Undergraduate Courses

Units: 3

System modeling; the elementary constructs and principles of system models including discrete time, discrete-state system theory; finite state machines; modeling components, system coupling, and system experiments (simulation). System design including requirements, life-cycle, performance measures and cost measures, tradeoffs, alternative design concepts, testing plan, and documentation. Applications and case studies from engineering.

 

Prerequisite(s): ENGR 102

Usually offered: Fall, Spring

Units: 3

Fundamentals of economic analysis and the time value of money for engineers. Construction of financial models in EXCEL including Income, Cash Flow, and Balance Sheet. Estimation of required capital and project acceptance criteria.

 

Prerequisite(s): MATH 122B, MATH 124 or MATH 125

Usually offered: Fall, Spring

Units: 3
Basics of data structures, transformations, computer methods, their implementation in MATLAB, and their applications in solving engineering problems.

 

Usually offered: Spring, Summer

Units: 3
Modeling and design of complex systems using all views of the Unified Modeling Language (UML). Most effort will be in the problem domain (defining the problem). Some effort will be in the solution domain (producing hardware or software).

 

Usually offered: Fall, Spring

Units: 1
A colloquium designed to help students understand what SIE's do. Students will interact with speakers and take tours to local companies. The course helps students select course options within the SIE programs and helps focus on possible SIE applications areas.

 

Units: 3

Axioms of probability, discrete and continuous distributions, sampling distributions. Engineering applications of statistical estimation, hypothesis testing, confidence intervals.

 

Prerequisite(s): MATH 129

Usually offered: Fall, Spring, Summer

Units: 3
Probability, Markov chains, Poisson processes, queuing models, reliability models.

 

Prerequisite(s): SIE 305

Usually offered: Spring

Units: 3
Design and analysis of observational and factorial experiments employing numerical and graphical methods. Topics include control charts, probability plots, multiple regression analysis, confidence and prediction intervals and significance tests.

 

Prerequisite(s): SIE 305

Usually offered: Spring

Units: 3
Linear programming models, solution techniques, sensitivity analysis and duality.

 

Prerequisite(s): SIE 270

Usually offered: Fall, Summer

Units: 3
Strategic, tactical and operational planning; innovation and technological cycles; the elements of entrepreneurship, and human relations topics for technical managers.

 

Prerequisite(s): SIE 265

Units: 4
Boolean algebra, combinational and sequential logic circuits, finite state machines, simple computer architecture, assembly language programming, and real-time computer control.

 

Units: 3
Rapid prototyping of decision support systems using Visual Basic for Applications (VBA) and Excel. Use of VBA, Excel, and external packages to solve optimization problems, to perform simulations, and to perform forecasting. Rapid design and implementation of decision support systems for financial, supply chain, and facility location problems.

 

Usually offered: Fall

Units: 3
Introduction to the integrated manufacturing enterprise and automation. Topics include computer-aided design, process planning, computer numerical control machining, machine vision, application of robots and automation. Typical structure: 2 hours lecture, 2 hours laboratory.

 

Usually offered: Spring

Units: 3

Quality, improvement and control methods with applications in design, development, manufacturing, delivery and service. Topics include modern quality management philosophies, engineering/statistical methods (including process control, control charts, process capability studies, loss functions, experimentation for improvement) and TQM topics (customer driven quality, teaming, Malcolm Baldridge and ISO 9000). May be convened with SIE 506.

 

Usually offered: Fall

Units: 3

This is a three-credit course offered for well-qualified seniors, graduate students, and engineering professionals and practitioners. This is an introductory text and will be supplemented with material on many everyday reliability engineering problems e.g. root cause analysis. The course will make use of Minitab™ software. The scope of this course includes: (1) failure distributions, (2) failure rate models and reliability concepts, (3) reliability systems and state-space models, (4) accelerated testing, (5) repair process and availability, (6) Bayesian reliability estimates, (7) case studies. May be convened with SIE 508.

