EEE 470 Industrial Automation and Robotics
A. Course General Information:
Course Code: |
EEE 470 |
Course Title: |
Industrial Automation and Robotics |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture |
Prerequisites: |
EEE 373 Embedded Systems Design EEE 373 Embedded Systems Design Laboratory |
Co-requisites: |
None |
Equivalent Course |
ECE 470 Industrial Automation and Robotics |
B. Course Catalog Description (Content):
Introduction to mechanics and control of robotic manipulators. Topics include spatial transformations, dynamics, trajectory generation, actuators and control, and relations to product design and flexible automation. This course also emphasizes on Robot Structure and Workspace, Orientation Matrices, Forward Kinematics, Inverse Kinematics. Jacobian and Singularities,Trajectory Generation, discussion on robot controller will also be covered in this course.
C. Course Objective:
The objectives of this course are to
a. develop the students’ knowledge in various robot structures and their workspace.
b. enhance students’ skills in performing spatial transformations associated with rigid body motions.
c. develop students’ skills in performing kinematics analysis of robot systems
d. provide the student with knowledge of the singularity issues associated with the operation of robotic systems, trajectory planning and robot control.
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Demonstrate knowledge of the relationship between mechanical structures of industrial robots and their operational workspace characteristics |
CO2 |
Apply spatial transformation to obtain forward kinematics equation of robot manipulators |
CO3 |
Solve inverse kinematics of simple robot manipulators |
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 |
Demonstrate knowledge of the relationship between mechanical structures of industrial robots and their operational workspace characteristics |
a |
Cognitive/ Apply |
Lectures, notes |
Quiz, Exam |
CO2 |
Apply spatial transformation to obtain forward kinematics equation of robot manipulators |
a |
Cognitive/ Apply |
Lectures, notes |
Quiz, Exam |
CO3 |
Solve inverse kinematics of simple robot manipulators |
b |
Cognitive/ Create |
Lectures, notes |
Assignment, Exam |
Sl. CO Description POs Bloom’s taxonomy domain/level Delivery methods and activities Assessment tools
CO1 Demonstrate knowledge of the relationship between mechanical structures of industrial robots and their operational workspace characteristics a Cognitive/ Apply Lectures, notes Quiz, Exam
CO2 Apply spatial transformation to obtain forward kinematics equation of robot manipulators a Cognitive/ Apply Lectures, notes Quiz, Exam
CO3 Solve inverse kinematics of simple robot manipulators b Cognitive/ Create Lectures, notes Assignment, Exam
EEE 472 Artificial Intelligence
EEE 472IL Artificial Intelligence Laboratory
A. Course General Information:
Course Title |
EEE 472 EEE 472IL |
Course Title |
Artificial Intelligence Artificial Intelligence Laboratory |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 3 |
Category: |
Program Elective |
Type: |
Elective, Engineering, Lecture + Laboratory |
Prerequisites: |
STA 201 Elements of Statistics and Probability |
Co-requisites: |
None |
Equivalent Course |
CSE 422 Artificial Intelligence |
B. Course Catalog Description (Content):
This course is an introduction to the basic principles, techniques, and applications of Artificial Intelligence. Topics includes concepts of artificial intelligence, rationality, knowledge representation, logic, inference, perception, learning, planning, and problem solving intelligent agents and their structures. Problem representation; task environments, search strategies, constraint satisfaction problems, constraint propagation, rule chaining, inference and learning in intelligent systems; systems of general problem solving, game playing, expert consultation, concept formation and natural language processing, recognition, understanding and translation. Use of heuristic vs. algorithmic programming; cognitive simulations- vs. machine intelligence; study of some expert systems such as robotics and understanding, solving problems in AI language. Students will also experience programming in AI language tools. There will be a 3-hours per week mandatory laboratory class in this course. This course has 3 hours/week integrated laboratory session (EEE472IL).
