Typical Job Titles
| Satellite Applications and Research Scientist | Machine Learning/Deep Learning Engineer |
| Research Programmer | System Engineer |
| Image Scientist | Camera Component Engineer |
Tuition Fee
Start Date
Medium of studying
Duration
Program Facts
Program Details
Degree
Masters
Major
Biomedical Engineering | Optical Engineering | Computer Engineering
Area of study
Engineering | Natural Science
Course Language
English
About Program
Program Overview
Imaging Science Master of Science Degree
Overview
The Imaging Science MS program at RIT provides training and research opportunities in imaging systems used in remote sensing, environmental science, and beyond. The program is geared toward advancing and broadening the skills of professionals working and researching in industries where various imaging modalities are used to research and solve problems in engineering and science.
Careers and Experiential Learning
- Typical Job Titles:
- Satellite Applications and Research Scientist
- Machine Learning/Deep Learning Engineer
- Research Programmer
- System Engineer
- Image Scientist
- Camera Component Engineer
- Cooperative Education: RIT science and math education includes cooperative education and internship experiences in industry and health care settings, as well as research in an academic, industry, or national lab.
- National Labs Career Events and Recruiting: The Office of Career Services and Cooperative Education offers National Labs and federally-funded Research Centers from all research areas and sponsoring agencies a variety of options to connect with and recruit students.
Curriculum
Imaging Science (thesis option), MS degree, typical course sequence
- First Year
- IMGS-606: Graduate Seminar I
- IMGS-607: Graduate Seminar II
- IMGS-617: Image Processing and Discrete Fourier Methods
- Choose three of the following:
- IMGS-619: Radiometry
- IMGS-620: The Human Visual System
- IMGS-613: Noise and System Modeling
- IMGS-621: Computer Vision
- IMGS-633: Optics for Imaging
- IMGS-790: Research and Thesis
- IMGS Elective
- Second Year
- IMGS-790: Research & Thesis
- IMGS Elective
- Choose between the following:
- IMGS-790: Research & Thesis
- IMGS Electives
Imaging Science (project option), MS degree, typical course sequence
- First Year
- IMGS-617: Image Processing and Discrete Fourier Methods
- Choose two of the following:
- IMGS-619: Radiometry
- IMGS-620: The Human Visual System
- IMGS-613: Probability, Noise, and System Modeling
- IMGS-633: Optics for Imaging
- IMGS-621: Computer Vision
- IMGS Elective
- Second Year
- IMGS-740: Imaging Science MS Systems Project Paper
- IMGS Electives
Electives
- ASTP-613: Astronomical Observational Techniques and Instrumentation
- CLRS-601: Principles of Color Science
- CLRS-602: Color Physics and Applications
- CLRS-720: Computational Vision Science
- CLRS-820: Modeling Visual Perception
- CSCI-603: Computational Problem Solving
- CSCI-630: Foundations of Artificial Intelligence
- CSCI-631: Foundations of Computer Vision
- EEEE-780: Digital Video Processing
- ENVS-650: Hydrologic Applications of Geographic Information Systems
- IMGS-606: Graduate Seminar I
- IMGS-607: Graduate Seminar II
- IMGS-609: Graduate Laboratory I
- IMGS-613: Noise and System Modeling
- IMGS-617: Image Processing and Discrete Fourier Methods
- IMGS-619: Radiometry
- IMGS-620: The Human Visual System
- IMGS-621: Computer Vision
- IMGS-622: Vision Sciences Seminar
- IMGS-624: Interactive Virtual Env
- IMGS-628: Design and Fabrication of Solid State Cameras
- IMGS-632: Advanced Environmental Applications of Remote Sensing
- IMGS-633: Optics for Imaging
- IMGS-635: Optical System Design and Analysis
- IMGS-639: Principles of Solid State Imaging Arrays
- IMGS-640: Remote Sensing Systems and Image Analysis
- IMGS-642: Testing of Focal Plane Arrays
- IMGS-643: Mathematical Methods of Imaging Science I
- IMGS-644: Mathematical Methods of Imaging Science II
- IMGS-684: Deep Learning for Vision
- IMGS-689: Graduate Special Topics
- IMGS-699: Imaging Science Graduate Co-op
- IMGS-712: Multi-view Imaging
- IMGS-719: Radiative Transfer I
- IMGS-720: Radiative Transfer II
- IMGS-723: Remote Sensing: Spectral Image Analysis
- IMGS-724: Introduction to Electron Microscopy
- IMGS-730: Magnetic Resonance Imaging
- IMGS-732: Synthetic Aperture Radar Image Formation Processing
- IMGS-740: Imaging Science MS Systems Project Paper
- IMGS-765: Performance Modeling and Characterization of Remote Sensing System
- IMGS-766: Geometric Optics and Lens Design
- IMGS-789: Graduate Special Topics: Machine Learning for Difficult Data
- IMGS-790: Research & Thesis
- IMGS-799: Imaging Science Independent Study
- IMGS-830: Advanced Topics in Remote Sensing
- IMGS-890: Research & Thesis
- MATH-605: Stochastic Processes
- MATH-645: Graph Theory
- MCSE-712: Nonlinear Optics
- MCSE-713: Lasers
- MCSE-731: Integrated Optical Devices & Systems
- STAT-641: Applied Linear Models - Regression
- STAT-758: Multivariate Statistics for Imaging Science
Admissions and Financial Aid
- On Campus
- Offered: Full-time
- Admit Term(s): Fall or Spring
- Application Deadline: Fall - January 15 priority deadline, rolling thereafter; Spring - rolling
- STEM Designated: Yes
- Online
- Offered: Part-time
- Admit Term(s): Fall or Spring
- Application Deadline: Rolling
- STEM Designated: No
- Application Details
- Complete an online graduate application
- Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, including any transfer credit earned
- Hold a baccalaureate degree (or US equivalent) from an accredited university or college
- Satisfy prerequisite requirements and/or complete bridge courses prior to starting program coursework
- Submit a current resume or curriculum vitae
- Submit a personal statement of educational objectives
- Submit two letters of recommendation
- Entrance exam requirements: GRE optional but recommended
- Submit English language test scores (TOEFL, IELTS, PTE Academic), if required
- Cost and Financial Aid
- Graduate tuition varies by degree, the number of credits taken per semester, and delivery method
- View the general cost of attendance or estimate the cost of your graduate degree
- A combination of sources can help fund your graduate degree
- Learn how to fund your degree
Faculty
- Jie Qiao: Associate Professor
- Anthony Vodacek: Professor
- Jan van Aardt: Director of Carlson Center for Imaging Science
- All Program Faculty
Research
- The College of Science consistently receives research grant awards from organizations that include the National Science Foundation, National Institutes of Health, and NASA
- The Chester F. Carlson Center for Imaging Science is a unique, interdisciplinary academic unit bringing together the fields of physics, mathematics, engineering, and computer science in the development of new imaging systems and their applications across a wide range of disciplines
