CSEE Research Review Friday May 6

MS student WIll Murnane presents his work on named entity recognition in Twitter at the 2010 CSEE Research Review

The CSEE department will hold its annual research review this Friday from 9:30am to 4:00pm at the UMBC Technology Center at South Campus. Faculty, research staff and students from all of the department's programs present and discuss their latest research results. The Research Review is open to the public and is a good way for prospective collaborators and students to find out about the research our department is doing and meet and network with current faculty and students. There is ample free parking and refrehsments and lunch are provided. Directions: Take Gun Road off Rolling Road (Rt. 166) or take the UMBC “Satellite” Shuttle to South Campus.

CSEE students at the Graduate Research Conference

Twenty CSEE graduate students had oral or poster presentations at the 2011 GRC and CSEE alumnus Dr. Ralph Semmel gave a the keynote address. We have some photographs from the event.

Three of our graduates students were also honored by receiving awards for making outstanding presentations.

  • Varish Mulwad received an award for an outstanding oral presentation on his dissertation research on "Generating Knowledge from Tables".

     

    Vast amounts of information are available in structured forms like spreadsheets, database relations, and tables found in documents and on the Web. We describe a framework for automatically understanding and interpreting information encoded in tables and generating knowledge from it. To accurately interpret a table, our framework maps every column header to class (or property wherever appropriate) from appropriate ontologies, link the data values to existing entities from a knowledge base (or map them as values of a property wherever appropriate) and identify and discover relations between various columns. Of the many domains where this work will be useful, we explore the application of this in two important domains — the medical research domain and the open government data. We also present preliminary results evaluating our framework against tables obtained from Google Squared, Wikipedia and a Google dataset.

  • Kavita Krishnaswamy received an award for an outstanding oral presentation on her thesis research on "Path planning a roboticarm efficiently".

     

    Individuals with physical disabilities need assistance because of their lack of physical strength. In order to provide a solution, we propose to build an assistive robot to provide physical assistance services for individuals with disabilities in real-time through the Internet. For an assistive robotic arm with a collaborative mode of control, given the initial arm configuration we will attempt to increase the speed of response times for the user requesting physical assistance by calculating the paths of reach to the possible target goals before the time of command. The specific aims of our research project are to a) determine the trajectory path of a human arm to perform tasks; b) identify the paths of frequent use; and c) analyze ways to improve path planning performance. In the first stage, we will investigate the most frequently requested tasks of physical assistance in cases where a robotic arm will be preferred over a personal assistant. For example, if the target task is eating, we may calculate the percentage of volunteering participants picking up a fork, returning the fork, reaching for a cup, and returning the cup. In this study, we utilize the TUM kitchen data set from the Technische Universität München for motion tracking data analysis. With the collected information, we will compute the statistics of the motions to identify paths of frequent use and design the architecture with caching. We anticipate an increase in performance and time will be saved for the computation of path planning. We will begin implementing and testing the new strategies for the proposed architecture.

  • Akshya Iyengar received an award for an outstanding poster presentation for her thesis research on "Estimating Temporal Boundaries of Events using Social Media Data".

     

    Social media websites like Twitter, Flickr and YouTube generate a high volume of user generated content as a major event occurs. Our goal is to automatically determine as accurately as possible when the event starts and when it ends by analyzing the volume and content of social media data. We describe a technique that estimates the temporal boundaries of anticipated events like wildfires and hurricanes and helps to monitor changes as events unfold. Estimating these temporal boundaries segments the event"related data into three major phases: the buildup to the event, the event itself, and the post"event effects and repercussions. The technique can also detect the presence and scope of significant sub"events occurring during the course of an event. For events that transpire over time and space, such as the dispersal of an oil spill, a hurricane or a spreading wildfire, we can analyze how event progressed, traveled geographically and major sub"events that occurred within the event. When applied to natural disasters and man-made disturbances, the derived data can help organizations involved in mediation efforts to track and analyze evolving events.

