Curriculum: Master of Engineering in Electrical Engineering

April 19, 2021

curriculum icon Curriculum at a Glance

The Master of Engineering in Electrical Engineering requires the student to successfully complete a minimum of 30 semester credit hours of specific coursework.

Industry 4.0 Domains of Knowledge

Industry 4.0 describes the evolution of industry toward inter-connectivity, automation, machine learning, and basing decisions on real-time data acquisition. The course provides students a broad understanding of the industrial internet of things (IIOT) which encompasses physical production and operations with smart digital technology, machine learning, and big data analysis to create a connected ecosystem for organizations that focus on manufacturing and supply chain management.

Effectiveness in Technical Organizations

This course examines the non-technical factors that enable engineers and other technical professionals to maximize their contribution to organizational effectiveness. The course covers communication processes and impediments to effective communication including written communication, presentations, and meeting facilitation. Models of motivation as regards technical professionals are presented and their application to the work setting are examined. Leadership models and the interaction of leaders and followers are also presented. Conflict management and appropriate methods for constructively dealing with this are discussed. Students develop personal development plans for continued learning and performance improvement.

Intelligent Industrial Controllers

Study the current technology and use of intelligent industrial controllers utilized in electric energy, manufacturing, material handling / processing, mass transit, and other industrial plants. Selection and programming of Programmable Automation Controllers (PACs), Programmable Logic Controllers (PLCs), and Distributed Control Systems (DACs) are covered.

Intelligent Systems Theory

This is a course for students in their first or second year of their graduate studies and for undergraduate students in the senior year. This course introduces and analyzes intelligent systems used in flexible manufacturing systems. The coursework includes expert systems, fuzzy logic, neural networks and applications with intelligent systems in manufacturing and material handling. The student's understanding gained from this course will be evaluated by a midterm and final exams, homework assignments and a course project.

AI & Machine Learning

Students are introduced to tools and methodologies to apply AI and machine learning concepts to various engineering problems.  Database and programming essentials are presented to provide the necessary foundational knowledge.  Concepts including supervised and unsupervised learning, self-organized maps, search problems, and Markov decision problems are presented then utilized to illustrate applications.

Introduction to Industrial Artificial Intelligence

Industrial big data includes all types of data generated from industry applications consisting of machine operation, manufacturing process and maintenance events, etc. In today’s competitive business environments, companies have urgent needs to use advanced analytical tools to manage their industrial data to gain more insights of their operations. This course will introduce students to advanced technologies—such as advanced signal processing, pattern recognition & machine learning and predictive analytics—that ultimately enable the conversion of industrial big data into actionable information that can be used to improve the design, the productivity and the efficiency of manufacturing operations.

Master of Engineering Capstone Project

Student works under the direction of a faculty member to complete the MEng capstone project.

Management of Innovation

The Management of Innovation course is a comprehensive review of the concepts of imagination, creativity, innovation, and entrepreneurship. This practical course focuses on the twelve elements of innovation and the twelve innovation competencies: innovative behaviors, thinking, problem solving, knowledge, creativity insights, culture building, innovation, entrepreneurship, strategy, leadership, ecosystems, and technology accelerators. This online course is designed for student-centered learning and integrates personalization, challenge-based learning teams that develop a meaningful value-added prototype, and student produced evidence-based research mini-documentary videos.

Security and Trust in Cyber-Physical Systems

Cyber-physical systems integrate computation, communication, and physical processes.  They interact with the world in real-time.  With the growth of powerful networks and the Internet of Things (IoT), these systems are charged with performing many tasks for which reliability, safety, and security are critical.  This course focuses on the development of trusted cyber-physical systems, which can be counted on to perform their tasks at defined levels of reliability, safety, and security.

Sensor Embedded Systems

Concepts in sensor system design, coverage, connectivity, Poisson distribution, regular topology, localization, synchronization, data aggregation, location of the base station, sensor operating system design, security issues, a project utilizing sensor boards.

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