Online courses directory (548)

The course introduces you to digital circuits and their merits and demerits over analog circuits. Basic building blocks of both combinational and sequential circuits or introduces and many examples of circuit design using these building blocks are presented. You will also be introduced to the design of digital systems such as traffic light controller.

El curso es bilingüe español-inglés, con videos y materiales en ambos idiomas. Lectures and materials are provided both in Spanish and English.

The C programming language is one of the most popular and widely used programming languages and is commonly used to program operating systems such as Unix. It is a general-purpose programming language and one of the main advantages of programming in C is that it allows the programmer to write directly to memory. This means that key constructs within C-generated software programs can be used within the memory in an efficient and machine-independent fashion. <br /><br />ALISON's free online C programming Diploma course introduces you to the important concepts when programming in C. You will learn how the C programming language works with data, what program flow is, and how to use functions, methods and routines. You will be introduced to conditional flow statements, the mechanisms for controlling flow statements, and how to implement simple statements. Logical operators such as OR, GOTO and the While loop are also discussed in detail. The course covers topics such as using pointers for direct memory access and manipulation in C, changing the memory address contained within a pointer and introduces constants and string literals. You will learn why pointers contain memory addresses of multi-byte variables, and how to visualize RAM in a new way. You will also get step by step instructions on how to create simple C programs and how to run them. <br /><br />This free online C programming Diploma course will be of great interest to all IT, software and computer professionals who would like a greater knowledge and understanding of the C programming language and its key concepts and features, and to learners who would like to learn more about this widely used programming language.<br />

The Diploma in Information Technology Management is a free online course that explains why managers must understand how Information Technology plays a fundamental role in both the structure and control of the modern business. A successful manager must have an excellent grasp of the functionality, capabilities and effects of the technology that he or she must implement and manage. This free online course presents an array of the core concepts of Information Technology management. The course will review such key areas as corporate frameworks, software, databases, information systems, communications and management of personnel in relation to technology and from the point of view of the business manager. This course is ideal for management professionals who wish to gain an in depth understanding of the management of Information Technology in the modern corporation. The course is also ideal for those who would like to learn about the central role of Information Technology in today’s workplace.<br />

This free online Diploma in Multimedia Development course covers the skills and techniques needed to create professional-looking videos, visual effects, motion graphics and animations. The course offers a rigorous exploration of key multimedia tools including Adobe Photoshop, Windows Movie Maker, Adobe Flash, Audacity, Director MX and Adobe After Effects. <br /><br />The video-based tutorials are delivered in a non-technical and step-by-step manner by the award-winning multimedia teacher Russell Stannard. The Diploma course is ideal for those who want to gain a thorough knowledge and understanding of multimedia development tools and techniques and who want to enhance their career prospects in the process.<br />

We will present the state of the art energy minimization algorithms that are used to perform inference in modern artificial vision models: that is, efficient methods for obtaining the most likely interpretation of a given visual input. We will also cover the popular max-margin framework for estimating the model parameters using inference.

Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming.

Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of these processes. The range of areas for which discrete stochastic-process models are useful is constantly expanding, and includes many applications in engineering, physics, biology, operations research and finance.

This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: Discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flowgraph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multirate techniques; Hilbert transforms; Cepstral analysis and various applications.

Acknowledgements

I would like to express my thanks to Thomas Baran, Myung Jin Choi, and Xiaomeng Shi for compiling the lecture notes on this site from my individual lectures and handouts and their class notes during the semesters that they were students in the course. These lecture notes, the text book and included problem sets and solutions will hopefully be helpful as you learn and explore the topic of Discrete-Time Signal Processing.

Distributed algorithms are algorithms designed to run on multiple processors, without tight centralized control. In general, they are harder to design and harder to understand than single-processor sequential algorithms. Distributed algorithms are used in many practical systems, ranging from large computer networks to multiprocessor shared-memory systems. They also have a rich theory, which forms the subject matter for this course.

The core of the material will consist of basic distributed algorithms and impossibility results, as covered in Prof. Lynch's book Distributed Algorithms. This will be supplemented by some updated material on topics such as self-stabilization, wait-free computability, and failure detectors, and some new material on scalable shared-memory concurrent programming.

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.

The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.

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 course provides an introduction to nonlinear deterministic dynamical systems. Topics covered include: nonlinear ordinary differential equations; planar autonomous systems; fundamental theory: Picard iteration, contraction mapping theorem, and Bellman-Gronwall lemma; stability of equilibria by Lyapunov's first and second methods; feedback linearization; and application to nonlinear circuits and control systems.

Dieser Kurs vermittelt einen Überblick über die Grundlagen des Maschinellen Sehens an Hand der Extraktion von 3D-Information aus dem Stereokamerabild einer Szene.

This course teaches the principles and analysis of electromechanical systems. Students will develop analytical techniques for predicting device and system interaction characteristics as well as learn to design major classes of electric machines. Problems used in the course are intended to strengthen understanding of the phenomena and interactions in electromechanics, and include examples from current research.

This course 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.

Acknowledgments

The instructor would like to thank Thomas Larsen and Matthew Pegler for transcribing into LaTeX the homework problems, homework solutions, and exam solutions.

6.632 is a graduate subject on electromagnetic wave theory, emphasizing mathematical approaches, problem solving, and physical interpretation. Topics covered include: waves in media, 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.

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.

This course explores electromagnetic phenomena in modern applications, including wireless and optical communications, circuits, computer interconnects and peripherals, microwave communications and radar, antennas, sensors, micro-electromechanical systems, and power generation and transmission. Fundamentals include quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided waves; resonance; acoustic analogs; and forces, power, and energy.