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DEPARTMENT OF ELECTRICAL AND

COMPUTER ENGINEERING

McMASTER UNIVERSITY

GRADUATE COURSE DESCRIPTIONS

2020/2021

Electrical and Computer Engineering 707

Linear Systems

Instructor: Dr. T. Kirubarajan Office: ITB-A112/A, ext. 24819 Email: [email protected] Web: http://www.ece.mcmaster.ca/~kiruba/ Prerequisites: Engineering mathematics and basic linear algebra - - For M.Eng. and

M.A.Sc. students ONLY Course Summary: This course is intended as a first semester graduate course on linear

systems theory, design and implementation with application to signal processing, communications, estimation and control. The objective is to present a comprehensive coverage of the basic tools needed by an electrical engineering graduate student specializing in the above areas.

Course Outline:

1. Linear spaces and linear operators 2. Mathematical descriptions of systems 3. State-space models, solutions and realizations 4. Controllability and observability of linear systems 5. Minimal realizations and coprime fractions 6. State feedback, state estimators and observers 7. Stability of linear and non-linear systems 8. Applications

Textbook/Reading: C.T. Chen, Linear System Theory and Design, Oxford University Press,

3rd Edition, 1999. Additional Reading: W. Rugh, Linear System Theory Prentice Hall, Second Edition, 1996. G. Strang, Linear Algebra and its Applications, Third Edition, 1988. Grading:

Assignments and projects 30% Mid-term 30% Final exam 40% Term: I


Engineering Optimization Instructor: Dr. Tim Davidson,

ITB-A111, Ext. 27352. e-mail: [email protected]

Course web page: http://www.ece.mcmaster.ca/~davidson/ECE710 Recommended Text: Boyd and Vandenberghe, Convex Optimization, Cambridge University

Press, Cambridge, 2004 (Book web page can be found at: http://www.stanford.edu/~boyd/cvxbook.html

Recommended Reading: Bertsekas, with Nedic and Ozdaglar, Convex Analysis and

Optimization, Athena Scientific, Belmont, MA, 2003. Nocedal and Wright, Numerical Optimization, Springer, New York,

1999. Bertsekas, Nonlinear Programming, 2nd edition, Athena Scientific, Belmont, MA, 1999. Gill, Murry and Wright, Practical Optimization, Academic Press, London, 1986.

Prerequisite: A solid background in linear algebra. Exposure to numerical

computing, programming, optimization and engineering design will be helpful, but is not required.

Course Outline: Principles of engineering optimization: modelling, formulation,

solution and verification A taxonomy of optimization problems and solution methods Convex sets, convex functions and convex optimization Duality Unconstrained optimization Constrained optimization, including interior point methods Computational complexity and NP-completeness Applications to engineering design

Lectures: There will be two lectures a week, each of about 90 minutes in

duration. Assessment: Midterm Test: 20%

Final Exam: 35% Design Project: 45%

Term: II


SILICON PHOTONICS – FUNDAMENTALS AND DEVICES

Instructor: Dr. Jamal Deen, email: [email protected]

Text: M.J. Deen and P.K. Basu, "Silicon Photonics - Fundamentals and

Devices", Wiley Series in Materials for Electronic &

Optoelectronic Applications, ISBN-13: 978-0-470-51750-5 - John

Wiley & Sons, 2012.

Description: The creation of affordable high speed optical communications using

standard semiconductor manufacturing technology is a principal aim

of silicon photonics research. This would involve replacing copper

connections with optical fibers or waveguides, and electrons with

photons. With applications such as telecommunications and

information processing, light detection, spectroscopy, holography and

robotics, silicon photonics has the potential to revolutionize electronic-

only systems. This course will provide an overview of the physics,

technology and device operation of exclusively silicon and related

alloys.

Course Outline: Basic Properties of Silicon; Quantum Wells, Wires, Dots and

Superlattices; Absorption Processes in Semiconductors; Light

Emitters in Silicon; Photodetectors , Photodiodes and Phototransistors;

Raman Lasers including Raman Scattering; Guided Lightwaves;

Planar Waveguide Devices and Fabrication Techniques and Material

Systems.

Project: The project can be a detailed review or investigation of any of

the topics covered in the course. It should include a discussion of the

key papers in the topic as well as the most recent results. Students are

expected to demonstrate a mastery of their chosen topic in the project

report and presentation.

mailto:[email protected]

Grading: Assignments - 50%

Project - 30%

Presentations - 20%

Term: I


Matrix Computations in Signal Processing Instructor: Dr. J.P. Reilly Web page: www.ece.mcmaster.ca/~reilly Text: "Matrix Computations", 3rd edition, Golub and Van Loan, Johns

Hopkins University Press References: "Linear Algebra and Its Applications", 3rd edition, G. Strang "Applied Numerical Linear Algebra", James W. Demmel Course Outline: 1. Review of fundamental concepts of linear algebra

2. Covariance matrices and the Karhunen-Loeve expansion, applications

3. Singular value decomposition (svd), eigendecomposition (ed) 4. Gaussian elimination, condition number, and error analysis 5. Cholesky decomposition and applications 6. Linear Least Squares Estimation: background, normal equations,

variance of solution, full-rank and rank-deficient solution using the svd.

7. The QR decomposition: Householder, Givens, fast Givens, and modified Gram-Schmit techniques, systolic arrays.

8. Solving least-squares using the QR decomposition: the full-rank and rank-deficient case.

9. Toeplitz systems Grading: Assignments - 2 @ 20% each = 40%

Final Exam - 60% Term: I

ECE 716 Switched Reluctance Machines

Fall 2020 Course Outline

COURSE OBJECTIVE The objective of the course is to develop an understanding of the fundamental operational principles and control of switched reluctance machines (SRM). The student will be provided the multidisciplinary principles and design aspects of SRM. The topics that will be covered throughout the course include electromagnetic principles, modeling, controls, converters, and materials used in SRMs, and design of SRM. The course also targets helping the student gain hands-on experience in simulation tools used in electric machine design.

INSTRUCTOR AND CONTACT INFORMATION

Class Timing: TBD All times referenced in this document are Eastern.

Dr. Berker Bilgin

Instructor Assistant Professor

Department of Electrical and Computer Engineering 1280 Main Street West, ITB A218

Tel: (905) 525 9140 Ext 27080 [email protected]

COURSE DESCRIPTION Switched Reluctance Machine (SRM) differs from other electric machines due to its simple construction and lack of coils or permanent magnets on the rotor. These features enable the operation at higher speeds and harsh environment. SRM is a promising candidate for various motor drive applications, but it can suffer from high torque ripple and acoustic noise. The simple and low-cost construction of SRM makes the modelling, analysis, and controls more challenging. In this course, we will explore various characteristics of SRM including modeling, converters, control, materials, and design. Students will utilize the course material to design an SRM in their group project assignment.

