MS defense: Numerical Integration Techniques for Volume Rendering

MS Thesis Defense

Numerical Integration Techniques for Volume Rendering

Preeti Bindu

10:00am Monday, 7 May 2012, ITE 352, UMBC

Medical image visualization often relies on 3D volume rendering. To enable interaction with 3D rendering of medical scans, improvements in the performance of Volume Rendering Algorithms need significant attention. Real-time visualization of 3D image data set is one of the key tasks of Augmented Reality Systems required by many medical imaging applications. Over past five years the development of the Graphics Processing Unit (GPU) has proved beneficial when it comes to Real Time Volume Rendering. We propose a GPU based volume rendering system for medical images using adaptive integration to improve performance. Our system is able to read and render DICOM images, implementing adaptive integration techniques that increase frame rate for volume rendering with the same quality of output images.

Committee: Dr. Marc Olano (advisor), Dr. Penny Rheingans and Dr. Samir Chettri

Mulwad, Van Tassel, and Ordonez win poster competition at CSEE Research Review

Congratulations to the three winners of the poster competition at the Computer Science and Electrical Engineering Department's annual Research Review, which took place in the UMBC Technology Center's business incubator and accelerator building last Friday. Winners were chosen by UMBC faculty who scored their top five choices with [-9, +9] range voting.

1st place (26 points): 
Varish Mulwad (CS, Ph.D.) "A Probabilistic Model for Generating Linked Data from Tables"
Advisor: Tim Finin

Vast amount of information is encoded in tables found in documents, on the Web, and in spreadsheets or databases. Integrating or searching over this information benefits from understanding its intended meaning and making it explicit in a semantic representation language like RDF. Most current approaches to generating Semantic Web representations from tables requires human input to create schemas and often results in graphs that do not follow best practices for linked data. Evidence for a table's meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-specific background knowledge. Approaches that work well for one domain, may not necessarily work well for others. We describe a domain independent framework for interpreting the intended meaning of tables and representing it as Linked Data. At the core of the framework are techniques grounded in graphical models and probabilistic reasoning to infer meaning associated with a table. Using background knowledge from resources in the Linked Open Data cloud, we jointly infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns and represent the inferred meaning as graph of RDF triples. A table's meaning is thus captured by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns.

2nd place (18 points): 
Richard Van Tassel  (CS, M.S.)  "Visual Obstruction Resistance for Emotion Detection"
Advisor: Marie desJardins

There is an increasing interest in developing systems that can determine a user's emotion by analyzing a video feed of the user's face. However, it cannot always be assumed that the user's face will be completely unobstructed by facial hair or apparel. If the system is a recreational or consumer good, it could be considered too restrictive to require a perfect view of the face at all times. Obstructions can prevent the system from identifying all of the facial expression components, called action units, present in the input face. It is therefore important that such emotion detection systems are capable of coping with partially obstructed faces. I propose a technique for reducing the effect of face obstructions. The technique will learn association rules between sets of action units from a set of unobstructed faces. Then, for a given input obstructed face, the technique will infer what action units are likely to be obstructed based on the visible ones, and will use this hypothetical set of action units to infer the emotion. This technique is tested on real face data, with simulated face obstructions. It will provide a statistically significant improvement in emotion detection accuracy over the same process without the technique applied.

3rd place (16 points): 
Patricia Ordonez (CS, Ph.D) (pictured) "Multivariate Time Series Analysis of Physiological and Clinical Data"
Advisor: Marie desJardins, Tim Oates

The complexity and volume of collected medical data is greater now than at any point in the history of medicine. Providers are expected to examine large volumes of data and identify correlations between parameters based on their own clinical experience to detect significant medical events. The information overload that providers face may hinder the diagnostic process. Existing visualizations to assist the provider in analyzing information consist mainly of tables or plots of values for a particular parameter over time. Multivariate Time Series Amalgams (MTSAs) provide an integrated, multivariate approach to represent clinical and physiological data. The hybrid representation automates the personalization of baselines and threshold values based on a patient’s medical history, while also incorporating traditional baselines and thresholds. MTSA visualizations capture the rate of change of provider-selected parameters and the relationships among them.