- The Imaging Science graduate program has over 100 students doing research on topics such as computer vision, astronomical imaging, satellite-based imaging systems and applications, virtual and augmented reality, and the use of UAVs in precision agriculture
- Students are supported by 18 core program faculty in the Center and another 15+ affiliated faculty members across RIT
- Faculty from the Center conduct research on a broad variety of topics including astronomy, cultural heritage imaging, detectors and imaging systems, human and computer vision, remote sensing, nanoimaging, magnetic resonance, and optical imaging
Events
- Imaging Science Thesis Defense: Macroscopic Roughness Modeling of Satellite Multi-Layer Insulation Reflectance
Related News
- Virtually endless possibilities
- University researchers measure the sun during the eclipse to assess impact on solar arrays
- Cause of temporary AT&T service outage remains unclear
Contact
- Admissions Contact: Lindsay Lewis, Senior Assistant Director, Office of Graduate and Part-Time Enrollment Services, Enrollment Management, 585-475-5532
- Program Contact: Anthony Vodacek, Professor, Chester F. Carlson Center for Imaging Science, College of Science, 585-475-7816
Program Outline
As digital image use continues to expand there is an increasing demand to fill dynamic imaging science careers. RIT’s imaging science MS provides the training and research opportunities needed to excel in industries like computer vision, astronomical imaging, satellite-based imaging systems and applications, virtual and augmented reality, and the use of UAVs (drones) in precision agriculture.
Master’s in Imaging Science at RIT: On-Campus or Online
Housed within the Chester F. Carlson Center for Imaging Science, RIT’s master's in imaging science is geared toward advancing and broadening the skills of professionals working and researching in the many industries where various imaging modalities are used to research and solve problems in engineering and science. This emerging field integrates engineering, math, physics, computer science, and psychology to understand and develop imaging systems and technology.
Students choose two courses from a variety of tracks such as: digital image processing, computer vision, electro-optical imaging systems, remote sensing, color imaging, optics, hard copy materials and processes, and nanoimaging. Tracks may be created for students interested in pursuing additional fields of study. The degree can be completed on-campus or online, and on a full or part-time basis.
An Imaging Science Curriculum Packed with High-Demand Skills
The MS degree in Imaging Science curriculum emphasizes a systems approach to the study of imaging science. With a background in science or engineering, this degree will prepare you for positions in research, product development, and management in the imaging industry. The curriculum was developed in collaboration with industry partners to emphasize skills needed by their scientists, engineers, and managers. You may concentrate on one of several "system tracks," or customize your own track. You may choose to complete either a research thesis or a project and a paper in the non-thesis option. You may also perform your thesis research at your place of employment.
- Research: Graduates apply their knowledge to research in areas of astronomy and space science, cultural artifact and document imaging, nanoimaging and materials, optics, remote sensing, and vision.
- Careers: Graduates work in various research and applied roles like Imaging Scientist, Imaging Systems Scientist, Imaging Systems Engineer, and Systems Engineer.
- Systems: Employers appreciate the ability of our graduates to apply the imaging chain towards a full systems perspective.
- Biomedical: Graduates are well prepared to contribute to both research and applied work in biomedical systems.
Research Thesis Option: The research thesis is based on experimental evidence obtained by the student in an appropriate field, as arranged between the student and their adviser. The minimum number of thesis credits required is four and may be fulfilled by experiments in the university’s laboratories. In some cases, the requirement may be fulfilled by work done in other laboratories or the student's place of employment, under the following conditions:
- The results must be fully publishable.
- The student’s advisor must be approved by the graduate program coordinator.
- The thesis must be based on independent, original work, as it would be if the work were done in the university’s laboratories.
Faculty within the Chester F. Carlson Center for Imaging Science supervise thesis research in areas of the physical properties of radiation-sensitive materials and processes, digital image processing, remote sensing, nanoimaging, electro-optical instrumentation, vision, computer vision, color imaging systems, and astronomical imaging. Interdisciplinary efforts are possible with the Kate Gleason College of Engineering and the College of Science.
Graduate Paper/Project Option: Students with demonstrated practical or research experience, approved by the graduate program coordinator, may choose the graduate project option (3 credit hours). This option takes the form of a systems project course. The graduate paper is normally performed during the final semester of study. Both part- and full-time students may choose this option, with the approval of the graduate program coordinator.
Imaging Science Careers
Our imaging science graduates are in high demand across many government and industrial sectors including the mobile phone industry, consumer electronics, aerospace, precision agriculture and remote sensing, national defense, and a wide array of other application areas. Many of our students work in industry on internships during their graduate career at RIT, further expanding their education. Recent students have been hired as either interns or in permanent positions by companies such as Apple, Google, Motorola, Lockheed Martin, L3Harris, Corning, Los Alamos National Laboratory, MITRE Corporation, and many others.
Students are also interested in: Physics MS, Astrophysical Sciences and Technology MS, Imaging Science Ph.D.
This program is offered on-campus or online.
Careers and Experiential Learning
Salary and Career Information for Imaging Science MS
Cooperative Education
What makes an RIT science and math education exceptional? It’s the ability to complete science and math co-ops and gain real-world experience that sets you apart. Co-ops in the College of Science include cooperative education and internship experiences in industry and health care settings, as well as research in an academic, industry, or national lab. These are not only possible at RIT, but are passionately encouraged.
