ECE 221 Energy Conversion I
ECE 221L Energy Conversion I Laboratory – v3
ECE 224 Energy Conversion Laboratory (1.5 credits) – v1, v2
A. Course General Information:
Course Code: |
ECE221 ECE221L |
Course Title: |
Energy Conversion I Energy Conversion I Laboratory |
Credit Hours (Theory + Laboratory): |
3 + 1 |
Contact Hours (Theory + Laboratory): |
3 + 3 |
Category: |
Program Core |
Type: |
Required, Engineering, Lecture + Laboratory |
Prerequisites: |
ECE 203 Electrical Circuits II ECE 203L Electrical Circuits II Laboratory |
Co-requisites: |
None |
Equivalent Course |
EEE 221 Energy Conversion I
EEE 221L Energy Conversion I Laboratory ECE 224 Energy Conversion Laboratory (1.5 credits) – v1, v2 EEE 224 Energy Conversion Laboratory (1.5 credits) – v1, v2 |
B. Course Catalog Description (Content):
This course gives a brief idea about the fundamental concepts of some DC and AC energy conversion machines. It starts with the basic principle, construction, performance analysis and designing of a transformer. Then it covers the construction, operating principle, effect of parameter changes and starting procedure of induction motor, synchronous generator and synchronous motor. Students also learn about the basic operating principle, procedure of speed control and starting of DC machines. This course has 3 hours/week separate mandatory laboratory session.
C. Course Objective:
The objective of this course are to
a. help students to understand the construction and basic principle of operation of a complex energy conversion system
b. provide the students with knowledge to analyze and design transformer, induction motor, synchronous motor, synchronous generator, DC motor and DC generator.
c. enable students to develop an understanding how different parameters like load, field current, supply voltage, frequency change the performance of an electrical machine
d. equip students with necessary skills to construct, run and observe the operation of basic electrical machines
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Describe the construction and basic operation principles of transformer, induction motor, synchronous machine and DC machine |
CO2 |
Examine the performance of transformer, induction motor, synchronous machine and DC machine |
CO3 |
Design transformer, induction motor, synchronous machine and DC machine for practical applications with various requirements of torque and speed using simulation tools |
CO4 |
Explain the effect of different parameter changes on the operation of induction motor, synchronous machine and DC machine |
CO5 |
Demonstrate proficiency in using laboratory tools to carry out experiments. |
E. Mapping of CO-PO-Taxonomy Domain & Level- Delivery-Assessment Tool:
Sl. |
CO Description |
POs |
Bloom’s taxonomy domain/level |
Delivery methods and activities |
Assessment tools |
EEE 221 Energy Conversion I |
|||||
CO1 |
Describe the construction and basic operation principles of transformer, induction motor, synchronous machine and DC machine |
a |
Cognitive/ Understand |
Lecture, Notes |
Quiz, Exam |
CO2 |
Examine the performance of transformer, induction motor, synchronous machine and DC machine |
a |
Cognitive/ Analyze |
Lecture, Notes |
Quiz, Assignment, Exam, project |
CO3 |
Design transformer, induction motor, synchronous machine and DC machine for practical applications with various requirements of torque and speed using simulation tools |
c |
Cognitive/ Create |
Lecture, Notes |
Assignment, Exam, Project |
CO4 |
Explain the effect of different parameter changes on the operation of induction motor, synchronous machine and DC machine |
a |
Cognitive/ Understand |
Lecture, Notes |
Quiz, Assignment, Exam |
EEE 221L Energy Conversion I Laboratory |
|||||
CO5 |
Demonstrate proficiency in using laboratory tools to carry out experiments. |
e |
Cognitive/ Understand Psychomotor/ Precision |
Lab Class |
Lab Work, Lab Exam |
F. Text and Reference Books:
Sl. |
Title |
Author(s) |
Publication Year |
Edition |
Publisher |
ISBN |
1 |
Electric Machinery and Fundamentals |
Stephen J. Chapman |
2012 |
5th |
McGraw Hill
|
978-007-108617-2 |
2 |
Electric Machines- Theory, Operation, Applications, Adjustment and Control |
Charles I Hubert |
2002 |
4th |
Pearson |
978-0675211369 |
ECE 303 Measurement & Instrumentation
ECE 303IL Measurement & Instrumentation Laboratory – v3
ECE 304 Measurement & Instrumentation Laboratory (1.5 credits) – v1, v2
A. Course General Information:
Course Code: |
ECE 303 ECE 303IL |
Course Title: |
