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Course Criteria
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3.00 Credits
Statistical methods for analyzing and improving business processes. Advanced control chart methods for eliminating assignable cause variation. Cusum and EWMA charts. Control procedures for autocorrelated process data. Experimental design methods for reducing common cause variation. One-way and two-way ANOVA. Fractional factorial designs. Prerequisite: DSC 615.
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1.00 - 3.00 Credits
Intensive reading or research in a selected field of advanced decision sciences. Prerequisite: graduate standing and permission of instructor.
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3.00 Credits
(DST-Arts and Science)
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1.00 Credits
This course introduces students to the computing and engineering professions and their role in society. Students will explore the unique features of different engineering and computing disciplines as well as the disciplines' common bonds, such as problem solving, math and science, teamwork, and communication. Students will examine ethical and societal issues related to the disciplines and their impact on society and the world. In addition, the students will be engaged in an active forum for dissemination and discussion of ideas, topics, and issues related to their learning at Miami, the School, and the community.
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3.00 Credits
This course introduces an approach to problem solving for engineering students. The students will learn systematic approaches to problem solving. Topics covered include: problem identification, requirement analysis, research on existing and alternative solutions, and quantitative analysis of solutions, synthesis and evaluation of data, prototyping, and testing. Students will also develop their oral and written communication skills as well as team work skills.
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3.00 Credits
Study of electric circuits and networks. Includes resistive circuits and first-order transients. Emphasizes the basic principles and their application to circuit analysis using calculus and linear algebra. Prerequisite: PHY 182. Concurrent course: MTH 249 or MTH 251 or MTH 257H. 2 Lec 1 Lab.
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3.00 Credits
Principles of Von Neumann computer architecture. Data representation and computer arithmetic. Memory hierarchy. CPU structure and instruction sets. Assembly language programming to better understand and illustrate computer architecture concepts. Performance considerations and alternative computer architectures. Prerequisite: CSA 271 or equivalent. Cross-listed with CSA 278.
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4.00 Credits
Topics include switching algebra and switching functions, logic design of combinational and sequential circuits using TTL, combinational logic design with MSI and LSI, busing, flip-flops, registers, counters, programmable logic devices, memory devices, register-level design, and microcomputer system organization. Students must show competency in the computer-aided design (CAD) and laboratory implementation of digital systems. 3 Lec. 2 Lab.
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4.00 Credits
Advanced topics in electric circuit analysis are combined with an in-depth study of theory and application of instrumentation and experimentation; power analysis, transformer principles, frequency response and filters, second order systems, LaPlace transform, and signal conditioning circuits are covered as well as components, and concepts of computer-machine interface systems; design of computer-controlled experimentation for real-time measurement, monitoring, and control of automated-industrial processes. Prerequisite: ECE 205, MME 211, STA 368 or STA 301. 3 Lec. 1 Lab. Cross-listed with MME 303.
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3.00 Credits
Analysis and design of electronic circuits and subsystems; study of diodes, transistors, and operational amplifier characteristics; amplification, frequency response and feedback in small signal amplifiers; applications of electronic devices and circuits. Prerequisite: ECE 305 or ECE/ MME 303. 2 Lec. 1 Lab.
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