C. Course Objective:
The objectives of this course are to:
a. Introduce the concept of Artificial Intelligence, rationality, understanding of different task environments and the appropriate use of different intelligent agents in these environments.
b. Prepare student to analyze different problem solving strategies for informed, uninformed problems, deterministic or stochastic games, and constraint satisfaction problem.
c. Teach different algorithms and the analysis of complexity, optimality and completeness of these algorithms.
d. Help student in developing the critical skill to formulate problems and strategies to solve problems. As well as using the concept of knowledge representation and logical inference in knowledge based agents.
e. Introduce the concept of uncertain knowledge and probabilistic reasoning and probabilistic models in various decision making problems.
f. Introduce the basic concept of machine learning and natural language processing
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Apply basic Artificial Intelligent (AI) principles in solutions of computing problem that require problem solving, inference, perception, rationality, task environment and agents, knowledge representation and learning, |
CO2 |
Distinguish different informed, uninformed and local and adversarial search algorithms and strategies, constraint satisfaction problems, deterministic and stochastic environments, supervised and unsupervised learning, knowledge representation by logical inference and probabilistic inference etc. |
CO3 |
Construct AI based search and adversarial (game) algorithms for informed search and constraint satisfaction problems |
CO4 |
Develop intelligent agents, expert systems, artificial neural networks and other machine learning models by applying probabilistic theories like naïve Bayes theory and Bayesian networks in developing in |
CO5 |
Assess the complexity, optimality and completeness of AI based algorithms and techniques. |
CO6 |
Demonstrate the independent research capability on artificial intelligence and write literature review on them. |
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 basic Artificial Intelligent (AI) principles in solutions of computing problem that require problem solving, inference, perception, rationality, task environment and agents, knowledge representation and learning, |
a |
Cognitive/ Apply |
Lecture, Discussion |
Assignment, Quiz, Exam |
CO2 |
Distinguish different informed, uninformed, local and adversarial search algorithms and strategies, constraint satisfaction problems, deterministic and stochastic environments, supervised and unsupervised learning, knowledge representation by logical inference and probabilistic inference. |
a |
Cognitive/ Analyze |
Lecture, Discussion |
Assignment, Quiz, Exam |
CO3 |
Construct AI based search and adversarial (game) algorithms for informed search and constraint satisfaction problems |
c |
Cognitive/ Create |
Lecture, Discussion, Lab class |
Assignment, Lab Work, Project |
CO4 |
Develop intelligent agents, expert systems, artificial neural networks and other machine learning models by applying probabilistic theories like naïve Bayes theory and Bayesian networks in developing in |
c |
Cognitive/ Create |
Lecture, Discussion Lab class |
Assignment, Lab Work, Project |
CO5 |
Assess the complexity, optimality and completeness of AI based algorithms and techniques. |
b |
Cognitive/ Evaluate |
Lecture, Discussion |
Assignment, Exam |
CO6
|
Demonstrate the independent research capability on artificial intelligence and write literature review on them. |
l |
Cognitive/ Understand, Affective/ Valuing |
Research Work |
Writing Review, Presentation |
EEE 474 Neural Networks
EEE 474IL Neural Networks
A. Course General Information:
Course Code: |
EEE 474 EEE474IL |
Course Title: |
Neural Networks Neural Network Laboratory |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 3 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture + Laboratory |
Prerequisites: |
EEE 343 Digital Signal Processing EEE 343L Digital Signal Processing Laboratory EEE 385 Machine Learning |
Co-requisites: |
None |
Equivalent Course |
CSE 425 Neural Networks |
B. Course Catalog Description (Content):
An extensive course on neural network architectures and learning algorithms with theory and applications. Temporal and optimal linear associative memories, fuzzy control. Cohen-Grossberg theorem. Unsupervised learning. Higher-order competitive, differential Hebbian learning networks. Supervised learning. Adaptive estimation and stochastic approximation. Adaptive vector quantization, mean-square approach. Kohonen self-organizing maps. Grossberg theory. Simulated annealing. Boltzman and Cauchy learning. Adaptive resonance. Gabor functions and networks, Radial basis function, recurrent neural networks, Convolution neural networks. This course has 3 hours/week integrated laboratory session (EEE474IL).