Measurement & Instrumentation Measurement & Instrumentation Laboratory |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Lab): |
3 + 3 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture + Laboratory |
Prerequisites: |
ECE 203 Electrical Circuits II ECE 203L Electrical Circuits Laboratory |
Co-requisites: |
None |
Equivalent Course |
EEE 303 Measurement & Instrumentation
EEE 303IL Measurement & Instrumentation Laboratory ECE 304 Measurement & Instrumentation Laboratory (1.5 credits) – v1, v2 EEE 304 Measurement & Instrumentation Laboratory (1.5 credits) – v1, v2 |
B. Course Catalog Description (Content):
Applications, functional elements of a measurement system and classification of instruments. Measurement of electrical quantities: Current and voltage, power and energy measurement. Current and potential transformer. Transducers: mechanical, electrical and optical. Measurement of non-electrical quantities: Temperature, pressure, flow, level, strain, force and torque. Basic elements of dc and ac signal conditioning: Instrumentation amplifier, noise and source of noise, noise elimination compensation, function generation and linearization, A/D and D/A converters, sample and hold circuits. Data Transmission and Telemetry: Methods of data transmission, DC/AC telemetry system and digital data transmission. Recording and display devices. Data acquisition system and microprocessor applications in instrumentation. This course has 3 hours/week integrated laboratory session (ECE303IL).
C. Course Objective:
This subject is offered in response to observations that measurement & instrumentation which plays a dominant part in the realization of most systems developed by electrical & electronics engineers in all sub-disciplines, and to insistence from an industry that our graduates should be adequately equipped to deal with the real engineering.
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Explain working principles and process for modern laboratory instrumentation and automated Data Acquisition systems. |
CO2 |
Characterize system behavior, evaluate the type of measurement to be performed and select the appropriate sensor |
CO3 |
Design and conduct experiments and acquire data analysis and interpretation skills. |
CO4 |
Prepare written documentation including formal written reports containing the data acquired from lab experiment. |
CO5 |
Use real-time operating system kernel, discrete time system. |
E. Mapping of CO-PO-Taxonomy Domain & Level- Delivery-Assessment Tool:
Sl. |
CO Description |
POs |
Bloom’s taxonomy domain/level |
Delivery methods and activities |
Assessment tools |
CO1 |
Explain working principles and process for modern laboratory instrumentation and automated Data Acquisition systems. |
a |
Cognitive/ Understand |
Lectures, notes |
Quiz, Exam |
CO2 |
Characterize system behavior, evaluate the type of measurement to be performed and select the appropriate sensor |
b |
Cognitive/ Evaluate |
Lectures, notes |
Quiz, Exam |
CO3 |
Design and conduct experiments and acquire data analysis and interpretation skills. |
b |
Cognitive/ Apply |
Lectures, notes |
Quiz, Exam |
CO4 |
Prepare written documentation including formal written reports containing the data acquired from lab experiment. |
j |
Cognitive/ Apply, Psychomotor// Precision |
Lab Class |
Lab Work, Lab Exam |
CO5 |
Use real-time operating system kernel, discrete time system. |
e |
Cognitive/ Apply, Psychomotor/ Manipulation |
Lab Class |
Lab Work, Lab Exam |
ECE 371 Introduction to Biomedical Engineering
A. Course General Information:
Course Code: |
ECE 371 |
Course Title: |
Introduction to Biomedical Engineering |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
CHE110 Principles of Chemistry Higher Secondary/Equivalent Biology |
Equivalent Course |
EEE 371 Introduction to Biomedical Engineering |
B. Course Catalog Description (Content):
Fundamentals of biological systems and the applications of engineering principles for solving problems in medicine. To understand the constitutive and structural behaviour of complex biological tissue: mechanical and electro-chemical properties of biological tissues, nonlinear and time-dependent behaviour, functional adaptation to load, structural and micro-structural behaviour, hierarchical modelling from system to tissue to cell, failure theories. Applications may include cardiovascular, neural and musculo-skeletal systems. Detailed discussion on bioengineering and biomedical engineering, including current local and international research and industry, emphasis on local strengths..