LEARNING OUTCOMES

Students will go through all relevant topics to understand the operational principles and design of SRM. Upon the completion of this course, students will be able to complete the following:

Understand the operational principles of SRM

Gain an understanding of multidisciplinary design aspects in SRM

Develop simulation models to analyze the performance of SRM

Conduct finite element analysis (FEA) simulations to characterize SRM

Gain an experience in designing an SRM for a selected application

RECOMMENDED READING

R. Krishnan, Switched Reluctance Motor Drives Modeling, Simulation, Analysis, Design and Applications, Boca Raton, FL: CRC, 2001.

B. Bilgin, J. W. Jiang, and A. Emadi, Switched Reluctance Motor Drives: Fundamentals to Applications, CRC Press, 2018, ISBN: 9878-1138304598

EVALUATION

The final student grade will be calculated as follows: Components and Weights

Group Assignment 6th week of the term (group) 10%

Midterm Exam 7th week of the term (individual) 25%

Group Project Assessment 11th week of the term (individual) 30%

Group Project Presentation 12th and 13th weeks of the term (group) 5%

Group Project Report 13th week of the term (group) 30%

Total 100%

Group Project Students will complete a group project due in the 13th week of the term. Students will form groups of three of their choice. Each group will select an application, calculate the design requirements in the group assignment (due in the 6th week of the term) and design a switched reluctance machine for that application. A project report must be submitted on the due date along with the final simulation files. The report should be written in double-column IEEE paper format. The report should not be more than 10 pages including references. Your mark in this assignment will be based on how well your design satisfies the requirements and how comprehensive your simulations are. Reports are expected to be free of spelling and grammatical mistakes. All references must be included and properly cited. On the 12th and 13th weeks of the semester, each group will present their designs to the class. By the 3rd week of the term, the groups have to select their application. A group project outline must be submitted, which includes the names and contact information of the team members, and the selected application. Group Assignment The purpose of this assignment is to identify the specifications of the switched reluctance machine to be designed in the group project. Some of these requirements are

Torque-speed envelop (continuous and peak)

Phase current (continuous and peak)

Duration of the peak power

Input voltage

Ambient temperature

Allowable temperature rise

Dimensional constraints

Current density and cooling constraints In this assignment, the groups are expected to conduct research and report the following details for their application:

Details of the application, advantages and challenges

Industry analysis (e.g. number of motors manufactured, major manufacturers, units sold, sample purchasing price, etc.)

Electric motor types currently used in the application

Specifications for sample motors This report should not exceed 5 pages in length (1.5 spacing, 1” margin on all sides, single column, single space, Time New Roman 12 font) inclusive of any

exhibits and/or appendices but exclusive of the title page, table of contents, and references. Some of the information in this report can be used in the introduction of the group project report. Group Project Assessment The purpose of this assessment is to evaluate the contribution of each member of a group to the group project. The Group Project Assessment will be held on the 11th week of the term. The instructor will meet the project groups separately. The schedule of the meetings will be announced during the course of the term. The group members will make a five-minute presentation to the instructor to describe the progress of their SRM design. Then, the instructor will ask questions to each member of the team to evaluate their contribution to the group project and to assess how they applied the course learnings in the design. In this assessment, the students will be graded for their individual performance and contribution to the group project. Midterm test The midterm test, written in the 7th week of the term, may consist of a combination of problems, short-answer questions, and drawings. More details of the format, structure, and content coverage will be provided during the course of the term.

Grade Conversion At the end of the course your overall percentage grade will be converted to your letter grade in accordance with the following conversion scheme.

Letter Grade Percent Points

A+ 90-100 12

A 85-89 11

A- 80-84 10

B+ 75-79 9

B 70-74 8

B- 60-69 7

F 00-59 0

COURSE SCHEDULE

Week Date Topic Due

1 Sep. 8 2020

Course Overview Electric Motor Industry and SRM

2 Sep. 15 2020

Electromagnetic Principles of SRM

3 Sep. 22 2020

Derivation of Pole Configuration in SRM Operational Principles of SRM

Group project outline due Sep. 22, 4PM ET

4 Sep. 29 2020

Modeling of SRM Tutorial: Modeling an SRM Drive in MATLAB/Simulink

5 Oct. 6 2020

SRM in Generating Mode Tutorial: Modeling and Analysis of SRM in Finite Element Software

6 Oct. 13 2020

Control of SRM Converters in SRM Tutorial: Optimization of control parameters of an SRM

Group Assignment Report due Oct. 13, 4PM ET

7 Oct. 20 2020 Midterm Exam

8 Oct. 27 2020

Design Considerations in SRM

9 Nov. 3 2020

SRM Design Examples

10 Nov. 10 2020

Materials used in SRM Mechanical Construction of SRM

11 Nov. 17 2020

Group Project Assessment No Lecture

12 Nov. 24 2020

Group Project Presentations I Advanced topic: Introduction to acoustic noise and vibration in SRM

13 Dec. 1 2020

Group Project Presentations II Advanced topic: Thermal Management in SRM

Group Project Report due Friday Dec. 4th, 7PM ET

Term 1

ECE 718 Special Topics in Computation

Predictable Computer Architecture for Real-Time Embedded Systems

Course Objective Understand design approaches of computer architecture with a deep focus on predictable architecture

for real-time embedded systems and cyber-physical systems.

Textbook: There is NO textbook used in this course. The main sources of information are labs, lecture and project

material, data sheets, application notes, online presentations, research papers, etc.

Labs and Projects The course has the following components:

Three labs that are using architectural simulators

A paper presentation that is conducted during Lectures 10 and 11 (tentative)

A project that is conducted individually and is chosen by students (with the guidance from the

instructor) to apply the concepts learned throughout the course. Potential ideas for the project will

also be discussed during lectures when the time is due.

Assessment

The exam will be scheduled by end of semester (to be confirmed in due time). It is worth noting that

there is also a quiz in the first week of classes. Although the quiz does not count toward the final grade,

it is mandatory for each student’s background to be assessed. Unless this quiz is written and a plan is

devised on a proper course of action to move forward with ECE744, no lab/project submissions will be

accepted.

Labs 20%

Presentation 10%

Project 40%

Exam 30%

Conversion from percentage to letter grade will be by way of the standard scale used in the Office of the

Registrar. Statistical adjustments (such as bell curving) will not normally be used. Please note to pass this

course you must also obtain at least at least 50% on each of the two projects.

