SYLLABUS

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SYLLABUS


Department:
CSIS
Course Number:

CSC 508
Credit Hours
: 3
Semester
: Fall
1999

I.

Course Name
: Techniques in Signal and Data Processing

II.
Instructor's Name
:

R. A. Pilgrim

Office Location
:

BB652


Phone Number
:

MSU 762
-
6220

Home 527
-
2657


E
-
Mail Address
:

pilgrim@apex.net


Web Page URL
: campus.murraystate.edu/acade
mic/faculty/bob.pilgrim/index.htm


Office Hours:
See Academic Web page: UserID: pilgrim, Password: access

III.

Class Location
: BB255


IV.
Catalog Description:

Covers the numerical methods and algorithms used rto obtain iseful information from
noisy measur
ements. The performance characteristics of various types of signal generating and sensing devices and
the associated methods of signal conditioning, sampling, and analog
-
to
-
digital conversion are reviewed. Common
techniques in linear and non
-
olinear filt
ering, responsivitiy correction, data object detection, classification,
correlation, feature extraction, identification and state estimation are studied. Applications in speech recognition,
sound & image analysis, data compression, and telecommunications a
re presented.

V.

Purpose
: The purpose of this course is to provide the student with a theoretical foundation in the principles
of signal and data processing and an understanding of their application to solving problems in real
-
world signal
processing appl
ications.

VI.
Course Objectives
:


1. Have a solid foundation in the mathematical and algorithmic methods in signal & data processing.


2. Understand the major issues in extracting signals from noisy measurements.


3. Understand the digit
ization process and its effects on the quality of signals.


4. Have experience with the various interface ports of desktop computers.


5. Be familiar with the frequency domain and its relationship to the time domain.


6. Underdanst the various image and sound file formats and how to access them in a program.


7. Have a practical knowledge of a library of signal and data processing functions.

VII.
Content Outline
:


We
ek 1


Introduction


Overview of Signal and Data Processing


Measurements and Noise



Week 2



Statistical Methods


Probability
-

A Simple Example


Some Probability Distributions


Uniform, Binomial, Gaussian,
Poisson


Generating Random Numbers



Week 3



Methods in Data Reduction

1The Random Vector and Propagation of Errors

Bayes Rule

Hyphthesis Testing

Curve Fitting
-

Least Squares




Week 4


Time Domain Analysis

Basics of Time Varying Signals

Analog to Digital Conversion

Sampling and Aliasing

Decimation in Amplitude and Time

Types and Sources of Noise

A Virtual Signal Generator

Accessing WAV Files



Week 5


Signal Detection and Adaptive Thresholds

Extracting Signals from Noisy Measurements

Convolution and Correlation



Week 6


Frequency Domain Analysis

Combining Sine

Waves

Building a Square Wave from Sine Waves

The Fourier Series



Week 7


The Fourier Transform and Its Properties

The Fast Fourier Transform (FFT) Algorithm

Frequency Analysis and the Power Density Spectrum



Week 8


The Z
-
Transform and Its Properties

The Effects of the Measurement System on a Measurement

Discrete Time Filtering IIR and FIR

High Pass, Low Pass and Band Pass Filters

Designing Discrete Time Filters



Week 9


Two
-
Dimensional Signals
-

Images

Accessing BMP Files
-

FOURCC

Image Filtering
-

Templates

Clustering Techniques

Feature Selection and Extraction



Week 10


Metho
ds in Pattern Recognition

Edge Detection

Image Segmentation and Partitioning

Shape Recognition

Texture Analysis





Week 11


Overview of Data Processing

Building and Tracking Data Objects

Categorization of Data Objects
-

Classification

Knowledge Extraction
-

State Estimation






Week 12


An Overview of Signal Processing Methods in Telecommuncations

Compression and Decompression in Voice Communicati
ons

Speaker Identification and Voice Recognition



Week 13


Compression and Decompression of Images

Teleconferencing

Wireless Networks



Week 14


Hot
-
Spot Tracking and Scan
-
to
-
Scan Correlation

Remote Sensing
-

LandSat

Automated Security and Surveillance



Week 15


Student Presentations



Week 16


Student Presentations

VIII.
Instructional Activities
: Instructional activities include, lectures, instructor
-
directed laboratory exercises,
programming assignments, and 3 or 4 tests. In addition, lecture outlines, answers to selected homework problems,
sample programs and other cour
se related information are available on the Web.

IX.
Field and clinical Experiences
: none

X.
Resources
: Open and closed programming laboratories, compiler/IDE, Web page.

XI.
Grading Procedures
:


90
-
100

A 3/4 In
-
Class
Quizes...........

50%


80
-
89

B Prog Assignments............. 20%


70
-
79

C Homework......................... 10%


60
-
69

D Presentation/Participation.. 20%


<60

E Final Exam........................

50%

Missing quiz grades will be made up during the
Final Exam. The Final Exam is required but
will be considered only if it improves your grade.
XII.
Attendance Policy
: The class role will be taken periodically. Students

are expected to attend class regularly.
Frequent absences could affect your homework/class participation grade. Late homework will not be graded for
credit.

XIII.
Text
: None

XIV.
Prerequisites
: Permission of Instructor

XV.
Academic Honesty Policy
: Che
ating, plagiarism (submitting another person's work as your own), or doing
work for another person which will receive academic credit are all unacceptable forms of conduct and constitute
academic dishonesty. This includes the use of unauthorized books, no
tebooks or other sources in order to secure or
give help during an examination; the unauthorized copying of examinations, assignments, reports, computer files or
term papers; or the presentation of unacknowledged material as if it were your own work.
It is

the policy of the
College of Business and Public Affairs that,
(1)
all

instances of academic dishonesty will receive appropriate
punitive action from the faculty member in whose class such dishonesty occurs
and

(2) the names of students
involved in acts of

academic dishonesty will be reported to the Dean.
Notice:

Any student requiring additional
assistance due to a disability should inform the instructor as soon as possible.