ECE 385 Machine Learning
ECE 385IL Machine Learning Laboratory
A. Course General Information:
Course Code: |
ECE 385 ECE 385IL |
Course Title: |
Machine Learning Machine Learning Laboratory |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 3 |
Category: |
Program Core |
Type: |
Required, Engineering, Lecture + Laboratory |
Prerequisites: |
STA 201 Elements of Statistics and Probability |
Co-requisites: |
None |
Equivalent Course |
EEE 385 Machine Learning EEE 385IL Machine Learning Laboratory |
B. Course Catalog Description (Content):
Machine learning is the science of getting computers to act without being explicitly programmed. In this class, students will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for themselves. Students will learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks), Unsupervised learning (clustering, dimensionality reduction, PCA), Neural Networks, Deep learning, Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that student will also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. This course has 3 hours/week mandatory integrated laboratory session (EEE385IL).
C. Course Objective:
The objectives of this course are to
a. Introduce the core concepts and fundamental elements of machine learning.
b. Provide students with sound understanding and knowledge of practical applications of different forms of machine learning techniques.
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Discuss the core concepts of Logistic Regression. |
CO2 |
Analyze the performance of different machine learning algorithms through various evaluation metrics. |
CO3 |
Design neural network systems for classification, segmentation or object detection from different forms of data. |
CO4 |
Apply the knowledge of machine learning to develop practical problem solving system. |
E. Mapping of CO-PO-Taxonomy Domain & Level- Delivery-Assessment Tool:
Sl. |
CO Description |
POs |
Bloom’s taxonomy domain/level |
Delivery methods and activities |
Assessment tools |
CO1 |
Discuss the core concepts of Logistic Regression. |
a |
Cognitive/ Understand |
Lecture, Notes |
Quiz, Assignment, Exam |
CO2 |
Analyze the performance of different machine learning algorithms through various evaluation metrics. |
a |
Cognitive/ Analyze |
Lecture, Notes |
Assignment, Exam, Project |
CO3 |
Design neural network systems for classification, segmentation or object detection from different forms of data. |
c |
Cognitive/ Create |
Lecture, Notes |
Assignment, Project |
CO4 |
Apply the knowledge of machine learning to develop practical problem solving system. |
a |
Cognitive/ Apply |
Lectures, Tutorial |
Assignment, Lab Work, Project |
F. Text and Reference Books:
Sl. |
Title |
Author(s) |
Publication Year |
Edition |
Publisher |
ISBN |
01 |
Hands-On Machine Learning with Scikit-Learn and TensorFlow. |
Aurélien Géron |
2017 |
1st |
O’ Reilly Media |
13: 978-1491962299 |
02 |
Deep Learning with Python |
François Chollet |
2017 |
1st |
Manning Publications |
13: 978-1617294433 |
03 |
MATLAB Machine Learning |
Michael Paluszek |
2016 |
1st |
Apress |
13: 978-1484222492 |
ECE 461 Biomedical Instrumentation
A. Course General Information:
Course Code: |
ECE 461 |
Course Title: |
Biomedical Instrumentation |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
ECE 343 Digital Signal Processing ECE 343L Digital Signal Processing Laboratory |
Co-requisites: |
None |
Equivalent Course |
EEE 461 Biomedical Instrumentation |
B. Course Catalog Description (Content):
Origin and major types of biological signals: Human body: cells and physiological systems, bioelectric potential, bio-potential electrodes and amplifiers, blood pressure, flow, volume and sound, electrocardiogram, electromyogram, electroencephalogram, phonocardiogram, vector cardiogram. Interpretation of bio-signals. Noise in bio-signals. Measurement of bio-signals: transducers, amplifiers and filters. Measurement and detection of blood pressure. Blood flow measurement: Plethysmograph and electromagnetic flow meter. Measurement of respiratory volumes and flow, related devices. X-ray. Tomography: positron emission tomography and computed tomography. Magnetic resonance imaging. Ultra-sonogram. Patient monitoring system and medical telemetry. Therapeutic devices: cardiac pacemakers and defibrillators. Electrical safety in bi instrumentation and sensing.
