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"The world of
science and engineering is filled with signals: images from remote space
probes, voltages generated by the heart and brain, radar and sonar echoes,
seismic vibrations, and countless other applications. Digital Signal
Processing is the science of using computers to understand these types of
data. This includes a wide variety of goals: filtering, speech recognition,
image enhancement, data compression, neural networks, and much more. DSP is
one of the most powerful technologies that will shape science and
engineering in the twenty-first century. Suppose we attach an
analog-to-digital converter to a computer, and then use it to acquire a
chunk of real world data. DSP answers the question: What next?".
From the
The Scientist and Engineer's Guide to Digital Signal Processing.
This course covers the
fundamentals of digital signal processing systems and their use in various
applications, in particular, image and multi-media processing. Theoretical
fundamentals will be reinforced by studying real life practical applications and
carrying out hands on projects (sample application areas highlighted in the
figures shown on this page). The course will run in the form of lectures and
tutorials and practical project based learning.
Prerequisites:
One of
EECE 359, EECE 369,
or permission from instructor.
Objectives:
This course will give student a solid understanding of DSP including;
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Differences
between analog and digital signal representation and processing along
with their associated implications.
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Advantages
and limitations of
digital signal processing along with their
fundamental tradeoffs.
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Principles of
signal and image generation and acquisition/capture.
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Signal
representations in various dimensions (1D, 2D, nD).
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The
relationship between frequency and time/space representations.
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Representation and processing of signals in the temporal/spatial as well
as the frequency domain.
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Processing of
high dimensional data such as 2D pictures, 3D medical images.
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Standard
filtering techniques such as denoising, enhancement, and restoration.
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Where and how
digital signal processing techniques are used in real life and practical
applications.
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How to develop
simple DSP applications in MATLAB and/or other development platforms.
Course topics:
Both theoretical and practical DSP topics will be taught including;
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Discrete-time
signals and systems, sampling and reconstruction, frequency domain
representations including the Discrete Fourier Transform (DFT).
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Frequency analysis
of digital signals and systems, linear time invariant (LTI) systems.
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Implementations of
discrete-time systems, design of digital filters (FIR, IIR).
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Image and
multi-media processing including image filtering, image enhancement in the
spatial and frequency domains, image restoration.
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Applications
of DSP e.g. audio signal processing, biomedical data
analysis, robotics.
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On-site visits and invited speakers related to
the area.
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MATLAB practice exercises.
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Course project work.
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