Courses tagged with "Infor" (203)

The course is designed to provide a practical - hands on - introduction to electronics with a focus on measurement and signals. The prerequisites are courses in differential equations, as well as electricity and magnetism. No prior experience with electronics is necessary. The course will integrate demonstrations and laboratory examples with lectures on the foundations. Throughout the course we will use modern "virtual instruments" as test-beds for understanding electronics. The aim of the course is to provide students with the practical knowledge necessary to work in a modern science or engineering setting.

This course is a broad introduction to a host of sensor technologies, illustrated by applications drawn from human-computer interfaces and ubiquitous computing. After extensively reviewing electronics for sensor signal conditioning, the lectures cover the principles and operation of a variety of sensor architectures and modalities, including pressure, strain, displacement, proximity, thermal, electric and magnetic field, optical, acoustic, RF, inertial, and bioelectric. Simple sensor processing algorithms and wired and wireless network standards are also discussed. Students are required to complete written assignments, a set of laboratories, and a final project.

This course will explore the state of the art in common sense knowledge, and class projects will design and build interfaces that can exploit this knowledge to make more usable and helpful interfaces.

This year's theme will be about how common sense knowledge differs in different languages and cultures, and how machine understanding of this knowledge can help increase communication between people, and between people and machines.

6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.

This course introduces the basic computational methods used to understand the cell on a molecular level. It covers subjects such as the sequence alignment algorithms: dynamic programming, hashing, suffix trees, and Gibbs sampling. Furthermore, it focuses on computational approaches to: genetic and physical mapping; genome sequencing, assembly, and annotation; RNA expression and secondary structure; protein structure and folding; and molecular interactions and dynamics.

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

This course surveys techniques to fabricate and analyze submicron and nanometer structures, with applications. Optical and electron microscopy is reviewed. Additional topics that are covered include: surface characterization, preparation, and measurement techniques, resist technology, optical projection, interferometric, X-ray, ion, and electron lithography; Aqueous, ion, and plasma etching techniques; lift-off and electroplating; and ion implantation. Applications in microelectronics, microphotonics, information storage, and nanotechnology will also be explored.

Acknowledgements

The Instructors would like to thank Bob Barsotti, Bryan Cord, and Ben Wunsch for their work on the Atomic Force Microscope video. They would also like to thank Bryan Cord for creating each video.

This course covers abstractions and implementation techniques for the design of distributed systems. Topics include: server design, network programming, naming, storage systems, security, and fault tolerance. The assigned readings for the course are from current literature. This course is worth 6 Engineering Design Points.

This short course provides an introduction to reactor dynamics including subcritical multiplication, critical operation in absence of thermal feedback effects and effects of Xenon, fuel and moderator temperature, etc. Topics include the derivation of point kinetics and dynamic period equations; techniques for reactor control including signal validation, supervisory algorithms, model-based trajectory tracking, and rule-based control; and an overview of light-water reactor startup. Lectures and demonstrations employ computer simulation and the use of the MIT Research Reactor.

This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.

6.630 is an introductory subject on electromagnetics, emphasizing fundamental concepts and applications of Maxwell equations. Topics covered include: polarization, dipole antennas, wireless communications, forces and energy, phase matching, dielectric waveguides and optical fibers, transmission line theory and circuit concepts, antennas, and equivalent principle. Examples deal with electrodynamics, propagation, guidance, and radiation of electromagnetic waves.

MASLab (Mobile Autonomous System Laboratory), also known as 6.186, is a robotics contest. The contest takes place during MIT's Independent Activities Period and participants earn 6 units of P/F credit and 6 Engineering Design Points. Teams of three to four students have less than a month to build and program sophisticated robots which must explore an unknown playing field and perform a series of tasks.

MASLab provides a significantly more difficult robotics problem than many other university-level robotics contests. Although students know the general size, shape, and color of the floors and walls, the students do not know the exact layout of the playing field. In addition, MASLab robots are completely autonomous, or in other words, the robots operate, calculate, and plan without human intervention. Finally, MASLab is one of the few robotics contests in the country to use a vision based robotics problem.

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 aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, machine learning, finance, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software.

Acknowledgements

The course materials were developed jointly by Prof. Stephen Boyd (Stanford), who was a visiting professor at MIT when this course was taught, and Prof. Lieven Vanderberghe (UCLA).

6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

This course covers concepts of computation used in analysis of engineering systems. It includes the following topics: data structures, relational database representations of engineering data, algorithms for the solution and optimization of engineering system designs (greedy, dynamic programming, branch and bound, graph algorithms, nonlinear optimization), and introduction to complexity analysis. Object-oriented, efficient implementations of algorithms are emphasized.

Principles of Applied Mathematics is a study of illustrative topics in discrete applied mathematics including sorting algorithms, information theory, coding theory, secret codes, generating functions, linear programming, game theory. There is an emphasis on topics that have direct application in the real world.

This course was recently revised to meet the MIT Undergraduate Communication Requirement (CR). It covers the same content as 18.310, but assignments are structured with an additional focus on writing.

The topics covered in this course include interactive proofs, zero-knowledge proofs, zero-knowledge proofs of knowledge, non-interactive zero-knowledge proofs, secure protocols, two-party secure computation, multiparty secure computation, and chosen-ciphertext security.

Wavelets are localized basis functions, good for representing short-time events. The coefficients at each scale are filtered and subsampled to give coefficients at the next scale. This is Mallat's pyramid algorithm for multiresolution, connecting wavelets to filter banks. Wavelets and multiscale algorithms for compression and signal/image processing are developed. Subject is project-based for engineering and scientific applications.

6.831/6.813 examines human-computer interaction in the context of graphical user interfaces. The course covers human capabilities, design principles, prototyping techniques, evaluation techniques, and the implementation of graphical user interfaces. Deliverables include short programming assignments and a semester-long group project. Students taking the graduate version also have readings from current literature and additional assignments.