The 16 lectures in this course cover the topics of adaptive antennas and phased arrays. Both theory and experiments are covered in the lectures. Part one (lectures 1 to 7) covers adaptive antennas. Part two (lectures 8 to 16) covers phased arrays. Parts one and two can be studied independently (in either order). The intended audience for this course is primarily practicing engineers and students in electrical engineering. This course is presented by Dr. Alan J. Fenn, senior staff member at MIT Lincoln Laboratory.
Online Publication

This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.

This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data. Enrollment preference is given to juniors and seniors.

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.

Are you interested in building and testing your own imaging radar system? MIT Lincoln Laboratory offers this 3-week course in the design, fabrication, and test of a laptop-based radar sensor capable of measuring Doppler, range, and forming synthetic aperture radar (SAR) images. You do not have to be a radar engineer but it helps if you are interested in any of the following; electronics, amateur radio, physics, or electromagnetics. It is recommended that you have some familiarity with MATLAB®. Teams of three students will receive a radar kit and will attend a total of 5 sessions spanning topics from the fundamentals of radar to SAR imaging. Experiments will be performed each week as the radar kit is implemented. You will bring your radar kit into the field and perform additional experiments such as measuring the speed of passing cars or plotting the range of moving targets. A final SAR imaging contest will test your ability to form a SAR image of a target scene of your choice from around campus; the most detailed and most creative image wins.
Acknowledgement and Disclaimer
This work is sponsored by the Department of the Air Force under Air Force Contract #FA8721-05-C-0002. Opinions, interpretations, conclusions and recommendations are those of the authors and are not necessarily endorsed by the United States Government.

TEXTBOOK ABSTRACT:

This book is intended for use in teaching undergraduate courses on continuous-time signals and systems in engineering (and related) disciplines. It has been used for several years for teaching purposes in the Department of Electrical and Computer Engineering at the University of Victoria and has been very well received by students. It has also been used to teach courses at numerous other universities, including Memorial University (NL, Canada) and Concordia University (QC, Canada).

This book provides a detailed introduction to continuous-time signals and systems, with a focus on both theory and applications. The mathematics underlying signals and systems is presented, including topics such as: properties of signals, properties of systems, convolution, Fourier series, the Fourier transform, frequency spectra, and the bilateral and unilateral Laplace transforms. Applications of the theory are also explored, including: filtering, equalization, amplitude modulation, sampling, feedback control systems, circuit analysis, and Laplace-domain techniques for solving differential equations. Other supplemental material is also included, such as: a detailed introduction to MATLAB, a review of complex analysis, and an exploration of time-domain techniques for solving differential equations. Throughout the book, many worked-through examples are provided. Problem sets are also provided for each major topic covered.

This course was developed in 1987 by the MIT Center for Advanced Engineering Studies. It was designed as a distance-education course for engineers and scientists in the workplace.
Advances in integrated circuit technology have had a major impact on the technical areas to which digital signal processing techniques and hardware are being applied. A thorough understanding of digital signal processing fundamentals and techniques is essential for anyone whose work is concerned with signal processing applications.
Digital Signal Processing begins with a discussion of the analysis and representation of discrete-time signal systems, including discrete-time convolution, difference equations, the z-transform, and the discrete-time Fourier transform. Emphasis is placed on the similarities and distinctions between discrete-time. The course proceeds to cover digital network and nonrecursive (finite impulse response) digital filters. Digital Signal Processing concludes with digital filter design and a discussion of the fast Fourier transform algorithm for computation of the discrete Fourier transform.

This class introduces studies in the algorithmic manipulation of type as word, symbol, and form. Problems covered will include semantic filtering, inherently unstable letterforms, and spoken letters. The history and traditions of typography, and their entry into the digital age, will be studied. Weekly assignments using Java® will explore new ways of looking at and manipulating type.

Filtering is the process of removing or separating the unwanted part of a mixture. In signal processing, filtering is specifically used to remove or extract part of a signal, and this can be accomplished using an analog circuit or a digital device (such as a computer). In this lesson, students learn the impact filtering can have on different types of signals, the concepts of frequency and spectrum, and the connections these topics have to real-world signals such as musical signals. Students also learn the roles that these concepts play in designing different types of filters. The lesson content prepares students for the associated activity in which they use an online demo and a variety of filters to identify the message in a distress signal heavily corrupted by noise.

