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Digital Signal Processing
Microelectronics, 3-rd year
No. of credits: 6
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
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 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
 Lecture 1:
 More info: 1 | 2 | 3 | 4
 Lab 1  | HW 1
Overview of discrete-time signals and systems. General structure of a DSP system.
 Lecture 2:
 Lab 2
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
 Lecture 3:
 More info: 1 | 2
 Lab 3 | HW 2
Realization of linear discrete-time filters
 Lecture 4:
 Lab 4
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
 Lecture 5:
 More info: 1 | 2 | 3 | 4
 Lab 5  | HW 3
Implementation of DSP algorithms: finite precision effects
     Fixed-point and floating-point representation of numbers
     Quantization and round-off errors
 Lecture 6:
 More info: 1
 Lab 6  
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.
 Lecture 7:
 More info: 1 | 2 | 3 | 4
 Lab 7  | HW 4
Multirate signal processing: Interpolation and decimation. Case study: delta-sigma A/D data converters.
 Lecture 8:  
 More info: 1
 Lab 8
 | HW 5
Data compression algorithms
 Lecture 9:
 More info: 1
 Lab 9  
Case studies: a) biometric technologies; b) artificial neural networks.
 Lecture 10: Pipelining. Parallel processing. Retiming.
 Lecture 11:
 More info:
1 | 2
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