This course is broad, covering a wide range of topics that have to do with the post-PC era of computing. It is a hands-on project course that also includes some foundational subjects. Students will program iPAQ handheld computers, cell phones (series 60 phones), speech processing, vision, Cricket location systems, GPS, and more. Most of the programming will be using Pythonĺ¨, but Pythonĺ¨ can be learned and mastered during the course. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5508 (Pervasive Computing).

Students download the software needed to create Arduino programs and make sure their Arduino microcontrollers work correctly. Then, they connect an LED to the Arduino and type up and upload programs to the Arduino board to 1) make the LED blink on and off and 2) make the LED fade (brighten and then dim). Throughout, students reflect on what they've accomplished by answering questions and modifying the original programs and circuits in order to achieve new outcomes. A design challenge gives students a chance to demonstrate their understanding of actuators and Arduinos; they design a functioning system using an Arduino, at least three actuators and either a buzzer or toy motor. For their designs, students sketch, create and turn in a user's manual for the system (text description, commented program, detailed hardware diagram). Numerous worksheets and handouts are provided.

Fundamental principles of the processes used in the fabrication of silicon monolithic integrated circuits. Physical models of bulk crystal growth, thermal oxidation, solid-state diffusion, ion implantation, epitaxial deposition, chemical vapor deposition, and physical vapor deposition. Refractory metal silicides, plasma and reactive ion etching, and rapid thermal processing. Process modeling and simulation. Technological limitations on integrated circuit design and fabrication. VLSI fundamentals.

An introduction to the basic principles of computer systems with emphasis on the use of rigorous techniques as an aid to understanding and building modern computing systems. Particular attention paid to concurrent and distributed systems. Topics include: specification and verification, concurrent algorithms, synchronization, naming, Networking, replication techniques (including distributed cache management), and principles and algorithms for achieving reliability.

Coding for the AWGN channel; block and convolutional codes; lattice and trellis codes; capacity-approaching codes; equalization of linear Gaussian channels; linear, decision-feedback, and MLSD equalization; precoding; multicarrier modulation; and topics in wireless communication.

This course is an introduction to the design, analysis, and fundamental limits of wireless transmission systems. Topics to be covered include: wireless channel and system models; fading and diversity; resource management and power control; multiple-antenna and MIMO systems; space-time codes and decoding algorithms; multiple-access techniques and multiuser detection; broadcast codes and precoding; cellular and ad-hoc network topologies; OFDM and ultrawideband systems; and architectural issues.

Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example: The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science". A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions. Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google and Netflix to the Office of Management and Budget. The aim of this class is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition.

In computer science, program analysis is used to determine the behavior of computer programs. Flow charts are an important tool for understanding how programs work by tracing control flow. Control flow is a graphical representation of the logic present in the program. In this lesson, students learn about, design and create flow charts for different scenarios, including a game based on the Battleship® created by Hasbro©. In the associated activity, Flow Charting App Inventor, students apply their knowledge from this lesson and gain experience with a software application called App Inventor. This lesson and its associated activity can be stand-alone or used as a launching point for the Android Acceleration Application unit or any lesson involving App Inventor.

This course covers the entire family of programming languages, starting with an introduction to programming languages in general and a discussion of the features and functionality that make up a modern programming language. Upon successful completion of this course, the student will be able to: identify the common concepts used to create programming languages; compare and contrast factors and commands that affect the programming state illustrate how execution ordering affects programming; identify the basic objects and constructs in Object-Oriented Programming; explain the characteristics of pure functional functions in functional programming; describe the structures and components utilized in logical programming. (Computer Science 404)

Principles of functional, imperative, and logic programming languages. Meta-circular interpreters, semantics (operational and denotational), type systems (polymorphism, inference, and abstract types), object oriented programming, modules, and multiprocessing. Case studies of contemporary programming languages. Programming experience and background in language implementation required. From the course home page: The course involves substantial programming assignments and problem sets as well as a significant amount of reading. The course uses the SCHEME+ programming language for all of its assignments.

In this culminating activity of the unit, students bring together everything they've learned in order to write the code to solve the Grand Challenge. The code solution takes two images captured by robots and combines them to create an image that can be focused at different distances, similar to the way that humans can focus either near or far. They write in a derivative of C++ called QT; all code is listed in this activity.

Principles of mass transport and electrical signal generation for biological membranes, cells, and tissues. Mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Electric properties of cells: ion transport; equilibrium, resting, and action potentials. Kinetic and molecular properties of single voltage-gated ion channels. Laboratory and computer exercises illustrate the concepts. For juniors and seniors. Students engage in extensive written and oral communication exercises.

Studies how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Models of randomized computation. Data structures: hash tables, and skip lists. Graph algorithms: minimum spanning trees, shortest paths, and minimum cuts. Geometric algorithms: convex hulls, linear programming in fixed or arbitrary dimension. Approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.

Detection and measurement of radio and optical signals encountered in communications, astronomy, remote sensing, instrumentation, and radar. Statistical analysis of signal processing systems, including radiometers, spectrometers, interferometers, and digital correlation systems. Matched filters and ambiguity functions. Communications channel performance. Measurement of random electromagnetic fields. Angular filtering properties of antennas, interferometers, and aperture synthesis systems. Radiative transfer and parameter estimation.

In this case study, designed to help break down stereotypes about scientists and engineers, students research the personal and professional lives of researchers in their field. The case was designed for use in high school to graduate courses in a variety of scientific disciplines, including molecular biology, biochemistry, geology, chemistry, psychology, mathematics, computer science, engineering, and anthropology. In the teaching notes, the authors provide lists of scientists for each of these disciplines as well as suggestions for classroom activities that can be used in various combinations to build on the students' biographical research.

Most algorithms in computer vision and image analysis can be understood in terms of two important components: a representation and a modeling/estimation algorithm. The representation defines what information is important about the objects and is used to describe them. The modeling techniques extract the information from images to instantiate the representation for the particular objects present in the scene. In this seminar, we will discuss popular representations (such as contours, level sets, deformation fields) and useful methods that allow us to extract and manipulate image information, including manifold fitting, markov random fields, expectation maximization, clustering and others. For each concept -- a new representation or an estimation algorithm -- a lecture on the mathematical foundations of the concept will be followed by a discussion of two or three relevant research papers in computer vision, medical and biological imaging, that use the concept in different ways. We will aim to understand the fundamental techniques and to recognize situations in which these techniques promise to improve the quality of the analysis.

Computers are all around us: in cars, kitchen scales, digital cameras, etc. But how do they store the data they hold? This unit will help you to understand how the data in a computer represents something in the outside world. You will also explore how ASCII code and Unicode are used to control data.

In this seminar, students will design and perfect a digital environment to house the activities of large-scale organizations of people making bottom-up decisions, such as with citizen-government affairs, voting corporate shareholders or voting members of global non-profits and labor unions. A working Open Source prototype created last semester will be used as the starting point, featuring collaborative filtering and electronic agent technology pioneered at the Media Lab. This course focuses on development of online spaces as part of an interdependent human environment, including physical architectures, mapped work processes and social/political dimensions. A cross-disciplinary approach will be taken; students with background in architecture, urban planning, law, cognition, business, digital media and computer science are encouraged to participate. No prior technical knowledge is necessary, though a rudimentary understanding of web page creation is helpful.