Department of Computer Science and Electrical Engineering

CSEE Professor Gymama Slaughter received a two-year research research award from the National Science Foundation to develop and evaluate nanoelectrode probe arrays to better detect and extract intracellular signals. Data from these signals will help in restoring functional loss of limb control of individuals with spinal cord injury or stroke.

Conventional neural interfaces consist of microelectrode arrays (MEAs) that are in close contact with neurons to record extracellular potential or stimulate electrical activity. However, due to the relative large microelectrode size, these MEAs are not capable of extracting intracellular signals, which is of particular interest in restoring functional loss of limb control of individuals with spinal cord injury or stroke. MEAs electrophysiological recordings still faces two major challenges, the inherent noisy data and the limited spatial resolution. These problems especially limit the accuracy and reliability of the movement parameter due to the unreliable spike recording for long durations.

The objective of this research is the fabrication and characterization of independently addressable nanoelectrode arrays (NEAs) and nanoelectrode probe arrays (NEPAs) for high-throughput recording of extracellular and intracellular electrophysiological measurements of neural activity.  These will allow, for the first time, simultaneous extracellular and intracellular characterization of large number of neurons while maintaining high spatial resolution, high signal-to-noise ratio, and excellent selectivity of neural interfaces.

The NSF award,  a novel parallel extracellular and intracellular nanoelectrode and nanoelectrode probe array for high throughput electrophysiological recording, will provide over $150,000 over the next two years to fund Dr. Slaughter and her students research on this project.

The Baltimore Chapter of IEEE Electron Devices and Solid-State Circuits is co-hosting a free, one-day Colloquium on Sensor Devices from 10:00 to 5:00 on Wednesday, September 25. The event will be held in the Benjamin Banneker Room (2212) of the Stamp Union Building at the University of Maryland, College Park.

Invited speakers include Dr. Philip Perconti (Army Research Laboratory), Prof. M. Alam (Purdue University), Dr. Parvez Uppal (Army Research Laboratory), Prof. Mark Reed (Yale University), Dr. Herbert Bennett (NIST), Prof. Michael Shur (RPI), Dr. Anupama Kaul (National Science Foundation) and Prof. Agis Iliadis (UMCP).

Attendance is free. To register please contact: Dr. Naresh C. Das (naresh.c.das2.civ at mail.mil), Dr. Victor Veliadis (victor.veliadis at ngc.com).

Research Assistantship Available

Smart Plug-based Appliance Energy Profiling and

Prediction Portal for Green Buildings

New emerging “smart plugs” embed a micro-controller and low-power communication device that allows you to monitor the power consumption of individual devices (e.g., microwave, coffee machine, laptop) plugged into power sockets, and communicate such power consumption information over a wireless network to a central monitoring station. Such devices could lead to substantial savings of energy and money by enabling Internet-based monitoring and real-time control of the behavior of individual appliances. This project will use real-life microcontroller kits (ACME Plugs from Moteware) and real-life building measurement data to explore whether such measurement-based monitoring can be used to:

• Develop Smart Circuit Breaker — i.e., to lessen the burden of the user of plugging each and every appliance/device in the building with a smart plug; we will investigate connecting multiple devices together with an individual smart plug/smart circuit breaker and design a smart circuit breaker using energy metering chip (ADE7753), AC/DC power supply, Microcontroller with radio (TI MSP430F16 and CC2420 radio supported by TinyOS) and solid state AC relay (Sharp S216SE1) etc.
• Profile individual devices — i.e., use NILM (non-intrusive load monitoring) data analytics algorithm on the time-series of power consumption traces to infer the type of plugged-in device (e.g., distinguish between a laptop & a coffeemaker), thereby building a dynamic catalog of the types & number of devices connected by a consumer.
• Predict the power consumption of individual rooms — i.e., using the past history of the power consumption of individual devices to create predictive inferences of the usage patterns for individual devices (e.g., learn that the individual switches on a dehumidifier for ~3 hrs every Thursday).

Expertise: Technical knowledge of standard time-series & statistical mining techniques (e.g., regression, support vector machines) is needed. Significant programming knowledge of Java & ability to create simple Web Applications is a must. Knowledge of TinyOS, Embedded System and Networking protocols is a plus, although not essential. The project will utilize real ACME plugs, which are programmed using TinyOS & which communicate using a ZigBee radio.

