Courses tagged with "Vectors" (67)
This course introduces the various aspects of present and future Air Traffic Control systems. Among the topics in the present system that we will discuss are the systems-analysis approach to problems of capacity and safety, surveillance, including the National Airspace System and Automated Terminal Radar Systems, navigation subsystem technology, aircraft guidance and control, communications, collision avoidance systems and sequencing and spacing in terminal areas. The class will then talk about future directions and development and have a critical discussion of past proposals and of probable future problem areas.
General introduction to systems engineering using both the classical V-model and the new Meta approach. Topics include stakeholder analysis, requirements definition, system architecture and concept generation, trade-space exploration and concept selection, design definition and optimization, system integration and interface management, system safety, verification and validation, and commissioning and operations. Discusses the trade-offs between performance, lifecycle cost and system operability. Readings based on systems engineering standards and papers. Students apply the concepts of systems engineering to a cyber-electro-mechanical system, which is subsequently entered into a design competition.
Students will prepare a PDR (Preliminary Design Review)-level design intended for the Cansat Competition.This year's class will be taught in the form of a Small-Private-Online-Course (SPOC) and offered simultaneously to students at MIT under number 16.842 and Ecole Polytechnique Fédérale de Lausanne (EPFL) as ENG-421.
This course provides an introduction to the transportation industry's major technical challenges and considerations. For upper level undergraduates interested in learning about the transportation field in a broad but quantitative manner. Topics include road vehicle engineering, internal combustion engines, batteries and motors, electric and hybrid powertrains, urban and high speed rail transportation, water vessels, aircraft types and aerodynamics, radar, navigation, GPS, GIS. Students will complete a project on a subject of their choosing.
This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.
Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.
This course was created for the "product development" track of MIT's System Design and Management Program (SDM) in conjunction with the Center for Innovation in Product Development. After taking this course, a student should be able to:
- Formulate measures of performance of a system or quality characteristics. These quality characteristics are to be made robust to noise affecting the system.
- Sythesize and select design concepts for robustness.
- Identify noise factors whose variation may affect the quality characteristics.
- Estimate the robustness of any given design (experimentally and analytically).
- Formulate and implement methods to reduce the effects of noise (parameter design, active control, adjustment).
- Select rational tolerances for a design.
- Explain the role of robust design techniques within the wider context of the product development process.
- Lead product development activities that include robust design techniques.