Course Description: This course covers continuous time
signal models, convolution, and superposition integral and impulse
response. Students also study Fourier series and periodic signals,
Parseval's theorem, energy spectral density, Fourier transform and
filters, discrete time signals, difference equations, discrete
Fourier transform, and discrete convolution.
Course Overview: Signals is an introductory course that
develops mathematical techniques for modeling continuous and
discrete signals and linear systems. Analog and digital signal
processing theory is taught simultaneously to emphasize the power
of these mathematical abstractions. Classroom lectures are
supplemented by computer simulations that reinforce significant
concepts. Topics covered in this course include basic linear
system theory, time domain methods such as convolution, frequency
domain methods such as the Fourier transform, the Z-transform, and
filter design and implementation. This is a four credit hour
lecture course that includes three hours of lecture and one credit
hour of recitation.
Student Outcomes (SO):
- SO C: An ability to creatively design a system, component or
process to meet desired needs within realistic constraints
such as economic, environmental, social, political, ethical,
health and safety, manufacturability, and sustainability.
- SO D: An ability to function effectively on
multi-disciplinary teams.
- SO G: An ability to communicate effectively in writing,
speaking and making presentations.
Course Topics: Refer to the SOs above to understand how these
topics relate to our stated student outcomes.
- Signal and system classifications, properties and signal operations
(SO C).
- Continuous-time and discrete.time convolution and correlation
(SO C).
- Fourier Series, Parseval's theorem, line spectra and power spectrum
(SO C).
- Fourier Transforms, Parseval's theorem, system frequency response.
(SO C).
- Filters, Bode plots and filtering
(SO C).
- Z-plane plots and relationship to magnitude frequency response
(SO C),
- Signal detection and classification challenge (SO 2, 3).
- Team-based problem-solving in recitation (SO 2, 3).
- Writing assignments on contemporary topics in
signal processing (SO 2, 3).
Questions or comments about the material presented here can be
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