Special Topics and Advanced Courses

Computer Science
Computer Engineering
Electrical Engineering

Fall 2005

Revised  July 2005  (update in progress)

The following is a selection of special topics courses and advanced courses to be be offered by the UMBC CSEE Department for the Fall 2005 semester. Some are cross listed with other departments and programs and some are offered for both undergraduate and graduate credit. Undergraduates can always enroll in a graduate course with the permission of the instructor. For more information on the content, scope or expected workload for any of these courses, please contact the instructor

.

CMPE 491W / 691W Wireless Sensor Networks

(Also listed as CMSC 491W  and CMSC 691W)
TuThr 1:00-2:15pm M. Younis

Wide range of applications such as disaster management, military and security have fueled the interest in sensor networks during the past few years. Sensors are typically capable of wireless communication and are significantly constrained in the amount of available resources such as energy, storage and computation. Such constraints make the design and operation of sensor networks considerably different from contemporary wireless networks, and necessitate the development of resource conscious protocols and management techniques. This course provides a broad coverage of challenges and latest research results related to the design and management of wireless sensor networks. Covered topics include network architectures, node discovery and localization, routing protocols, medium access arbitration and network security.

CMSC 491I/691I Information Assurance

MW 2:30-3:45pm A. Sherman

(description will be added soon)

CMSC 491Q Quantum Computation

MW 7:10-8:45pm S. Lomonaco

(description will be added soon)

CMSC 491W/691W Wireless Sensor Networks

(Also listed as CMPE 491W and CMPE 691W)
TuThr 1:00-2:15pm M. Younis

Wide range of applications such as disaster management, military and security have fueled the interest in sensor networks during the past few years. Sensors are typically capable of wireless communication and are significantly constrained in the amount of available resources such as energy, storage and computation. Such constraints make the design and operation of sensor networks considerably different from contemporary wireless networks, and necessitate the development of resource conscious protocols and management techniques. This course provides a broad coverage of challenges and latest research results related to the design and management of wireless sensor networks. Covered topics include network architectures, node discovery and localization, routing protocols, medium access arbitration and network security.

CMSC 691C Computational Complexity Theory
TuTh 4:00-5:15pm R. Chang

This course will concentrate on the proof techniques commonly used in computational complexity theory, including: self-reducibility, one-way functions, tournament divide and conquer, isolation, witness reduction, polynomial interpolation, nonsolvable groups and random restriction.
Prerequisite: CMSC 651.

Textbook: The Complexity Theory Companion, Lane A. Hemaspaandra and Mitsunori Ogihara, Springer-Verlag, ISBN 3-540-67419-5, 2002

CMSC 691D Distributed Data Mining
TuThr 2:30-3:45pm H. Kargupta

Advances in computing and communication over wired and wireless networks have resulted in many pervasive distributed computing environments. The Internet, intranets, local area networks, ad hoc wireless networks, and sensor networks are some examples. These environments often come with different distributed sources of data and computation. Mining in such environments naturally calls for proper utilization of these distributed resources. Moreover, in many privacy sensitive applications different, possibly multi-party, data sets collected at different sites must be processed in a distributed fashion without collecting everything to a single central site. However, most off-the-shelf data mining systems are designed to work as a monolithic centralized application. They normally down-load the relevant data to a centralized location and then perform the data mining operations. This centralized approach does not work well in many of the emerging distributed, ubiquitous, possibly privacy-sensitive data mining applications. Distributed Data Mining (DDM) offers an alternate approach to address this problem of mining data using distributed resources. This course will offer an overview of the field distributed data mining.