Digital Signal Processing and Laboratory
*) Knowledge and understanding: Knowledge of the mathematical foundations for the representation, analysis and digital processing of signals. Knowledge of design methods of FIR and IIR filters.
*) Applied knowledge and understanding: analysis of a signal in the frequency domain, design of a digital filter, performing digital filtering of signals.
*) Making judgments: developing the ability to critically and synergistically use various tools for numerical processing of signals. Knowing how to evaluate the design constraints of a processing system in terms of error, computational complexity and stability of the algorithm.
*) Communication skills: Ability to present the topics in a clear and rigorous technical-scientific point of view. Knowing how to submit an application solution in a simple and comprehensive.
*) Learning skills: knowing how to integrate knowledge from various sources for the purpose of deepening. Knowing how to use the concepts covered for applications other than those disclosed.
For the successful achievement of objectives, the knowledge of signals and systems theory is required.
Digital signal processing systems. Advantages over analogue systems. Continuous time (TC) and discrete time (TD) signals. Deterministic and random signals. Examples of TD signals: complex sinusoids and exponentials. Energy and power. (3 hours)
TD systems. Input-output description. Block diagram. Properties of TD systems: causality, stability, linearity and time invariance. Impulsive response of an LTI system. Analysis of the properties of an LTI system from the impulse response. FIR and IIR systems. Systems described by constant coefficients difference equations. Step response. Steady state and transient response. Recursive and non-recursive realizations of FIR and IIR systems. Examples of AR filters and MA. (6 hours)
Zeta transform. ROC. Zeta transformed of elementary signals. Property of the Zeta transform. Inverse Zeta transform. Inverse Zerta transform of a ratio of polynomials. System function for an LTI system. System properties based on the system function. System function for systems described by constant coefficients difference equations. Poles and zeros. FiR and IIR filters. (5 hours)
Discrete time Fourier series. Discrete time and property Fourier transformation. CTFT calculation using DTFT. DFT and IDFT. DFT properties. Circular translation. Circular and linear conduction. Conduction via DFT. (4 hours)
Harmonic response of an LTI system. Properties of the harmonic response function for stable and causal LTI systems. Poles and zeros and system behavior. Resonator filters and notch filters. Realization of notch filters FIR and IIR. Synthesis of comb filters by positioning poles and zeros. Allpass filters. Reversible and minimum phase systems. (4 hours)
Sampling theorem. Sampled signal spectrum. Ideal reconstruction. Ideal A/De D/A conversion. Quantization. SQNR. (2 hours)
The problem of filter synthesis. Frequency selective filters. Ideal filters and physically feasible filters. Paley-Wiener conditions. Tolerance mask and specifications. Synthesis of FIR filters. Window method. Method of synthesis of FIR filters by spectral sampling. Minimax method. (4 hours)
Digital signal processing laboratory: exercises in Matlab and Python (see http://edi.uniparthenope.it/course/view.php?id=76 ) (20 hours)
Continuous time (TC) and discrete time (TD) signals. Energy abd power of signals.
TD systems and their properties. LTI systems. FIR and IIR systems. Systems described by constant coefficients difference equations.
Direct and inverse Zeta transform. System function for an LTI system. System function for systems described by difference equations. Poles and zeros. FiR and IIR filters.
Discrete Time Fourier series and properties. CTFT calculation using DTFT. DFT and IDFT. Properties of DFT. Circular translation. Circular and linear conduction. Conduction via DFT.
Harmonic response of an LTI system. Resonator, notch and comb filters. Allpass filters. Invertible and minimum phase systems.
A/De D/A conversion.
The problem of filter synthesis. Ideal filters and physically feasible filters. Synthesis of FIR filters. Window method and spectral sampling method. Minimax method.
Computer labs in Matlab e Python.
The course consists of a series of lectures and practical computer exercises carried out in Matlab and Python.
J. G. Proakis, D. G. Manolakis . Digital Signal. Processing. Principles, Algorithms, and Applications. Prentice Hall.
The final examination consists of an oral interview with the aim of assessing the knowledge acquired, the ability of understanding and the oral presentation. At the oral interview it is necessary to present a short written report on the exercises carried out in Matlab in the laboratory during the course. The interview will cover all the topics treated in the course and the discussion of the exercises.