What makes an RIT education exceptional? It’s the ability to complete relevant, hands-on career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate co-op and how it provides you with the career experience employers look for in their next top hires..
National Labs Career Events and Recruiting
The Office of Career Services and Cooperative Education offers National Labs and federally-funded Research Centers from all research areas and sponsoring agencies a variety of options to connect with and recruit students. Students connect with employer partners to gather information on their laboratories and explore co-op, internship, research, and full-time opportunities. These national labs focus on scientific discovery, clean energy development, national security, technology advancements, and more. Recruiting events include our university-wide Fall Career Fair, on-campus and virtual interviews, information sessions, 1:1 networking with lab representatives, and a National Labs Resume Book available to all labs.
SHOW MORE
About University
Rochester Institute of Technology (Dubai)
Overview:
Rochester Institute of Technology (Dubai) is a branch campus of the renowned Rochester Institute of Technology in the United States. Located in Dubai Silicon Oasis, a special economic zone for knowledge and innovation, RIT Dubai offers a comprehensive range of undergraduate and graduate programs in various fields, including engineering, business, computing, and design. The institution is committed to providing students with a high-quality American education in a dynamic and international setting.
Services Offered:
RIT Dubai provides a wide array of services to support student success, including:
Academic Support Center:
- Offers tutoring, study skills workshops, and other resources to enhance academic performance.
Advising Resources:
- Provides guidance on academic planning, career exploration, and personal development.
Health and Wellness:
- Offers access to healthcare services, counseling, and wellness programs.
Athletics and Recreation:
- Provides opportunities for students to participate in sports, fitness activities, and recreational programs.
Student Leadership:
- Encourages student involvement in clubs, organizations, and leadership initiatives.
Student Accommodation:
- Offers on-campus housing options for students.
Parking and Transportation:
- Provides parking facilities and transportation services for students.
Student Life and Campus Experience:
RIT Dubai fosters a vibrant and inclusive campus community where students can engage in a variety of activities and experiences, including:
Student Life at RIT Dubai:
- Offers opportunities for students to connect with peers, participate in social events, and explore cultural activities.
New Student Orientation:
- Provides a welcoming introduction to campus life and resources.
Co-op and Internship Program:
- Offers students practical work experience through co-op and internship opportunities.
Key Reasons to Study There:
American Degree:
- RIT Dubai offers a true American degree, recognized globally for its quality and rigor.
State-of-the-Art Campus:
- The campus features modern facilities and technology to support learning and research.
Co-op and Internship Program:
- Provides students with valuable work experience and career development opportunities.
Study Abroad Options:
- Offers students the chance to study at other RIT campuses or partner institutions around the world.
Global Connectivity:
- RIT Dubai is located in a dynamic and international hub, providing students with diverse perspectives and networking opportunities.
Academic Programs:
RIT Dubai offers a range of undergraduate and graduate programs, including:
Undergraduate Programs:
- Bachelor of Fine Arts in New Media Design
- Bachelor of Science in Psychology
- Bachelor of Science in Industrial Engineering
- Bachelor of Science in Cybersecurity
- Bachelor of Science in Computing and Information Technologies
- Bachelor of Science in Electrical Engineering
- Bachelor of Science in Mechanical Engineering
- Bachelor of Science in Marketing
- Bachelor of Science in Finance
- Bachelor of Science in Global Business Management
Graduate Programs:
- Master of Science in Organizational Leadership and Innovation
- Masters of Science in Professional Studies: Future Foresight and Planning
- Masters of Science in Engineering Management
- Masters of Science in Mechanical Engineering
- Masters of Science in Professional Studies: Data Analytics
- Masters of Science in Professional Studies: Smart Cities
- Masters of Science in Cybersecurity
- Masters of Science in Electrical Engineering
Other:
- RIT Dubai has a strong focus on innovation and entrepreneurship, with dedicated labs and centers supporting student projects and research.
- The institution boasts a diverse student body representing over 75 nationalities, creating a rich and multicultural learning environment.
- RIT Dubai has a high employability rate, with over 80% of graduates securing employment within six months of graduation.
- The institution has a strong network of alumni, providing students with valuable connections and career support.