C. Course Objective:
The objectives of this course is to teach the
a. Teach classification logic to make difference between supervised and unsupervised learning.
b. Introduce functional and mathematical components of neural network classifiers, multilayer perceptron upon nonlinear classification context.
c. Introduce limitation of existing neural networks and their extension process.
d. Prepare student to use of computational tools for experimentation leading to new theoretical insights, build and train neural networks for practical purposes.
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Apply classification logic to differ between networks for supervised and unsupervised learning; |
CO2 |
Develop mathematical competence for understanding neural networks |
CO3 |
Use Multilayer Perceptron upon nonlinear classification context problems. |
CO4 |
Apply neural networks for solving non-conventional real-life situations |
CO5 |
Use computational tools for experimentation leading to new theoretical insights, build and train neural networks for practical purposes |
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 classification logic to differ between networks for supervised and unsupervised learning; |
a |
Cognitive/ Apply |
Lecture, Notes |
Assignment, Quiz, Exam |
CO2 |
Develop mathematical competence for understanding neural networks |
a |
Cognitive/ Apply |
Lecture, Notes |
Assignment, Quiz, Exam |
CO3 |
Use Multilayer Perceptron upon nonlinear classification context problems. |
a |
Cognitive/ Apply |
Lecture, Notes |
Assignment, Quiz, Exam |
CO4 |
Apply neural networks for solving non-conventional real-life situations |
a |
Cognitive/ Apply |
Lecture, Notes |
Assignment, Quiz, Exam |
CO5 |
Use computational tools for experimentation leading to new theoretical insights, build and train neural networks for practical purposes |
e |
Cognitive/ Understand Psychomotor/ Precision |
Lecture, Notes |
Assignment |
EEE 476 lmage Processing
EEE 476IL Image Processing Laboratory
A. Course General Information:
Course Code: |
EEE 476 EEE 476IL |
Course Title: |
lmage Processing Image Processing Laboratory |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 3 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture + Laboratory |
Prerequisites: |
EEE 343 Digital Signal Processing EEE 343L Digital Signal Processing Laboratory EEE 385 Machine Learning |
Co-requisites: |
None |
Equivalent Course |
CSE 428 Image Processing |
B. Course Catalog Description (Content):
This course introduces the basic concepts and methodologists of digital image processing. The covered topics include image enhancement, 2D Fourier transform and sampling, high dimensional spectral analysis, filtering, edge detection, image segmentation, feature extraction, de-noising, digital image compression techniques and morphology.
C. Course Objective:
The objective of this course is to provide an introduction to basic concepts, methodologies and algorithms of digital image processing. It focuses on the following two problems associated with digital images: (1) image enhancement and restoration for easier interpretation of images, and (2) image analysis and object recognition. Some advanced image processing techniques will also be studied in this course. The primary goal of this course is to lay a solid foundation for students to study advanced image analysis. This course has 3 hours/week integrated laboratory session (EEE476IL).
