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Digital Signal Processing Microelectronics, 3-rd year No. of credits: 6
Textbooks: J.G. Proakis, D. G. Manolakis, Digital Signal Processing, 4/E, Prentice-Hall, 2007
K.K. Parhi, VLSI Digital Signal Processing Systems: Design and Implementation, Wiley, 1999
Tutorials: General description
The course focuses on linear filtering of discrete-time signals. Main topics are related to digital filter design/realization techniques, Discrete Fourier Transform/FFT implementation and its use, linear adaptive filtering. Fixed/floating point representation and finite precision effects are presented. Applications include audio signal processing, coding algorithms, multirate signal processing. Hardware implementations issues are also outlined, related to pipelining and algorithm transformation (retiming, folding, unfolding).
Course outline Overview of discrete-time signals and systems. General structure of a DSP system. Design techniques for linear discrete-time filters:
a) IIR filters: impulse invariance method, bilinear transformation, frequency transformations, state-vector approach, LS design
b) FIR filters: linear phase filters using windows, frequency-sampling, optimum equiripple
Realization of linear discrete-time filters Discrete Fourier Transform (DFT): definition, properties, and applications
DFT definition and properties
Linear filtering based on DFT
Filtering long data sequences: overlap-add and overlap-save methods
Efficient computation of DFT: Fast Fourier Transform (FFT) algorithms
Implementation of DSP algorithms: finite precision effects
Fixed-point and floating-point representation of numbers
Quantization and round-off errors
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Adaptive linear filtering - theory and applications
Optimal filtering problem and Wiener solution. Basics of adaptive filtering theory.
Gradient descent algorithm: definition, convergence analysis. LMS algorithm and its variants.
Adaptive filtering in the frequency domain.
Multirate signal processing: Interpolation and decimation. Case study: delta-sigma A/D data converters. Lecture 8:
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Lab 8 | HW 5
Data compression algorithms Lecture 9:
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Case studies: a) biometric technologies; b) artificial neural networks. Lecture 10: Pipelining. Parallel processing. Retiming. Lecture 11:
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Technology trends for DSP chips. Lecture 12: Case study: CORDIC processors. Lecture 13: System-level design of integrated circuits. Lecture 14: Course review. Q&A. Exercises. Subiecte examen: Exemplu 1 | Exemplu 2