Total programs
226
Average ranking globally
#442
Average ranking in the country
#132
Admission Requirements
View Printable Curriculum
Imaging Science (thesis option), MS degree, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| IMGS-606 | Graduate Seminar I |
1 |
This course is focused on familiarizing students with research activities in the Carlson Center, research practices in the university, research environment and policies and procedures impacting graduate students. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course also addresses issues and practices associated with technical presentation and technical writing. Credits earned in this course apply to research requirements. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Seminar 1 (Fall). | ||
| IMGS-607 | Graduate Seminar II |
1 |
This course is a continuation of the topics addressed in the preceding course Imaging Science Graduate Seminar I. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course addresses issues and practices associated with technical presentations. Credits earned in this course apply to research requirements. (Prerequisites: IMGS-606 or equivalent course.) Seminar 1 (Spring). | ||
| IMGS-616 | Fourier Methods for Imaging |
3 |
This course develops the mathematical methods required to describe continuous and discrete linear systems, with special emphasis on tasks required in the analysis or synthesis of imaging systems. The classification of systems as linear onlinear and shift variant/invariant, development and use of the convolution integral, Fourier methods as applied to the analysis of linear systems. The physical meaning and interpretation of transform methods are emphasized. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall). | ||
6 |
||
| IMGS-619 | Radiometry |
|
This course is focused on the fundamentals of radiation propagation as it relates to making quantitative measurements with imaging systems. The course includes an introduction to common radiometric terms and derivation of governing equations with an emphasis on radiation propagation in both non-intervening and turbid media. The course also includes an introduction to detector figures of merit and noise concepts. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall). | ||
| IMGS-620 | The Human Visual System |
|
This course describes the underlying structure of the human visual system, the performance of those structures and the system as a whole, and introduces psychophysical techniques used to measure them. The visual system's optical and neural systems responsible for collecting and detecting spatial, temporal, and spectral signals from the environment are described. The sources and extent of limitations in the subsystems are described and discussed in terms of the enabling limitations that allow practical imaging systems. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall). | ||
IMGS Elective |
||
9 |
||
| IMGS-613 | Probability, Noise, and System Modeling |
|
This course develops models of noise and random processes within the context of imaging systems. The focus will be on stationary random processes in both one dimension (time) and two dimensions (spatial). Power spectrum estimation will be developed and applied to signal characterization in the frequency domain. The effect of linear filtering will be modeled and applied to signal detection and maximization of SNR. The matched filter and the Wiener filter will be developed. Signal detection and amplification will be modeled, using noise figure and SNR as measures of system quality. At completion of the course, the student should have the ability to model signals and noise within imaging systems. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring). | ||
| IMGS-633 | Optics for Imaging |
|
This course provides the requisite knowledge in optics needed by a student in the graduate program in Imaging Science. The topics covered include the ray and wave models of light, diffraction, imaging system resolution. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring). | ||
| IMGS-682 | Image Processing and Computer Vision |
|
This course will cover a wide range of current topics in modern image processing and computer vision. Topics will include introductory concepts in supervised and unsupervised machine learning, linear and nonlinear filtering, image enhancement, supervised and unsupervised image segmentation, object classification, object detection, feature matching, image registration, and the geometry of cameras. Assignments will involve advanced computational implementations of selected topics from the current literature in a high-level language such as Python, MATLAB, or Julia and will be summarized by the students in written technical papers. The course requires computer programming, linear algebra, and calculus. Lecture 3 (Spring). | ||
IMGS Elective |
||
| Second Year | ||
| IMGS-790 | Research & Thesis |
2 |
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer). | ||
8 |
||
| IMGS-790 | Research & Thesis |
|
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer). | ||
IMGS Electives |
||
| Total Semester Credit Hours | 30 |
|
Imaging Science (project option), MS degree, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| IMGS-616 | Fourier Methods for Imaging |
3 |
This course develops the mathematical methods required to describe continuous and discrete linear systems, with special emphasis on tasks required in the analysis or synthesis of imaging systems. The classification of systems as linear onlinear and shift variant/invariant, development and use of the convolution integral, Fourier methods as applied to the analysis of linear systems. The physical meaning and interpretation of transform methods are emphasized. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall). | ||
6 |
||
| IMGS-619 | Radiometry |
|
This course is focused on the fundamentals of radiation propagation as it relates to making quantitative measurements with imaging systems. The course includes an introduction to common radiometric terms and derivation of governing equations with an emphasis on radiation propagation in both non-intervening and turbid media. The course also includes an introduction to detector figures of merit and noise concepts. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall). | ||
| IMGS-620 | The Human Visual System |
|
This course describes the underlying structure of the human visual system, the performance of those structures and the system as a whole, and introduces psychophysical techniques used to measure them. The visual system's optical and neural systems responsible for collecting and detecting spatial, temporal, and spectral signals from the environment are described. The sources and extent of limitations in the subsystems are described and discussed in terms of the enabling limitations that allow practical imaging systems. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall). | ||
IMGS Elective |
||
9 |
||
| IMGS-613 | Probability, Noise, and System Modeling |
|
This course develops models of noise and random processes within the context of imaging systems. The focus will be on stationary random processes in both one dimension (time) and two dimensions (spatial). Power spectrum estimation will be developed and applied to signal characterization in the frequency domain. The effect of linear filtering will be modeled and applied to signal detection and maximization of SNR. The matched filter and the Wiener filter will be developed. Signal detection and amplification will be modeled, using noise figure and SNR as measures of system quality. At completion of the course, the student should have the ability to model signals and noise within imaging systems. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring). | ||
| IMGS-633 | Optics for Imaging |
|
This course provides the requisite knowledge in optics needed by a student in the graduate program in Imaging Science. The topics covered include the ray and wave models of light, diffraction, imaging system resolution. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring). | ||
| IMGS-682 | Image Processing and Computer Vision |
|