This team-taught multidisciplinary course provides information relevant to the conduct and interpretation of human brain mapping studies. It begins with in-depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include: fMRI experimental design including block design, event related and exploratory data analysis methods, and building and applying statistical models for fMRI data; and human subject issues including informed consent, institutional review board requirements and safety in the high field environment.
Additional Faculty
Div Bolar
Dr. Bradford Dickerson
Dr. John Gabrieli
Dr. Doug Greve
Dr. Karl Helmer
Dr. Dara Manoach
Dr. Jason Mitchell
Dr. Christopher Moore
Dr. Vitaly Napadow
Dr. Jon Polimeni
Dr. Sonia Pujol
Dr. Bruce Rosen
Dr. Mert Sabuncu
Dr. David Salat
Dr. Robert Savoy
Dr. David Somers
Dr. A. Gregory Sorensen
Dr. Christina Triantafyllou
Dr. Wim Vanduffel
Dr. Mark Vangel
Dr. Lawrence Wald
Dr. Susan Whitfield-Gabrieli
Dr. Anastasia Yendiki

This course explores the ultimate limits to communication and computation, with an emphasis on the physical nature of information and information processing. Topics include: information and computation, digital signals, codes and compression, applications such as biological representations of information, logic circuits, computer architectures, and algorithmic information, noise, probability, error correction, reversible and irreversible operations, physics of computation, and quantum computation. The concept of entropy is applied to channel capacity and to the second law of thermodynamics.

This set of 10 lectures (about 11+ hours in duration) was excerpted from a three-day course developed at MIT Lincoln Laboratory to provide an understanding of radar systems concepts and technologies to military officers and DoD civilians involved in radar systems development, acquisition, and related fields. That three-day program consists of a mixture of lectures, demonstrations, laboratory sessions, and tours.
Online Publication

Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.

TEXTBOOK ABSTRACT:

This book is intended for use in the teaching of graduate and senior undergraduate courses on multiresolution signal and geometry processing in the engineering and related disciplines. It has been used for several years for teaching purposes in the Department of Electrical and Computer Engineering at the University of Victoria and has been well received by students.

This book provides a comprehensive introduction to multiresolution signal and geometry processing, with a focus on both theory and applications. The book has two main components, corresponding to multiresolution processing in the contexts of: 1) signal processing and 2) geometry processing.

The signal-processing component of the book studies one-dimensional and multi-dimensional multirate systems, considering multirate structures such as sampling-rate converters, filter banks, and transmultiplexers. A particularly strong emphasis is placed on filter banks. Univariate and multivariate wavelet systems are examined, with the biorthogonal and orthonormal cases both being considered. The relationship between filter banks and wavelet systems is established. Several applications of filter banks and wavelets in signal processing are covered, including signal coding, image compression, and noise reduction. For readers interested in image compression, a detailed overview of the JPEG-2000 standard is also provided. Some other applications of multirate systems are considered, such as transmultiplexers for communication systems (e.g., multicarrier modulation).

The geometry-processing component of the book studies subdivision surfaces and subdivision wavelets. Some mathematical background relating to geometry processing is provided, including topics such as homogeneous coordinate transformations, manifolds, surface representations, and polygon meshes. Several subdivision schemes are examined in detail, including the Loop, Kobbelt sqrt(3), and Catmull-Clark methods. The application of subdivision surfaces in computer graphics is considered.

A detailed introduction to functional analysis is provided, for those who would like a deeper understanding of the mathematics underlying wavelets and filter banks. For those who are interested in software applications of the material covered in the book, appendices are included that introduce the CGAL and OpenGL libraries. Also, an appendix on the SPL library (which was developed for use with this book) is included. Throughout the book, many worked-through examples are provided. Problem sets are also provided for each major topic covered.

The aim of the students from the Numeric Photography class at the MIT Media Laboratory was to present an exhibition of digital artworks which blend photography and computation, in the context of scene capture, image play, and interaction. Equipped with low end digital cameras, students created weekly software projects to explore aesthetic issues in signal processing and interaction design. The results are more than a hundred Java® applets, many of which are interactive, that suggest new avenues for image play on the computer. These weekly exercises led to the final product, an exhibition of the student work.

This course explores the detection and measurement of radio and optical signals encountered in communications, astronomy, remote sensing, instrumentation, and radar. Topics covered include: 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; and radiative transfer and parameter estimation.

This class teaches the fundamentals of signals and information theory with emphasis on modeling audio/visual messages and physiologically derived signals, and the human source or recipient. Topics include linear systems, difference equations, Z-transforms, sampling and sampling rate conversion, convolution, filtering, modulation, Fourier analysis, entropy, noise, and Shannon's fundamental theorems. Additional topics may include data compression, filter design, and feature detection. The undergraduate subject MAS.160 meets with the two half-semester graduate subjects MAS.510 and MAS.511, but assignments differ.

6.003 covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses). Applications are drawn broadly from engineering and physics, including feedback and control, communications, and signal processing.

This course was developed in 1987 by the MIT Center for Advanced Engineering Studies. It was designed as a distance-education course for engineers and scientists in the workplace.
Signals and Systems is an introduction to analog and digital signal processing, a topic that forms an integral part of engineering systems in many diverse areas, including seismic data processing, communications, speech processing, image processing, defense electronics, consumer electronics, and consumer products.
The course presents and integrates the basic concepts for both continuous-time and discrete-time signals and systems. Signal and system representations are developed for both time and frequency domains. These representations are related through the Fourier transform and its generalizations, which are explored in detail. Filtering and filter design, modulation, and sampling for both analog and digital systems, as well as exposition and demonstration of the basic concepts of feedback systems for both analog and digital systems, are discussed and illustrated.