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Describe basic principles of digital image processing |
CO2 |
Apply the knowledge of convolution, Fourier analysis, filtering, histogram analysis, image enhancement, linear and non-linear systems analysis and segmentation to manipulate digital images |
CO3 |
Compare among different image compression techniques and color processing techniques. |
CO4 |
Evaluate different sources of noise in digital images (i.e. acquisition noise, low contrast), and learn to reduce their impact through denoising and enhancement |
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 basic principles of digital image processing |
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 and segmentation to manipulate digital images |
a |
Cognitive/ Apply |
Lecture, Notes |
Assignment, Quiz, Exam |
CO3 |
Compare among different image compression techniques and color processing techniques. |
a |
Cognitive/ Evaluate |
Lecture, Notes |
Assignment, Quiz, Exam |
CO4 |
Evaluate different sources of noise in digital images (i.e. acquisition noise, low contrast), and learn to reduce their impact through denoising and enhancement |
a |
Cognitive/ Evaluate |
Lecture, Notes |
Assignment, Exam |
EEE 478 Speech Recognition and Synthesis
EEE 478IL Speech Recognition and Synthesis Laboratory
A. Course General Information:
Course Code: |
EEE 478 EEE 478IL |
Course Title: |
Speech Recognition and Synthesis Speech Recognition and Synthesis Laboratory |
Credit Hours (Theory + Laboratory): |
3 + 0 |
Contact Hours (Theory + Laboratory): |
3 + 0 |
Category: |
Program Elective |
Type: |
Optional, Engineering, Lecture + Laboratory |
Prerequisites: |
EEE 343 Digital Signal Processing EEE 343L Digital Signal Processing Laboratory EEE 385 Machine Learning |
Co-requisites: |
None |
Equivalent Course |
CSE 432 Speech Recognition and Synthesis |
B. Course Catalog Description (Content):
Speech production and Phonetics: speech organs, articulatory phonetics, Acoustic Theory of speech production, Vocal tract models. Speech analysis: time and frequency domain analysis, Formant and Pitch estimation, speech coding: Linear Predictive Coding (LPC), Vocoders, Vector quantization. Speech Enhancement Techniques. Speech Synthesis: Formant and LPC synthesizers, Effect of different speeches and languages. Automatic Speech and Speaker Recognition: Feature extraction, Hidden Markov models, Noise robustness, Measures of similarity, language and accent identification. This course has 3 hours/week integrated laboratory session (EEE478IL).
C. Course Objective:
The objectives of this course are to
a. Introduce the core concepts and fundamental elements of digital speech processing.
b. Familiarize students with different aspects of speech generation, enhancement, recognition and synthesis.
D. Course Outcomes (COs):
Upon successful completion of this course, students will be able to
Sl. |
CO Description |
CO1 |
Discuss various articulatory organs related with human speech generation and phonetics. |
CO2 |
Analyze human speech in time and frequency domain for pitch and formant estimation. |
CO3 |
Explain core components of automatic speech and speaker recognition considering different speeches and languages. |
CO4 |
Apply the knowledge of speech processing to develop artificial voice synthesizing 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 various articulatory organs related with human speech generation and phonetics. |
a |
Cognitive/ Understand |
Lecture, Notes |
Quiz, Exam |
CO2 |
Analyze human speech in time and frequency domain for pitch and formant estimation. |
b |
Cognitive/ Analyze |
Lecture, Notes |
Assignment, Exam |
CO3 |
Explain core components of automatic speech and speaker recognition considering different speeches and languages. |
a |
Cognitive/ Understand |
Lecture, Notes |
Quiz, Exam |
CO4 |
Apply the knowledge of speech processing to develop artificial voice synthesizing system. |
a |
Cognitive/ Apply |
Lecture, Notes |
Quiz, Assignment, Exam |
EEE 488 Data Science and Analytics
EEE 488IL Data Science and Analytics Laboratory
A. Course General Information:
Course Code: |
EEE 488 EEE 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 |
ECE 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 |
EEE 489 IoT for Critical Infrastructures
EEE 489IL IoT for Critical Infrastructures Laboratory
A. Course General Information:
Course Code: |
EEE 489 EEE 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: |
EEE 373 Embedded System Design EEE 373L Embedded System Design Laboratory |
Co-requisites: |
None |
Equivalent Course |
ECE 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 |
EEE 495 Special topic in Robotics and Intelligent System
A. Course General Information:
Course Code: |
EEE 495 |
Course Title: |
Special topic in Robotics and Intelligent System |
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 an area of current interest in Robotics and Intelligent System area of Electrical and Electronic Engineering. The emphasis will be on thorough study of a contemporary topics in C Robotics and Intelligent System area within EEE, and the course will be made accessible to students with an intermediate, undergraduate EEE 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