PhD student Yasaman Haghpanah scores a DC hat trick

CSEE Ph.D. student Yasaman Haghpanah achieved a Doctoral Consortium hat trick by being invited to participate in upcoming doctoral consortium events at the top three conferences in her research area: IJCAI, AAMAS, and AAAI. Doctoral Consortia have become common in computer science research communities and provide an opportunity for a group of Ph.D. students to discuss and explore their research interests and career objectives with a panel of established, senior researchers in their research area.  Students must apply to participate in a doctoral consortium and the selection process is usually quite competitive. Her dissertation research is developing and evaluating a trust model for supply chain management systems using multi-agent systems techniques. Yasaman is a member of the Maple Laboratory, directed by Professor Marie desJardins.

Yasaman also had a paper accepted to the 2011 Workshop on Agent Mediated Electronic Commerce which is collocated with AAMAS in Taipei. On top of all that, as part of the AAMAS Doctoral Consortium, she will also be spending two months in Rotterdam on an extended stay at Wolf Ketter's Learning Agents Research Group at the Erasmus Research Institute of Management, Erasmus Universiteit Rotterdam.

MS defense: Detection of Unsafe Action in Laparoscopic Cholecystectomy Video

MS defense

Detection of Unsafe Action in Laparoscopic Cholecystectomy Video

Ashwini Lahane

10:00am Thursday, 28 April 2011, ITE 346

Wellness and healthcare are central to the lives of all people. Information technology has already contributed in significant ways towards enhancement of healthcare delivery and to improving the quality of life. And it will continue to do so with the development of “smarter” technologies and environments. Recent years have seen context awareness as one of the most important aspects in the emerging pervasive computing paradigm. We focus our work on situation awareness; a more holistic variant of context awareness where situations are regarded as logically aggregated contexts. We demonstrate an application of situation aware computing in healthcare. We primarily focus on laparoscopic cholecystectomy, a complex yet commonly performed surgical procedure. The outcome of the surgery is influenced greatly by the training, skill, and judgment of the surgeon. Many surgical simulators have been developed to meet the training and practice needs of the surgeons. However few systems provide feedback during the actual surgery. We present a method to detect a situation, that shows possibility of injury to an artery by analyzing the laparoscopic cholecystectomy surgical video. The system can be used to provide feedback to the operating surgeon in case of a possible risk. We have also built a prototype to demonstrate the use of our system in telemedicine, in the form of a web service.

Thesis Committee:

  • Dr. Yelena Yesha (chair)
  • Dr. Anupam Joshi
  • Dr. Milton Halem
  • Dr. Michael Grasso

 

Complex-valued Adaptive Signal Processing: Applications in Medical Imaging

EE Graduate Seminar

Complex-valued Adaptive Signal Processing:
Applications in Medical Imaging

Prof. Tulay Adali
Machine Learning for Signal Processing Laboratory
CSEE Dept/UMBC

1-2pm Friday, 29 April 2011, ITE 237

Complex-valued signals arise frequently in applications as diverse as communications, radar, geophysics, optics, and biomedicine, as most practical modulation formats are of complex type and applications such as radar and magnetic resonance imaging lead to data that are inherently complex valued. The complex domain, however, presents unique challenges for signal processing, in particular for adaptive nonlinear processing, and as a result, until recently, most algorithms derived for the complex domain have taken engineering shortcuts limiting their usefulness. The most common one among those has been assuming the circularity of the signal, thus ignoring the information conveyed by the phase. Similarly when taking gradients in the complex domain, a "split" approach that performs optimization separately with respect to the real and imaginary variables has been the dominant practice.

There have been important advances in the area within the last decade that clearly demonstrate that noncircularity is an intrinsic characteristic of many signals of practical interest, and when taken into account, the methods developed for their processing may provide significant performance gains. Similarly, it has been shown that using Wirtinger calculus, all calculations can be carried out in a manner similar to real-valued calculus while keeping all the computations in the complex domain.

In this talk, after a brief introduction to optimization using Wirtinger calculus and statistics in the complex domain, and then I will give examples from some of the recent work conducted at the MLSP-Lab.

