A. Brinton Cooper III
Courses
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What is a
signal? Signals are as fundamental in the study of Electrical
Engineering as electromagnetic waves. Signals are found nearly
everywhere. In the body, signals convey the brain's instructions
to the musculo-skeletal system, resulting
in walking, running, and stopping. Fixed and portable wireless
telephone systems convey conversation and information via
signals. Pressing the accelerator of an automobile sends signals
to the engine to consume more fuel and increase speed. These and
countless other phenomena are well modeled as functions of continuous
time or as sequences of numbers occurring at discrete time
intervals. The choice of model is made to represent the physical
signal with greatest accuracy or fidelity. |
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What
is a system? Signals are of no use without entities to generate
and act upon them. Examples include human sensory apparatus, a
radio receiver, and an emergency siren. Taking "signal" to mean
"the mathematical model of a physical signal," we define a system as an
operator that accepts a given (input) signal and produces a new
(output) signal in response. In this course, we study the
mathematical representation of signals and systems. The most important
representations we introduce involve the frequency domain - a
transforming way of looking at signals and systems, and a complement to
the conventional viewpoint of the time domain viewpoint.
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520.460
Error Control Coding (Fall, 2007) [Minimum of 10
students required Fall, 2008] |
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Error
control codes map messages of symbols into blocks or streams of information symbols suitable for
transmission over a noisy communications channel in such a way as to
provide resistance to the effects (usually errors in the received
information) of channel noise. Coding is used because it works
and because it pays for itself in terms of reduced energy and/or
bandwidth required for robust information conveyance. Simply put,
coding detects and corrects errors caused by channel noise. More
subtly, coding affords the use of less energy and permits sending more
bits/second in one Hz of bandwidth. The focus of this course is
on how coding works, on codes and their decoders as well as performance
and efficiency. Prerequisites: Linear algebra and
probability. |
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520.465
Digital Communications I (Spring, 2008) [Minimum of 10
students required after 2008] |
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This course
introduces the basic tools and topics of modern digital communication
beginning with the mathematical representation and spectral properties
of random signals and a basic introduction to the detection of real and
complex signals in the presence of noise. Memoryless modulation and
demodulation schemes are thoroughly studied for the Gaussian channel,
and measures of performance are developed. 3 credits. Prerequisites: Basic
Communication (520.401) and Probability (550.310 or 550.420). |
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520.466
Digital Communications II (Fall, 2006) [Minimum of 10
students required Fall, 2008] |
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As a follow-on to 520.465, Digital Communications II
addresses fundamental and achievable bounds on digital communications
performance. It begins with "just enough Information Theory" to
convey an appreciation for the power and utility of channel (error
control) coding. The performance of coded communications is
studied first by bounding the performance of systems using coding, then
by examining how actual codes are used in communications. |
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520.766
Seminar in Error Control Coding (Occasional) [Please ask me
before registering] |
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A
seminar on emerging error control codes and decoding algorithms is held
when requested, meeting weekly for approximately two hours. Each
participant prepares one or more talks on topics of interest, in
consultation with the other participants. Frequently, a student
focuses on one topic throughout the semester, making several
presentations and submitting a 20-40 page report summarizing the
topic. Prerequisite: Error Control Coding (520.460). |
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