Outline and Tentative Schedule (Fall 2020) Lecture Topic Associated

Lab Assignment

Lecture 1

- Introduction - Core and pipeline

architectures

Computer Architecture Preliminaries

Lecture 2

- Caches - DRAMs

Lab Assignment

1

Lecture 3

- Multicore - Coherence and

Consistency

Lecture 4

Introduction to Embedded Systems, Real-Time Systems, and Cyber-Physical Systems

Lecture 5

Predictable Computer Architecture: Caches

Predictable Architecture for Embedded Systems

Lecture 6

Predictable Computer Architecture: Main Memory

Lab Assignment

2

Lecture 7

Predictable Computer Architecture: Interconnect

Lecture 8

Predictable Computer Architecture: Heterogenous SoCs

Lecture 9

OS and Real-Time OS (RTOS) Operating Systems Lab Assignment

3

Lecture 10

Applications of CPS 1 Research-Papers based

Lecture 11

Applications of CPS 2

Lecture 12

Project Presentations

Term 1

Policy Reminders: Senate and the Faculty of Engineering require all course outlines to include the following reminders: “The Faculty

of Engineering is concerned with ensuring an environment that is free of all adverse discrimination. If there is a

problem, that cannot be resolved by discussion among the persons concerned, individuals are reminded that they

should contact the Department Chair, the Sexual Harassment Officer, or the Human Rights Consultant, as soon as

possible.”

“Students are reminded that they should read and comply with the Statement on Academic Ethics and the Senate

Resolutions on Academic Dishonesty as found in the Senate Policy Statements distributed at registration and

available in the Senate Office.”

"Academic dishonesty consists of misrepresentation by deception or by other fraudulent means and can result in

serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript

(notation reads: "Grade of F assigned for academic dishonesty"), and/or suspension or expulsion from the

university. It is your responsibility to understand what constitutes academic dishonesty. For information on the

various kinds of academic dishonesty please refer to the Academic Integrity Policy, specifically Appendix 3, located

at https://secretariat.mcmaster.ca/app/uploads/Academic-Integrity-Policy-1-1.pdf. The following illustrates only

three forms of academic dishonesty:

1. Plagiarism, e.g. the submission of work that is not one's own or for which other credit has been obtained. 2.

Improper collaboration in group work. 3. Copying or using unauthorized aids in tests and examinations."

of 1

ELECTRICAL AND COMPUTER ENGINEERING

ECE 720 Power Converter Systems

Instructor: Dr. Mehdi Narimani

ITB-A320 Email: [email protected]

Recommended Textbook: Bin Wu and Mehdi Narimani “High-Power Converters and AC Drives,”

Wiley - IEEE Press, 2017 http://ca.wiley.com/WileyCDA/WileyTitle/productCd-1119156033.html

Course Description: A course on the analysis, simulation and design of power converter

systems. Main topics include: high-power multi-pulse rectifiers, multilevel voltage and current source converters, pulse width modulation, harmonic reduction techniques, modeling and simulation techniques, and industrial applications. Important concepts are illustrated with design projects using Matlab/Simulink.

Course Outline: Introduction

High-Power Semiconductor Devices Multipulse Diode Rectifiers Multipulse SCR Rectifiers Two-level Voltage Source Inverter Multilevel Cascaded H-Bridge Converters Multilevel Diode-Clamped Inverter Other Multilevel Voltage Source Converters Current Source Inverters

Course Evaluation Assignments (5Assignments) 75% Final Project 25% Total 100%

Term II

ECE 723

Information Theory and Coding Instructor - Dr. Steve Hranilovic [email protected] http://www.ece.mcmaster.ca/∼hranilovic Course Description This course will provide an introductory look into the broad areas of information theory and coding theory. As stated in the course text,

Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity C).

In later stages of the course, coding techniques will be discussed which approach these ultimate limits. Tentative Outline (time permitting):

Entropy: entropy, relative entropy, mutual information, chain rules, data processing

inequality, the asymptotic equipartition property, entropy rates for stochastic processes.

Data Compression: the Kraft inequality, Shannon-Fano codes, Huffman codes, arithmetic coding.

Channel Capacity: discrete channels, random coding bound and converse, Gaussian channels, coloured Gaussian noise and optimal “water-pouring” power allocation.

Error Control Coding: linear block codes and their properties, hard-decision decoding, cyclic codes, convolutional codes, soft-decision decoding, Viterbi decoding algorithm.

Advanced Coding Techniques: lattice codes, trellis coded modulation, coset codes, multi-level codes/multi-stage decoding, iterative decoding.

Course Text/Reference Materials: Thomas M. Cover and Joy A. Thomas, Elements of Information Theory, John Wiley & Sons, 1991. (ISBN 0-471-06259-6) Stephen B. Wicker, Error Control Systems for Digital Communication and Storage, Prentice-Hall, 1995. (ISBN 0-13-200809-2) Papers from the literature cited by instructor. Assessment: Project – 50%, Assignments – 50%. Term 1


Policy Reminders: The Faculty of Engineering is concerned with ensuring an environment that is free of all adverse discrimination. If there is a problem, that cannot be resolved by discussion among the persons concerned, individuals are reminded that they should contact the Department Chair, the Sexual Harassment Officer or the Human Rights Consultant, as soon as possible. Students are reminded that they should read and comply with the Statement on Academic Ethics and the Senate Resolutions on Academic Dishonesty as found in the Senate Policy Statements distributed at registration and available in the Senate Office. Academic dishonesty consists of misrepresentation by deception or by other fraudulent means and can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: ”Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from the university. It is your responsibility to understand what constitutes academic dishonesty. For information on the various kinds of academic dishonesty please refer to the Academic Integrity Policy, specifically Appendix 3, located at http://www.mcmaster.ca/senate/academic/ac integrity.htm The following illustrates only three forms of academic dishonesty: 1. Plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained. 2. Improper collaboration in group work. 3. Copying or using unauthorized aids in tests and examinations.

ELECTRICAL AND COMPUTER ENGINEERING

ECE 724: MODELING, CONTROL, AND DESIGN OF ELECTRIFIED VEHICLES

Instructor: Dr. Jennifer Bauman

Contact: [email protected]

ITB A217, 905-525-9140 x27784

Course Description:

This course covers the modeling, control, and design of electrified vehicles, including hybrid, plug-in

hybrid, and pure electric vehicles. The course will use the textbook “Hybrid Electric Vehicle System

Modeling and Control, 2nd Edition” by Wei Liu. The high-level goal of this course is to understand the

vehicle model as a testbed for evaluating future design and control ideas. By the end of the course,

students will be able to create accurate vehicle models validated to real-world data, and use these

models to evaluate new ideas. The course content is approximately divided as follows:

Week Topic Textbook Chapter

(if applicable)

1 Introduction, model types, powertrain architectures, data sources 1

2 Standard drive cycles, fuel economy and range, model structure, modeling of: driver, chassis, wheel, final drive

10, 3

3 Modeling of: motors, control, power electronics, electrical accessories 3, 4

4 Modeling of: batteries, fuel cells 2, 3, 5

5 Hybrid energy storage systems, ultracapacitors

6 Modeling of: engines, transmissions 2, 3

7 Control strategies 6

8 Control strategies, electrified vehicle design 7, 10.3

9 EV Charging 6, 7.7 – 7.9

10/11/12 Student project presentations

Course Delivery and Assessment:

The course will have one 3-hour lecture per week. The course will be heavily project-based, with the

goal of the main project being to perform a small research study in the electrified vehicle space that

utilizes vehicle modeling to investigate design and/or control ideas. Note that there will be no

extensions on any deadlines. The course assessment is as follows:

Deliverable Description % of Total

Mark

Project Proposal

The purpose of the project proposal is to get students reading the literature and generating ideas for their course project. Thus, work on the project will begin at the start of the term. Perform a literature review in a vehicle-related area of interest (power electronics, motors, batteries, hybrid control, etc.). The proposal should consist of two main parts:

- Summary of literature review relevant to your topic of interest

- Proposed topic for investigation using vehicle modeling for the project.

