CSEE
CSEE professor Dr. Tulay Adali receives USM Regents’ Faculty Award for Scholarship/Research/Creative Activity
posted Thursday, June 6th, 2013

adali_awardMore than twenty years ago, Tulay Adali stepped onto UMBC’s campus as an assistant professor right after receiving her PhD. Much has changed since then.

Now a professor of Computer Science and Electrical Engineering, Dr. Adali runs a highly active Machine Learning for Signal Processing Lab (MLSP­Lab). Her recent appointment as an IEEE Signal Processing Society Distinguished Lecturer has prompted invitations to speak around the world about her research in the theory and development of algorithms for signal processing. This March, Dr. Adali was awarded the University System of Maryland Regents’ Faculty Award for Scholarship, Research, or Creative Activity.

Her secret to success?

“Planning or thinking about the future is not something I do,” said Dr. Adali in her acceptance speech at the Presidential Faculty and Staff Ceremony where she was honored in March. “I rather make sure I enjoy what I do and have fun along the way.” Her technique seems to be paying off. For proof, just take a look at the recognition received by her research in two distinct areas: the development of powerful data­driven methods, and the analysis and fusion of medical imaging data. In 2008, Dr. Adali was elected a fellow of the American Institute for Medical and Biological Engineering (AIMBE). In 2009, the Institute of Electrical and Electronics Engineering (IEEE) elected her a fellow for her work on the theory and practice of statistical signal processing.

In 2011, a paper by Dr. Adali and colleagues titled “Complex ICA using nonlinear functions” received the 2010 IEEE Signal Processing Society Best Paper Award. The work develops a complete framework, allowing for the processing of complex data in a manner similar to the real­valued case, eliminating the need to make many of the simplifying assumptions commonly employed. The results of this NSF­funded study led to the development of a complete data­driven framework that enables joint use of sample dependence and higher­order­statistics.

Dr. Adali’s work in medical image analysis and fusion has also gained notoriety. She has been working on methods for data­driven analysis of medical imaging data, and for the analysis of functional magnetic resonance imaging (fMRI) data for understanding brain function. She and her colleagues discovered that fusing more than two modalities increases the sensitivity and specificity of the analyses of fMRI, electroencephalography (EEG) and structural MRI data. In March 2011, an IEEE Spectrum article mentioned her success in obtaining very high classification accuracy in identifying mental disorders in patients. Then in April 2011, in addition to her ongoing projects funded by the NSF, NIH, and the Mind Research Network, she received a grant from Michelin Research to study irregular wear detection in tires, where the new data-driven framework is applied to a completely new problem domain.

These notable research advances made Dr. Adali stand out as a nominee for this year’s Regents’ Faculty Award for Scholarship, Research, or Creative Activity. It is the highest honor given by the Board of Regents to faculty members, given to faculty members who have gone above and beyond the call of duty. This year, Dr. Adali joins only three other USM faculty members who were recognized for their exceptional research contributions. “Dr. Adali has been steadily building her research career and I am not surprised by the award since her research is remarkable,” says Dr. Carter, CSEE Department Chair. “I see her continuing to grow her research in areas of signal processing for medical applications and becoming a key UMBC faculty member

PhD defense: On Prediction and Estimation for Datastreams Utilizing Sparsity and Structure, 6/6
posted Wednesday, June 5th, 2013

Ph.D. Dissertation Defense

On Prediction and Estimation for Datastreams

Utilizing Sparsity and Structure

Shiming Yang

10:00am-12:00pm, 6 June 2013, ITE 325b, UMBC

With the unprecedented fast growth of data, we have better opportunities to understand our complex world, and simultaneously face pervasive challenges in efficiently inferring the meaning behind these vast amounts of data. It is particularly important to explore the intrinsic structures in data to increase our rational understanding of the latent mechanisms that generate them. In modeling, structures are features used to characterize the underlying systems, such as the rank of a system, the number of clusters, the levels of hierarchy, and the order of spatio-temporal correlations in multiple measurements.

In this thesis, we present our research contributions on utilizing structures and sparsity in observed data to improve estimation and prediction of trajectories of system states for two systems: the highway traffic system and the human physiology systems. Both systems exhibit features that are seen in many other applications.

