Advanced Digital Signal Processing with MATLAB(R) (15 + 25 hours (40 hours))

This course mainly deals with using MATLAB(R) Signal Processing toolbox for Digital signal processing, analysis, visualization, and algorithm development. The training covers various topics such as filter design, windowing techniques, transforms, multi-rate signal processing, statistical signal processing, parametric modeling etc.

COURSE CONTENT :
Introduction to DSP
(2 hours)
  • Introduction to DSP
  • Sampled data systems
  • Aliasing and antialiasing
  • Reconstruction
  • Practical limitations
  • Frequency & amplitude resolution
  • Quantization and timing errors
  • Correlation and convolution
  • Frequency analysis
  • Fourier transforms
  • Frequency ‘leakage’
  • Windowing
  • Multi-rate signal processing
Transforms (2 hours)
  • Fourier Transform• Z – Transform• DCT Transform
  • Hilbert Transform
  • Wavelet Transform
Filters (5 hours) FIR Filter – FIR digital Filters• FIR filter basics• Analysis of FIR filters

  • Frequency & impulse responses
  • The window design method
  • Optimization design methods
  • Practical limitations of FIR filters

IIR Filter –

  • IIR filter basics
  • Analysis of FIR filters
  • Frequency & impulse responses
  • IIR filter design
  • Poles, zeroes and filter response
Cepstral analysis (1 hour)
  • Complex Cepsturm
  • Inverse complex cepstrum
  • Real cepstrum and minimum phase reconstruction
Statistical signal processing
(3 hours)
  • Introduction to statistical parameters
  • Autocorrelation matrix
  • Power spectral density (PSD)
  • Cross power spectral density
  • Finding PSD using various Methods (periodogram, modified periodogram, covariance, Eigen vector, burg, yule walker, Welch, MUSIC Algorithm, Root MUSIC Algorithm)
  • Spectrogram
  • Transfer function estimation
Parametric modeling(2 hours)
  • Introduction to signal modelling
  • Study of Auto Regressive Moving Average Models (ARMA), ARModels and MA models
  • Estimation of model parameters using various methods like Yule-Walker, prony etc)
DSP with MATLAB(R)
(3 hours)
  • Introduction to DSP Toolbox
  • Signal processing functions in MATLAB(R) (conv, conv2, corrcoef,cov, cplxpair, deconv, fft, fft2, fftshift, filter2, freqspace, ifft, ifft2,unwrap)
  • Time domain analysis of a signal
  • Frequency domain analysis of a signal
Digital Filter Design in MATLAB(R)
(2 hours)
  • Discrete-Time Filters (Direct form I, Direct form II, lattice filters)
  • 1_D Median filtering
  • Butterworth filter design
  • Chebyshev Type I filter design (pass band ripple)
  • Chebyshev Type II filter design (stop band ripple)
  • Raised cosine FIR filter design
  • Recursive digital filter design
Window Design(2 hour)
  • Rectangular window
  • Hamming window
  • Hanning window
  • Bartlett window
  • Kaiser window etc
Transforms(2 hour)
  • Discrete fourier transform
  • Discrete cosine transform
  • Hilbert transform
  • Discrete wavelet transform
  • inverse transforms
Multi-rate Signal Processing(2 hours)
  • Decimation
  • Interpolation
  • Up-Sampling
  • Down-Sampling
  • Re-Sampling
Linear Systems(1 hour)
  • Stabilize polynomial
  • z-transform partial-fraction expansion
  • conversion of digital filter parameters to transfer function form/ pole-zero form etc
Cepstral analysis(1 hour)
  • Complex cepstral analysis
  • Inverse complex cepstrum
  • Real cepstrum and minimum phase reconstruction
Statistical signal processing(4 hours)
  • Cross Correlation
  • Covariance
  • Data matrix for autocorrelation matrix estimation
  • Power spectral density (PSD)
  • Cross power spectral density
  • Finding PSD using various Methods (periodogram, modified periodogram, covariance, Eigen vector, burg, yule walker, Welch, MUSIC Algorithm, Root MUSIC Algorithm)
  • Spectrogram
  • Transfer function estimation
Parametric Modeling(4 hours)
  • Autoregressive (AR) all-pole model parameters estimated usingBurg method
  • Estimate AR model parameters using covariance method
  • Estimate AR model parameters using modified covariance method
  • Estimate autoregressive (AR) all-pole model using Yule-Walker method
  • Cross power spectral density
  • Prony method for filter design
Waveform Generation(30 min)
  • Swept-frequency cosine
  • periodic sinc function
  • Pulse train
  • Saw-tooth or triangle wave
GUI’s(1 hour) 
  • Filter Design and Analysis Tool
  • GUI-based filter design
  • Open interactive digital signal processing tool
  • Open Filter Visualization Tool
Bi-level Waveform Measurements(30 min)
  • Duty cycle of pulse waveform
  • Fall time of negative going bi-level waveform transitions
  • Period of bi-level pulse
  • Separation between bilevel waveform pulses
  • Bilevel waveform pulse width
  • Slew rate of bilevel waveform
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