Be sure to include a discussion of what data will be needed and used. Relevant parts of the literature review can be re-used in your final paper. The project proposal should be ~2 pages, single-spaced, 12-point font.

10%

Assignment #1 – EV Model

Use the provided Chevrolet Spark EV data to create a vehicle model in MATLAB/Simulink. Compare the simulated battery state-of-charge to the real logged vehicle data for the given drive cycles. Write a technical report (4 - 5 pages, single-spaced, 12-point font, including figures) to describe your model and results, and comment on any discrepancies in your results.

30%

Project

In-class presentation of ~10 minutes. Initial modeling results must be presented. The modeling results can be improved and extended for the final project submission.

5%

Final submission: MATLAB/Simulink modeling files and IEEE Conference-style final paper

55%

Term 11


Wireless Communication Networks

Instructor: Dr. T. Todd

Office: ITB/A324, 529-7070 ext. 24343

Email: [email protected]

Course website: http://owl.mcmaster.ca/~todd/

Prerequisites: Comp Eng 4DK4 (Computer Communication Networks) or equivalent,

ECE 739 (Resource Management and Performance Analysis In Wireless

Communication Networks) or equivalent, CAS 736 (Analysis Of

Stochastic Networks) or equivalent, or permission of the instructor. C

programming experience required. Access to a Unix/Linux/Windows

workstation and a C compiler.

Course Objective: This is an advanced course on wireless networking which focuses on

various topics relating to cellular and wireless mesh networks. Much of

the course will be run using student presentations and discussion, and

each student will do a project containing a significant research

component.

Textbook/Reading: Various books, papers, articles and lecture notes.

Grading: Lab/Assignments 20%

Project 60%

Class Presentations and/or Final Exam 20%

Term: I


http://owl.mcmaster.ca/~todd/


Nonlinear Control Systems

Instructor: Dr. Shahin Sirouspour, ITB-A319, Ext. 26238

[email protected]

Objective: To develop an understanding of the state-of-the-art in applied

analysis and synthesis of nonlinear control systems with an emphasis

on robotic control systems. Topics to be covered range from phase-

plane analysis, Lyapunov and input-output stability, to feedback

linearization and backstepping control.

Lectures: 3 hours/week

Recommended Text: Instructor’s lecture notes.

Recommended Reading: - M. Vidyasagar, Nonlinear Systems Analysis, SIAM, 2002.

- S. Sastry, Nonlinear Systems, Analysis, Stability, and Control,

Springer, 1999.

- M. Krstic, et al., Nonlinear and Adaptive Control Design, John

Wiley, 1995.

- J.-J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice

Hall, 1991.

Prerequisite: An undergraduate course in control systems (e.g. EE4CL4 or MECH

ENG 4R03) and a solid background in mathematical analysis.

Course Content:

Linear vs. Nonlinear Control, an introduction

Phase Plane Analysis

Describing Functions

Lyapunov Stability

o Stability of Linear Systems

o Linearization and Local Stability

o Lyapunov’s Direct Method

o LaSalle’s Invariance Principle

o Instability Theorems


Input-output Stability

o Lp spaces and their Extension

o Small Gain Theorem

o Passivity

o Positive Real and Strictly Positive Real Transfer Functions

o The Lur’e problem

o Circle and Popov Criteria

Feedback Linearization

o Vector fields, Lie Brackets, and Lie Algebra

o Input-State Linearization

o Input-Output Linearization

o Zero Dynamics

o Feedback Linearization for MIMO Systems

Robust & Adaptive Nonlinear Control

o Sliding Mode Control

o Adaptive Control of Linear Systems

o Adaptive Control of Nonlinear Systems

Linear Parameterization Model

Prediction-Error-Based Estimation Methods

The Least-Squares Estimator

o Composite Adaptation

o Adaptive Backstepping Control

Application Examples

Evaluation: Final Exam: 40%

Course Project: 40%

Assignments: 20%

Term: II


Nonlinear Optimization for Engineers Instructor: Prof. Mohamed Bakr ITB-A219, ext. 24079, e-mail: [email protected] Office hours: One hour after each lecture Course Description: This course addresses advanced concepts in nonlinear optimization

with a special focus on electrical applications. Starting with classical optimization approaches and single dimensional methods, we move to cover unconstrained and constrained multidimensional optimization. Both gradient-based and value-based optimization approaches are covered. The theory of linear and convex optimization will be introduced. The course also addresses areas of research relevant to electrical engineering. These include Space Mapping (SM) optimization, global optimization approaches and Adjoint Variable Methods (AVM). The examples and projects mainly focus on applications relevant to electrical engineering. 1. Introduction 2. Classical Optimization Approaches 3. One Dimensional Search 4. Unconstrained Optimization 5. Constrained Optimization 6. Sequential Quadratic Programming 7. Linear Programming 8. Convex Optimization

9. Global Optimization Approaches 10. Space Mapping Optimization 11. Adjoint Variable Methods Format: One 3 hour lecture is offered per week. One office hour after each

lecture. Recommended Texts: 1. Singiresu S. Rao, Engineering Optimization Theory and Practice,

Third Edition, John Wiley and Sons 2. Jorge Nocedak and Stephen Wright, Numerical Optimization,

Second Edition, Springer 3. Collection of research papers

Evaluation: Four projects each for 25% , Students are expected to make

presentations of their projects. Term: I


NETWORK INFORMATION THEORY Instructor: Prof. Jun Chen ITB-A221, X20163, [email protected] Prerequisites: Undergraduate courses in linear algebra, signals and systems,

probability and digital communication. Prior Exposure to information theory is preferred, but not required.

General Description: Network information theory deals with the fundamental limits on

information flow in networks and optimal coding techniques and protocols that achieve these limits. It extends Shannon’s point-to-point information theory to networks with multiple sources and destinations. Although a complete theory is yet to be developed, several beautiful results and techniques have been developed over the past forty years with applications in wireless communication, the internet, and other networked systems. This course aims to provide a broad coverage of key results, techniques, and open problems in network information theory.

