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Course Criteria
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4.00 Credits
Topics and subject areas that are not among the courses listed here are frequently offered under the special topics title. Under the same title also may be found experimental courses that may be offered for the fi rst time. Such courses are offered in a formal format; that is, regularly scheduled class sessions with an instructor. The level of complexity is commensurate with a senior-level undergraduate/fi rst year graduate technical course. Class 4, Credit 4
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4.00 Credits
This course is a hands-on seminar style survey of mobile robotics. The development of the fi eld and an overview of the different approaches to mobile robot guidance (knowing where we are and where we want to go), navigation (formulating a plan to get where we want to go) and control (following a desired path) will be given. The emphasis will be on algorithms and techniques. (0306-451) Class 4. Credit 4
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4.00 Credits
One of the most useful qualities of a properly designed feedback control system is robustness, i.e., the ability of the closed-loop control system to continue to perform satisfactorily despite large variations in the (open-loop) plant dynamics and the environment. This new approach has been successfully applied to high performance servo drive systems, unmanned aerial vehicles, visual feedback systems and mobile robots among others. This course will provide an introduction to state-of-the-art techniques for analysis and design of robust feedback systems. Matlab will be used extensively for analysis, design and simulation. (0306-553 or equivalent, 1016-331 or equivalent is recommended) Class 4, Credit 4
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4.00 Credits
This is a fi rst course in digital image processing that emphasizes both theory and implementation. Two-dimensional sampling, transforms, and fi ltering are introduced and used for image enhancement, compression, restoration, segmentation, and applications in color and video processing. Project assignments involve Matlab implementation of algorithms and paper reviews. (0306-451) Class 4, Credit 4
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4.00 Credits
This course covers both fundamental concepts and the more advanced topics in Computer Vision. Topics include image formation, color, texture and shape analysis, linear fi ltering, edge detection and segmentation. In addition, students are introduced to more advanced topics, such as model based vision, object recognition, digital image libraries and applications. Homework, literature reviews, and programming projects are integrated with lectures to provide a comprehensive learning experience. (0306-451 or permission of instructor) Class 4, Credit 4
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4.00 Credits
Provides a unifi ed view of the broad fi eld of data and computer communications and networks. Emphasis is on the basic principles underlying the technology of data and computer networks. Critical issues on data communication networks as well as the current and evolving standards in computer communications architecture are discussed. The topology, access control and performance of various types of networks are studied in detail. A comprehensive student project is required. (1016-351 and at least fourth-year standing or permission of instructor) Class 4, Credit 4 (F, W)
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4.00 Credits
This course covers a set of advanced topics in wireless and wired network security design. It targets deep-level network security protocols design. The topics include applied cryptography fundamentals, Internet security (IPSec, Kerbos, email security, etc.), wireless LAN security, sensor network and security, and ad hoc network security. Class projects include Java/C-based RC4/ Hash design, Milinx-based TCP security experiments and Wireless security research. (0306-694 or equivalent) Class 4, Credit 4
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3.00 Credits
Allows upper-level graduate students an opportunity to independently investigate, under faculty supervision, aspects of the fi eld of computer engineering that are not suffi ciently covered in existing courses. Proposals for independent study activities are subject to approval by both the faculty member supervising the independent study and the department head. (Permission of the supervising faculty member and the department head required.) Credit variable 1-4
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4.00 Credits
Descriptive statistics; probability; measurement techniques; normal distribution and central limit theorem applied to confi dence intervals and statistical inference; control charts. Topics will be related to engineering through realworld examples. (Grade of C or better in 1016-283 or grade of C or better in 1016-282 and coregistration in 1016-283) Credit 4 (F)
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4.00 Credits
Statistics in engineering; enumerative and analytic studies; descriptive statistics and statistical control; sample spaces and events; axioms of probability; counting techniques; conditional probability and independence; distributions of discrete and continuous random variables; joint distributions; central limit theorem. (1016-283 or 1016-274) Credit 4 (F)
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