Digital Signal Processing Case Study Taghizadehgh

Real-Time Digital Signal Processing

These resources introduce real-time digital signal processing (DSP) applications, algorithms and hardware. They aim to bridge the mathematics introduced in an undergraduate DSP course with practical implementation issues. The interaction between hardware and software are studied in the context of the TMS320DM6437 (DaVinci Video Processor) using MATLAB/Simulink. Case studies involving real-time filter design and the implementation of common real-time audio, image and video processing algorithms are explored.


Learning Outcomes and Objectives

The notes, assignments and labs/demos on this page are developed, in part, for a course with the following learning outcomes and objectives.

It is the intent that students will:

  1. Be able to identify the basic architectural elements of DSP hardware;
  2. Understand common real-time DSP algorithms for filtering and multimedia processing applications;
  3. Be able to program DSP hardware to perform signal, image and video processing tasks using MATLAB/Simulink;
  4. Gain an appreciation for the trade-offs necessary in algorithm design for real-time DSP implementation;
  5. Be able to overcome technical obstacles through ingenuity and resourcefulness; and
  6. Acquire an appreciation of the importance of real-time DSP for a broad class of engineering applications.


Topics and Notes

Note: course notes are copyright Deepa Kundur.


Review Problems


Hardware Implementation Resources

  • MATLAB Primer, Simulink Primer
  • Introduction to DSP
  • Filter Design and Implementation
  • Real-time FFT Convolution
  • Static and Switched Audio Equalizer
  • Audio Effects
  • Real-Time Edge Detection in Image and Video, [support files]
  • Real-Time Video Compression and Encryption, [support files]
  • Real-Time Video Focus Identification and Assessment, [support files]
  • Real-Time Video Barcode Recognition, [support files]
  • Real-Time Video Aggie Color Correction, [support files]
  • Real-Time Video Night Vision Effect, [support files]
  • Real-Time Digital Video Watermarking, [support files]
  • Real-Time Edge Detection-Based Motion Tracking, [support files]
  • Real-Time Tracking of Round Objects, [support files]
  • Real-Time Optical Flow-Based Motion Tracking, [support files]
  • Real-Time Object Tracking Through Background Estimation, [support files]
  • Real-Time Video Coin Counting, [support files]

Credits: These labs and demos were tested and modified over the years with the help and support of numerous individuals ...

…the Mathworks team (Todd Atkins, Sandeep Hiremath), my TAs over the years (Shan Liu, Lin Lai, Chris Byers, Julien Jainsky, William Luh), course students who went the extra mile (especially Diwakar Panchamgam) and various undergraduate researchers (including David Rolando, Irfan Jafri, Jonathan Kellerman, Adam Hollock, Noraica Davila, Gilbert Rodriguez, Mark Ivey).


Related References

  • A. Singh and S. Srinivasan, Digital Signal Processing: Implementation Using DSP Microprocessors with Examples from TMS320C54XX, Brooks/Cole, Belmont, California, 2004;
  • T. B. Welch, C. H. G. Wright and M. G. Morrow, Real-Time Digital Signal Processing from MATLAB to C with the TMS320C6x DSK, Taylor & Francis Group, New York, 2006;
  • J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 4th ed., Pearson, New Jersey, 2007.

Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal.

The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as:

• big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,

We welcome also submissions in the fundamental areas such as information theory and on mathematical topics with potential applications in signal processing such as functional analysis, stochastic calculus, game theory and group theory. Papers in the classical application areas of signal processing such as telecommunications, speech, image and video processing are welcome only if they contain novel research into methodology.. Purely application oriented articles in these areas should be submitted to the relevant dedicated journals

Digital Signal Processing: A Review Journal also aims to publish quality review articles in addition to occasional focus issues with special emphasis on emerging topics. Discussion articles in which several leading researchers discuss the future of a specific research area are also welcome.

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