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Course Criteria
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3.00 Credits
Demand for petroleum alternative propulsion sources has focused attention on hybrid vehicles with fuel cells, electric motors and battery packs and internal combustion engines burning hydrogen and reformulated fuels. Comparison of performance, emissions, fuel efficiency, operational requirements and vehicle configurations is studied.
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3.00 Credits
This course explores internal combustion engine simulation, experimental analysis, and component design with a focus on thermodynamics, heat transfer, combustion and fluid dynamics. A term project integrates simulation software with experimental data analysis to allow students to design, build and test components. Preq: AUE 8160. Coreq: AUE 8181.
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0.00 Credits
Non-credit laboratory to accompany AUE 8180. Coreq: AUE 8180.
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3.00 Credits
Examines novel modes of combustion in internal-combustion engines, and provides in-depth study of the underlying phenomena and advanced engine systems required to translate the novel combustion concept into a viable technology. Advanced modeling and simulation tools aid in establishing a link between the fundamentals and system design decisions. Preq: AUE 8160.
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3.00 Credits
This course introduces the fundamental technologies for autonomous vehicle sensors, perception, and machine learning, from electromagnetic spectrum characteristics and signal acquisition, vehicle extrospective sensor data analysis, perspective geometry models, image and point cloud processing, to machine/deep learning approaches. Students also gain hands-on programming experience in vehicle perception problems through homework and class projects.
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3.00 Credits
This course is intended to be a computational introduction to modeling, analysis and control of autonomous systems for enhanced physical interaction (mobility and manipulation) with the world. The first part of the course deals with the theoretical frameworks for modeling, analysis (kinematics and dynamics) and control of generic articulated robotic systems, rooted in rich traditions of mechanics and geometry. The rest of the course examines harnessing and enhancing the mobility and manipulation performance of articulated multi?body mechanical systems in the context of serial-? and parallel-chain manipulators, as well as wheeled mobile robots (and hybrid combinations of these systems). Case studies are used to highlight this process of systematic performance (mobility and manipulation) evaluation and enhancement for exemplary multi-body robotic systems.
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3.00 Credits
This course introduces students to both the fundamental advances in science (formulations), as well as technology (ROS based implementation) behind the systems-of-systems.The course includes elements of: (i) definition of higher-level goals and deliverables at the beginning of the semester; (ii) a flipped classroom format for discussions/survey of state-of-the-art in research activities in connected and automated vehicles (as captured in concurrent archival/journal publications); (iii) interspersed lectures to provide references to the theoretical basics of analysis, sensing, planning and controls, as pertinent to the topics of the literature survey; and (iv) hands-on practical implementation of selected subtopics. Preq: Graduate standing in an automotive engineering, computer engineering, electrical engineering, mechanical engineering, or computer science program.
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3.00 Credits
Introduces typical autonomous driving technologies covering autonomous vehicle basic, sensing, planning and controls for autonomous driving, machine learning for autonomous driving and connected vehicles technologies. The course also provides hands-on projects to apply fundamental knowledge to autonomous driving vehicles.
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3.00 Credits
Discussion of legislated state, federal and international requirements. On-board automotive sensors to monitor vehicle operation and typical diagnostic algorithms are studied. Includes analytical methods for designing fault-tolerant systems and assessing vehicle reliability including safety-critical systems and "limp-home" modes, as well as use of hand-held scanners and specialized diagnostic equipment to classify faults.
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3.00 Credits
Investigation into derivation of models and design of control strategies for powertrain and chassis control modules and integration into automotive platforms. Also presents software design, sensor selection, system architecture, diagnostics and reliability issues. Application is made to engine management, transmission and chassis systems with a consideration of vehicle performance, safety and information provision. Preq: Graduate standing.
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