C. Course Objective:
The objectives of this course are to
a. -introduce the students to the application of biomedical instrumentation
b. familiarize the students with the analysis and design of different instrument to measure biosignals like EEG, ECG, and EMG etc.
c. -includes brief study of different medical instrument and their use in physiological measurements
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Describe the origin of bio potentials and the role of bio potential electrodes. |
CO2 |
Analyze common biomedical signals (ECG, EEG, EMG etc.) and distinguish characteristic features. |
CO3 |
Identify common signal artifacts, their sources and formulate strategies for their suppression. |
CO4 |
Explain the principles of operation and patient safety issues related to biomedical instruments (X-ray, MRI, Ultra sonogram etc.) |
E. Mapping of CO-PO-Taxonomy Domain & Level- Delivery-Assessment Tool:
Sl. |
CO Description |
POs |
Bloom’s taxonomy domain/level |
Delivery methods and activities |
Assessment tools |
CO1 |
Describe the origin of bio potentials and the role of bio potential electrodes |
a |
Cognitive/ Remember |
Lecture, Notes |
Quiz, Exam |
CO2 |
Analyze common biomedical signals (ECG, EEG, EMG etc.) and distinguish characteristic features; |
b |
Cognitive/ Analyze |
Lecture, Notes |
Assignment, Exam |
CO3 |
Identify common signal artifacts, their sources and formulate strategies for their suppression. |
b |
Cognitive/ Analyze |
Lecture, Notes |
Assignment, Exam |
CO4 |
Explain the principles of operation and patient safety issues related to biomedical instruments (X-ray, MRI, Ultra sonogram etc.) |
a |
Cognitive/ Understand |
Lecture, Notes |
Quiz, Exam |
ECE 462 Introduction to Photonics
A. Course General Information:
Course Code: |
ECE 462 |
Course Title: |
Introduction to Photonics |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
ECE 308 Electronic Circuits II ECE 308L Electronic Circuits II Laboratory ECE 309 Semiconductor Device Physics |
Co-requisites: |
None |
Equivalent Course |
EEE 462 Introduction to Photonics |
B. Course Catalog Description (Content):
The course covers the following concepts: Introduction to nanophotonics, Review of electromagnetism fundamentals, Finite difference time domain modeling method, Interaction of light with dipolar nanoparticles, Interaction of light with wavelength‐scale particles, Optical tweezers, Photonic crystals, Interaction of light with plasmonic metals,plasmonics, Optical antennas, Purcell effect, Microscopy and manipulation tools, Near Field Imaging, Meta-Material and Plasmon Based Devices, Nanoscale Waveguide Based Device, Microcavity Based Devices, Other emerging topics in nanophotonics.
C. Course Objective:
The objective of this course Ire to:
• Provides a comprehensive overview of the fundamental principles and primary applications of nanophotonics,
• Describes the behavior of light and its interactions with matter on the micro- and nanoscale.
• Introduce how scattering from small particles depends on particle size, shape, and composition.
