Price 4635 + VAT /15 Tcs
DURATION 3 Days

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 hands-on lab examples emphasizing on integrating the theoretical knowledge with practical experience

Prerequisite:

Familiarity with Basic Signal Processing Theory. Experience with MATLAB® programming.

Software Tools:

®MATLAB and ®Simulink

Topics

Algorithm Design and Simulation

  • Visualizing and Analyzing Simulation results
  • Improving algorithm performance
  • Improving programming skills

 

Course Outline:

1. 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 Non-periodic 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
    • Zero-Pole Map
    • Causality and Stability
  • Discrete Fourier Transform
    • DFT
    • IDFT
    • FFT
    • IFFT

Lab1: Deterministic Signals and Systems

2. 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
    • Cross-Correlation and Auto-Correlation Function
    • Power Spectral Density (PSD)

Lab 2: Random Signals Generation and Analysis

  • Signal and Parameter Quantization:
    • Uniform Quantization
    • Quantization Noise
    • Non-Uniform Quantization
    • Vector Quantization (Optional)
    • Lloyd Algorithm (Optional)

Lab 3: Scalar and Vector Quantization

  • Signal Compression and Coding:
    • PCM – Pulse Code Modulation
    • DPCM and ADPCM algorithms
    • Speech Signals
    • LPC algorithm (Optional)
    • Levinson algorithm (Optional)

Lab 4: Speech Signal Compression and Coding

3. 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)

 

Lab 5: 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)

Lab 6: Signal and Parameter Estimation Algorithms

  • Spectral Estimation:
    • Windowing- Bartlett, Hann, Hamming, Blackman
    • The Periodogram
    • Parametric and Non-parametric Estimation (Optional) 

Lab 7: Spectral Estimation Algorithms

  • Kalman Filter (Optional)
  • Kalman filter process
  • Kalman filter algorithm
  • Kalman Gain

Lab 8: Kalman Filter algorithm implementation for Signal Tracking (Optional)

4. 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
    • QPSK & QAM

Lab 9: Communication System Design

  • Channel Equalization
    • Linear Equalizers
    • Symbol-Spaced Equalizers
    • Fractionally Spaced Equalizers (Optional)
    • LMS Linear Equalizer

Lab 10:  Adaptive Equalization implementation

  • Carrier Synchronization
    • The Phase Locked Loop
    • PLL Based Frequency Synthesis
    • Costas loops for BPSK,QPSK and QAM signals (Optional)

Lab 11: Carrier Synchronization algorithms  implementation

  • OFDM Synchronization

Lab 12: OFDM Synchronization algorithm implementation

5. 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 12×8 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
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