For the traffic problem, it is useful to know the near–term traffic conditions after the occurrence of some events which have noticeable impact on the road traffic. Often used macroscopic models, which view road traffic as fluid flowing in pipes, suffer from various inaccuracies, which could be mitigated by incorporating past observations to correct predictions. However, we often have limited observation and computing resources (e.g., probe vehicles, smartphones, bandwidth, sensors) to gather and process past observations. We describe a novel low-overhead strategy to adaptively select observation sites in real-time by using the density of the mesh of the numerical solution of the underlying mathematical model to capture the variability of that solution. We show that our proposed strategy improves the numerical accuracy of near–term traffic forecasting with limited observation resources as compared with with uniform deployment of the observation resources. In addition to deploying limited observation resources, one is often concerned with detecting special traffic events. To this end, we propose a novel method to decompose traffic observations into normal background and sparse events. Our method couples multiple traffic datastreams so that they share a certain sparse spatio–temporal structure.

We also study the utility of sparseness and structure in physiological datastreams. Missing values hinder the use of many machine learning methods. We show how to incorporate ideas from compressive sensing into handling the missing values problem in continuous intracranial pressure (ICP) datastreams from patients with traumatic brain injury. We experimentally evaluate the proposed method in experiments where randomly selected ICP values are marked as missing. We find our method gives estimated missing values that are in better agreement with the true values as compared with k–nearest neighbor and expectation maximization data imputation methods.

Moreover, predicting the near–term intracranial pressure for traumatic brain injury patients is of great importance to clinicians. Traditional regression methods, need an explicit parametric form of the model to fit. However, due to our limited knowledge of the complex brain physiology, it is difficult to specify an accurate parametric model. To overcome this difficulty, our model uses Gaussian processes to quantify our prior beliefs on the smoothness of the regression model, and performs regression in an infinite dimensional space. We show that the proposed Gaussian process regression model shows predicts ICP changes in clinically useful timeframes and may support future development of minimally-invasive ICP monitoring systems, earlier intervention strategies, and better patient outcomes.

Committee: Drs. K. Kalpakis (Chair), Alain Biem (IBM TJ Watson), Chein-I Chang, Colin MacKenzie, Dhananjay Phatak, Yaacov Yesha

MS Defense: Nimbus: Scalable, Distributed, In-Memory Data Storage 6/6
posted Tuesday, June 4th, 2013

MS Defense

Nimbus: Scalable, Distributed, In-Memory Data Storage

Adam Shook

1:30pm Thursday, 6 June 2013, 325b ITE, UMBC

The Apache Hadoop project provides a framework for reliable, scalable, distributed computing. The storage layer of Hadoop, called the Hadoop Distributed File System (HDFS), is an append-only distributed file system designed for commodity hardware. The append-only nature of the file system limits the ability for applications to have random reads and writes of data. This was addressed by Apache HBase and Apache Accumulo, which both allow for quick random access to a highly scalable key/value store.

However, these projects still require data to be read from the local disk of the server, and therefore cannot handle the type of I/O throughput that many applications require. This limits the potential for "hot" data sets that cannot be stored in memory of one machine, but do not need the scalability of HBase, i.e. the ones that can be sharded and stored in memory on dozens of machines. These data sets are often referenced by many applications and can be dozens of gigabytes in size.

Nimbus is a project designed for Hadoop to expose distributed in-memory data structures, backed by the reliability of HDFS. By executing a series of I/O benchmarks against HBase, Nimbus's architecture and implementation are validated by demonstrating the performance advantage over HBase, allowing for high-throughput data fetch operations. The overall architecture and design of each component are discussed to validate Nimbus's design goals, as well as a description of relevant use cases and future work for the project.