• Make student familiarize with ways to numerically model light at the nanoscale.
• Explain how both localized surface plasmons and surface plasmon polaritons can be used to concentrate light into nanoscale volumes.
• Introduce how photonic crystals can be used to reflect, guide, or confine light.
• Conceptually explain and mathematically derive the diffraction limit of light.
• Introduce schemes to perform imaging with resolution beyond the diffraction limit.
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Explain how scattering from small particles depends on particle size, shape, and composition |
CO2 |
Explain the mechanism of light matter interaction |
CO3 |
Analyze different microscopy and imaging tools |
CO4 |
Apply knowledge of photonics in nanoscale waveguide, microcavity, antenna design, and medical applications. |
E. Mapping of CO-PO-Taxonomy Domain & Level- Delivery-Assessment Tool:
Sl. |
CO Description |
POs |
Bloom’s taxonomy domain/level |
Delivery methods and activities |
Assessment tools |
CO1 |
Explain how scattering from small particles depends on particle size, shape, and composition |
a |
Cognitive/ Understand |
Lectures, Notes |
Assignment, Quiz, Exam |
CO2 |
Explain the mechanism of light matter interaction |
a |
Cognitive/ Understand |
Lectures, Notes |
Assignment, Quiz, Exam |
CO3 |
Analyze different microscopy and imaging tools |
b |
Cognitive/ Analyze |
Lectures, Notes |
Assignment, Exam |
CO4 |
Apply knowledge of photonics in nanoscale waveguide, microcavity, antenna design, and medical applications. |
a |
Cognitive/ Apply |
Lectures, Notes |
Assignment, Quiz, Exam |
ECE 464 Nanotechnology
A. Course General Information:
Course Code: |
ECE 464 |
Course Title: |
Nanotechnology |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
ECE 308 Electronic Circuits II ECE 308L Electronic Circuits II Laboratory ECE 309 Semiconductor Device Physics |
Co-requisites: |
None |
Equivalent Course |
EEE 464 Nanotechnology |
B. Course Catalog Description (Content):
The course covers concepts in nanotechnology: Introduction to nanotechnology innovation, Nanomaterials, Processes & fabrication: In top-down processing, bottom-up, Characterization: Optical tools, Scanning probe techniques, Electron microscopies , Spectroscopy techniques, X-ray techniques, Other: contact angle, ellipsometry, Nanobiotechnology & medical applications, Energy: harvesting techniques, conversion, and storage, Nano-electronics, plasmonics & nano-photonics
C. Course Objective:
The objective of this course are to:
a. Introduce the unique properties of nanomaterials to the reduce dimensionality of the material.
b. Explain methods of fabricating nanostructures.
c. Describe tools for properties of nanostructures.
d. Make students familiarize with the applications of nanomaterials and implication of health and safety related to nanomaterials.
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Explain nanoscale and macroscale material and objects |
CO2 |
Explain nanotechnology enabling technology |
CO3 |
Analyze different fabrication and characterization tools |
CO4 |
Apply knowledge of nanotechnology in energy, nanoelectronics, photonics and medical applications. |
E. Mapping of CO-PO-Taxonomy Domain & Level- Delivery-Assessment Tool:
Sl. |
CO Description |
POs |
Bloom’s taxonomy domain/level |
Delivery methods and activities |
Assessment tools |
CO1 |
Explain nanoscale and macroscale material and objects |
a |
Cognitive/ Understand |
Lectures, Notes |
Assignment, Quiz, Exam |
CO2 |
Explain nanotechnology enabling technology |
a |
Cognitive/ Understand |
Lectures, Notes |
Assignment, Quiz, Exam |
CO3 |
Analyze different fabrication and characterization tools |
b |
Cognitive/ Analyze |
Lectures, Notes |
Assignment, Exam |
CO4 |
Apply knowledge of nanotechnology in energy, nanoelectronics, photonics and medical applications. |
a |
Cognitive/ Apply |
Lectures, Notes |
Assignment, Quiz, Exam |
ECE 471 Introduction to Biomedical Imaging and Image Analysis
A. Course General Information:
Course Code: |
ECE 471 |
Course Title: |
Introduction to Biomedical Imaging and Image Analysis |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
ECE 343 Digital Signal Processing ECE 343L Digital Signal Processing Laboratory |
Co-requisites: |
None |
Equivalent Course |
EEE 471 Introduction to Biomedical Imaging and Image Analysis |
B. Course Catalog Description (Content):
This course is about processing signals to obtain medical images for each modality (based on its physics, mathematical modeling and instrumentation). It introduces the fundamental principles of medical image analysis and visualization. It also covers the two main image geometry. It focuses on the main sources of medical imaging data such as ultrasound, MR, and X-ray images for the purpose of quantification and visualization to increase the usefulness of modern medical image data.
