectrum and power spectrum.
Two- channel analysis. Cross correlation and cross spettrum.
Time-frequency domain analysis, STFT, Gabor transform, continuous and discrete Wavelet transform.
Denoising e data compression.
Homomorphic analysis, real and complex cepstrum.
Hilbert transform, analytic signal, signal envelope.
Empirical mode decomposition, Hilbert-Huang transform