Committee: Drs. Tim Finin (chair), Anupam Joshi and Konstantinos Kalpakis

Phd Defense: Dingkai Guo, Mid-Infrared Photonic Integration 6/4
posted Monday, June 3rd, 2013

Ph.D. Dissertation Defense

Mid-Infrared Photonic Integration

Dingkai Guo

10:00am Tuesday, 4 June 2013, TRC CASPR conference room

The mid-Infrared (Mid-IR) wavelength range is important for applications including medical and security imaging, environmental trace gas sensing and free space communications. However, photonic integrated circuits (PICs) in the mid-IR range are completely under-developed which significantly slows the reduction of mid-IR system size, weight, and coupling losses and limits the development of highly functional mid-IR photonic modules with lower cost. In this dissertation, a solution to mid-IR photonic integration was demonstrated using a compact widely tunable mid-IR transmitter and a mid-IR amplifying photo-detector, which can be integrated with the mid-IR source.

This integrated widely tunable mid-IR source is fabricated by incorporating super structure grating (SSG) to the mid-IR quantum cascade laser (QCL) waveguide. The emission wavelength of the fabricated SSG-DBR QCL can be well controlled by varying the injection currents to the two grating sections. The wavelength can be tuned from 4.58μm to 4.77μm (90cm-1) with a supermode spacing of 30nm. This SSG-DBR QCL can be a compact replacement for the external cavity QCL used in current mid-IR sensors.

Mid-IR amplification and detection can be achieved using the same material as the mid-IR source. This QCL amplifier has an adjustable bandwidth and tunable gain peak, so it can function as a tunable mid-IR filter. By biasing the QCL just below its threshold, we demonstrated more than 11dB optical gain and over 28dB electrical gain at specified wavelengths. In the electrical gain measurement process, the resonant amplifier also functioned as a detector. This indicates that intersubband-based gain materials are ideal candidates for mid-IR photonic integrations.

Beside the optimized fabrication processes, new characterization technique based on the electrical derivative of the QCL I-V curves is used to quickly acquire the QCL threshold and leakage current, and explore the device carrier transport. The leakage currents present in different QCL waveguide structures are also studied and compared using this technique.

Finally, we report that the telecom wavelengths induced optical quenching effects on mid-IR QCLs when the QCLs are operated well above their threshold. The quenching effect is a result of intersubband bandbending and it depends on the coupled near-IR intensity, wavelength, and the QCL voltage bias. The quenching effects not only can be used for mid-IR QCL optical switching and modulation but also reveal that the mid-IR QCLs can function as “converters” to convert the telecom optical signal into the mid-IR optical signal at the near-IR fiber end.

A coherent mid-IR transceiver with both transmitting and receiving functions can be realized based on each integrated component introduced in this dissertation. This compact transceiver includes an integrated widely tunable mid-IR source, a mid-IR filter, amplifier, and detector based on the same material system.

Committee: Drs. Fow-Sen Choa (Chair), Anthony Johnson, Terrance Worchesky (Physics) , Li Yan, Gymama Slaughter

MS defense: A Multilayer Framework to Catch Data Exfiltration
posted Sunday, June 2nd, 2013

MS Thesis Defense

A Multilayer Framework to Catch Data Exfiltration

Puneet Sharma

10:30am Wednesday, 5 June 2013, 325b ITE, UMBC

Data exfilteration is the unauthorized leakage of confidential data from a particular system. It is a specific form of intrusion that is particularly hard to catch due to the most common cause: an insider entity who is responsible for the leak. That entity could be a person employed in the organization or a malicious hardware component bought from an unreliable third party. Catching such intrusions, therefore, can be extremely difficult. We describe a framework comprising multiple parameters that are constantly monitored in a system. These parameters can cover the entire stack of the computer architecture, from the hardware up to the application layer. Malicious behavior is detected by different modules monitoring these parameters and an aggregated attack alert is produced if multiple modules detect malicious activity within a short period of time. A more distributed and comprehensive monitoring framework should ensure that designing an attack becomes extremely difficult since an attack must go through multiple detectors present in the system without raising any alarms.