The second half of my research consists of developing automated techniques for discovering correlations among parameters over time to assist providers in making a diagnosis. The underlying premise of my research is that the complexity of a highly integrated system such as a human being is better captured by examining patterns as multivariate temporal abstractions as opposed to conjunctions of univariate ones — the more common approach for multivariate time series analysis and in medicine. The objective of such an approach is to assist in the identification of latent patterns within the data associated with specific medical conditions or significant medical events. Thus, in addition to the MTSA visualizations, I will present two novel multivariate time series representations, Stacked Bags-of-Patterns and Multivariate Bag-of-Patterns, which have been effective at classifying medical data. These representations are more compact than the raw multivariate time series and would facilitate the retrieval of patients from large medical databases based on physiological similarity and ideally on the presence of similar medically significant events or medical conditions. These techniques been compared to two other multivariate versions of univariate time series representations, Piecewise Dynamic Time Warping and Ensemble Voting using Bag-of-Patterns. Results demonstrate the potential of using these representations for multivariate time series analysis.


MS defense: A Modular, Power-Intelligent Wireless Sensor Node Architecture

MS Thesis Defense

A Modular, Power-Intelligent Wireless Sensor Node Architecture

David Riley

10:30am Monday, 7 May 2012, ITE 346

The current state of the art in wireless sensor nodes, both in academia and the commercial world, is a fractured landscape of designs which mostly address individual problems. The most common commercial design derives directly from a mote developed at the University of California, Berkeley around 1999, and presents only moderate, incremental improvements over the original design. No designs yet present a comprehensive, intelligent design befitting a modern system.

By using dynamic power management, deep system configurability, autonomous peripheral modules, and multiple CPU architectures, this thesis presents a flexible and efficient node architecture. Modules in a system communicate between each other to coordinate their activities and power levels. Special attention is given to power sourcing and distribution. Individual peripheral boards supply their own drivers to the CPU using architecture-independent code. The platform may be configured to work with most networks, sensor types and power sources due to its improved connectivity and hierarchical design.

The resulting Configurable Sensor Node (CoSeN) architecture is competitive with existing designs on price, size and power while greatly exceeding most of them on performance, configurability and application potential. The CoSeN architecture is validated through a prototype implementation.

Committee: Professors Mohammed Younis, Tim Oates and Gymama Slaughter

Josiah Dykstra and Han Dong awarded for best Computer Science research

Congratulations to CSEE graduate students Josiah Dykstra (Computer Science, Ph.D.) and Han Dong (Computer Science, M.S.) for winning the Computer Science and Electrical Engineering (CSEE) Department's 2011-2012 awards for best research by a Ph.D. student and best research by an M.S. student, respectively.

Winners were chosen based on the scientific merit (significance, originality, notriviality, correctness) and the writing style of their research papers.

Josiah's (pictured left) research, entitled "Acquiring Forensic Evidence from Infrastructure-as-a-Service Cloud Computing: Exploring and Evaluating Tools, Trust, and Techniques", deals with digital forensics for cloud computing, including frameworks, tools, and legal analysis to facilitate forensic investigations of remote Infrastructure-as-a-Service clouds. You can read Josiah's full paper here

Han's (pictured right) research, entitled "Cross-Platform OpenCL Code and Performance Portability for CPU and GPU Architectures Investigated with a Climate and Weather Physics Model", investigates the portability of OpenCL across CPU and GPU architectures in terms of code and performance via a

representative NASA GEOS-5 climate and weather physics model. Han discovered that OpenCL's vector-oriented programming paradigm assists compilers with implicit vectorization and creates significant performance gains. You can read Han's full paper here.

CSEE graduate students Karuna Joshi (Computer Science, Ph.D.) and James MacGlashan (Computer Science, Ph.D.) were awarded honorable mention.

As this year's winners, both Josiah and Han will present their work at this year's CSEE Research Review, which takes place this Friday, May 4 from 9 a.m. to 4 p.m. in the large conference room of the UMBC Technology Center's business incubator and accelerator building.



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