C. Course Objective:
The objective of this course is to:
a. Provide a broad overview of this field as well as the foundation techniques required to process, analyze, and use images for medical applications.
b. Familiarize with many of the current methods used to enhance and extract useful information from medical images.
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Interpret digital images as 2D mathematical functions. |
CO2 |
Apply the knowledge of convolution, Fourier analysis, filtering, histogram analysis, image enhancement, linear and non-linear systems analysis to manipulate digital images. |
CO3 |
Compute an image analysis algorithm from first principles (i.e. block diagrams, mathematics) and learn how to implement, debug and test functionality |
CO4 |
Identify sources of noise in medical images (i.e. acquisition noise, low contrast), and learn to reduce their impact through denoising and enhancement |
CO5 |
Describe the physics and advantages/disadvantages of X-rays, CT, PET, MRI, EIT and ultrasound. |
E. Mapping of CO-PO-Taxonomy Domain & Level- Delivery-Assessment Tool:
Sl. |
CO Description |
POs |
Bloom’s taxonomy domain/level |
Delivery methods and activities |
Assessment tools |
CO1 |
Interpret digital images as 2D mathematical functions. |
a |
Cognitive/ Understand |
Lecture, Notes |
Quiz, Exam |
CO2 |
Apply the knowledge of convolution, Fourier analysis, filtering, histogram analysis, image enhancement, linear and non-linear systems analysis to manipulate digital images. |
a |
Cognitive/ Apply |
Lecture, Notes |
Quiz, Assignment, Exam |
CO3 |
Compute an image analysis algorithm from first principles (i.e. block diagrams, mathematics) and learn how to implement, debug and test functionality |
e |
Cognitive/ Apply |
Lecture, Notes |
Assignment |
CO4 |
Identify sources of noise in medical images (i.e. acquisition noise, low contrast), and learn to reduce their impact through denoising and enhancement |
b |
Cognitive/ Analyze |
Lecture, Notes |
Assignment, Exam |
CO5 |
Describe the physics and advantages/disadvantages of X-rays, CT, PET, MRI, EIT and ultrasound. |
a |
Cognitive/Remember |
Lecture, Notes |
Quiz, Exam |
ECE 473 Advanced Magnetic Resonance Imaging and Applications
A. Course General Information:
Course Code |
ECE 473 |
Course Code Title |
Advanced Magnetic Resonance Imaging and Applications |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Elective, Engineering, Lecture |
Prerequisites: |
ECE 343 Digital Signal Processing ECE 343L Digital Signal Processing Laboratory |
Co-requisites: |
None |
Equivalent Course |
EEE 473 Advanced Magnetic Resonance Imaging and Applications |
B. Course Catalog Description (Content):
In depth study of the physics and mathematical principles of MRI. MRI machines, RF coils, NMR phenomenon for proton spin system, resonance condition, frame of reference,RF excitation, relaxation, signal localization. Bloch equations, k-space acquisition, imaging sequences. MRI image reconstruction, contrast imaging, artifacts. Applications of MRI: neuroimaging and musculoskeletal imaging. MRI based diagnosis of cancer and stroke.
