Unit DIGITAL AND STATISTICAL SIGNAL PROCESSING
- Course
- Computer engineering and robotics
- Study-unit Code
- 70A00910
- Curriculum
- In all curricula
- Teacher
- Fabrizio Frescura
- Teachers
-
- Fabrizio Frescura
- Hours
- 72 ore - Fabrizio Frescura
- CFU
- 9
- Course Regulation
- Coorte 2021
- Offered
- 2021/22
- Learning activities
- Affine/integrativa
- Area
- Attività formative affini o integrative
- Academic discipline
- ING-INF/03
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- The course is divided in few units:
Numerical formats for Digital Signal Processing
Discrete Time Signals and systems
Discrete Fourier Transform and Spectral Estimation
FIR Filter Design
Z-Transforms and Transfer Functions
Interpolation, Decimation and Rate Conversion
Introduction to Digital Statistical Signal Processing
Non-Parametric siSpectral Estimation
Linear Prediction - Reference texts
- S. Orfanidis, “Introduction To Signal Processing”, Prentice Hall
M. H. Hayes, “Statistical Digital Signal Processing And Modeling”, John Wiley & Sons
S. Orfanidis, “Optimum Signal Processing”, Prentice Hall - Educational objectives
- Understanding simulating and designing Numerical filters (FIR / IIR) with design software tools (eg Matlab)
Understanding, simulating, and designing (Matlab) spectral estimation schemes based on the use of DFT / FFT for both stationary and non-stationary signals
Understanding, simulating, and designing (Matlab) sampling frequency conversion schemes with direct and polyphase technique
Understanding, simulating and designing (Matlab) statistical signal analysis schemes (Linear Prediction, Spectral Estimate). - Prerequisites
- Signal Theory
System Theory
Digital Systems Electronic
Telecommunication Basics
Measurement and Probability Theory
Computer Architecture and Operating Systems - Teaching methods
- Lectures are based on a presentation of PowerPoint Slides integrated with blackboard examples and design exercises. Interactivity with students is highly encouraged throughout the whole lecture.
For each module Matlab Exercises are carried out for theory refinement and applications. - Learning verification modality
- The exam final test is made up if two separate tests:
1) A signal processing project on the PC (Matlab)
2) An oral exam - Extended program
- Teaching Module: Numerical formats for Digital Signal Processing (8 Hours)
Fixed Point
Floating Point
Pdf Estimation of Random Processes: Stationary, Stationary and Ergodic, Non-Stationary.
Uniform and Optimal Quantization. Non-Linear Quantization. Design and Implementation of full-digital non linear quantization schemes.
Teaching Module: Discrete Time Signals and systems (4 Hours)
Introduction on Digital Signal Processing. Discrete time signals, vs Analog Signals, vs Digital Signals.
Discrete-Time Systems, stability and causality of discrete-time systems (LTI) – Discrete Time Impulse Response
Discrete time convolution
Difference Equation for IIR systems
Matlab Exercises
Teaching Module: Discrete Fourier Transform and Spectral Estimation (10 Hours)
DTFT (Discrete Time Fourier Transform) – Frequency Spectrum of Discrete Signals. Frequency Sampling of DTFT – DFT and IDFT. Fast Fourier Transform FFT.
Circular Convolution, implementation of Circular Convolution with Linear Convolution and Circular Prefix
DFT/FFT for spectral analysis of stationary signals. Frequency resolution. Windowing of data. - DFT/FFT for spectral analysis of non-stationary signals. Spectrogram. Matlab Exercise.
Teaching Module: FIR Filter Design(10 Hours)
FIR Filter Design with Windows Methods – traditional windows – Kaiser Window.
FIR Filter Design with Frequency Sampling method
FIR Filter design in Matlab – Exercises
Teaching Module: z-Transforms and Transfer Functions (12 hours)
Main Properties. Region of Convergence (RoC), Causality and Stability. Frequency Spectrum – relation with DTFT.