 

Prerequisite(s): SIE 305

Usually offered: Fall

Units: 3

Consideration of human characteristics in the requirements for design of systems, organizations, facilities and products to enable human-centered design which considers human abilities, limitations and acceptance.

 

Prerequisite(s): SIE 305

Usually offered: Fall, Spring

Units: 3

Basic concepts, methods, principles and skills in designing and evaluating various human-machine interfaces. Machine here is generally defined as any physical systems that can be operated by human operators. By taking this course, students can not only use several effective methods to design and prototype human-machine interfaces based on the needs and characteristics of users (e.g., PPT method, Visual Basic Applications user interface programming skills; simple Web design techniques etc.), but also apply both quantitative and qualitative evaluation methods to optimize the human performance, mental workload and aesthetics. To broaden students’ view in HMI, relative new topics in HMI are also introduced in this course.

 

Prerequisite(s): ECE 175 or CSC 110, SIE 305

Units: 3

Topics covered in this course include patents, trade secrets, trademarks, copyrights, product liability contracts, business entities, employment relations and other legal matters important to engineers and scientists.

 

Units: 3
Principles of the engineering sales process in technology-oriented enterprises; selling strategy, needs analysis, proposals, technical communications, electronic media, time management and ethics; practical application of concepts through study of real-world examples.

 

Units: 3
Application of principles of probability and statistics to the design and control of engineering systems in a random or uncertain environment. Emphasis is placed on Bayesian decision analysis.

 

Prerequisite(s): SIE 305

Usually offered: Fall

Units: 3
Statistical methodology of estimation, testing hypotheses, goodness-of-fit, nonparametric methods and decision theory as it relates to engineering practice. Significant emphasis on the underlying statistical modeling and assumptions.

 

Usually offered: Fall

Units: 3
Discrete event simulation, model development, statistical design and analysis of simulation experiments, variance reduction, random variate generation, Monte Carlo simulation.

 

Prerequisite(s): SIE 305

Usually offered: Fall, Spring, Summer

Units: 3
This course provides fundamental analytical skills necessary to analyze data and make decisions using sports examples. These skills include critical thinking, statistical analysis, computer programming, and data visualization which are generally applicable to other areas of engineering and business.

 

Prerequisite(s): SIE 305

Usually offered: Summer

Units: 3
This course will provide senior undergraduate and graduate students from a diverse engineering disciplines with fundamental concepts, principles and tools to extract and generalize knowledge from data. Students will acquire an integrated set of skills spanning data processing, statistics and machine learning, along with a good understanding of the synthesis of these skills and their applications to solving problem. The course is composed of a systematic introduction of the fundamental topics of data science study, including: (1) principles of data processing and representation, (2) theoretical basis and advances in data science, (3) modeling and algorithms, and (4) evaluation mechanisms. The emphasis in the treatment of these topics will be given to the breadth, rather than the depth. Real-world engineering problems and data will be used as examples to illustrate and demonstrate the advantages and disadvantages of different algorithms and compare their effectiveness as well as efficiency, and help students to understand and identify the circumstances under which the algorithms are most appropriate.

 

Usually offered: Spring

Units: 3
Survey of methods including network flows, integer programming, nonlinear programming, and dynamic programming. Model development and solution algorithms are covered. May be convened with SIE 540.

 

Prerequisite(s): SIE 340

Usually offered: Spring

Units: 3

This course will provide senior undergraduate and graduate students an introduction to mathematical nonlinear optimization with applications in machine learning and data science. This course will involve analysis of optimization algorithms, in particular, scalability of algorithms to large datasets will be discussed in theory and in implementation. The fundamental algorithms for nonlinear optimization are studied and applied to supervised learning models, including but not limited to nonlinear regression, logistic regression, support vector machines, and deep neural networks. Students will write their own implementation of the algorithms in the MATLAB/Python programming language and explore their performance on realistic data sets. May be convened with SIE 549.

