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Digital Signal ProcessingMicroelectronics, 3-rd yearNo. 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:An Introduction to DSP (Analog Devices) | Why use DSP ? (Analog Devices)

The Breadth and Depth of DSP (Chapter 1 from Smith's book:)The Scientist and Engineer's Guide to DSP

Universal microprocessors vs. DSPs | Choosing a DSP | DSP Benchmark | New challenges for DSP | Alternatives to DSPs(BDTI)General descriptionThe 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 outlineOverview 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 equirippleRealization 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) algorithmsImplementation of DSP algorithms: finite precision effects

Fixed-point and floating-point representation of numbers

Quantization and round-off errors 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:

More info:1

|Lab 8HW 5Data compression algorithms Case studies: a) biometric technologies; b) artificial neural networks. Lecture 10:Pipelining. Parallel processing. Retiming. Lecture 11:

More info:1 | 2Technology 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