Course Overview:Attending the Signal Processing Applications and Algorithms class will give you a theoretical background on Signal Processing Algorithms and demonstrates Applications used in the industry. You will be mastering the MATLAB® and Simulink® tools during the training in the lab exercises embedded into the training
Who should attend:This course is intended for engineers having some background in Signal Processing and Programming skills in MATLAB® that want to broaden their knowledge in Designing and Simulating DSP algorithms. The course includes handson lab examples emphasizing on integrating the theoretical knowledge with practical experience.
Prerequisite:Familiarity with Basic Signal Processing Theory. Experience with MATLAB® programming.
Topics Include:
Algorithm Design and Simulation
Visualizing and Analyzing Simulation results
Improving algorithm performance
Improving programming skills
Software Tools:
®MATLAB and ®Simulink
Course Outline:

Introduction – Deterministic Signals
In this chapter we introduce the theory of Deterministic Signal Processing that is the basics of every DSP system.
We demonstrate the basic principles with lab examples.

Continuous Time Signals:

Periodic and Nonperiodic Signals

Spectral representation

Continues Fourier Transform

Discrete Time Signals:

Sampling Theorem

Spectral representation

Discrete Time Fourier Transform

Z Transform

Linear Time Invariant discrete t time systems

The LTI discrete time system

The linear convolution

Difference Equations

Transfer Functions

ZeroPole Map

Causality and Stability

Discrete Fourier Transform
Lab1: Deterministic Signals and Systems

Statistical Signal Processing
In this chapter we develop the theory of Random Signal Processing that is based on Statistical Modeling. We show the relations between Statistical and Spectral properties. We develop algorithms for Quantization and Compression of Random and Deterministic Signals.
We demonstrate the Statistical Signal Processing Algorithms with lab examples.

Introduction

Random Signals:

Probability Density Function (PDF)

The Histogram

Gaussian Distribution

Expectation

Variance

White Gaussian Noise

CrossCorrelation and AutoCorrelation Function

Power Spectral Density (PSD)
Lab2: Random Signals Generation and Analysis

Signal and Parameter Quantization:

Uniform Quantization

Quantization Noise

NonUniform Quantization

Vector Quantization (Optional)

Lloyd Algorithm (Optional)
Lab3: Scalar and Vector Quantization

Signal Compression and Coding:

PCM – Pulse Code Modulation

DPCM and ADPCM algorithms

Speech Signals

LPC algorithm (Optional)

Levinson algorithm (Optional)
Lab4: Speech Signal Compression and Coding

Advanced Signal Processing
In this chapter we develop Methods for Advanced Signal Processing. We Design and Implement Digital FIR and IIR filters. We develop algorithms Adaptive filters and Spectral Estimation.
We demonstrate Advanced Signal Processing Algorithms with lab examples.

Digital filter Design:

Introduction

FIR – GLP filters – Type I,II,III,IV

IIR filters – Butterworth, Chebyshev, Elliptic

Filter implementation

Quantization effects (Optional)
Lab5: Digital Filter Design and Implementation

Signal and Parameter Estimation:

Wiener filter

Method of steepest descent

Least Mean Square algorithm

Method of Least Square

Recursive Least Square algorithm (Optional)
Lab6: Signal and Parameter Estimation Algorithms

Spectral Estimation:

Windowing Bartlett, Hann, Hamming, Blackman

The Periodogram

Parametric and Nonparametric Estimation (Optional)
Lab7: Spectral Estimation Algorithms

Kalman Filter (Optional)

Kalman filter process

Kalman filter algorithm

Kalman Gain
Lab8: Kalman Filter algorithm implementation for Signal
Tracking (Optional)

Communication Signal Processing
The field of Digital Communications is very much related to Signal Processing implementation. In this chapter we show the relation between these two fields by introducing applications from the Telecommunication world. We analyze the performance of a variety of Signal Processing algorithms used in Digital Communication Systems.

Signal Modulation & Demodulation

IQ Modulation

IQ Demodulation

Pulse Shaping

BPSK modulation with Raised Cosine shaping filter

Signal Constellations
Lab9: Communication System Design

Channel Equalization

Linear Equalizers

SymbolSpaced Equalizers

Fractionally Spaced Equalizers (Optional)

LMS Linear Equalizer
Lab10: Adaptive Equalization implementation

Carrier Synchronization

The Phase Locked Loop

PLL Based Frequency Synthesis

Costas loops for BPSK,QPSK and QAM signals (Optional)
Lab11: Carrier Synchronization algorithms implementation
Lab12: OFDM Synchronization algorithm implementation

Xilinx System Generator and HDL code generation
This chapter is a workshop demonstrating the use of FPGA based FIR filter implementation using Xilinx System Generator and HDL coder

DSP Design Flow – System Generator

Create a 12x8 MAC using System Generator for DSP

Signal routing

Implementing System Control

Designing a MAC FIR

Designing a FIR filter

Filter implementation with HDL coder (Optional)

Basic FIR filter

Optimized FIR filter

IIR filter