 

Prerequisite(s): SIE 270, SIE 305, SIE 340

Usually offered: Spring

Units: 3
Fundamentals of space systems engineering; The system engineering process for space missions; Model-based design for spacecrafts and space flight systems; Elements of mission analysis and design; Elements of analysis and design for spacecraft subsystems (structure and mechanisms, thermal control; attitude control and orbit determination; command and data handling; propulsion; communication; power); The course will involve preliminary design of a full space system (spacecraft, lander, rover) to accomplish specific mission goals and objectives (e.g. scientific); The course will include lectures on special topics that are specific to the targeted space system design project developed during the semester.

 

Usually offered: Spring

Units: 3
Process and tools for systems engineering of large-scale, complex systems: requirements, performance measures, concept exploration, multi-criteria tradeoff studies, life cycle models, system modeling, etc.

 

Usually offered: Fall, Spring

Units: 3

The primary purpose of this course is to provide students with a system-level understanding of sensor development. The student will see the development of remote sensing techniques beginning with high level requirements through concept of operations, architecture development, subsystem modeling and culminating in integration, validation and verification. The student will be exposed to key design parameters for radar and Electro Optical sensing systems that drive both system cost and performance. Advanced multi-sensor systems and adaptive signal processing will also be discussed.

Prerequisite(s): SIE 305

Units: 3
Foundations, principles, methods and tools for effective design and management of projects in technology-based organizations. This course focuses on the scope, time, cost, performance and quality concerns of engineering projects characterized by risk and uncertainty. Initiating, planning, executing, monitoring, controlling and closing process are addressed. Students design and complete a project from concept through completion. Project management software is utilized. May be convened with SIE 557.

 

Usually offered: Fall, Spring

Units: 3
An introduction to model-based systems engineering (MBSE), which is the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases. The course emphasizes practical use of the Systems Modeling Language (SysML) and MBSE methods.

 

Prerequisite(s): SIE 454A

Usually offered: Spring

Units: 3

Production systems, quantitative methods for forecasting, aggregate planning, inventory control, materials requirement planning, production scheduling, manpower planning and facility design.

Usually offered: Spring

Units: 3

Fundamentals of Supply Chain Management including inventory/logistics planning and management, warehouse operations, procurement, sourcing, contracts and collaboration.

 

Usually offered: Spring

Units: 3
This course will provide senior undergraduate and graduate students the conceptual, methodological, and scientific bases to quantify and reduce the impact of engineering decisions on the environment, with a focus on applying life cycle analysis (LCA) to support the material choice, product/process design, and manufacturing/engineering decisions. Main topics covered include concept of life cycle thinking, computational structure of LCA, process and economic input-output based LCA, LCA software demonstration, LCA case studies, environmental product declaration, and recent development and advanced topics in LCA.

 

Usually offered: Spring

Units: 3

The purpose of this course is to introduce selected topics, issues, problems, and techniques in the area of System Cyber Security Engineering (SCSE), early in the development of a large system. Students will explore various techniques for eliminating security vulnerabilities, defining security specifications / plans, and incorporating countermeasures in order to achieve overall system assurance. SCSE is an element of system engineering that applies scientific and engineering principles to identify, evaluate, and contain or eliminate system vulnerabilities to known or postulated security threats in the operational environment. May be convened with SIE 571.

Prerequisite(s): Programming experience recommended
Usually offered: Fall

Units: 3
This course engages students in diverse and varied national cybersecurity/information systems security problems, under an existing and very successful umbrella program called 'INSuRE', that enables a collaboration across several universities, cyber professionals and cross-disciplined cyber related technologies. Led by Stevens Institute of Technology and made possible by a grant from the NSA and NSF, INSuRE has fielded a multi-institutional cybersecurity research course in which small groups of undergraduate and graduate students work to solve unclassified problems proposed by NSA, other US government agencies, and/or private organizations and laboratories. Students will learn how to apply research techniques, think clearly about these issues, formulate and analyze potential solutions, and communicate their results with sponsors and other participating universities. Working in small groups under the mentorship of technical experts from government and industry, each student will formulate, carry out, and present original research on current cybersecurity / information assurance problems of interest to the nation. This course will be run in a synchronized distance fashion, coordinating activities with other INSuRE technical clients and sponsors, along with partnering universities which are all National Centers of Academic Excellence in Cyber Defense Research (CAE-R).