Committee: Drs. Anupam Joshi (chair), Tim Finin, Chintan Patel

PhD proposal: Yu Wang, Solving the Physically-Based Modeling and Animation Problem with a Unified Solution
posted Saturday, June 1st, 2013

Ph.D. Dissertation Proposal

The Modeling Equation: Solving the Physically-Based

Modeling and Animation Problem with a Unified Solution

Yu Wang

11:00am Monday, June 3, 2013, VANGOGH Lab, ITE 352

Physically-based modeling, i.e. the ability to model sophisticated geometrical shapes and objects in complex physical environments, is an important and popular research area in computer graphics, especially in animation and modeling. Rigid body dynamics studies how solid objects react to external forces without considering collisions (unconstrained), or the interaction between rigid bodies without inter-penetration (constrained). Deformable object modeling accounts for the effects of material properties, external forces, and environment constrains on object deformation. Fluid simulation in computer graphics heavily studies efficient way of solving and/or approximating the physically-based Navier-Stokes equations.

It’s difficult to account for these behaviors from a mechanics point of view, but they have analogous rheological equations. To be exact, rheology studies deformation and flow of matters by accounting for the movements of particles that comprise the material relative to each other. There are three different rheological properties: if we apply definite forces to a material to make it reach a definite deformation, and the deformation goes back when the forces are removed, the material is elastic; if the deformation remains permanent, the material is plastic; or under definite forces, the deformation keeps increase without a limit, the material flows.

I’m proposing to create physically-accurate material behaviors using a generalized formulation based on rheological theories, i.e. kinematic and dynamic properties of rigid bodies, deformable objects, fluid-like materials can be represented by the same formulation with different weights to their rheological properties.

Committee: Drs. Marc Olano (Chair and Advisor), Matthias K. Gobbert (Mathematics and Statistics), Penny Rheingans, Lynn Sparling (Physics), Jian Chen

MS defense: Extracting cybersecurity related entities, terms and concepts from text
posted Friday, May 24th, 2013

MS Thesis Defense

Extracting cybersecurity related entities,
terms and concepts from text

Ravendar Lal

10:30am Tuesday, 28 May 2013, ITE325b, UMBC

Securing computers, data, cyber-physical systems and networks is a growing problem as society's dependence on them increases while they remain vulnerable to attacks by both criminals and rival nation states. Creating 'situationally aware' computer systems that defend against new "zero day" software vulnerabilities requires them to automatically integrate and use new security-related data from a wide variety of sources. One important source is information found in text from security bulletins, vulnerability databases, news reports, cybersecurity blogs and Internet chat rooms.

We describe an information extraction framework to extract cybersecurity-relevant entities, terms and concepts from text. We use a Conditional Random Field based model trained on manually annotated data to identify and extract the relevant terms. These are then mapped to a previously developed OWL ontology and represented as RDF linked data. We evaluated the system's performance by comparing its results on test data from the National Vulnerability Database and security bulletins from Microsoft and Adobe.

Committee: Drs. Tim Finin (Advisor), Anupam Joshi, Tim Oates

Five COEIT Students Selected as Federal CyberCorps Scholars
posted Thursday, May 23rd, 2013

Five students in UMBC's College of Engineering and Information Technology have been selected for major scholarships to pursue studies in cybersecurity-related fields under UMBC's participation in the National Science Foundation's Scholarship for Service (SFS) Federal CyberCorps program. As SFS Scholars, students receive full tuition, fees, annual reimbursement of professional development expenses ($3,000), a nine-month stipend ($20,000 for undergraduates, $25,000 for MS/MPS students, and $30,000 for PhD students) for up to two years (three years for PhD), and assistance with federal cybersecurity internships and career placement.

The awardees for AY13-14 are:

Nathan Price, MS (CMPE)
Punlada Muangrat, BS (IS)
Shannon Mcpherson, MPS (CYBR)
Alex Cooke, BS (IS)
Denis Danilin, MS (IS)

These new awardees will join existing UMBC SFS Scholars Oliver Kubik (BS, CMSC), Mary Mathews (PhD, CMSC), Brendan Masiar (MPS, CYBR) and Brandyn Schult (MPS, CYBR).

The CyberCorps program produces highly-qualified professionals to meet the United States government's increasing need to protect American's cyber infrastructure. While in the program at UMBC, SFS CyberCorps Scholars participate in special SFS program activities, have opportunities to engage in mentored research opportunities both at UMBC and its partners from industry and government, and must complete a paid summer internship for the federal government. Upon graduation, each student must work for the government (for pay) for one year for each year of scholarship received. Drs. Alan Sherman and Richard Forno direct the program under a five-year $2.5 million NSF grant received in 2012.

