Digital Signal Processing Case Study Taghizadeh Maryland

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A Pragmatic* Introduction to Signal Processing

 with applications in scientific measurement

An illustrated essay with free software and spreadsheet templates to download

This is a work in progress, last updated March 13, 2018. (Recently updated files)

A retirement project by Tom O'Haver, Professor Emeritus
Department of Chemistry and Biochemistry, University of Maryland at College Park

orcid.org/0000-0001-9045-1603

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This essay is also available as a 153-page,indexed, printable document written in OpenOffice and saved in
Open Document**, Word   , and PDF formats.March 13
, 2018 version.  Readability index
A paperback book version is available from Amazon CreateSpace in black-and-white ($11.00 US) and in color ($30.00 US).
The current web site can be downloaded in archived ZIP format complete with all linked software (156 MBytes).
Who uses this site?    Feedback from users
   Site search:

Comparison of Interactive Signal Processing Tools for Matlab

Spreadsheets for Measurement Calibration 


  

Octave, a freely downloadable alternative to Matlab, almost completely compatible. For Windows, Macintosh, and Linux.


 

SPECTRUM: simple, freeware, 90s-era signal processing program, for Macintosh OS 8.


   

      Microsoft Excel 2013                    OpenOffice Calc 4.1.1



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First edition created in 1995. Last updated March 2018. Created with SeaMonkey. This site is a retirement project and international community service, maintained by Prof. Tom O'Haver, Professor Emeritus, Department of Chemistry and Biochemistry, The University of Maryland at College Park, currently residing in Naples, FL, and in Silver Spring, MD. Comments, suggestions and questions should be directed to Prof. O'Haver at toh@umd.edu, currently residing in Naples, FL.

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Course Description:  Sampling as a modulation process, aliasing, the sampling theorem, the Z-transform and discrete-time system analysis, direct and computer-aided design of recursive and nonrecursive digital filters, the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT), digital filtering using the FFT, analog-to-digital and digital-to-analog conversion, effects of quantization and finite-word-length arithmetic

Prerequisite(s): ENEE 322 and completion of all lower division courses

Corequisite(s): None

Course Objectives:

  • Understand how analog signals are represented by their discrete-time samples, and in what ways digital filtering is equivalent to analog filtering
  • Master the representation of discrete-time signals in the frequency domain, using the notions of z-transform, discrete-time Fourier transform (DTFT) and discrete Fourier transform (DFT)
  • Learn the basic forms of FIR and IIR filters, and how to design filters with desired frequency responses
  • Understand the implementation of the DFT in terms of the FFT, as well as some of its applications (computation of convolution sums, spectral analysis)

Topics Covered:

  • Uniform sampling: sampling as a modulation process; aliasing; ideal impulse sampling; sampling theorem; sampling bandpass signals
  • Data reconstruction by polynomial interpolation and extrapolation: zero-order hold; first order hold; linear point connector
  • The z-transform: definition; inverse; useful transform relationships; Parseval's theorem; difference equations
  • Analysis of sampled-data systems by transform methods: transfer functions for discrete-time systems; sinusoidal steady-state frequency response; structures for realizing transfer functions; stability; decimation and interpolation
  • The design of transfer functions for digital filtering: bilinear transformation method for IIR filters; Fourier series, windowing and the Remez algorithm for FIR filters
  • Effects of quantization and finite word length arithmetic in digital filters
  • The discrete Fourier transform (DFT): definition of the DFT and its inverse; transform relationships; cyclic convolution and correlation; fast Fourier transform (FFT); filtering long sequences using the FFT

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