Textbook: Abbas El Gamal and Young-Han Kim, Lecture Notes on Network Information Theory, {online} http://circuit.ucsd.edu/_yhk/Init.html. Tentative Outline (Time Permitting: 1. Entropy, Mutual Information, and Typicality

2. Point-to-Point Communication 3. Multiple Access Channels 4. Degraded Broadcast Channels 5. Interference Channels 6. Channels with State 7. General Broadcast Channels 8. Distributed Lossless Source Coding 9. Source Coding with Side Information 10. Distributed Lossy Source Coding 11. Multiple Descriptions 12. Joint Source-Channel Coding

Grading: Lecture Report 50%, Presentation 50%, Project (optional) 20%

Term: I

2

Policy Reminders: Senate and the Faculty of Engineering require all course outlines to include the following reminders: “The Faculty of Engineering is concerned with ensuring an environment that is free of all adverse discrimination. If there is a problem, that cannot be resolved by discussion among the persons concerned, individuals are reminded that they should contact the Department Chair, the Sexual Harassment Officer or the Human Rights Consultant, as soon as possible.” “Students are reminded that they should read and comply with the Statement on Academic Ethics and the Senate Resolutions on Academic Dishonesty as found in the Senate Policy Statements distributed at registration and available in the Senate Office.” “The instructor and university reserve the right to modify elements of the course during the term. The university may change the dates and deadlines for any or all course in extreme circumstances. If either type of modification becomes necessary, reasonable notice and communication with the students will be given with explanation and the opportunity to comment on changes. It is the responsibility of the student to check their McMaster email and course websites weekly during the term and to note any changes.” “Academic dishonesty consists of misrepresentation by deception or by other fraudulent means and can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: “Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from the university. It is your re-sponsibility to understand what constitutes academic dishonesty. For information on the various kinds of academic dishonesty please refer to the Academic Integrity Policy, specifically Appendix 3, located at http://www.mcmaster.ca/senate/academic/ac integrity.htm The following illustrates only three forms of academic dishonesty: 1. Plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained. (Insert specific course information, e.g. style guide) 2. Improper collaboration in group work. (Insert specific course information) 3. Copying or using unauthorized aids in tests and examinations. (If applicable) In this course we will be using a software package designed to reveal plagiarism. Students will be required to submit their work electronically and in hard copy so that it can be checked for academic dishonesty.”

Term I

Instructor: ECE 740 - Semiconductor Device Theory and Modeling

Instructor: Prof. Y. Haddara - E-mail: [email protected]

Lectures: Three hours per week

Office Hours: One hour immediately after each class or by appointment.

Text: D.A. Neaman - Semiconductor Physics and Devices, 3rd Ed., McGraw Hill (2002).

Course Description: This course provides a fundamental in-depth knowledge of the theory of operation,

modeling, parameter extraction, scaling issues, and higher order effects of active and

passive semiconductor devices that are used in mainstream semiconductor technology and

emerging devices of practical interest. There will be a comprehensive review of the theories

and latest models for the devices that are valid out to very high frequencies and the use of

physical device modeling. A review of the latest device technologies and architectures will

be presented. The course will be a prerequisite to the other applied courses in

microelectronics and photonics.

Course Outline

1. Review of semiconductor fundamentals.

2. Homo- and hetero-junction devices - theory; modeling; parameter extraction.

3. MOS capacitors and transistors - theory; modelling; parameter extraction; scaling issues; reliability.

4. Bipolar transistors - theory; modeling; parameter extraction; scaling issues; reliability.

5. Photodetectors – theory; modelling; parameter extraction; and scaling issues.

6. Transport and modeling of disordered semiconductors (organic and polymeric) devices.

Project Description: The project can be a detailed review or investigation of a specific part of the course. Examples

are Nano-scale MOS architectures and performance; Advanced silicon-based photodetectors;

SiGe HBTs or Nanowire silicon-based transistors; Transistor design and performance for

specific (e.g low-noise) applications; Device (MOS, BJT or HBT) parameter extraction

techniques; Modeling issues of silicon diodes at high frequencies; Carrier transport in nano-

scale MOS transistors; Conductivity of organic devices; Carrier scattering in nano-MOS

transistors; Modeling issues of passive components in silicon technology at microwave

frequencies; etc.

Grading: Assignments - 35% Project - 35% Final Exam - 30%

Selected References

IEEE Transactions on Electron Devices, Solid-State Electronics, Journal of Applied Physics etc.

ECS, ICMTS, IEDM, ESSDERC, DRC Proceedings.

Device simulators and manuals – Synopsis, Silvaco, TMA etc..

Y.P.Tsividis - Operation and Modelling of the MOS Transistor, 2nd Ed., McGraw Hill (1999), (TK 7871.99.M44.T77)

D.J. Roulston - Bipolar Semiconductor Devices, McGraw Hill (1990), TK 7871.86.R68.

D. Ferry, L. Akers and E. Greeneich - Ultra Large Scale Integrated Microelectronics, Prentice Hall (1988).

C.T. Sah - Fundamentals of Solid-State Electronics, World Scientific, Singapore (1991), TK 7871.85.S23

M. Shur, Physics of Semiconductor Devices, Prentice Hall (1990), QC 611.S563.

S.M. Sze - Physics of Semiconductor Devices, John Wiley & Sons (1981), TK 7871.85.S988.

S.M. Sze (Ed.)- Modern Semiconductor Device Physics, John Wiley & Sons (1998), QC 611.M674.

M.S. Tyagi - Introduction to Semiconductor Materials and Devices, John Wiley (1991), TK7871.85.T93.

S. Wang- Fundamentals of Semiconductor Theory and Device Physics, Prentice Hall (1989), QC 611.W32.

R. Warner & B. Grung - Semiconductor Device Electronics, Holt Rinehart & Winston (1991), ISBN 0-03-009559-X.

Electrical and Computer Engineering 746 Analysis and Design of RF ICs for Communications Instructor: Dr. C.H. (James) Chen Texts: Bosco Leung, VLSI for Wireless Communications, Prentice-Hall,

TK7874.75.L48, 2002 Reference Texts: 1. B. Razavi, RF Microelectronics, Prentice-Hall Inc., 1998.