NOTE:  We expect to have additional SFS slots coming available in January 2014.  Applications for SFS support beginning in the Spring 2014 semester will be accepted through 12PM on Friday, 15 November 2013. All levels of study are welcome to apply — however, students must be admitted to (or already enrolled at) UMBC first before applying for SFS.

For information about SFS (including application information and instructions) and other US government-related cybersecurity scholarships available, please visit cisa.umbc.edu.

CE21-Maryland Summit raises awareness about need for more women and minority Computer Science majors
posted Thursday, May 16th, 2013

You don’t know until you try.

This is what the leaders behind Computing Education for the 21st Century (CE21-Maryland) have figured out. Drs. Marie desJardins, Penny Rheingans, and Susan Martin say that removing the mystery and misconceptions surrounding Computer Science is the key to getting more women and minorities to purse careers in the field.  

Historically, Computer Science has been dominated by white males. Just take a look at the numbers. In 2010-11 only 12.7% of computing degrees went to women. A mere 4.6% of Computer Science graduates were African Americans and 6.5% were Hispanics.

CE21 copyIt’s not that women and minorities are no good at Computer Science.  It’s that they often think they’re no good. When you walk into a classroom and no one looks like you, it’s easy to feel like you don’t belong.

Dr. Marie desJardins, a CSEE professor, gives one part of the explanation for this trend. “Youth gaming culture is really dominating Computer Science majors,” she says. Boys who love video games naturally gravitate to Computer Science in college. Women aren’t typically avid gamers. Instead, they tend to gravitate towards social careers–not something they associate with the field of Computer Science.

Herein lies the problem: most of us don’t know what computer scientists actually do. “There are some iconic careers that we understand,” says CSEE Professor Penny Rheingans. “We know what doctors do. We know what lawyers do. But, nobody knows what Computer Scientists do.”

Only by experiencing the discipline firsthand will kids understand if the field is for them. Take Dr. Rheingans, who was headed down the path to becoming a lawyer when she took a Computer Science class: “And I fell in love,” she says. It was hard and sometimes frustrating, but that challenge got her hooked. “Not enough students have the opportunity to experience that.”

despullquoteEven if women and minorities want to take Computer Science, they don’t always have the opportunity in high school. Throughout Maryland, inconsistent curriculum and spotty availability has made it hard for students to be introduced to the subject.  

Computer Science is not a graduation requirement, nor is it even offered at every Maryland high school. Most schools offer it as an elective like ceramics or woodshop. Since the class doesn’t count for credit, those students– who aren’t otherwise encouraged by parents, or a longstanding love of computers–don’t have much incentive to take it.

Computers are becoming an increasingly ubiquitous part of our lives. As such, Dr. desJardins thinks that Computer Science should be a requirement for high school students. “We make them take Government, Math, Science, and English. But, we don’t make them take Computer Science—but it’s the fastest growing job market of any discipline,” she says. “I think it’s morally wrong that we’re not teaching children how to master this technology.”

In March 2012, Drs. desJardins, Rheingans, and Martin formed CE21-Maryland to get a deeper understanding of the shortcomings of Computer Science education in Maryland, and to help solve this problem. The group is supported by NSF’s Computing Education for the 21st Century (CE21) program.

CE21picsLast August, CE21-Maryland held its first mini-summit to raise awareness of these issues among Computer Science high school teachers across the state. The summit successfully helped establish connections among teachers who share this passion for change. “Having a community is absolutely important to helping empower people,” says Dr. Rheingans, who has proven the importance of community first-hand as the director of UMBC’s Center for Women in Technology (CWIT).

On May 17, CE21-Maryland will hold its second Summit for Computing Education. Teachers, administrators, legislators, and industry leaders will gather at UMBC to explore these issues, network, and discuss plans for increasing the number and diversity of students studying Computer Science in our state.

