Department of Computer Science and Electrical Engineering

Hi Tea is back with a bang! Registration is now open for the Fall 2013 CSEE Hi Tea Competition. Hi Tea is a student-run social event held (nearly) every Friday from 3:00 to 3:30 in the third floor hallway of the ITE building outside the CSEE Department suite (325 ITE). All students, staff, faculty and friends of the CSEE Department are welcome to attend. Each week, a group of students will plan and assemble simple refreshments for the event. This Fall we will repeat the popular competition used in the Spring (see some photos).

Let's come together to cheer and vote for the competitors, have some food, mingle with old friends and make new ones. Here are the rules:

• Form groups of one to four members. Teams do not have to have all members from the same lab. So feel free to form a group with any of your friends from the CSEE Department.
• Create a name for your team.
• The winning team will be chosen from a weighted combination of votes. One vote will result from attendees. Another vote will come from faculty judges. Ties will be resolved by faculty judges.
• Each team should limit their presentation to $15. Therefore, each team will be reimbursed up to $15. Each team must save their receipts and submit them to Jane Gethman to obtain their reimbursement.
• Teams will be judged on creativity, presentation, and budget planning. It is preferred that you list how you managed your expenses for the judges to verify limit-to-$15 rule.
• Each week, two teams will compete against each other (). The winner will proceed to the next round.

Hi Tea will proceed for eight weeks. After that, the winner, first runner-up, and second runner-up will receive a $100, $75, and $50 gift certificate, respectively.

The following dates are reserved for fun-filled special editions of Hi Tea. No teams will compete on these days. Stay tuned for more details!

• 25-Oct-2013: Halloween
• 1-Nov-2013: Diwali
• 22-Nov-2013: Thanksgiving

For questions, comments, and registration, email the following Hi Tea committee members:

• Raghavendra Shruti Rao ( )
• Genaro Hernandez Jr ()
• Rukmini Ramakrishnan ()

The first meeting of the UMBC Game Developers Club (GDC) will be held at Noon on Wednesday September 11 in Engineering 005a. Potential new members and people curious about the process behind video games are welcome. In the first meeting, club president Paul Tschirgi will review the club purpose, organization and activities and describe the guidelines for the game idea selection process. Information on last year's projects can be found on the GDC project page.

The GDC was originally formed in 2005 with the goal of giving students from varied backgrounds a chance to work together and make games. The GDC accepts members from any major or background, including computer science, digital art, computer modeling, information systems, and music. If you would like to know more about the organization, feel free to visit our forums or come to one of our meetings. Currently, the GDC meets every Wednesday from 12:00 pm to 1:00 pm in the the GAIM Lab (ENGR 005A). Dates for meeting, workshops and other GDC related events are posted on the GDC Google Calendar.

The UMBC ACM student chapter is glad to announce the first meetup of Baltimore Software Craftsmanship User Group. This meetup is for the students and software developers in the Baltimore, MD area that care about the quality of their work and want to practice and improve their programming skills, share what they know and learn new things from others.

Please RSVP for the event by completing the form. More details can be read on the form or below.

Note: Registration for this initial meetup is limited to only twelve people from the UMBC community. If you are UMBC Student or Faculty please don't RSVP on the meetup site. Use the above form only.

Event Details

This is a HANDS-ON coding user group with no presentations. Each meeting will be a dojo where we will go through a challenging software craftsmanship exercise that focuses on clean code, test-driven development, design patterns, and refactoring. We will pair up and practice on a kata in order to learn and apply the values, principles, and disciplines of software craftsmanship. Come with your LAPTOP equipped with your favorite programming and automated unit testing environment. If you don't have a laptop COME ANYWAY, we will need only one laptop for every two people. Be prepared to pair up, learn, share and have fun!

The event is open to all UMBC students, however programming ability is REQUIRED. Interested Faculty members can join in too! This can also be a good opportunity to network with professionals from various companies and get yourself noticed for any job opportunities that exist.

Questions or Suggestions? Send email to Primal Pappachan (primal1 at umbc.edu) or Vladimir Korolev (vkorol1 at umbc.edu).

The Association for Computing Machinery (ACM) is the world’s largest educational and scientific computing society. UMBC has an active ACM student chapter that is open to all UMBC undergraduate and graduate students of any major.

This year the chapter is planning to organize various events where faculty members, ACM distinguished speakers, and local tech companies will talk about various interesting topics. The first event of the year is the Welcome Back Picnic which be held from 11:30am to 1:30pm on September 18th in the Engineering Atrium. Other activities like Hi-Tea competitions, Code Craftsmanship, Reading groups and Peer mentorship are also in the works. Suggestions on speakers or other events are welcome and can be sent to .

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.

Please stop by for these events for which we will send out detail as they get confirmed. Sign up for the UMBC ACM mailing list to become a part of the local chapter and receive updates and news of its activities and events. You can also follow us on Twitter or like us on Facebook to keep track of chapter’s events.

