Introduces abstraction as an important mechanism for problem decomposition and solution formulation in the biomedical domain, and examines computer representation, storage, retrieval, and manipulation of biomedical data. Examines effect of programming paradigm choice on problem-solving approaches, introduces data structures and algorithms. Presents knowledge representation schemes for capturing biomedical domain complexity. Teaches principles of data modeling for efficient storage and retrieval. The final project involves building a medical information system that encompasses the different concepts taught in the subject.

This course features a rigorous introduction to modern cryptography, with an emphasis on the fundamental cryptographic primitives of public-key encryption, digital signatures, pseudo-random number generation, and basic protocols and their computational complexity requirements.

Modeling of the control processes in conventional and high-speed data communication networks. Develops and utilizes elementary concepts from queueing theory, algorithms, linear and nonlinear programming to study the problems of line and network protocols, distributed algorithms, quasi-static and dynamic routing, congestion control, deadlock prevention. Treats local and wide-area networks, and high-speed electronic and optical networks. 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 intends to provide a rigorous introduction to the most important research results in the area of distributed algorithms, and prepare interested students to carry out independent research in distributed algorithms. Topics covered include: design and analysis of concurrent algorithms, emphasizing those suitable for use in distributed networks, process synchronization, allocation of computational resources, distributed consensus, distributed graph algorithms, election of a leader in a network, distributed termination, deadlock detection, concurrency control, communication, and clock synchronization. Special consideration is given to issues of efficiency and fault tolerance. Formal models and proof methods for distributed computation are also discussed.

The course addresses dynamic systems, i.e., systems that evolve with time. Typically these systems have inputs and outputs; it is of interest to understand how the input affects the output (or, vice-versa, what inputs should be given to generate a desired output). In particular, we will concentrate on systems that can be modeled by Ordinary Differential Equations (ODEs), and that satisfy certain linearity and time-invariance conditions. We will analyze the response of these systems to inputs and initial conditions. It is of particular interest to analyze systems obtained as interconnections (e.g., feedback) of two or more other systems. We will learn how to design (control) systems that ensure desirable properties (e.g., stability, performance) of the interconnection with a given dynamic system.

This book collects posts from the Educational Media Program's blog. The EdMedia Program is run by the Teaching and Learning Centre at Simon Fraser University and is designed to help faculty, staff and grad students make sense of and educational media and incorporate it into their teaching practice.

Treatment of electromechanical transducers, rotating and linear electric machines. Lumped-parameter electromechanics of interaction. Development of device characteristics: energy conversion density, efficiency; and of system interaction characteristics: regulation, stability, controllability, and response. Use of electric machines in drive systems. Problems taken from current research. This course explores concepts in electromechanics, using electric machinery as examples. It teaches an understanding of principles and analysis of electromechanical systems. By the end of the course, students are capable of doing electromechanical design of the major classes of rotating and linear electric machines and have an understanding of the principles of the energy conversion parts of Mechatronics. In addition to design, students learn how to estimate the dynamic parameters of electric machines and understand what the implications of those parameters are on the performance of systems incorporating those machines.

6.641 examines electric and magnetic quasistatic forms of Maxwell's equations applied to dielectric, conduction, and magnetization boundary value problems. Topics covered include: electromagnetic forces, force densities, and stress tensors, including magnetization and polarization; thermodynamics of electromagnetic fields, equations of motion, and energy conservation; applications to synchronous, induction, and commutator machines; sensors and transducers; microelectromechanical systems; propagation and stability of electromechanical waves; and charge transport phenomena.

Subject on electromagnetic wave theory, emphasizing mathematical approaches, problem solving, and physical interpretation. Topics include: equivalence principle, duality and complementarity, Huygens' principle, Fresnel and Fraunhofer diffraction, dyadic Green's functions, Lorentz transformation, and Maxwell-Minkowski theory. Examples deal with limiting cases of Maxwell's theory and diffraction and scattering of electromagnetic waves.

