MS Defense
Computer Science and Electrical Engineering

Detection Performance and Computational Complexity of
Radar Compressive Sensing for Noisy Signals

Asmita Korde

2:00-4:00 Wednesday, 24 July 2013, ITE 325

In recent years, compressive sensing has received a lot of attention due to its ability to reducethe sampling bandwidth, yet reproduce a good reconstructed signal back. Compressivesensing is a new theory of sampling which allows the reconstruction of a sparse signal bysampling at a much lower rate than the Nyquist rate. This concept can be applied to severalimaging and detection techniques. In this thesis, we explore the use of compressive sensing for radar applications. By using this technique in radar, the use of matched filter can be eliminated and high rate sampling can be replaced with low rate sampling. We analyze compressive sensing in the context of radar by applying varying factors such as noise and different measurement matrices. Different reconstruction algorithms are compared by generating ROC curves to determine their detection performance, which in turn are also compared against a traditional radar system. Computational complexity and MATLAB run time are also measured for the different algorithms. We also propose an algorithm called simplified OMP, which works well in noisy environments and has a very low computational complexity.

Committee: Professors Tinoosh Mohsenin (Chair), Joel Morris, Tulay Adali, and Mohamed Younis

Computer Science and Electrical Engineering
MS Thesis Defense

Multicast Routing with Byzantine Robustness

Debdatta Mukherjee

2:30-4:30 Tuesday, july 23, 2013, ITE 346

Network problems arise when nodes behave in arbitrary ways such as sending malformed messages, sending incorrect messages or not forwarding messages at all to other nodes in the network. These faults are called Byzantine failures. In a real network, these faults can be a result of hardware failure, cyber-attacks or network congestion. Due to the serious problems these faults can cause, it becomes important to make the network robust against them, so that the network continues to operate properly or degrades in an acceptable way in the presence of such faults. In this thesis, we propose methods that include multiple node disjoint path calculations and robust flooding to find byzantine-free multicast trees. By finding such trees, we can guarantee the delivery of the messages from a source to a particular multicast group.

Committee: Professors Deepinder Sidhu (chair), Kostas Kalpakis and Sergei Nirenburg

MS Defense
Computer Science and Electrical Engineering

Social Media Data Analytics Applied to Hurricane Sandy

Han Dong

12:30-2:30 Monday, 29 July 2013, ITE 325b

Social media websites are an integral part of many people’s lives in delivering news and other emergency information. This is especially true during natural disasters. Furthermore, the role of social media websites is becoming more important due to the cost of recent natural disasters. These online platforms are usually the first to deliver emergency news to a wide variety of people due to the significantly large number of users registered. During disasters, extracting useful information from this pool of social media data can be useful in understanding the sentiment of the public; this information can then be used to improve decision making. In this work, I am presenting a system that automates the process of collecting and analyzing social media data from Twitter. I also explore a variety of visualizations that can be generated by the system in order to understand the public sentiment. I demonstrate an example of utilizing this system on the Hurricane Sandy disaster from October 26, 2012 to October 30, 2012. Finally, a statistical analysis is performed to explore the causality correlation between an approaching hurricane and the sentiment of the public.

As a result of the large amount of data collected by this system; scalable machine learning algorithms are needed for analysis. Boosting is a popular and powerful ensemble method in the area of supervised machine learning algorithms due to its theoretical convergence guarantees, simple implementation and ability to use different learning algorithms to produce a classifier with high accuracy. A novel parallel implementation of the multiclass version of Boosting (AdaBoost.MH) is proposed and our experimental results show that the parallel implementation achieves classification error percentages similar to serial implementation with fewer execution iterations. By distributing the tasks, the number of Boosting iterations decreased linearly at least up to 16 computational threads.

Committee: Professors Milton Halem (chair), Yelena Yesha, John Dorband and Shujia Zhou

MS Defense
Computer Science and Electrical Engineering

Linked Data for Cybersecurity Vulnerability Descriptions

Arnav Joshi

9:00-11:00 Monday, 22 July 2013, 325b ITE

The Web is typically our first source of information about new software vulnerabilities, exploits and cyber-attacks. Information is found in semi-structured vulnerability databases as well as in text from security bulletins, news reports, cybersecurity blogs and Internet chat rooms. It can be useful to cybersecurity systems if there is a way to recognize and extract relevant information and represent it as easily shared and integrated semantic data. We describe such an automatic framework that generates and publishes a RDF linked data representation of cybersecurity concepts and vulnerability descriptions extracted from the National Vulnerability Database and other text sources. Entities, relations and concepts are represented using custom ontologies for the cybersecurity domain and also mapped to objects in the DBpedia knowledge base, producing a rich resource of machine-understandable linked data. The resulting cybersecurity linked data collection can be used for many purposes, including automating early vulnerability identification, mitigation and prevention efforts.

Committee: Professors Tim Finin (chair), Anupam Joshi and Tim Oates

"Grace Hopper was a totally exceptional experience.  I had the opportunity for three on-site interviews, which offered great practice with technical interviews." -- Emily Scheerer, Computer Science

"It was great meeting a bunch of new people from all around the country at the conference as well as networking with many of the biggest names in technology, like Facebook, Twitter, Apple, and Microsoft.  I know getting to meet these recruiters in person was a huge opportunity and I am very optimistic in my chances of getting an internship on the west coast, which is one of my dreams." -- Alec Pulianas, Computer Engineering

UMBC Professor Helen J. Burgess was recognized has for creating "the first truly digital dissertation in the United States" by the Networked Digital Library of Theses and Dissertations. Dr. Burgess is an Associate Professor of English in the Communication and Technology track and specializes in digital humanities.

Her 2003 doctoral dissertation, Highways of the mind: the haunting of the superhighway from the World's Fair to the World Wide Web, was completed at West Virginia University.

In addition to the recognition, Dr. Burgess received an Innovative ETD Award from NDLTD for her work.

“In Helen J. Burgess’ doctoral dissertation ‘Highways of the mind: the haunting of the superhighway from the World's Fair to the World Wide Web’ she produced the first truly digital dissertation in the United States which took full advantage of a Web-based format — incorporating an HTML and multimedia environment. Her dissertation consists of well-organized Web pages, persistence index links, photographs as well as video and audio clips linked throughout the document. This allows the reader to completely immerse into the digital environment and provide immediately accessible examples indicated in textual format. As a true testament to time and durability, Burgess’ ETD is still as functional today as the day it was posted online a decade ago. The bundle of dissertation files contain the Web page version as well as a "linear" book format version in PDF format, along with copies of all multimedia objects preserved in their original format, for preservation purposes.”

Are you away from UMBC this summer? Do you want to share how you are spending your experience with the greater UMBC community? 

Using the hashtag #UMBCIntern, share your best photos with us about your Summer internship, co-op, or research experience. 

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