This course will cover a wide range of current topics in modern image processing and computer vision. Topics will include introductory concepts in supervised and unsupervised machine learning, linear and nonlinear filtering, image enhancement, supervised and unsupervised image segmentation, object classification, object detection, feature matching, image registration, and the geometry of cameras. Assignments will involve advanced computational implementations of selected topics from the current literature in a high-level language such as Python, MATLAB, or Julia and will be summarized by the students in written technical papers. The course requires computer programming, linear algebra, and calculus. Lecture 3 (Spring). | ||
IMGS Elective |
||
| Second Year | ||
| IMGS-740 | Imaging Science MS Systems Project Paper |
3 |
The analysis and solution of imaging science systems problems for students enrolled in the MS Project capstone paper option. Research 3 (Fall, Spring, Summer). | ||
IMGS Electives |
9 | |
| Total Semester Credit Hours | 30 |
|
Electives
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| ASTP-613 | Astronomical Observational Techniques and Instrumentation |
3 |
This course will survey multi-wavelength astronomical observing techniques and instrumentation. The design characteristics and function of telescopes, detectors, and instrumentation in use at the major ground based and space based observatories will be discussed as will common observing techniques such as imaging, photometry and spectroscopy. The principles of cosmic ray, neutrino, and gravitational wave astronomy will also be briefly reviewed. (Prerequisites: This course is restricted to students in the ASTP-MS and ASTP-PHD programs.) Lecture 3 (Fall). | ||
| CLRS-601 | Principles of Color Science |
3 |
This course covers the principles of color science including theory, application, and hands-on experience incorporated into the lectures. Topics include color appearance (hue, lightness, brightness, chroma, saturation, colorfulness), colorimetry (spectral, XYZ, xyY, L*a*b*, L*C*abhab, ΔE*ab, ΔE00), the use of linear algebra in color science and color imaging, metamerism, chromatic adaptation, color inconstancy, color rendering, color appearance models (CIECAM02), and image appearance models (S-CIELAB, iCAM). (Prerequisites: Graduate standing in CLRS-MS, IMGS-MS, CLRS-PHD or IMGS-PHD.) Lecture 3 (Fall). | ||
| CLRS-602 | Color Physics and Applications |
3 |
This course explores the relationship between a material’s color and its constituent raw materials such as colorants, binding media, substrates, and overcoats. These can be determined using a variety of physical models based on absorption, scattering, luminescence, and interference phenomena. These models enable the production of paints, plastics, colored paper, printing, and others to have specific colors. Accompanying laboratories will implement and optimize these models using filters, artist opaque and translucent paints and varnishes including metallic and pearlescent colorants, and inkjet printing. Statistical techniques include principal component analysis and linear and nonlinear optimization. (Prerequisites: CLRS-601 or equivalent course.) Lecture 3 (Spring). | ||
| CLRS-720 | Computational Vision Science |
3 |
Computational Vision Science This course provides an introduction to modern computer-based methods for the measurement and modeling of human vision. Lectures will introduce the experimental techniques of visual psychophysics including threshold measurement, psychometric functions, signal detection theory, and indirect, direct, and multidimensional scaling. Lectures will also introduce the MATLAB technical computing environment and will teach how to use MATLAB to run computer-based psychophysical experiments and to analyze experimental data and visualize results. Laboratory exercises will provide practical experience in using computer-based tools to conduct psychophysical experiments and to develop computational models of the results. Prior experience in vision science and/or scientific computing will be helpful but is not required. (Prerequisites: Graduate standing in CLRS-MS, IMGS-MS, CLRS-PHD or IMGS-PHD.) Lecture 3 (Fall). | ||
| CLRS-820 | Modeling Visual Perception |
3 |
This course presents the transition from the measurement of color matches and differences to the description and measurement of color appearance in complex visual stimuli. This seminar course is based mainly on review and student-led discussion of primary references. Topics include: appearance terminology, appearance phenomena, viewing conditions, chromatic adaptation, color appearance modeling, image appearance, image quality, and material appearance. (Prerequisites: CRLS-601 and CLRS-720 or equivalent courses.) Lecture 3 (Spring). | ||
| CSCI-603 | Computational Problem Solving |
3 |
This course focuses on the application of computational thinking using a problem-centered approach. Specific topics include: expression of algorithms in pseudo-code and a programming language; elementary data structures such as lists, trees and graphs; problem solving using recursion; and debugging and testing. Assignments (both in class and homework) requiring a pseudo-code solution and implementation in a programming language are an integral part of the course. Note: This course serves as a bridge course for graduate students and cannot be taken by undergraduate students without permission from the CS Undergraduate Program Coordinator. (This course is restricted to students in COMPSCI-MS.) Lecture 3 (Fall, Spring). | ||
| CSCI-630 | Foundations of Artificial Intelligence |
3 |
An introduction to the theories and algorithms used to create artificial intelligence (AI) systems. Topics include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments and oral/written summaries of research papers are required. (Prerequisites:((CSCI-603 or CSCI-605) &CSCI-661) with grades of B or better or ((CSCI-243 or SWEN-262)&(CSCI-262 or CSCI-263)).If you have earned credit for CSCI-331 or you are currently enrolled in CSCI-331 you won't be permitted to enroll in CSCI-630.) Lecture 3 (Fall, Spring). | ||
| CSCI-631 | Foundations of Computer Vision |
3 |
An introduction to the underlying concepts of computer vision and image understanding. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming assignments are an integral part of the course. Note: students who complete CSCI-431 may not take CSCI-631 for credit. (Prerequisites:(CSCI-603 and CSCI-605 and CSCI-661 with grades of B or better) or ((CSCI-243 or SWEN-262) and (CSCI-262 or CSCI-263)) or equiv courses. If earned credit for/or currently enrolled in CSCI-431 you will not be permitted to enroll in CSCI-631.Prerequisites:(CSCI-603 and CSCI-605 and CSCI-661 with grades of B or better) or ((CSCI-243 or SWEN-262) and (CSCI-262 or CSCI-263)) or equiv courses. If earned credit for/or currently enrolled in CSCI-431 you will not be permitted to enroll in CSCI-631.) Lecture 3 (Fall, Spring). | ||
| CSCI-737 | Pattern Recognition |
3 |
An introduction to pattern classification and structural pattern recognition. Topics include Bayesian decision theory, evaluation, clustering, feature selection, classification methods (including linear classifiers, nearest-neighbor rules, support vector machines, and neural networks), classifier combination, and recognizing structures (e.g. using HMMs and SCFGs). Students will present current research papers and complete programming projects such as optical character recognizers. Offered every other year. (Prerequisites: CSCI-630 or CSCI-331 or equivalent course.) Lecture 3 (Fall). | ||
| EEEE-780 | Digital Video Processing |
3 |
In this graduate level course the following topics will be covered: Representation of digital video - introduction and fundamentals; Time-varying image formation models including motion models and geometric image formation; Spatio-temporal sampling including sampling of analog and digital video; two dimensional rectangular and periodic Sampling; sampling of 3-D structures, and reconstruction from samples; Sampling structure conversion including sampling rate change and sampling lattice conversion; Two-dimensional motion estimation including optical flow based methods, block-based methods, Pel-recursive methods, Bayesian methods based on Gibbs Random Fields; Three-dimensional motion estimation and segmentation including methods using point correspondences, optical flow & direct methods, motion segmentation, and stereo and motion tracking. (Prerequisites: EEEE-779 or equivalent course.) Lecture 3 (Spring). | ||