 

Usually offered: Spring

Units: 3
Driven by efforts to improve human health and healthcare systems, this course will cover relevant topics at the intersection of people, health information, and technology. Specifically, we will survey the field of biomedical informatics that studies the effective uses of biomedical data, information, and knowledge from individuals (patients), populations, biomolecules, and cellular processes, for scientific inquiry, problem solving, and decision making. We will explore foundations and methods from both biomedical and computing perspectives, including hands-on experiences with systems, tools, and technologies in the healthcare ecosystem. May be convened with SIE/BME 577.

 

Usually offered: Fall

Units: 3

An introduction to the engineering design process with a focus on understanding constraints and opportunities associated with additive manufacturing (AM). Students will gain an understanding of how to exploit AM to manufacture parts with complex geometry, while also considering economic viability and manufacturability. Opportunities and constraints associated with various AM technologies, from fused-filament fabrication (often called 3D printing) to metal AM processes, will be surveyed. The course will culminate in a hands-on design project where students will use design-for-additive-manufacturing (DfAM) frameworks and tools to design a novel product. This course aims to promote creativity and critical thinking, which are necessary to effectively use AM technology in the context of product design. May be convened with SIE 581.

Usually offered: Fall

Units: 3
Survey of lean and variability reduction principles as applied to manufacturing and non-manufacturing environments.

 

Prerequisite(s): SIE 305

Usually offered: Spring

Units: 3

Modern manufacturing systems with emphasis on information requirements and data management. Includes CAD, CAM, CAPP, real-time scheduling, networking, and system justification. May be convened with SIE 583.

Prerequisite(s): SIE 383
Usually offered: Fall

Units: 1

Directed research is one of the best ways for an undergraduate to engage in interesting research and get individual guidance from faculty. Please download the proposal form and contact the faculty member with whom you have interest in working. (1-3 units)

Usually offered: Fall, Spring, Summer

Units: 3
Specialized work on an individual basis, consisting of training and practice in actual service in a technical, business, or governmental establishment.

 

Units: 3
This course is designed to provide a flexible topics course across several domains in the field of Systems Engineering, Industrial Engineering, and Engineering Management. Students will develop and exchange scholarly information in a small group setting. Selected advanced topics in Systems and Industrial Engineering and Operations Research, such as 1) optimization, 2) stochastic systems, 3) systems engineering and design, 4) human cognition systems, and 5) informatics. Course may be repeated for a maximum of 9 units or 3 completions.

 

Prerequisite(s): SIE 305

Usually offered: Fall, Spring, Summer

Units: 3

Teams of students will use material taught in the SIE curriculum to address a customer's needs and help a real-world client design or improve a system. Students will use a system design process, discover system requirements, identify project and technical risks, and develop a project plan and schedule. Students will communicate orally and in writing. A series of design reviews will monitor project goals, schedule, risk and progress. 498A should be taken in the student's second-to-last semester. (2-3 units)

Applies to Yuma Campus.  Main Campus students should take ENGR 498A/B.

Prerequisite(s): Senior status. Prerequisites vary by major.

Usually offered: Fall

Units: 3

Teams of students will use material taught in the SIE curriculum to address a customer's needs and help a real-world client design or improve a system. Students will use a system design process, discover system requirements, identify project and technical risks, and develop a project plan and schedule. Students will communicate orally and in writing. A series of design reviews will monitor project goals, schedule, risk and progress. Continuation of SIE-498A.

Applies to Yuma Campus.  Main Campus students should take ENGR 498A/B.

Prerequisite(s): SIE 498A
Usually offered: Spring