Please contact Dr. Nirmalya Roy at for research assistantship for this project.

Professor Curtis R. Menyuk of the UMBC Computer Science and Electrical Engineering Department has been awarded the 2013 IEEE Photonics Society William Streifer Scientific Achievement Award. The award recongnizes Dr. Menyuk

"For seminal advances in the fundamental understanding and mitigation of polarization effects in high-performance optical fiber communication systems."

He will receive the award at presentation during the awards Ceremony at the 2013 IEEE Photonics Conference at the Hyatt Regency Bellevue, Bellevue, Washington in September.

The William Streifer Scientific Achievement Award is given to recognize an exceptional single scientific contribution which has had a significant impact in the field of lasers and electro-optics in the past 10 years. The award is given for a relatively recent, single contribution, which has had a major impact on the Photonics Society research community. It may be given to an individual or a group for a single contribution of significant work in the field.

Professor Curtis Menyuk was born March 26, 1954. He received the B.S. and M.S. degrees from MIT in 1976 and the Ph.D. from UCLA in 1981. He has worked as a research associate at the University of Maryland, College Park and at Science Applications International Corporation in McLean, VA. In 1986 he became an Associate Professor in the Department of Electrical Engineering at the University of Maryland Baltimore County, and he was the founding member of this department. In 1993, he was promoted to Professor. He was on partial leave from UMBC from Fall, 1996 until Fall, 2002. From 1996 – 2001, he worked part-time for the Department of Defense, co-directing the Optical Networking program at the DoD Laboratory for Telecommunications Sciences in Adelphi, MD from 1999 – 2001. In 2001 – 2002, he was Chief Scientist at PhotonEx Corporation. In 2008 – 2009, he was a JILA Visiting Fellow at the University of Colorado.

For the last 25 years, his primary research area has been theoretical and computational studies of lasers, nonlinear optics, and fiber optic communications. He has authored or co-authored more than 230 archival journal publications as well as numerous other publications and presentations, and he is a co-inventor of 5 patents. He has also edited three books. The equations and algorithms that he and his research group at UMBC have developed to model optical fiber systems are used extensively in the telecommunications and photonics industry. He is a member of the Society for Industrial and Applied Mathematics. He is a fellow of the American Physical Society, the Optical Society of America, and the IEEE. He is a former UMBC Presidential Research Professor.

Professor Alan Sherman received two research awards from the National Science Foundation to support work at UMBC on cybersecurity.

Sherman is a co-principal investigator on a two year, $300K Eager award to foster research cooperation among four successful and mature Centers of Academic Excellence in Information Assurance Research: Purdue University, UMBC, UC Davis, and Mississippi State. The project will provide opportunities for students to work on problems proposed and mentored by practitioners in the real world rather than just faculty led research. As a result, more pressing and urgent problems will be addressed, the students will benefit from the guidance of multiple and interdisciplinary research faculty from multiple institutions and the student-lead research may produce solutions for pressing national problems.

Professor Sherman also received a supplement of $271K to his UMBC Cybersecurity SFS Program award to support graduate research assistants who will work on two new projects. One will develop new algorithms for verifiable randomness that can generate random bits in a way that the recipients will have verifiably high assurance that the bits were generated in a truly random fashion. The work will improve upon the (unverifiable) NIST random beacon project. The second project will develop a new security education game, inspired by the UMBC-developed classroom game SecurityEmpire, to be fielded as a Facebook application for free use by anyone. This is joint work with UMBC faculty Marc Olano and Linda Oliva.

The research will be carried out in the UMBC Center for Information Security and Assurance under Professor Sherman’s supervision.

UMBC's Mobile, Pervasive and Sensor Systems Laboratory focuses on three key areas: renewable energy, healthcare applications and mobile phone systems. Their crowdsourcing-based technology for accurate and low cost detection of weed infestations was cited recently as one of the top ten technologies changing farm machinery by Farm Industry News.

D. Saraswat and N. Banerjee, Crowdsourcing App for Precision Agriculture Decision Making, ASABE Annual International Meeting, Dallas TX, August 2012.

The research was begun while Professor Banerjee was at the University of Arkansas, where the complex software system was  implemented by students Brenna Blackwell and Mahbub Rahman, who is continuing his PhD studies at UMBC.

Like the MPSSL Facebook page to follow their work or visit the MPSSL page

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