2. T.H. Lee, The Design of CMOS Radio-Frequency Integrated Circuits, Cambridge University Press, 1998.

3. G. Gonzalez, Microwave Transistor Amplifiers: Analysis and Design, 2nd ed., Prentice-Hall Inc., 1997.

4. Lawrence P. Huelsman, Active and Passive Analog Filter Design: An Introduction, McGraw-Hill, 1993.

5. D.A. Johns and K. Martin, Analog Integrated Circuit Design, John Wiley & Sons, Inc., New York, 1997.

6. P.E. Allan and D.R. Holberg, CMOS Analog Circuit Design, 2nd ed., Oxford Press, 2002.

7. B. Razavi, Design of Analog CMOS Integrated Circuits, McGraw-Hill, 2001.

8. Clarke and Hess, Communication Circuits: Analysis and Design, Krieger, Reprint, 1994.

9. H.L. Krauss, C.W. Bostian, F.H. Raab, Solid State Radio Engineering, Wiley, 1980.

Course Description: This course provides a fundamental and in-depth knowledge of the

analysis and design of radio-frequency (RF) integrated circuits (IC) in the CMOS technology for wireless communications. The topics include the modelling of active and passive components for AC and noise analysis, design examples of amplifiers, filters, oscillators, PLL and frequency synthesizers. Circuit performance will be evaluated by both hand calculations and computer simulations. A good understanding of circuit analysis and CAD tools (e.g., HSPICE or SpectreRF) is required.

Course Outline: 1. Passive and active components at RF

2. Design of low-noise amplifiers 3. Active and passive filters 4. Operation and design of mixers 5. Oscillators 6. Phase locked loop 7. Frequency synthesizers

Project: Project will be the design and detailed analysis, including both hand calculations and computer simulation of an RF integrated circuit for a specific purpose/application. Possible projects will be discussed at the beginning of class.

Grading: Assignments - 50%

Term Project - 50% Term: II

1

ECE 753: MODERN ANTENNAS IN WIRELESS TELECOMMUNICATIONS Objectives and outline: This course provides fundamental knowledge in the theory and practice of antenna design and deployment in modern wireless telecommunication systems. The theory of electromagnetic radiation is introduced and the fundamental antenna parameters are explained. Basic antenna measurement techniques are introduced and practiced in a 6-hour laboratory session. The principles of analysis and design of antenna arrays are discussed. Special attention is paid to antennas used in mobile (cellular, satellite) communications. The fundamental limitations of electrically small antennas as well as the principles of smart antennas are briefly introduced through seminar sessions. Instructor: Prof. Natalia K. Nikolova

ITB-A308, ext. 27141 e-mail: [email protected]

Course web page: http://www.ece.mcmaster.ca/faculty/nikolova/antennas.htm Courseware and text: 1. Lecture Notes (distributed in class and available for download)

2. C. A. Balanis, Antenna Theory, 3rd ed., Wiley-Interscience, New York, 2005.

Additional sources: 3. W. L. Stutzman and G. A. Thiele, Antenna Theory and Design, 2nd ed., Wiley, 1998. 4. J. D. Kraus and R. J. Marhefka, Antennas (for all Applications), 3rd ed., McGraw-Hill, 2002.

(the previous editions authored by Kraus only are fine, too). 5. R. S. Elliot, Antenna Theory and Design, A Classical Reissue, IEEE Press, 2003. 6. Elsherbeni and Inman, Antenna Design & Visualization Using MATLAB, Scitech, 2006. On antennas and propagation: 7. R. E. Collin, Antennas and Radiowave Propagation, McGraw-Hill, Inc. 1985. 8. K. Siwiak, Radiowave Propagation and Antennas for Personal Communications, 2nd ed.,

Artech House, Inc., Norwood, MA, 1998. 9. J. Doble, Introduction to Radio Propagation for Fixed and Mobile Communications, Artech

House, Inc., Norwood, MA, 1996. On smart antennas: 10. T. K. Sarkar, M. C. Wicks, M. Salazar-Palma, R. J. Bonneau, Smart Antennas, Wiley, 2003. Course Outline: 1. Introduction into antenna theory and practice. 2. Radiation integrals and auxiliary potential functions; basic EM theorems in antenna problems. 3. Fundamental antenna parameters. 4. Antenna measurements. 5. Infinitesimal dipole; wire and loop radiating elements.

2

6. Wire antennas – dipoles, monopoles, Yagi-Uda array. 7. Arrays – analysis and design. 8. Printed antennas. 9. Reflector antennas. 10. Horn antennas. Seminars: 11. Fundamental limitations of electrically small antennas. 12. Smart antennas and signal processing antennas. Assessment: Laboratory 20 % Weekly Assignments 40 % Project 40 % Term: I Lectures: There will be one lecture a week, of about 3 hours in duration. Policy reminder:

Academic dishonesty consists of misrepresentation by deception or by other fraudulent means and can result in serious consequences, e.g., the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: "Grade of F assigned for academic dishonesty"), and/or suspension or expulsion from the university.

It is your responsibility to understand what constitutes academic dishonesty. For information on the various kinds of academic dishonesty please refer to the Academic Integrity Policy, specifically Appendix 3, located at

http://www.mcmaster.ca/senate/academic/ac_integrity.htm

The following illustrates only three forms of academic dishonesty:

Plagiarism, e.g. the submission of work that is not one's own or for which other credit has been obtained.

Improper collaboration in group work.

Copying or using unauthorized aids in tests and examinations. Last update: May 26, 2019


Modeling and Simulation of Photonic Devices

Instructor: Dr. Xun Li

ITB-A313, Ext. 27698

E-mail: [email protected]

Course Description:

Photonic devices are key components to lightwave generation, amplification,

transmission, and detection in many application systems. Photonic devices that utilize

primarily photons, in conjunction with electrons can offer a tremendous bandwidth in

these applications, especially in broadband communication systems and networks. This

course focuses on the modeling of various passive, active, and functional photonic

devices through numerical approaches, simulation of device terminal performances

through mixed analytical and numerical methods, and extraction of device behavior

models.

Course Outline:

1. Introduction to photonic device modeling

2. Optical wave propagation

3. Material optical property

4. Numerical solution techniques

5. Selected photonic device modeling and simulation examples

Grading: Midterm minor project 40%, Final major project 60%

Term: I or II

Textbooks and/or other references:

Lecture notes will be offered (no textbook)

Reference books:

1. Physics of Photonic Devices, 2nd Edition, by S. L. Chuang, Wiley Inter-Science,

ISBN9780470293195

2. Optoelectronic Devices - Design, Modeling, and Simulation, by X. Li, Cambridge

University Press, ISBN9780521875103


Design of Lightwave Communication Systems and Networks Instructor: Dr. S. Kumar

CRL-204, ext: 26008 Email: [email protected]

Prerequisite: Communication Systems (3TI4), Discrete Time Systems and Random

Processes (3TJ4), Computer Communication Networks (4DK4) Recommended Texts: "Optical Fiber Telecommunications IIIA and IIIB", edited by I.P.

Kaminow and T.L. Koch, Academic Press, ISBN 0123951704 (IIIA) and ISBN 0123951712 (IIIB)

"Fiber-Optic Communication Systems", Govind P. Agrawal, John Wiley and Sons, Inc., 1997, ISBN 0-471-17540-4

Course Description: Lightwave communication has emerged as the undisputed transmission

method of choice in almost all areas of telecommunication, mainly because it offers unrivaled transmission capacity at low cost. Starting with the design of photonic devices for lightwave generation, modulation, amplification and detection and optical fibers for lightwave transmission, this course will mainly focus on the design of lightwave communication systems and networks based on these photonic devices and optical fibers.