The summit will include a college student panel, where current computing majors will share their journey to becoming Computer Science majors. One session will take a look at the AP CS Principles course, a proposed AP course being developed by the College Board and National Science Foundation, with pilots offered around the country.

Dr. desJardins realizes that recognizing the Computer Science curriculum problem is a lot easier than fixing it.  “It’s a chicken or the egg problem,” she says. Regulating Computer Science classes across the state can’t happen until teachers are trained to teach it. Training is not likely to happen unless enrollment increases. CE21-Maryland envisions a two-pronged approach to train teachers and make connections with legislators who can make a difference.

The women behind CE21-Maryland are working hard to change the compostion of Computer Science majors. But, why?

“First of all it’s a numbers problem. Second of all it’s a diversity problem,” explains Dr. Rheingans. By 2018, nearly 40,000 new computing-related jobs will be available in Maryland each year. But, only about 2,000 bachelor’s degrees in computing and information systems are awarded by Maryland institutions annually. Recruiting more women and minorities to the major will help satisfy the huge need for computing majors in the future.   

But, perhaps more importantt is the chance to add diversity to the next generation of technological problem-solvers. “Different perspectives leads you to stronger, more robust solutions,” she says.

PhD defense: Quantum Cascade Laser Arrays for Standoff Photoacoustic Chemical Detection, 5/17
posted Thursday, May 16th, 2013

Ph.D. Dissertation Defense

High Power Mid-infrared Quantum Cascade Laser Array
for Standoff Photoacoustic Chemical Detection

Xing Chen

1:00-3:00pm Friday, 17 May 17 2013, TRC CASPR Conference Room

Quantum cascade lasers (QCLs) are compact, portable, powerful semiconductor laser sources with emission wavelengths from mid-infrared (mid-IR) to terahertz (THz) regions of the electromagnetic spectrum. Mid-IR (i.e. wavelengths from 3 to 20 µm) QCLs are of great importance in a wide range of applications such as trace gas sensing, environmental monitoring, free space communication, medical diagnosis and so on. High power QCLs are particularly important to applications such as infrared counter measure (IRCM) and standoff chemical detections. In such applications, the system performances critically depend on the amount of power a QCL can produce. This dissertation includes two major studies: the first part of the dissertation includes design, fabrication and characterization of high power mid-IR QCL arrays; the second part involves standoff chemical detection using QCLs as laser sources and photoacoustic effect as sensing technologies.

In the first part of the dissertation, we design, fabricate and characterize multi-emitter QCL arrays consisting of multiple narrow laser stripes. Simulation results indicate that the proposed multi-emitter laser arrays present much better thermal performance than a broad area laser device, while having the same thermal management ability as a single narrow stripe device. We have successfully fabricated edge emitting and surface emitting QCL arrays with 5 and 16 emitters. Experimental results show that, with the same laser cavity length, a QCL array with 5 emitters produces over 3 times more power than a single emitter laser device. QCL array with 16 emitters generates about 4 W output peak power at wavelength ~7.9 µm. We have also fabricated surface emitting QCL arrays and demonstrated single mode emission.

The second part of the dissertation involves using high power mid-IR QCLs to perform standoff chemical detections based on photoacoustic sensing technologies. Photoacoustic effect is a light-matter interaction effect that involves generation of acoustic waves when a medium absorbs electromagnetic energy from light. It has been known as a sensitive spectroscopic technique for chemical sensing.

Standoff photoacoustic chemical detection with distance more than 41 feet using quantum cascade laser operated at relatively low power, less than 40 mW, is demonstrated. A simplified theoretical model is developed for pulsed laser photoacoustic effect in open-air environment. The standoff photoacoustic signal can be calibrated as a function of different parameters such as laser pulse energy, gas vapor concentration and detection distance. The results yield good agreements with theoretical model. Standoff detection of solid phase explosive chemicals has also been demonstrated by the use of an ultra-sensitive microphone and acoustic reflector. More than 8 feet detection distance is obtained for standoff photoacoustic sensing of explosives.

Committee: Drs. Fow-Sen Choa (Chair), Brian Cullum, Yordan Kostov, Ryan Robucci, Chen-Chia Wang and Li Yan