Inverse Z transform
Equivalent descriptions of Discrete Time Systems, Transfer Function – Sinusoidal response, Stationary time Response, Transient Response.
IIR Filter Design with Pole-Zero placement: First Order Filters, Parametric Filters, Notch and Comb Filters. Matlab Exercise
Teaching Module: Interpolation, Decimation and Rate Conversion (12 Hours)
Interpolation and oversampling. Interpolation Filter Design – Polyphase interpolators. DAC Equalization, Multistage Interpolation.
Decimation, Decimation Filter design.
L/M Sampling rate Converters. Multistage implementation.
Matlab Exercise
Teaching Module: Introduction to Digital Statistical Signal Processing (20 Hours)
Non-Parametric Spectral Analysis: Introduction, Power Spectral Density estimation of correlated Gaussian Signals – Spectral Resolution and Spectral Leakage. Polarization of Spectral Estimation. Periodogram, Auto Correlation Estimation
Parametric Spectral Analysis: Introduction, Teaching Module: Numerical formats for Digital Signal Processing (8 Hours)
Fixed Point
Floating Point
Pdf Estimation of Random Processes: Stationary, Stationary and Ergodic, Non-Stationary.
Uniform and Optimal Quantization. Non-Linear Quantization. Design and Implementation of full-digital non linear quantization schemes.
Teaching Module: Discrete Time Signals and systems (4 Hours)
Introduction on Digital Signal Processing. Discrete time signals, vs Analog Signals, vs Digital Signals.
Discrete-Time Systems, stability and causality of discrete-time systems (LTI) – Discrete Time Impulse Response
Discrete time convolution
Difference Equation for IIR systems
Matlab Exercises
Teaching Module: Discrete Fourier Transform and Spectral Estimation (10 Hours)
DTFT (Discrete Time Fourier Transform) – Frequency Spectrum of Discrete Signals. Frequency Sampling of DTFT – DFT and IDFT. Fast Fourier Transform FFT.
Circular Convolution, implementation of Circular Convolution with Linear Convolution and Circular Prefix
DFT/FFT for spectral analysis of stationary signals. Frequency resolution. Windowing of data. - DFT/FFT for spectral analysis of non-stationary signals. Spectrogram. Matlab Exercise.
Teaching Module: FIR Filter Design(10 Hours)
FIR Filter Design with Windows Methods – traditional windows – Kaiser Window.
FIR Filter Design with Frequency Sampling method
FIR Filter design in Matlab – Exercises
Teaching Module: z-Transforms and Transfer Functions (12 hours)
Main Properties. Region of Convergence (RoC), Causality and Stability. Frequency Spectrum – relation with DTFT.
Inverse Z transform
Equivalent descriptions of Discrete Time Systems, Transfer Function – Sinusoidal response, Stationary time Response, Transient Response.
IIR Filter Design with Pole-Zero placement: First Order Filters, Parametric Filters, Notch and Comb Filters. Matlab Exercise
Teaching Module: Interpolation, Decimation and Rate Conversion (12 Hours)
Interpolation and oversampling. Interpolation Filter Design – Polyphase interpolators. DAC Equalization, Multistage Interpolation.
Decimation, Decimation Filter design.
L/M Sampling rate Converters. Multistage implementation.
Matlab Exercise
Teaching Module: Introduction to Digital Statistical Signal Processing (20 Hours)
Non-Parametric Spectral Analysis: Introduction, Power Spectral Density estimation of correlated Gaussian Signals – Spectral Resolution and Spectral Leakage. Polarization of Spectral Estimation. Periodogram, Auto Correlation Estimation
Parametric Spectral Analysis: Introduction, MA Analysis, truncation of Autocorrelation function. Analysis AR, AR spectral Analysis, Linear Prediction, Prediction Error. Levinson Recursion, Computational advantages, non-recursive lattice filter.