This is intended as an introduction to embedded controllers for students in Electrical Engineering and Technology at the AAS and/or BS level. It begins with a discussion of the C programming language and then shifts to using the open source Arduino hardware platform. Uses both the Arduino library and more direct coding of the controller.

This course introduces the theory of error-correcting codes to computer scientists. This theory, dating back to the works of Shannon and Hamming from the late 40's, overflows with theorems, techniques, and notions of interest to theoretical computer scientists. The course will focus on results of asymptotic and algorithmic significance. Principal topics include: Construction and existence results for error-correcting codes. Limitations on the combinatorial performance of error-correcting codes. Decoding algorithms. Applications in computer science.

This course considers the interaction between law, policy, and technology as they relate to the evolving controversies over control of the Internet. In addition, there will be an in-depth treatment of privacy and the notion of "transparency" -- regulations and technologies that govern the use of information, as well as access to information. Topics explored will include: Legal Background for Regulation of the Internet Fourth Amendment Law and Electronic Surveillance Profiling, Data Mining, and the U.S. PATRIOT Act Technologies for Anonymity and Transparency, The Policy-Aware Web

Inspired by the work of the architect Antoni Gaudi, this research workshop will explore three-dimensional problems in the static equilibrium of structural systems. Through an interdisciplinary collaboration between computer science and architecture, we will develop design tools for determining the form of three-dimensional structural systems under a variety of loads. The goal of the workshop is to develop real-time design and analysis tools which will be useful to architects and engineers in the form-finding of efficient three-dimensional structural systems.

Foundations of Computation is a free textbook for a one-semester course in theoretical computer science. It has been used for several years in a course at Hobart and William Smith Colleges. The course has no prerequisites other than introductory computer programming. The first half of the course covers material on logic, sets, and functions that would often be taught in a course in discrete mathematics. The second part covers material on automata, formal languages, and grammar that would ordinarily be encountered in an upper level course in theoretical computer science.

This course will provide a gentle, yet intense, introduction to programming using Python for highly motivated students with little or no prior experience in programming. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. The course is designed to help prepare students for 6.01 Introduction to EECS. 6.01 assumes some knowledge of Python upon entering; the course material for 6.189 has been specially designed to make sure that concepts important to 6.01 are covered. 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.

This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of "computer science beyond computers": that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. Beginning in antiquity--with Euclid's algorithm and other ancient examples of computational thinking--the course will progress rapidly through propositional logic, Turing machines and computability, finite automata, GĚŚdel's theorems, efficient algorithms and reducibility, NP-completeness, the P versus NP problem, decision trees and other concrete computational models, the power of randomness, cryptography and one-way functions, computational theories of learning, interactive proofs, and quantum computing and the physical limits of computation. Class participation is essential, as the class will include discussion and debate about the implications of many of these ideas.

6.776 covers circuit and system level design issues of high speed communication systems, with primary focus being placed on wireless and broadband data link applications. Specific circuit topics include transmission lines, high speed and low noise amplifiers, VCO's, and high speed digital circuits. Specific system topics include frequency synthesizers, clock and data recovery circuits, and GMSK transceivers. In addition to learning analysis skills for the above items, students will gain a significant amount of experience in simulating circuits in SPICE and systems in CppSim (a custom C++ simulator).

6.976 covers system level issues of high speed communication systems and their impact on circuit requirements, with primary focus being placed on wireless and broadband data link applications. Course topics include: transistor level design techniques for high speed amplifiers, mixers, VCO's, registers and gates, and phase locked loops, and the impact of transmission line effects on circuit designs for narrowband and broadband systems. Finally, behavioral level simulation techniques are presented for phase locked loops and other communication circuits.

The goal of this book is to teach you to think like a computer scientist. I like the way computer scientists think because they combine some of the best features of Mathematics, Engineering, and Natural Science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating trade offs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.The single most important skill for a computer scientist is problem-solving. By that I mean the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That’s why this chapter is called “The way of the program.”

The single most important skill for a computer scientist is problem solving. The goal of this book is to teach you to think like a computer scientist.