Design Digital Signal Processing System
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       Tasks Number : 20
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Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

  • Curriculum
  • 1. Introduction
    • videocam
      The tasks to do in this course

      11m26s
    • videocam
      Overview of DSP

      11m26s
    • videocam
      Classification of DSP

      11m26s
    • videocam
      DSP Toolbox in Matlab

      11m26s
  • 2. Mathematical Transformations of DSP
    • videocam
      Time and frequency domain

      11m26s
    • videocam
      The DFT transformation

      11m26s
    • videocam
      The Z transformation

      11m26s
    • videocam
      Fast Fourier Transform

      11m26s
  • 3. Design of Digital Filters
    • videocam
      Overview of Digital Filters

      11m26s
    • videocam
      Design IIR Filter

      11m26s
    • videocam
      Design FIR Filter

      11m26s
    • videocam
      Image Filters

      11m26s
    • videocam
      Audio Filters

      11m26s
  • 4. DSP Programming on TMS320 Platform
    • videocam
      Introduction to DSP of Texas Instruments

      11m26s
    • videocam
      Install tools of TI

      11m26s
    • videocam
      I/O basic programming

      11m26s
    • videocam
      The FIR filter programming

      11m26s
    • videocam
      The IIR filter programming

      11m26s
    • videocam
      Data frame processing

      11m26s
    • videocam
      DFT transformation programming

      11m26s
Signal
Design Digital Signal Processing System


Digital signal processing (DSP) is the use of digital processing, such as by computers, to perform a wide variety of signal processing operations. The signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency.

Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech processing, sonar, radar and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, seismology, among others.

DSP can involve linear or nonlinear operations. Nonlinear signal processing is closely related to nonlinear system identification and can be implemented in the time, frequency, and spatio-temporal domains.

The application of digital computation to signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression. DSP is applicable to both streaming data and static data.

The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, digital synthesizers, radar, sonar, financial signal processing, seismology and biomedicine.

Specific examples are speech compression and transmission in digital mobile phones, room correction of sound in hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, medical imaging such as CAT scans and MRI, MP3 compression, computer graphics, image manipulation, hi-fi loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers.

DSP algorithms have long been run on general-purpose computers and digital signal processors. DSP algorithms are also implemented on purpose-built hardware such as application-specific integrated circuit (ASICs). Additional technologies for digital signal processing include more powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors.

Depending on the requirements of the application, digital signal processing tasks can be implemented on general purpose computers. Often when the processing requirement is not real-time, processing is economically done with an existing general-purpose computer and the signal data (either input or output) exists in data files.

This is essentially no different from any other data processing, except DSP mathematical techniques (such as the FFT) are used, and the sampled data is usually assumed to be uniformly sampled in time or space. For example: processing digital photographs with software such as Photoshop.

However, when the application requirement is real-time, DSP is often implemented using specialized microprocessors such as the DSP56000, the TMS320, or the SHARC. These often process data using fixed-point arithmetic, though some more powerful versions use floating point.

For faster applications FPGAs might be used. Beginning in 2007, multicore implementations of DSPs have started to emerge from companies including Freescale and Stream Processors, Inc. For faster applications with vast usage, ASICs might be designed specifically. For slow applications, a traditional slower processor such as a microcontroller may be adequate. Also a growing number of DSP applications are now being implemented on embedded systems using powerful PCs with multi-core processors.

Table of Content

1. Introduction

  • The tasks to do in this course
  • Overview of DSP
  • Classification of DSP
  • DSP Toolbox in Matlab

2. Mathematical Transformations of DSP

  • Time and frequency domain
  • The DFT transformation
  • The Z transformation
  • Fast Fourier Transform

3. Design of Digital Filters

  • Overview of Digital Filters
  • Design IIR Filter
  • Design FIR Filter
  • Image Filters
  • Audio Filters

4. DSP Programming on TMS320 Platform

  • Introduction to DSP of Texas Instruments
  • Install tools of TI
  • I/O basic programming
  • The FIR filter programming
  • The IIR filter programming
  • Data frame processing
  • DFT transformation programming