MS defense: Boosting Base Station Anonymity in Wireless Sensor Networks

MS Defense

Exploiting Architectural Techniques for Boosting
Base Station Anonymity in Wireless Sensor Networks

Zhong Ren

2:00pm Thursday, 28 April 2011, ITE 346

Wireless Sensor Networks (WSNs) can be deployed to serve mission-critical applications in hostile environments such as battlefield and territorial borders. In these setups, the WSN may be subject to attacks in order to disrupt the network operation. The most effective way for an adversary to do so is by targeting the Base-Station (BS), where the sensor data are collected in the field. By identifying and locating the BS, the adversary can launch attacks to damage or disrupt the operation of the BS. Therefore, maintaining the BS anonymity is of utmost importance in WSNs.

In this thesis we propose three novel approaches to boost the anonymity of the BS nodes to protect them from potential threats. We first explore the deployment of more BS nodes. We compare the BS anonymity of one versus multiple stationary BS under different network topologies. Our results show that having more base-stations can boost both the average and max anonymity of BS nodes. We further provide guidelines on a cost versus anonymity trade-off to determine the most suitable BS count for a network. Second we exploit the mobility of base-stations and explore the effect of relocating some of the existing BS nodes to the lowest anonymity regions. Our results show that having one mobile BS can dramatically boost the anonymity of the network and moving multiple BS does not provide much value. Finally, we propose to pursue dynamic sensor to cluster re-association to confuse the adversary. This can be employed when base-stations cannot safely move.

Committee members:

  • Mohamed Younis (Chair)
  • Yun Peng
  • Charles Nicholas

CSEE students presenting at the 2011 Graduate Research Conference

Congratulations to the record number of CSEE graduate students who will make oral or poster presentations at the 2011 UMBC Graduate Research Conference on Friday April 29th. The presentations will take place in two sessions, one 9:00 to 10:50 and another 11:00 to 12:50. Poster sessions will be on the seventh floor of the library and oral presentations in UC 312. See the GRC site for a program booklet with abstracts and the session schedules.

Oral presentations:

  • Yousef Ebrahimi, Increasing Transmission Power for Higher Base-station Anonymity in Wireless Sensor Network
  • Zamon Granger, Stabilizing Air Pressure in a Hermetic Environment with Embedded Technology
  • Congchong Liu, Optimizing MapReduce Programming Model through Adaptive Load Balance
  • Kavita Krishnaswamy, Path planning a robotic arm efficiently
  • Varish Mulwad, Generating knowledge from tables
  • Darshana Dalvi, Genome-based Clinical Decision Support System
  • Ashwini Lahane, Detection of unsafe actions in laparoscopic cholecystectomy surgical videos
  • Karuna Pande Joshi, Lifecycle of virtualized services on the cloud
  • Jon Ward, ICF: A Physical Layer Metric for Measuring Anonymity in Wireless Sensor Networks
  • Theoplis E. Stewart Sr., Smart Grid: What are some underlying implications to overlaying digital technology onto the electric power grids?

Poster presentations:

  • Sai Ma, Tom Eichele and Nicolle M. Correa, Hierarchical and graphical analysis of fMRI network connectivity in healthy and schizophrenic groups.
  • Shiming Yang , Near Real"time Data Assimilation for the HYSPLIT Aerosol Dispersion Model.
  • Yu Wang , A Framework for GPU 3D Model Reconstruction using Structure-from-Motion
  • Akshaya Iyengar, Estimating Temporal Boundaries of Events using Social Media Data
  • Brice Cannon, Paveen Apiratikul and John Hryniewicz, Characterization of semiconductor optical waveguides
  • Brian White Jr., The Mobile Memory
  • Xing Chen, Standoff photoacoustic chemical detection using quantum cascade lasers
  • Yi Xin, On Gas Detection and Concentration Estimation via Mid-IR-based Gas Detection System Analysis Model
  • Robert J. Weiblen, Calculation of the expected bandwidth for a mid-infrared supercontinuum source based on As2S3 chalcogenide photonic crystal fibers
  • Pramod Jagtap, Privacy Preservation in Context Aware Geo-social Networking Applications

MS defense: Privacy Preservation in Context-Aware Systems

Mobile devices will provide better services if then can model, recognize and adapt to their users' context.