| ENVS-650 | Hydrologic Applications of Geographic Information Systems |
4 |
Aerial photography, satellite imagery, Global Positioning Systems (GPS), and Geographic Information Systems (GIS) are extremely useful tools in hydrologic modeling and environmental applications such as rainfall runoff modeling, pollution loading, landscape change analyses, and terrain modeling. This course will: 1) introduce students to spatial analysis theories, techniques and issues associated with hydrologic and environmental applications; 2) provide hands-on training in the use of these spatial tools and models while addressing a real problem; 3) provide experience linking GIS and model results to field assessments and monitoring activities; 4) enable students to solve a variety of spatial and temporal hydrologic and environmental problems; and 5) provide tools useful for addressing environmental problems related to the graduate thesis or project. (Prerequisites: ENVS-250 or equivalent course or graduate standing in the ENVS-MS program.) Lec/Lab 6 (Spring). | ||
| IMGS-606 | Graduate Seminar I |
1 |
This course is focused on familiarizing students with research activities in the Carlson Center, research practices in the university, research environment and policies and procedures impacting graduate students. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course also addresses issues and practices associated with technical presentation and technical writing. Credits earned in this course apply to research requirements. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Seminar 1 (Fall). | ||
| IMGS-607 | Graduate Seminar II |
1 |
This course is a continuation of the topics addressed in the preceding course Imaging Science Graduate Seminar I. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course addresses issues and practices associated with technical presentations. Credits earned in this course apply to research requirements. (Prerequisites: IMGS-606 or equivalent course.) Seminar 1 (Spring). | ||
| IMGS-609 | Graduate Laboratory I |
2 |
This laboratory course is intended to familiarize graduate students with many concepts, tools, and techniques necessary for completion of the Imaging Science graduate curriculum. Students will work in a variety of areas including scientific programming, numerical analysis, imaging system analysis, and characterization. (Pre-requisite: Graduate standing in Imaging Science or permission of the instructor.) (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lab 4 (Fall). | ||
| IMGS-613 | Probability, Noise, and System Modeling |
3 |
This course develops models of noise and random processes within the context of imaging systems. The focus will be on stationary random processes in both one dimension (time) and two dimensions (spatial). Power spectrum estimation will be developed and applied to signal characterization in the frequency domain. The effect of linear filtering will be modeled and applied to signal detection and maximization of SNR. The matched filter and the Wiener filter will be developed. Signal detection and amplification will be modeled, using noise figure and SNR as measures of system quality. At completion of the course, the student should have the ability to model signals and noise within imaging systems. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring). | ||
| IMGS-616 | Fourier Methods for Imaging |
3 |
This course develops the mathematical methods required to describe continuous and discrete linear systems, with special emphasis on tasks required in the analysis or synthesis of imaging systems. The classification of systems as linear onlinear and shift variant/invariant, development and use of the convolution integral, Fourier methods as applied to the analysis of linear systems. The physical meaning and interpretation of transform methods are emphasized. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall). | ||
| IMGS-619 | Radiometry |
3 |
This course is focused on the fundamentals of radiation propagation as it relates to making quantitative measurements with imaging systems. The course includes an introduction to common radiometric terms and derivation of governing equations with an emphasis on radiation propagation in both non-intervening and turbid media. The course also includes an introduction to detector figures of merit and noise concepts. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall). | ||
| IMGS-620 | The Human Visual System |
3 |
This course describes the underlying structure of the human visual system, the performance of those structures and the system as a whole, and introduces psychophysical techniques used to measure them. The visual system's optical and neural systems responsible for collecting and detecting spatial, temporal, and spectral signals from the environment are described. The sources and extent of limitations in the subsystems are described and discussed in terms of the enabling limitations that allow practical imaging systems. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Fall). | ||
| IMGS-622 | Vision Sciences Seminar |
1 |
This seminar course provides a forum in which students, faculty, and researchers with an interest in the Vision Sciences (visual neuroscience, perception psychology, computational vision, computer graphics) can interact through reading, presentation, and discussion of classic texts and contemporary research papers in the field. Students will read and summarize weekly readings in writing and will periodically prepare presentations and lead discussions. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 1 (Fall, Spring). | ||
| IMGS-624 | Interactive Virtual Env |
3 |
This course provides experience in the development of real-time interactive three-dimensional environments, and in the use of peripherals, including virtual reality helmets, motion tracking, and eye tracking in virtual reality. Students will develop expertise with a contemporary Game Engine, along with an understanding of the computations that facilitate 3D rendering for interactive environments. Projects will cover topics such as lighting and appearance modelling, mathematics for vertex manipulation, 3D to 2D projection, ray tracing, the integration of peripherals via software development kits, and the spatial and temporal calibration of an eye tracker embedded within a head-worn display. Students will complete homework tutorials on game/application development in a contemporary computer gaming engine. This course involves a substantial programming component, and prior programming experience is required. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lab 4 (Fall). | ||
| IMGS-628 | Design and Fabrication of Solid State Cameras |
3 |
The purpose of this course is to provide the student with hands-on experience in building a CCD camera. The course provides the basics of CCD operation including an overview, CCD clocking, analog output circuitry, cooling, and evaluation criteria. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lab 6 (Fall). | ||
| IMGS-632 | Advanced Environmental Applications of Remote Sensing |
3 |
This course will focus on a broader selection of analytical techniques with an application-centric presentation. These techniques include narrow-band indices, filtering in the spatial and frequency domains, principal component analysis, textural analysis, hybrid and object-oriented classifiers, change detection methods, and structural analysis. All of these techniques are applied to assessment of natural resources. Sensing modalities include imaging spectroscopy (hyperspectral), multispectral, and light detection and ranging (lidar) sensors. Applications such as vegetation stress assessment, foliar biochemistry, advanced image classification for land use purposes, detecting change between image scenes, and assessing topography and structure in forestry and grassland ecosystems (volume, biomass, biodiversity) and built environments will be examined. Real-world remote sensing and field data from international, US, and local sources are used throughout this course. Students will be expected to perform a more comprehensive final project and homework assignments, including literature review and discussion and interpretation of results. (This course requires permission of the Instructor to enroll.) Lab 3 (Spring). | ||
| IMGS-633 | Optics for Imaging |
3 |