ECE 488 Data Science and Analytics
ECE 488IL Data Science and Analytics Laboratory
A. Course General Information:
Course Code: |
ECE 488 ECE 488IL |
Course Title: |
Data Science and Analytics Data Science and Analytics Laboratory |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 3 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture + Laboratory |
Prerequisites: |
STA 201 Elements of Statistics and Probability MAT 216 Mathematics I Lecture + Laboratory Linear Algebra and Fourier Analysis |
Co-requisites: |
None |
Equivalent Course |
EEE 488 Data Science and Analytics |
B. Course Catalog Description (Content):
Properties of data: variables and types, measurement scales, central tendency, dispersion, correlation, and contingency. Data distribution: probability, random variables and probability distribution. Statistical inference: interval estimation, hypothesis testing, analysis of variance, and multivariate analysis of variance. Data preprocessing: data cleaning, integration, data transformation and outlier detection. Feature engineering: data dimensionality reduction, component analysis, factor analysis, discriminant analysis and feature selection. Time-series analysis: trend and seasonal components, stationary processes, non-stationary processes, forecasting methods, and advanced time-series analysis methods. Building models from data: predictive modeling through regression and sequential data models, classification modeling through supervised learning, cluster modeling through unsupervised learning. Data visualization and model evaluation. This course has 3 hours/week integrated laboratory session (EEE488IL).
C. Course Objective:
The objective of this course is to describe both theoretical and practical approach for making sense out of data. A step-by-step process is introduced, which is designed to walk you through the steps and issues that you will face in data analysis projects. It covers the more common tasks relating to the analysis of data including (1) how to prepare data prior to analysis, (2) how to generate summaries of the data, (3) how to identify non-trivial facts, patterns, and relationships in the data, and (4) how to create models from the data to better understand the data and make predictions. The hands-on exercise will help the students to apply data science methodology to solve a range of real-world problems.
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Apply statistical inference methods for hypothesis testing and variance analysis of data. |
CO2 |
Use data preprocessing and feature engineering methods for feature extraction and selection |
CO3 |
Analyze appropriate data modeling methods suitable for different types of problems and data |
CO4 |
Examine, select and implement proper data analytics steps for solving interesting real-life data science problems and write report to show the results of data analysis. |
E. Mapping of CO-PO-Taxonomy Domain & Level- Delivery-Assessment Tool:
Sl. |
CO Description |
POs |
Bloom’s taxonomy domain/level |
Delivery methods and activities |
Assessment tools |
CO1 |
Apply statistical inference methods for hypothesis testing and variance analysis of data. |
a |
Cognitive/ Apply |
Lecture, Notes |
Assignment, Quiz, Exam |
CO2 |
Use data preprocessing and feature engineering methods for feature extraction and selection |
a |
Cognitive/ Apply |
Lecture, Notes |
Assignment, Quiz, Exam Project |
CO3 |
Analyze appropriate data modeling methods suitable for different types of problems and data |
a |
Cognitive/ Analyze |
Lecture, Notes |
Assignment, Quiz, Exam |
CO4 |
Examine, select and implement proper data analytics steps for solving interesting real-life data science problems and write report to show the results of data analysis. |
a |
Cognitive/ Apply |
Lecture, Notes |
Project |
ECE 489 IoT for Critical Infrastructures
ECE 489IL IoT for Critical Infrastructures Laboratory
A. Course General Information:
Course Code: |
ECE 489 ECE 489IL |
Course Title: |
IoT for Critical Infrastructures IoT for Critical Infrastructures Laboratory |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture + Laboratory |
Prerequisites: |
ECE 373 Embedded System Design ECE 373L Embedded System Design Laboratory |
Co-requisites: |
None |
Equivalent Course |
EEE 489 IoT for Critical Infrastructures |
B. Course Catalog Description (Content):
This course will introduce the internet of things, its sensor network and required protocol including Cyber security of IoT and Cyber-Physical Systems. This course will also discuss the Programming for IoT devices/embedded systems. Students will design and build IoT based project for critical infrastructures. This course has 3 hours/week integrated laboratory session (EEE489IL).
