Students are introduced to the concept of engineering biological organisms and studying their growth to be able to identify periods of fast and slow growth. They learn that bacteria are found everywhere, including on the surfaces of our hands. Student groups study three different conditions under which bacteria are found and compare the growth of the individual bacteria from each source. In addition to monitoring the quantity of bacteria from differ conditions, they record the growth of bacteria over time, which is an excellent tool to study binary fission and the reproduction of unicellular organisms.

A computational camera attempts to digitally capture the essence of visual information by exploiting the synergistic combination of task-specific optics, illumination, sensors and processing. In this course we will study this emerging multi-disciplinary field at the intersection of signal processing, applied optics, computer graphics and vision, electronics, art, and online sharing through social networks. If novel cameras can be designed to sample light in radically new ways, then rich and useful forms of visual information may be recorded — beyond those present in traditional photographs. Furthermore, if computational process can be made aware of these novel imaging models, them the scene can be analyzed in higher dimensions and novel aesthetic renderings of the visual information can be synthesized.
We will discuss and play with thermal cameras, multi-spectral cameras, high-speed, and 3D range-sensing cameras and camera arrays. We will learn about opportunities in scientific and medical imaging, mobile-phone based photography, camera for HCI and sensors mimicking animal eyes. We will learn about the complete camera pipeline. In several hands-on projects we will build physical imaging prototypes and understand how each stage of the imaging process can be manipulated.

This is a foundation subject in modern software development techniques for engineering and information technology. The design and development of component-based software (using C# and .NET) is covered; data structures and algorithms for modeling, analysis, and visualization; basic problem-solving techniques; web services; and the management and maintenance of software. Includes a treatment of topics such as sorting and searching algorithms; and numerical simulation techniques. Foundation for in-depth exploration of image processing, computational geometry, finite element methods, network methods and e-business applications. This course is a core requirement for the Information Technology M. Eng. program.
This class was also offered in Course 13 (Department of Ocean Engineering) as 13.470J. In 2005, ocean engineering subjects became part of Course 2 (Department of Mechanical Engineering), and the 13.470J designation was dropped in lieu of 2.159J.

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 lesson shows how to use Python and skimage to do basic image processing. With support from an NSF iUSE grant, Dr. Tessa Durham Brooks and Dr. Mark Meysenburg at Doane College, Nebraska, USA have developed a curriculum for teaching image processing in Python. This lesson is currently being piloted at different institutions. This pilot phase will be followed by a clean-up phase to incorporate suggestions and feedback from the pilots into the lessons and to make the lessons teachable by the broader community. Development for these lessons has been supported by a grant from the Sloan Foundation.

This is an accelerated introduction to MATLAB® and its popular toolboxes. Lectures are interactive, with students conducting sample MATLAB problems in real time. The course includes problem-based MATLAB assignments. Students must provide their own laptop and software. This is great preparation for classes that use MATLAB.

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.

6.637 covers the fundamentals of optical signals and modern optical devices and systems from a practical point of view. Its goal is to help students develop a thorough understanding of the underlying physical principles such that device and system design and performance can be predicted, analyzed, and understood.
Most optical systems involve the use of one or more of the following: sources (e.g., lasers and light-emitting diodes), light modulation components (e.g., liquid-crystal light modulators), transmission media (e.g., free space or fibers), photodetectors (e.g., photodiodes, photomultiplier tubes), information storage devices (e.g., optical disk), processing systems (e.g., imaging and spatial filtering systems) and displays (LCOS microdisplays). These are the topics covered by this course.

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

Short Description:
A guide to processing multi-spectral imagery MetaShape. Image alignment, generation of dense point clouds, digital surface and terrain models and orthomosaics is covered as well as the export of dense point clouds and orthomosaics to external software.

Long Description:
A complete guide (including sample data set) to processing multi-spectral imagery using Agisoft MetaShape Pro with ground control points. Image alignment, generation of dense point clouds, digital surface and terrain models and orthomosaics is covered as well as the export of dense point clouds and orthomosaics to external software.

Word Count: 4900

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)