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)