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

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.

BEL@UMBC is UMBC's New Bioelectronics Laboratory

Professor Gymama Slaughter joined the department as an Assistant Professor in 2010 and established the Bioelectronics Laboratory as a group doing research on Bioelectronics and optimization methods for physical circuit design, low-voltage and biologically inspired computing, sensor-processor integration, and wireless networking and communications. Its current research projects focus on developing sense-and-respond systems for blood metabolites and vital signs as well as volatile organic compounds/gas detection in trauma patients and crowded areas.

Dr. Slaughter has a broad background that makes her uniquely qualified to work in Bioelectronics. She received three degrees from Virginia Commonwealth University: a BS in Chemistry, a MS in Chemical Engineering and a Ph.D in Computer Engineering. Before joining UMBC in August 2010, she was Director of the Center for Biosystems and Engineering and professor of Computer Engineering at Virginia State University.

Visit the new BEL@UMBC Web site to find out more about this new and exciting research group at UMBC.


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

Two bioinfomatics talks, Wed 27 April

The UMBC Biological Sciences Department will host two talks on bioinformatics on Wednesday, April 27.

Bioinformatics: Illustrations from 20 years at NCB*I, Dr. Jim Ostell, NCBI, NIH, 11:00am Wednesday 27 April, BS-004

Jim Ostell is one of the founders of NCBI, he will give a general audience talk, ideal for students and faculty from Biology, CS, Chemistry, Statistics and Math that would like to learn what Bioinformatics is about and the history of one of the main bioinformatic center in the world.

Network and state space models: science and science fiction approaches to cell fate predictions, John Quackenbush, Harvard, 12:00pm Wednesday 27 April BS-004

Two trends are driving innovation and discovery in biological sciences: technologies that allow holistic surveys of genes, proteins, and metabolites and a realization that biological processes are driven by complex networks of interacting biological molecules. However, there is a gap between the gene lists emerging from genome sequencing projects and the network diagrams that are essential if we are to understand the link between genotype and phenotype. ‘Omic technologies were once heralded as providing a window into those networks, but so far their success has been limited, in large part because the high-dimensional they produce cannot be fully constrained by the limited number of measurements and in part because the data themselves represent only a small part of the complete story. To circumvent these limitations, we have developed methods that combine ‘omic data with other sources of information in an effort to leverage, more completely, the compendium of information that we have been able to amass.Here we will present a number of approaches we have developed, with an emphasis on the how those methods have provided into the role that particular cellular pathways play in driving differentiation, and the role that variation in gene expression patterns influences the development of disease states. Looking forward, we will examine more abstract state-space models that may have potential to lead us to a more general predictive, theoretical biology.

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.


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

MS defense: Recovering from Soft Node Failures in Wireless Sensor Networks using Neural Networks

MS Thesis Defense

Recovering from Soft Node Failures in
Wireless Sensor Networks using Neural Networks

Shivvasangari Subramani

9:00am Tuesday, 26 April 2011, ITE 346

In the past few years, wireless sensor networks (WSNs) have become important in different applications because of their robustness in hostile environments. WSNs need to perform in a timely manner in the face of interference, attacks, accidents, and failures. Being a battery operated system, there is a trade-off between performance and energy utilization. In this thesis we focus on WSN accuracy and consider ways to improve the performance of WSNs when sensors become damaged, resulting in poor input signal quality. When all other components of the sensor like the processor, memory, and battery are working, our proposed solution is to learn to undo the damage in a node by training on neighbors sensor values.

Thesis Committee:

  • Dr. Anupam Joshi
  • Dr. Tim Oates (chair)
  • Dr. Mohamed Younis
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