C. Course Objective:
The objectives of this course is to teach the
a. Introduce different infrastructure components, network systems, and design the basic network for IoT ideas.
b. Expose students to software solutions for different IoT systems
c. Teach IoT security and privacy risks, and concept design secure hardware and software.
d. Prepare student on finding viable IoT concept design, develop prototype and testing as well as identify route to market..
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Explain the IoT systems and the requirements to design IoT solutions |
CO2 |
Apply sensors, network systems and standard protocols in IoT infrastructure |
CO3 |
Evaluate the security and privacy implications of the IoT and Cyber-Physical Systems |
CO4 |
Design and develop IoT system with given requirements and constraints |
E. Mapping of CO-PO-Taxonomy Domain & Level- Delivery-Assessment Tool:
Sl. |
CO Description |
POs |
Bloom’s taxonomy domain/level |
Delivery methods and activities |
Assessment tools |
CO1 |
Explain the IoT systems and the requirements to design IoT solutions |
a |
Cognitive/ Understand |
Lecture, Notes |
Assignment, Quiz, Exam |
CO2 |
Apply sensors, network systems and standard protocols in IoT infrastructure |
a |
Cognitive/ Apply |
Lecture, Notes |
Assignment, Quiz, Exam Project |
CO3 |
Evaluate the security and privacy implications of the IoT and Cyber-Physical Systems |
a |
Cognitive/ Evaluate |
Lecture, Notes |
Assignment, Quiz, Exam |
CO4 |
Design and develop IoT system with given requirements and constraints |
a |
Cognitive/ Create |
Lecture, Notes |
Project |
ECE 490 Special Topics
A. Course General Information:
Course Code: |
ECE 490 |
Course Title: |
Special Topics |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
Set by Department/Instructor |
B. Course Catalog Description (Content):
This course will explore emerging topics and interest in Electrical and Electronic Engineering. The emphasis will be on thorough study of a contemporary field within ECE, and the course will be made accessible to students with an intermediate, undergraduate ECE background. The syllabus should be approved by the department chair prior to commencement of the term, and a detailed description will be provided before the registration period.
ECE 491 Independent Study
A. Course General Information:
Course Code: |
ECE 491 |
Course Title: |
Independent Study |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
Set by Department/Instructor |
B. Course Catalog Description (Content):
For students interested in studying Electrical and Electronic Engineering: independently exploring an advanced topic under a faculty instructor. The student must first identify a faculty member within the EEE department to oversee his/her work, and then write a proposal to the department chair outlining the means and objectives of the study. The proposal must be approved by the intended faculty supervisor and department
ECE 497 Internship
A. Course General Information:
Course Code: |
ECE 402 |
Course Title: |
Internship |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
Set by Department/Instructor |
B. Course Catalog Description (Content):
For students interested in doing an internship in industry under the supervision of industry and faculty advisors. the student must first identify a faculty member within the EEE department to oversee his/her work, and then write a proposal to the department chair outlining the means and objectives of the nature of Internship work The proposal must be approved by the intended faculty supervisor and department chair.
ECE 498 Directed Research
A. Course General Information:
Course Code: |
ECE 498 |
Course Title: |
Directed Research |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
Set by Department/Instructor |
B. Course Catalog Description (Content):
For students interested in conducting significant research under a faculty supervisor. The student must first identify a faculty member within the EEE department to oversee his/her research work, and then write a proposal to the department chair outlining the means and objectives of the research project. The proposal must be approved by the intended faculty supervisor and department chair. At the end of the term, the student must submit a detailed research report and/or give a presentation of the results, before the final course grade may be awarded.