"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?".
The Scientist and Engineer's Guide to Digital Signal Processing.
This course covers core topics in digital signal and image processing and their use in
practical applications. Theoretical
fundamentals will be reinforced by studying real life practical applications and
carrying out hands on projects. The course will run in the form of lectures, tutorials/labs
and hands on course projects.
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This course will give students a solid understanding of digital signal and
image processing including;
Digital signal/image representation and associated implications.
Advantages and limitations of digitization
and fundamental tradeoffs.
signal/image generation and acquisition/capture.
relationship between frequency and time/space representations.
Representation and processing in the temporal/spatial
Representation and processing
in the frequency domain, duality to
multi-dimensional data such as 2D pictures, 3D medical images, videos
techniques, e.g., for denoising, enhancement, and restoration.
Digital signal processing
uses in real life practical
Development of DSP applications in MATLAB and/or
other software platforms.