The UMBC ACM Student Chapter invites CSEE students, staff and faculty to the third annual Welcome Back Picnic. Join us this coming Wednesday, 10 October 2012, between 11:30 am and 1:30pm in the Atrium of the Engineering Building for lunch and to meet new and returning students.

The picnic is sponsored by the CSEE Department and the ACM Student Chapter with additional support by the UMBC Graduate Students Association. RSVP via then event on Facebook or my.umbc.edu.

You can stay informed about the chapter activities and events by liking its Facebook page or signing up for its mailing list

ACM, the Association for Computing Machinery, is the the world’s largest educational and scientific computing society. It provides members with resources that advance computing both as a science and a profession. UMBC's chapter meetings are open to all undergraduate and graduate students of any major. While you do not need to join ACM to be a part of the local chapter, the annual membership dues for students is only $19, heavily discounted from the non-student rate. See the ACM site for more information on student membership and its benefits. If you have any questions about the UMBC chapter or suggestions for activities for the coming year, send them to the acmofficers at lists.umbc.edu.

UMBC CSEE Colloquium

Real-time Causal Anomaly Detection for Hyperspectral Imagery

Yu-Lei Wang
Information and Communication Engineering College
Harbin Engineering University, China

1:00pm Friday, 12 October 2012, ITE 227, UMBC

Due to availability of very high spectral resolution, a hyperspectral imaging sensor is capable of uncovering many subtle signal sources which cannot be visually inspected or known by prior knowledge. Such signal sources generally appear as anomalies in the data. As a result, anomaly detection has received considerable interest in hyperspectral imaging. In anomaly detection real time causal processing is particularly important and crucial. This is because many anomalies, such as moving targets, may not stay long enough and the duration of their presence is very short. Most importantly, they may show up suddenly and instantly, then disappear quickly afterwards. Therefore, for an algorithm to be able to detect these targets in a timely fashion, the process must be real time. In addition, the data that can be used should be only those which have been visited and processed. So, the data processing must be also causal as well. Such causality is a very important pre-requisite to real time processing. Our work is believed to be the first work devoted to exploring this concept into anomaly detection. Specifically, it further derives a causal innovations information update equation for implementing real time causal anomaly detection. This concept which makes use of only innovations information provided by the pixel currently being processed without re-processing previous pixels is similar to those derived in Kalman filtering.

Yu-Lei Wang received her BS degree in Electrical Engineering from Harbin Engineering University, China in 2009 and is currently a Ph.D. student in the same university. Since December 2011 Ms. Wang has been working in the Remote Sensing Signal and Image Processing Laboratory at UMBC on hyperspectral anomaly detection under a China State Scholarship awarded by China Scholarship Council for a two-year visit to UMBC. Ms. Wang's research interest includes remote sensing image processing and vital sign signal processing.

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