Course Outline: 1. Lightwave generation and modulation

2. Fiber Transmission 3. Lightwave amplification 4. Lightwave detection 5. Advanced components for multiplexing and networking 6. Transmitter design 7. Amplifier design 8. Receiver design 9. Transmission protocols and line coding 10. Design of point to point WDM system 11. Transport networks, access networks and packet switched networks.

Grading: Project - 50%

Final Exam - 50% Term: II


TRACKING AND SENSOR INFORMATION FUSION Instructor: Dr. R. Tharmarasa Pre-requisites: ECE760 (Stochastic Processes) or ECE730 (Linear Systems) or

ECE771 (Algorithms for Parameter and State Estimation) or instructor’s permission.

Text: Multisensor-multitarget tracking, Y. Bar-Shalom and X. Li,

YBS Publications, 1995. Course description: This is intended as a follow-up course for ECE771, which

deals with single-sensor single-target tracking in a clean environment. This new course will introduce the advanced concepts and algorithms for multisensor-multitarget tracking under realistic conditions (with imperfect sensors and measure-ment uncertainties). In addition, this course will deal with multisource information fusion with applications to communications, signal processing and target tracking.

Course outline:

1) Review of target tracking and state estimation 2) Single-sensor single-target tracking in a clean environment 3) Single-target tracking in clutter 4) Multitarget tracking in clutter 5) Introduction to multisensor fusion 6) Multisensor fusion architectures 7) Multisensor fusion algorithms 8) Distributed sensor fusion 9) Sensor resource management 10) Computational issues 11) Application to target tracking, communications and signal

processing Project description: The students are expected to formulate and solve an advanced

multisensor-multitarget tracking/fusion problem and present simulations results from their MATLAB or C/C++ implementation.

Grading: 25% problems, 25% take-home exam and 50% project Term: I

Electrical and Computer Engineering 771 Algorithms for Parameter and State Estimation Instructor: Dr. T. Kirubarajan Office: ITB-A313, ext. 24819 e-mail: [email protected] Outline: This course presents parameter and state estimation algorithms for

noisy dynamic systems. The objective is to present a comprehensive coverage of advanced estimation techniques with applications to communications, signal processing and control. In addition to theory, the course also covers practical issues like filter initialization, software implementation, and filter model mismatch. Advanced topics on nonlinear estimation and adaptive estimation will be discussed as well. The concepts will be put into practice by the students on realistic estimation projects.

Prerequisites: Engineering mathematics, linear systems, probability and stochastic

processes References: 1. Y. Bar-Shalom, X. Rong Li and T. Kirubarajan, Estimation with

Applications to Tracking and Navigation, John Wiley & Sons, 2001.

2. R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, John Wiley & Sons, 1992.

3. F. L. Lewis, Optimal Estimation, John Wiley & Sons, 1986. 4. D. Manolakis, Statistical and Adaptive Signal Processing: Spectral

Estimation, Signal Modeling, Adaptive Filtering and Array Processing, McGraw-Hill, 2000.

Course Outline: 1. Basic concepts:

a. Maximum likelihood (ML) estimation b. Maximum a posteriori (MAP) estimation c. Least squares (LS) estimation d. Minimum mean square error (MMSE) estimation e. Linear MMSE (LMMSE) estimation

2. LS estimation for linear and nonlinear systems 3. Modeling stochastic dynamic systems 4. The Kalman filter for discrete time linear dynamic systems with

Gaussian noise

5. Steady static filters for noisy dynamic systems 6. Adaptive multiple model estimation techniques 7. Nonlinear estimation techniques 8. Computational aspects of discrete time estimation 9. Extensions to autocorrelated noise and smoothing 10. Continuous time state estimation

Grading: Exams 50%; Projects 40%; Homework assignments 10% Term: I

ECE-780: Medical Imaging Systems II _____________________________________________________________________________

Page 1 of 3

Electrical and Computer Engineering, ECE-780

MEDICAL IMAGING SYSTEMS II Instructor: Dr. Michael Noseworthy, PhD, PEng (LEL) Office: SJH, F-130, 905-522-1155, ext. 35218 Email: [email protected] Lab: Imaging Research Centre, St. Joseph’s Healthcare, Fontbonne

Room F-126 Web: http://www.ece.mcmaster.ca/~mikenose/web/HOME.html From Grad Calendar: This course will compliment Medical Imaging Systems I. In this course

imaging methods that rely on non-ionizing radiation will be discussed. The course content focuses on magnetic resonance imaging (MRI), multinuclear spectroscopy (MNS) and in vivo nuclear magnetic resonance (NMR) methods. Advanced concepts such as multi-modality imaging approaches, image fusion, and functional medical image processing will be discussed. (Note: Ultrasound and optical methods will not be discussed).

Course Objective: This course is designed to allow students to become familiar with

medical imaging technologies both from a physics and engineering perspective through to a practical perspective. The course will focus primarily on magnetic resonance techniques (e.g. MRI, in vivo NMR, etc.). Occasional comparisons with other imaging modalities (e.g. PET, SPECT, ultrasound, mammography, CT, EEG, MEG) will be made where appropriate. In addition, throughout the course, students will learn the most frequent artefacts, their causes and potential solutions.

Course Schedule: One 3 hour lecture every week. There will be three 2hr MRI labs

booked after every 4 lectures (timing TBA) to demonstrate materials from lectures.

Expected Background Knowledge: Students taking this course are expected to already have solid knowledge

in anatomy, physiology, electromagnetics and magnetic resonance imaging (MRI).

Course Outline: Week 1. Classical response of a single nucleus to a magnetic field,

rotating and lab frames of reference; magnetization, relaxation and Bloch equation.

Week 2. Quantum mechanical description of MRI. quantum mechanical

basis of precession and excitation, thermal equilibrium and longitudinal relaxation.

Week 3. RF pulses and signal detection. RF coils (surface coil, T/R switches, birdcage, phased array), B1

- and B1+ fields, B1

+ mapping. Week 4. Introductory signal acquisition methods: free induction decay,

spin echoes, inversion recovery, and spectroscopy Week 5. One and multidimensional Fourier imaging. Slice excitation

and k-space. Week 6. Sampling (uniform and non-uniform), image reconstruction,

signal, contrast and noise. Week 7. Water/fat imaging and chemical selective / suppression

methods. Week 8. Fast imaging in steady state, fast/turbo spin echo, echo planar

imaging, spiral and irregularly sampled imaging. Week 9. Magnetic field inhomogeneity effects and T2∗ dephasing Week 10. Motion artifacts, motion sensitizing gradients, measuring spin

motion using phase contrast, time-of-flight and diffusion. Week 11. Electromagnetic properties of tissues, magnetic susceptibility,

current density imaging, quantitative susceptibility mapping (qSM). Week 12. Multinuclear spectroscopy and imaging. Physiologically

important non-proton nuclei, quadrupolar nuclei. Week 13. Multi-modal image fusion. Atlases. Data mining, Big data. Textbook/Reading: The following texts are required: 1). Handbook of MRI Pulse Sequences (2004) Matt A. Bernstein et al. 2). Magnetic Resonance Imaging: Physical Principles and Sequence

Design, 2nd edition. (2014) Robert W. Brown, et al. Grading: Students will each give 3 presentations throughout the term detailing

advanced MRI topics. The topics will align with that being discussed that week in lecture (see schedule above). Student presentations will be 25minutes each, followed by 5min of Q/A. Marking will be done by the instructor and all students in the course (rubric attached). These will be discussed with the instructor early in the term.