If you are a senior planning to apply to a graduate program next year or a current graduate student early in your program of study, you should consider applying for a National Science Foundation Graduate Research Fellowship.

NSF has opened the application for the 2014 Graduate Research Fellowship Program. These Fellowships give three years of funding to students in research based science, technology, engineering, and mathematics master’s and doctoral programs.

Funds are awarded to the student and can be used in any appropriate program. If you will be applying to graduate programs for next year, you only need describe the insititutions that you plan to apply to.

NSF's Computer & Information Science & Engineering Directorate is looking for strong applicants in this year’s program. The deadline for submission is November 4, 2013 and the application can be found here.  If you are interested, you should start by talking with your advisor and or other faculty members about the program and how to submit a strong application.

The third Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL 2013) is a one day event that will bring together faculty, researchers and students from universities in the Mid-Atlantic area doing research on speech, language or machine learning. The colloquium is an opportunity to present preliminary, ongoing or completed work and to network with other students, faculty and researchers working in related fields.

The first MASC-SLL was held in 2011 at Johns Hopkins University and the second in 2012 at the University of Maryland, College Park. This year the event will be held at the University of Maryland, Baltimore County (UMBC) in Baltimore, MD from 9:30 to 5:00 on Friday, 11 October 2013. There will be no registration charge and lunch and refreshments will be provided.

Students and postdocs are encouraged to submit abstracts describing ongoing, planned, or completed research projects, including previously published results and negative results. Research in any field applying computational methods to any aspect of human language, including speech and learning, from all areas of computer science, linguistics, engineering, neuroscience, information science, and related fields, is welcome. All accepted submissions will be presented as posters and some will also be invited for short oral presentations. Student-led breakout sessions will also be held to discuss papers or topics of interest and stimulate interaction and discussion. Suggest breakout session topics via easychair.

Computer Science and Electrical Engineering
Ph.D. Dissertation Proposal

Framework for Accurate Estimation of Dynamic

Power Supply Noise and its Effect on Path Delays

Sushmita K. Rao

11:00am-1:00pm Monday, July 29, 2013, ITE 346

Power-supply noise is a major contributing factor for yield loss in sub-micron designs. Excessive switching in test mode causes supply voltage to droop more than in functional mode leading to failures in delay tests that would not occur otherwise under normal operation. Thus, there exists a need to accurately estimate on-chip supply noise early in the design phase to meet power requirements in normal mode and during test to prevent over-stimulation during test cycle and avoid false failures.

Simultaneous switching activity (SSA) of several logic components is one of the main sources of power-supply noise (PSN) which results in reduction of supply voltages at the power-supplies of the logic gates. Current research concentrate on static IR-drop which accounts for only part of the total voltage drop on the power grid and therefore insufficient for nanometer designs. To our knowledge, inductive drop is not included in current noise analysis techniques for simplification. The power delivery networks in today’s very deep-submicron chips are susceptible to slight variations and cause sudden large current spikes leading to higher Ldi/dt drop than resistive drop essentiating the need to be accounted. Simultaneous switching in localized areas in a chip too result in large instantaneous current to be drawn from a particular power bump or pad reducing supply voltage further. Thus, there arises a growing need to accurately characterize the resistive and inductive voltage drop caused by simultaneous switching of multiple paths. Power-supply noise also impacts circuit operation incurring a significant increase in path delays. It is critical to account for this increase in delay during the ATPG process else it can lead to overkill during transition and delay testing. However, it is infeasible to carry out full-chip SPICE-level simulations on a design to validate the large number of ATPG generated test patterns. Accurate and efficient techniques are required to quantify supply noise and its impact on path delays to ensure reliable operation in both mission mode and during test.

A scalable current-based dynamic method is presented to estimate both IR and Ldi/dt drop caused by simultaneous switching activity. Also presented is a technique to predict the increase in path delays caused by supply noise. The noise and delay estimation techniques use simulations of individual extracted paths in comparison to time-consuming full-chip simulations and thus it can be integrated with existing ATPG tools. Simulation results for combinational and sequential benchmark circuits are presented demonstrating the effectiveness of the convolution-based techniques.

Committee: Professors Chintan Patel (Chair), Mohamed Younis, Ryan Robucci and Nilanjan Banerjee

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

Computer Science and Electrical Engineering
MS Thesis Defense

Sentiment Analysis on Tweets and their
Relationship with Stock Market Trends

Jay Sharma

10:00 AM – 12:00 PM Monday, July 29, 2013, ITE 325

We investigate whether sentiment derived from micro-blogging site Twitter can be used to identify important events (product launch, quarter results etc.) and help to infer the future movement of the stock. We used the volume and key performance index of Apple Company’s financial tweets to identify important events and infer the future movement. We present the results of machine learning algorithms (Naïve Bayes, Maximum Entropy, and SVM) for classifying the sentiment of Apple Company’s financial tweets. Statistical analysis using Granger causality test showed that we were able to infer the movement of Apple Company’s stock close price in advance.

Committee: Professors Yelena Yesha (chair), Shujia Zhou, and Tim Finin

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