This course provides the requisite knowledge in optics needed by a student in the graduate program in Imaging Science. The topics covered include the ray and wave models of light, diffraction, imaging system resolution. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring). | ||
| IMGS-635 | Optical System Design and Analysis |
3 |
The primary objectives of this course are to teach critical optics and system concepts, and skills to specify, design, simulate, and evaluate optical components and systems. A modern optical design program and various types of optical systems will be used to illustrate how to solve real-world optical engineering problems. The course is not a traditional lens design course, which usually focuses on designing and optimizing individual lens elements. Instead the course will emphasize analyzing systems, which are often made with off-the-shelf optical components. (Prerequisites: IMGS-321 or IMGS-633 or (EEEE-505 and EEEE-705) or (IMGS-322 or PHYS-365) or equivalent course.) Lecture 1 (Spring). | ||
| IMGS-639 | Principles of Solid State Imaging Arrays |
3 |
This course covers the basics of solid state physics, electrical engineering, linear systems and imaging needed to understand modern focal plane array design and use. The course emphasizes knowledge of the working of CMOS and infrared arrays. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lecture 3 (Fall). | ||
| IMGS-640 | Remote Sensing Systems and Image Analysis |
3 |
This course introduces the students to the governing equations for radiance reaching aerial or satellite based imaging systems. It then covers the temporal, geometric, spectral, and noise properties of these imaging systems with an emphasis on their use as quantitative scientific instruments. This is followed by a treatment of methods to invert the remotely sensed image data to measurements of the Earth’s surface (e.g. reflectance and temperature) through various means of inverting the governing radiometric equation. The emphasis is on practical implementation of multidimensional image analysis and examining the processes governing spatial, spectral and radiometric image fidelity. (Prerequisite: IMGS-251 or equivalent course.) Lecture 3 (Fall). | ||
| IMGS-642 | Testing of Focal Plane Arrays |
3 |
This course is an introduction to the techniques used for the testing of solid state imaging detectors such as CCDs, CMOS and Infrared Arrays. Focal plane array users in industry, government and university need to ensure that key operating parameters for such devices either fall within an operating range or that the limitation to the performance is understood. This is a hands-on course where the students will measure the performance parameters of a particular camera in detail. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lab 6 (Spring). | ||
| IMGS-682 | Image Processing and Computer Vision |
3 |
This course will cover a wide range of current topics in modern image processing and computer vision. Topics will include introductory concepts in supervised and unsupervised machine learning, linear and nonlinear filtering, image enhancement, supervised and unsupervised image segmentation, object classification, object detection, feature matching, image registration, and the geometry of cameras. Assignments will involve advanced computational implementations of selected topics from the current literature in a high-level language such as Python, MATLAB, or Julia and will be summarized by the students in written technical papers. The course requires computer programming, linear algebra, and calculus. Lecture 3 (Spring). | ||
| IMGS-684 | Deep Learning for Vision |
3 |
This course will review neural networks and related theory in machine learning that is needed to understand how deep learning algorithms work. The course will include the latest algorithms that use deep learning to solve problems in computer vision and machine perception, and students will read recent papers on these systems. Students will implement and evaluate one or more of these systems and apply them to problems that match their interests. Students are expected to have taken multiple computer programming courses and to be comfortable with linear algebra and calculus. No prior background in machine learning or pattern recognition is required. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lecture 3 (Fall). | ||
| IMGS-699 | Imaging Science Graduate Co-op |
0 |
This course is a cooperative education experience for graduate imaging science students. CO OP (Fall, Spring, Summer). | ||
| IMGS-712 | Multi-view Imaging |
3 |
Images are 2D projections gathered from scenes by perspective projection. By making use of multiple images it is possible to construct 3D models of the scene geometry and of objects in the scene. The ability to derive representations of 3D scenes from 2D observations is a fundamental requirement for applications in robotics, intelligence, medicine and computer graphics. This course develops the mathematical and computational approaches to modeling of 3D scenes from multiple 2D views. After completion of this course students are prepared to use the techniques in independent research. (Prerequisites: IMGS-616 or IMGS-682 or equivalent course.) Lecture 3 (Spring). | ||
| IMGS-719 | Radiative Transfer I |
3 |
This course is the first course in a two-semester course sequence that covers the theory of radiative transfer in disordered media. The course begins with a brief review of basic electromagnetism and models for scattering and absorption by single particles and progresses to the theory of radiative transfer in semi-infinite media. Various approximations that allow closed-form solutions are presented, and related phenomenology, such as the shadow-hiding opposition effect and coherent backscatter opposition effects, are described in terms of these models. (Prerequisites: IMGS-619 and IMGS-633 or ASTP-615 or equivalent courses.) Lecture 3 (Spring). | ||
| IMGS-720 | Radiative Transfer II |
3 |
This course covers advanced topics related to the theory of radiative transfer in disordered media. The course begins with a review of topics presented in the first semester course, including the radiative transfer solutions due to Hapke’s solution for a semi-infinite medium and the opposition effect. Students will complete a project focused on one or more advanced topics related to radiative transfer in disordered media, such as effects of surface roughness, scattering in layered media, oriented scattering layers, more advanced treatments of multiple scattering or polarization, or radiative transfer in the water column. (Prerequisites: IMGS-719 | ||
Remote Sensing: Spectral Image Analysis
3
This course is focused on analysis of high-dimensional remotely sensed data sets. It begins with a review of the properties of matter that control the spectral nature of reflected and emitted energy. It then introduces three mathematical ways to characterize spectral data and methods to perform initial analysis of spectral data to characterize and preprocess the data. These include noise characterization and mitigation, radiometric calibration, atmospheric compensation, dimensionality characterization, and reduction. Much of the course focuses on spectral image analysis algorithms employing the three conceptual approaches to characterizing the data. These analytical tools are aimed at segmentation, subpixel or pixel unmixing approaches and target detection including treatment of signal processing theory and application. There is also a significant emphasis on incorporation of physics based algorithms into spectral image analysis. The course concludes with an end-to-end treatment of image fidelity incorporating atmospheres, sensors, and image processing effects. (Prerequisites: IMGS-619 and IMGS-722 or equivalent courses.) Lecture 3 (Fall).
IMGS-724
Introduction to Electron Microscopy
3
The course will introduce the basic concepts and practice of electron microscopy, including transmission electron microscopy (TEM), scanning electron microscopy (SEM) and x-ray microanalysis. During the second half of the course students will do an 8-10 hour hands-on project in SEM or TEM or both, including a project paper and a poster presentation. Laboratory demonstrations will be held in the NanoImaging Lab to reinforce the lecture material. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering.) Lecture 3 (Spring).
