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This is an advanced course on modeling, design, integration and best practices for use of machine elements such as bearings, springs, gears, cams and mechanisms. Modeling and analysis of these elements is based upon extensive application of physics, mathematics and core mechanical engineering principles (solid mechanics, fluid mechanics, manufacturing, estimation, computer simulation, etc.).
- Subjects:
- Mechanical Engineering
- Keywords:
- Engineering design Machine design
- Resource Type:
- Courseware
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Courseware
This course is an introduction to designing mechatronic systems, which require integration of the mechanical and electrical engineering disciplines within a unified framework. There are significant laboratory-based design experiences. Topics covered in the course include: Low-level interfacing of software with hardware; use of high-level graphical programming tools to implement real-time computation tasks; digital logic; analog interfacing and power amplifiers; measurement and sensing; electromagnetic and optical transducers; control of mechatronic systems.
- Subjects:
- Mechanical Engineering
- Keywords:
- Mechatronics
- Resource Type:
- Courseware
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Courseware
This course studies the fundamentals of how the design and operation of internal combustion engines affect their performance, efficiency, fuel requirements, and environmental impact. Topics include fluid flow, thermodynamics, combustion, heat transfer and friction phenomena, and fuel properties, with reference to engine power, efficiency, and emissions.
- Subjects:
- Mechanical Engineering
- Keywords:
- Internal combustion engines
- Resource Type:
- Courseware
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Courseware
This course is an introduction to numerical methods and MATLAB®: Errors, condition numbers and roots of equations. Topics covered include Navier-Stokes; direct and iterative methods for linear systems; finite differences for elliptic, parabolic and hyperbolic equations; Fourier decomposition, error analysis and stability; high-order and compact finite-differences; finite volume methods; time marching methods; Navier-Stokes solvers; grid generation; finite volumes on complex geometries; finite element methods; spectral methods; boundary element and panel methods; turbulent flows; boundary layers; and Lagrangian coherent structures (LCSs).
- Subjects:
- Mechanical Engineering
- Keywords:
- Fluid mechanics
- Resource Type:
- Courseware
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Courseware
This is an interdisciplinary, project-based course, centered around a design project in which small teams of students work closely with a person with a disability in the Cambridge area to design a device, piece of equipment, app, or other solution that helps them live more independently.
- Subjects:
- Biomedical Engineering, Mechanical Engineering, Rehabilitation Sciences, Computing, and Electrical Engineering
- Keywords:
- Self-help devices for people with disabilities
- Resource Type:
- Courseware
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Courseware
This course explores cutting-edge neurotechnology that is essential for advances in all aspects of neuroscience, including improvements in existing methods as well as the development, testing and discussion of completely new paradigms. Readings and in-class sessions cover the fields of electrophysiology, light microscopy, cellular engineering, optogenetics, electron microscopy, MRI / fMRI, and MEG / EEG.
- Subjects:
- Biomedical Engineering and Biology
- Keywords:
- Neurotechnology (Bioengineering)
- Resource Type:
- Courseware
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Courseware
This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.
- Subjects:
- Biomedical Engineering and Medical Imaging
- Keywords:
- Biomedical engineering Signal processing Image processing
- Resource Type:
- Courseware
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Courseware
This course develops and applies scaling laws and the methods of continuum and statistical mechanics to biomechanical phenomena over a range of length scales, from molecular to cellular to tissue or organ level.
- Subjects:
- Biomedical Engineering and Biology
- Keywords:
- Biomedical engineering Biomechanics
- Resource Type:
- Courseware
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Courseware
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
- Subjects:
- Computing
- Keywords:
- Pattern perception -- Statistical methods Machine learning
- Resource Type:
- Courseware
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Courseware
The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control. The course is presented in a standard format of lectures, readings and problem sets.
- Subjects:
- Mechanical Engineering
- Keywords:
- Robotics
- Resource Type:
- Courseware