A take home final exam will be held that covers the entire term.

Presentations (3) each worth 20% 60% Final Exam (take home) 40% Term: II

Note: This course will only be taught if 6 or more students are registered.

ECE 788 SPECIAL TOPICES

Control of Adjustable Speed Drives

Instructor: Dr. Babak Nahid-Mobarakeh

ITB-A310

[email protected]

https://www.eng.mcmaster.ca/ece/people/faculty/babak-nahid-mobarakeh

Course Summary:

AC machine modeling and their control design tools are presented. The focus is

on permanent-magnet and induction machines supplied by voltage-source

inverters. Field-Oriented Control, with and without mechanical sensor, is

developed. Common failures in adjustable speed drives are introduced and their

effect on the drive performance is analyzed. Fault-tolerant drives are studied and

some practical examples from industry are presented. Principal concepts are

developed with projects using Matlab/Simulink.

Course Outline:

1. Adjustable speed drives: architectures and applications

2. Modeling of AC machines for control purposes

3. Modeling of voltage-source inverters

4. Field-oriented control of AC machines

5. Common failures in adjustable speed drives

6. Modeling of AC machines under fault condition

7. Fault-tolerant capability of AC drives

8. Fault-tolerant control of adjustable speed drives

Recommended Textbook:

Jean‐Paul Louis, Control of Synchronous Motors, Wiley ISTE 2011 https://onlinelibrary.wiley.com/doi/book/10.1002/9781118601785

Grading:

Assignments 40%

Final Project 60%

Term: I


https://www.eng.mcmaster.ca/ece/people/faculty/babak-nahid-mobarakeh

https://onlinelibrary.wiley.com/doi/book/10.1002/9781118601785


Models of the Neuron

Instructor: Dr. Ian Bruce,

ITB/A213, Ext. 26984.

e-mail: [email protected]

Objective: To provide a solid conceptual and quantitative background in the

modelling of biological neurons and how they function as computational

devices. Practical experience will be gained in modelling neurons from a

number of perspectives, including equivalent electrical circuits, nonlinear

dynamical systems, and random point-processes, and an introduction to

the mathematics required to understand and implement these different

engineering methodologies will be given.

Text: C. Koch, Biophysics of computation: information processing in single

neurons, Oxford University Press, 1998. (ISBN: 0195104919)

References: P. Dayan and L. F. Abbott, Theoretical neuroscience, MIT Press, 2001.

(ISBN: 0262041995)

D. Johnston and S. M.-S. Wu, Foundations of cellular neurophysiology,

MIT Press, 1994. (ISBN: 0262100533)

C. Koch and I. Segev, Methods in neuronal modeling - 2nd edition, MIT

Press, 1998. (ISBN: 0262112310)

H. Wilson, Spikes decisions and actions: Dynamical foundations of

neuroscience, Oxford University Press, 1999. (Hdbk: ISBN

0-19-852431-5; Pbk: ISBN 0-19-852430-7)

W. Gerstner and W. Kistler, Spiking neuron models: single neurons,

populations, plasticity, Cambridge University Press , 2002. (Hdbk: ISBN

0-521-81384-0; Pbk: ISBN 0-521-89079-9) Link to online version.

F. Rieke, D. Warland, R. de Ruyter van Steveninck, and W. Bialek,

Spikes: exploring the neural code, MIT Press, 1996. (ISBN: 0262181746)

D. L. Snyder and M. I. Miller, Random point processes in time and space,

Springer-Verlag, 1991. (ISBN: 0387975772)

S. H. Strogatz, Nonlinear dynamics and chaos: with applications in

physics, biology, chemistry, and engineering, Perseus Books, 2001.

(ISBN: 0738204536)

Lectures: There will be eleven 3-hour lectures, with the possibility of one extra, if

required.

Prerequisite: A basic undergraduate understanding of electrical circuits, linear systems,

ordinary and partial differential equations, probability and random

processes.

Course Outline: Introduction to Biological Neurons and Neural Computation (1 Lecture)

Basic anatomy and physiology of neurons, membrane potential, spiking,

spike propagation, synapses, excitation and inhibition, basics of neural

computation;

Simple Deterministic Models of Neural Excitation (2 Lectures)

Integrate-and-fire models, discharge-rate models, simple neural networks;

Stochastic Models of Neural Activity (2 Lectures)

Poisson- and renewal-process models, random-walk models;

Nonlinear Dynamical Models of Neural Excitation (3 Lectures)

The Hodgkin-Huxley model, ionic channels, activation and inactivation

states, action potential generation, phase-plane analysis of neural

excitability, nonlinear dynamics;

Axons and Dendritic Trees (3 Lectures)

Linear cable theory, modeling dendritic trees, action potential

propagation, compartmental models.

Grading: Assignments (45%); Midterm (25%); Final (30%).

Term: II


Biomedical Signal Modeling and Processing Instructor: Dr. Aleksandar Jeremic Room: ITB-A214 Phone: 905-525-9140 ext. 27894 Email:[email protected] Prerequisites: For MASc and MEng students only. Course Objectives: A key to efficient biomedical signal processing is a fundamental understanding of physical models, simplified but adequate mathematical models, and statistically efficient signal processing algorithms. This course will expose students to advanced signal processing techniques and illustrate their application to biomedical signal processing and diagnostic imaging. Recommended Reading: E. Bruce, Biomedical Signal Processing and Signal Modeling, JohnWiley & Sons, 2003, New York. R. M. Gulrajani, Bioelectricity and Biomagnetism, John Wiley & Sons, 1998, New York. Prerequisites: Linear algebra and basic signal processing theory. Students should also be familiar with MATLAB. Grading: 3 Projects 70% Final Exam 30% Tentative Outline: • Biological signals – heart (ECG/MCG) – brain (EEG/MEG) – muscle (EMG) • Physiological Modeling : brain and heart dipole models, spatio-temporal analysis, finite elements and boundary elements models. • Signal Processing : estimation of physiological parameters using repeated measurements, analysis of deterministic and random growth curves, model identification and selection. TERM I

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