IMGS-730
Magnetic Resonance Imaging
3
This course is designed to teach the principles of the imaging technique called magnetic resonance imaging (MRI). The course covers spin physics, Fourier transforms, basic imaging principles, Fourier imaging, imaging hardware, imaging techniques, image processing, image artifacts, safety, and advanced imaging techniques. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Spring).
IMGS-740
Imaging Science MS Systems Project Paper
3
The analysis and solution of imaging science systems problems for students enrolled in the MS Project capstone paper option. Research 3 (Fall, Spring, Summer).
IMGS-765
Performance Modeling and Characterization of Remote Sensing System
3
This course introduces the techniques utilized for system performance predictions of new imaging platforms during their design phase. Emphasis will be placed on systems engineering concepts and their impact on final product quality through first principles modeling. In addition, the student will learn techniques to characterize system performance during actual operation to monitor compliance to performance specifications and monitor system health. Although the focus of the course will be on electro-optical collection systems, some modality specific concepts will be introduced for LIDAR, broadband infrared, polarimetric, and hyperspectral systems. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring).
IMGS-766
Geometric Optics and Lens Design
3
This course leads to a thorough understanding of the geometrical properties of optical imaging systems and detailed procedures for designing any major lens system. Automatic lens design, merit functions, and optimization are applied to real design problems. The course will utilize a modern optical design program and examples carried out on a number of types of lenses to illustrate how the process of design is carried out. (Prerequisites: IMGS-633 or equivalent course.) Lab 2 (Fall).
IMGS-789
Graduate Special Topics: Robot Vision
1-3
This is a graduate-level course on a topic that is not part of the formal curriculum. This course is structured as an ordinary course and has specific prerequisites, contact hours, and examination procedures. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lec/Lab (Fall, Spring, Summer).
IMGS-790
Research & Thesis
1-6
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
IMGS-799
Imaging Science Independent Study
1-4
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their graduate studies. (Enrollment in this course requires permission from the department offering the course.) Ind Study (Fall, Spring, Summer).
IMGS-830
Advanced Topics in Remote Sensing
3
This course is an in-depth examination of emerging techniques and technologies in the field of remote sensing at an advanced level. Examples of topics, which will differ each semester, are typically formed around a specific remote sensing modality such as lidar, polarimetry, radar, and hyperspectral remote sensing. (Prerequisites: IMGS-723 or equivalent course.) Lecture 3 (Spring).
IMGS-890
Research & Thesis
1-6
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
MATH-605
Stochastic Processes
3
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows. (Prerequisites: ((MATH-241 or MATH-241H) and MATH-251) or equivalent courses or graduate standing in ACMTH-MS or MATHML-PHD or APPSTAT-MS programs.) Lecture 3 (Spring).
MATH-645
Graph Theory
3
This course introduces the fundamental concepts of graph theory. Topics to be studied include graph isomorphism, trees, network flows, connectivity in graphs, matchings, graph colorings, and planar graphs. Applications such as traffic routing and scheduling problems will be considered. (This course is restricted to students with graduate standing in the College of Science or Graduate Computing and Information Sciences.) Lecture 3 (Fall).
MATH-711
Advanced Methods in Scientific Computing
3
MCSE-712
Nonlinear Optics
3
This course introduces nonlinear concepts applied to the field of optics. Students learn how materials respond to high intensity electric fields and how the materials response: enables the generation of other frequencies, can focus light to the point of breakdown or create waves that do not disperse in time or space solitons, and how atoms can be cooled to absolute zero using a(laser. Students will be exposed to many applications of nonlinear concepts and to some current research subjects, especially at the nanoscale. Students will also observe several nonlinear-optical experiments in a state-of-the-art photonics laboratory. (Prerequisites: EEEE-374 or equivalent course or graduate student standing in the MCSE-PHD program.) Lecture 3 (Spring).
MCSE-713
Lasers
3
This course introduces students to the design, operation and (applications of lasers (Light Amplification by Stimulated Emission of (Radiation). Topics: Ray tracing, Gaussian beams, Optical cavities, (Atomic radiation, Laser oscillation and amplification, Mode locking and Q switching, and Applications of lasers. (Prerequisites: EEEE-374 or equivalent course or graduate student standing in the MCSE-PHD program.) Lecture 3 (Fall).
MCSE-731
Integrated Optical Devices & Systems
3
This course discusses basic goals, principles and techniques of integrated optical devices and systems, and explains how the various optoelectronic devices of an integrated optical system operate and how they are integrated into a system. Emphasis in this course will be on planar passive optical devices. Topics include optical waveguides, optical couplers, micro-optical resonators, surface plasmons, photonic crystals, modulators, design tools and fabrication techniques, and the applications of optical integrated circuits. Some of the current state-of-the-art devices and systems will be investigated by reference to journal articles. Lecture 3 (Fall).
STAT-641
Applied Linear Models - Regression
3
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
STAT-758
Multivariate Statistics for Imaging Science
3
This course introduces multivariate statistical techniques and shows how they are applied in the field of Imaging Science. The emphasis is on practical applications, and all topics will include case studies from imaging science. Topics include experimental design and analysis, the multivariate Gaussian distribution, principal components analysis, singular value decomposition, orthogonal subspace projection, cluster analysis, canonical correlation and canonical correlation regression, regression, multivariate noise whitening.
This course is not intended for CQAS students unless they have particular interest in imaging science. CQAS students should be taking the course STAT-756-Multivariate Analysis. (Prerequisites: This class is restricted to students in APPSTAT-MS, SMPPI-ACT, IMGS-MS, IMGS-PHD, CLRS-MS or CLRS-PHD.) Lecture 3 (Summer).
Note for online students
The frequency of required and elective course offerings in the online program will vary, semester by semester, and will not always match the information presented here. Online students are advised to seek guidance from the listed program contact when developing their individual program course schedule.
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