course outline fs15 ee800 stochastic systems

7/23/2019 Course Outline FS15 EE800 Stochastic Systems

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National University of Sciences & Technology (NUST)School of Electrical Engineering and Computer Science (SEECS)Department of Electrical Engineering

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Analysis of Stochastic SystemsCourse Code: EE-800 Semester: 1st

Credit Hours: 3+0 Prerequisite Codes: None

Instructor: Dr. Muhammad Usman

Ilyas

Class: MS-EE 7 (TCN / DSSP)

Office: Room A-312, SEECS Telephone: +92-51-90852133

Lecture Days: Tu 17001750h, Th 1800

1950h

E-mail: [email protected]

Class Room: RIMMS CR-21 Consulting Hours:

Lab Engineer: - Lab Engineer Email: -

Knowledge Group: Telecommunications &

Networking

Updates on LMS: Every Saturday / Sunday

Course Description:

This course covers the fundamental tools of probabilistic modeling and random processes as they are useful for

communication, signal processing and control. The course introduces axiomatic definition of probability, set

theory, conditional probability, permutations and combinations, random variables, distribution functions,

probability density functions, mean, variance, characteristic functions, joint distributions, concepts of

stochastic process, correlation and covariance, Poisson process, Markov chain and Markov process.

Course Objectives:

The course objective is that its successful completion should develop understanding of random variables,random processes and their applications in mathematical modeling for engineering problems. Further, it

should introduce students to the application of random variables in information theory. It should introduce

students to basic mathematical tools to distinguish random processes from chaotic processes.

Course Learning Outcomes (CLOs):

At the end of the course the students will be able to: PLO BT Level*

1.

Understand the properties and relationships of different random variables 1, 2 C-1

2. Understand the properties of random processes 1, 2 C-1

3.

Understand the relationships between multiple random processes 1, 2 C-1, C-44.

Use random variables and random processes for mathematical modeling 2, 3 C-3, C-4

5. Use random variables in information theory 3 C-3

6. Distinguish between random ad chaotic processes 4 C-4

* BT= Blooms Taxonomy, C=Cognitive domain, P=Psychomotor domain, A= Affective

domain

C-1=Knowledge, C-3=Application, C-4=Analysis
mailto:[email protected]:[email protected]:[email protected]


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Mapping of CLOs to Program Learning Outcomes

PLOs/CLOs CLO1 CLO2 CLO3 CLO4 CLO5 CLO6

PLO 1 (Engineering Knowledge)

PLO 2 (Problem Analysis)

PLO 3 (Design/Development of Solutions)

PLO 4 (Investigation)

PLO 5 (Modern tool usage)PLO 6 (The Engineer and Society)

PLO 7 (Environment and Sustainability)

PLO 8 (Ethics)

PLO 9 (Individual and Team Work)

PLO 10 (Communication)

PLO 11 (Project Management)

PLO 12 (Lifelong Learning)

Mapping of CLOs to Assessment Modules and Weightages (In accordance with NUST statutes)

To be filled in at the end of the course.

Assessments/CLOs CLO1 CLO2 CLO3 CLO4 CLO5 CLO6

Quizzes: 10%

Assignments: 15%

OHT-1: 15%

OHT-2: 15%

Labs: 0%

End Semester Exam:45%

Total : 100 %

Books:

Text Book: Alberto Leon-Garcia, Probability and Random Processes for Electrical Engineering, 2nded., Addison-

Wesley.

Reference

Books:1. Sheldon Ross, Introduction to Probability Models, 9thed., Academic Press, 2007.

2. Thomas M. Cover, Joy A. Thomas, Elements of Information Theory, Wiley Interscience, 1991.

3. Garnett P. Williams, Chaos Theory Tamed, Joseph Henry Press, 2001.

Main Topics to be Covered:

1. Introduction to probability

2.

Random variables

3. Multiple random variables

4. Limits and equalities of properties of random variables


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5.

Application of random variables in information theory

6.

Random processes

7.

Properties of multiple random processes8.

Introduction to chaotic processes

Lecture Breakdown:

Week No. Topics Sections Remarks

1 Lecture 1: Introduction to probability theory

Lecture 2: contd

Lecture 3: contd

2 Lecture 4:Random variablesLecture 5:

Lecture 6:

3 Lecture 7 Random variables

Lecture 8:

Lecture 9:

4 Lecture 10: Multiple random variables

Lecture 11: contd

Lecture 12: contd

5 Lecture 13: contd

Lecture 14: contdLecture 15: contd

6 OHT-1

7 Lecture 16: Limits and inequalities

Lecture 17: contd

Lecture 18: contd

8 Lecture 19: Applications of random variables in information theory

Lecture 20: contd.

Lecture 21: contd.

9 Lecture 22: Random processes

Lecture 23: contd

Lecture 24: contd

10 Lecture 25: Random processes

Lecture 26: contd

Lecture 27: contd

11 Lecture 28: contd

Lecture 29: contd

Lecture 30: contd

12 OHT-2

13 Lecture 31: Properties of multiple random processes

Lecture 32: contd

Lecture 33: contd



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Lecture 35: contd

Lecture 36: contd

15 Lecture 37: contdLecture 38: Markov chains

Lecture 39: contd


Lecture 41: Introduction to chaos theory

Lecture 42: Chaos theory


Lecture 44: contd

Lecture 45: Revision

18 Week 18: ESE

Lab Experiments:

Lab - N/A

Tools / Software Requirement:

Mathworks Matlab R2013, Python 2.7 on Ubuntu

Grading Policy:Quiz Policy: In order to give practice and comprehensive understanding of subject, in-class quizzes will

be given. Approximately 10 quizzes will be taken during the entire semester.

There will be no retakes opportunities for quizzes.

Quizzes will be unannounced and normally last for 10-15 minutes.

The question framed is to test the concepts involved in current or last lecture.

There will be no best-of grading policy.

Grading for quizzes will be on a scale of 0 to 10.

A score of 10 indicates an exceptional attempt towards the answer and a score of 1

indicates your answer is entirely wrong but you made a reasonable effort towards the

solution. Scores in between indicate very good (8-9), good (6-7), satisfactory (4-5), and

poor (2-3) attempt. Failure to make a reasonable effort to answer a question scores a 0.

Assignment Policy: In order to give practice and comprehensive understanding of subject, home assignments

will be given.

Late assignments will be accepted with a 30% penalty of the assignment total.

All assignments will count towards the total (No best-of policy). The students are

advised to do the assignment themselves. Copying of assignment is highly discouraged

and taken as cheating case and will be forwarded for disciplinary action. The questions in

assignment are more challenging to give students the confidence and extensive

knowledge about the subject and enable them to prepare for the exams.

Lab Conduct: N/A

Plagiarism:

SEECS maintains a strict no tolerance plagiarism policy that applies for quizzes,assignments, exams and any other assessment tools.

While collaboration in this course is highly encouraged, you must ensure that you do not

claim other peoples work/ idea as your own. Plagiarism occurs when the words, ideas,


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assertions, theories, figures, images, programming codes of others is presented as your

own work.

You must cite and acknowledge all sources of information in your assignments. Failing to comply with the SEECS plagiarism policy will lead to strict penalties including

zero marks in assignments and report to the academic coordination office for disciplinary

action.

course outline fs15 ee800 stochastic systems

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