Mumbai University

DIGITAL SIGNAL PROCESSING

DIGITAL SIGNAL PROCESSING

Discrete Time Signals and Systems: Discrete time signal sequences, Linear Shift Invariant system, Stability, Linear Constant

Coefficient difference equations, Frequency domain representation of discrete time systems and signals, symmetry properties of

Fourier Transform, Sampling of continuous time signal, Two dimensional sequences and system.

Z Transform: Z-transform, Inverse z transform theorem and properties, System functions, Two-dimensional transforms.

The Discrete Fourier Transform: Representation of periodic sequences, The Discrete Fourier Series, Properties of the discrete

Fourier series, Sampling the z-transform, Fourier representation of finite deviation sequences, the discrete fourier transform, properties of the DFT, Linear convolution using the DFT, two dimensional DFT.

Flow Graph and Matrix Representation of Digital Filters: Signal flow graph representation of digital networks, Matrix representation of digital networks, Basic network structures for IIR, Transposed forms, Basic network structures for FIR systems, Parameter Quantization effects, Tellegen’s theorem for digital filters and its applications.

Digital Filter Design Techniques: Design of IIR digital filters from analog filters, Properties of FIR digital filters, Design of FIR filters using windows, Comparison of IIR and FIR filters.

Computation of The Discrete Fourier Transform: Goertzel’s Algorithm, Decimation in time algorithms, Decimation in frequency algorithms, FFT algorithms for a N composite number, General computational considerations in FFT algorithms, Chirps Z transform algorithm.

Discrete Hilbert Transform: Real and Imaginary part sufficiency for causal sequences, Minimum phase condition, Hilbert Transform relation for the DFT and the complex sequences.

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