MS Defense

Privacy Preservation in Context-Aware Systems

Pramod Jagtap

1:00pm Wednesday, 27 April 2011, ITE 210

Recent years have seen a confluence of two major trends – the increase of mobile devices such as smart phones as the primary access point to networked information and the rise of social media platforms that connect people. Their convergence supports the emergence of a new class of context-aware geosocial networking applications. While existing systems focus mostly on location, our work centers on models for representing and reasoning about a more inclusive and higher-level notion of context, including the user’s location and surroundings, the presence of other people and devices, feeds from social networking systems they use, and the inferred activities in which they are engaged. A key element of our work is the use of collaborative information sharing where devices share and integrate knowledge about their context. This introduces the need for privacy and security mechanisms. We present a framework to provide users with appropriate levels of privacy to protect the personal information their mobile devices are collecting including the inferences that can be drawn from the information. We use Semantic Web technologies to specify high-level, declarative policies that describe user’s information sharing preferences. We have built a prototype system that aggregates information from a variety of sensors on the phone, online sources, and sources internal to the campus intranet, and infers the dynamic user context. We show how our policy framework can be effectively used to devise better privacy control mechanisms to control information flow between users in such dynamic mobile systems.

Presentation

Thesis Committee:

  • Dr. Anupam Joshi (chair)
  • Dr. Tim Finin
  • Dr. Yelena Yesha
  • Dr. Laura Zavala

 

MS defense: A Framework for GPU 3D Model Reconstruction Using Structure-from-Motion

MS Defense

A Framework for GPU 3D Model Reconstruction
Using Structure-from-Motion

Yu Wang

10:00am Thursday, 28 April 2011, ITE 352

A framework for three-dimensional (3D) model reconstruction is described in this thesis. The primary application is scanning forest canopies and assisting scientific applications such as fire hazard evaluation and vegetation biomass estimation, using photos taken from a radio-controlled helicopter, although the methods apply to any series of photographs taken along a path. The approach is based on the fact that the photos are taken in a continuous path, thus taking advantage of the adjacency between images. The major contributions of this project are 1) introduce a linear time complexity algorithm that reduces number of image pairs to match for datasets obtained in a continuous path. 2) present an optimized high performance framework for GPU 3D reconstruction using structure-from-motion.

Thesis Committee:

  • Dr. Marc Olano, Chair
  • Dr. Penny Rheingans
  • Dr. Erle Ellis

MS defense: eXtensible Dynamic Form (XDF) for Supplier Discovery

MS Thesis Defense

eXtensible Dynamic Form (XDF) for Supplier Discovery

Yan Kang

1:00PM Tuesday, 26 April 2011, ITE 346

Discovery of suppliers (supplier discovery) is essential for building a flexible network of suppliers in a supply chain. The first step for supplier discovery is to collect manufacturing capabilities of suppliers and requirements of customers. In traditional e-marketplaces, online form interfaces are typically used to collect the requirements and capabilities. However, those forms are mostly lack of flexibility to capture a variety of requirements and capabilities in a structured way. In this thesis, we propose new innovative form architecture called eXtensible Dynamic Form (XDF) to facilitate data collection process of supplier discovery.

This architecture provides several key innovations including: 1) architecture for users (suppliers or customers) to create new structure of form for their own contents; 2) an intelligent search engine facilitating users to reuse the existing form components 3) hierarchical representation of the requirements and capabilities as XML instances. Experimental results demonstrate that the proposed architecture is valuable for facilitating the supplier discovery process.

Thesis Committee:

  • Dr. Charles Nicholas
  • Dr. Yun Peng (Chair)
  • Dr. Yelena Yesha
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