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  Partner: UNT Libraries
 Department: Department of Computer Science and Engineering
3D Reconstruction Using Lidar and Visual Images

3D Reconstruction Using Lidar and Visual Images

Date: December 2012
Creator: Duraisamy, Prakash
Description: In this research, multi-perspective image registration using LiDAR and visual images was considered. 2D-3D image registration is a difficult task because it requires the extraction of different semantic features from each modality. This problem is solved in three parts. The first step involves detection and extraction of common features from each of the data sets. The second step consists of associating the common features between two different modalities. Traditional methods use lines or orthogonal corners as common features. The third step consists of building the projection matrix. Many existing methods use global positing system (GPS) or inertial navigation system (INS) for an initial estimate of the camera pose. However, the approach discussed herein does not use GPS, INS, or any such devices for initial estimate; hence the model can be used in places like the lunar surface or Mars where GPS or INS are not available. A variation of the method is also described, which does not require strong features from both images but rather uses intensity gradients in the image. This can be useful when one image does not have strong features (such as lines) or there are too many extraneous features.
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Adaptive planning and prediction in agent-supported distributed collaboration.

Adaptive planning and prediction in agent-supported distributed collaboration.

Date: December 2004
Creator: Hartness, Ken T. N.
Description: Agents that act as user assistants will become invaluable as the number of information sources continue to proliferate. Such agents can support the work of users by learning to automate time-consuming tasks and filter information to manageable levels. Although considerable advances have been made in this area, it remains a fertile area for further development. One application of agents under careful scrutiny is the automated negotiation of conflicts between different user's needs and desires. Many techniques require explicit user models in order to function. This dissertation explores a technique for dynamically constructing user models and the impact of using them to anticipate the need for negotiation. Negotiation is reduced by including an advising aspect to the agent that can use this anticipation of conflict to adjust user behavior.
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An Approach Towards Self-Supervised Classification Using Cyc

An Approach Towards Self-Supervised Classification Using Cyc

Date: December 2006
Creator: Coursey, Kino High
Description: Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles is also guided by the ontology. The research conducted shows that a system can be created that utilizes statistical text classification methods to extract information from an ad-hoc generated information source like Wikipedia for use in a formal semantic ontology like Cyc. Benefits and limitations of the system are discussed along with future work.
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Automated Classification of Emotions Using Song Lyrics

Automated Classification of Emotions Using Song Lyrics

Date: December 2012
Creator: Schellenberg, Rajitha
Description: This thesis explores the classification of emotions in song lyrics, using automatic approaches applied to a novel corpus of 100 popular songs. I use crowd sourcing via Amazon Mechanical Turk to collect line-level emotions annotations for this collection of song lyrics. I then build classifiers that rely on textual features to automatically identify the presence of one or more of the following six Ekman emotions: anger, disgust, fear, joy, sadness and surprise. I compare different classification systems and evaluate the performance of the automatic systems against the manual annotations. I also introduce a system that uses data collected from the social network Twitter. I use the Twitter API to collect a large corpus of tweets manually labeled by their authors for one of the six emotions of interest. I then compare the classification of emotions obtained when training on data automatically collected from Twitter versus data obtained through crowd sourced annotations.
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Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements

Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements

Date: August 2013
Creator: Kumara, Muthukudage Jayantha
Description: The effectiveness of colonoscopy depends on the quality of the inspection of the colon. There was no automated measurement method to evaluate the quality of the inspection. This thesis addresses this issue by investigating an automated post-procedure quality measurement technique and proposing a novel approach automatically deciding a percentage of stool areas in images of digitized colonoscopy video files. It involves the classification of image pixels based on their color features using a new method of planes on RGB (red, green and blue) color space. The limitation of post-procedure quality measurement is that quality measurements are available long after the procedure was done and the patient was released. A better approach is to inform any sub-optimal inspection immediately so that the endoscopist can improve the quality in real-time during the procedure. This thesis also proposes an extension to post-procedure method to detect stool, bite-block, and blood regions in real-time using color features in HSV color space. These three objects play a major role in quality measurements in colonoscopy. The proposed method partitions very large positive examples of each of these objects into a number of groups. These groups are formed by taking intersection of positive examples with a hyper plane. ...
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Automated Syndromic Surveillance using Intelligent Mobile Agents

Automated Syndromic Surveillance using Intelligent Mobile Agents

Date: December 2007
Creator: Miller, Paul
Description: Current syndromic surveillance systems utilize centralized databases that are neither scalable in storage space nor in computing power. Such systems are limited in the amount of syndromic data that may be collected and analyzed for the early detection of infectious disease outbreaks. However, with the increased prevalence of international travel, public health monitoring must extend beyond the borders of municipalities or states which will require the ability to store vasts amount of data and significant computing power for analyzing the data. Intelligent mobile agents may be used to create a distributed surveillance system that will utilize the hard drives and computer processing unit (CPU) power of the hosts on the agent network where the syndromic information is located. This thesis proposes the design of a mobile agent-based syndromic surveillance system and an agent decision model for outbreak detection. Simulation results indicate that mobile agents are capable of detecting an outbreak that occurs at all hosts the agent is monitoring. Further study of agent decision models is required to account for localized epidemics and variable agent movement rates.
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Automatic Tagging of Communication Data

Automatic Tagging of Communication Data

Date: August 2012
Creator: Hoyt, Matthew Ray
Description: Globally distributed software teams are widespread throughout industry. But finding reliable methods that can properly assess a team's activities is a real challenge. Methods such as surveys and manual coding of activities are too time consuming and are often unreliable. Recent advances in information retrieval and linguistics, however, suggest that automated and/or semi-automated text classification algorithms could be an effective way of finding differences in the communication patterns among individuals and groups. Communication among group members is frequent and generates a significant amount of data. Thus having a web-based tool that can automatically analyze the communication patterns among global software teams could lead to a better understanding of group performance. The goal of this thesis, therefore, is to compare automatic and semi-automatic measures of communication and evaluate their effectiveness in classifying different types of group activities that occur within a global software development project. In order to achieve this goal, we developed a web-based component that can be used to help clean and classify communication activities. The component was then used to compare different automated text classification techniques on various group activities to determine their effectiveness in correctly classifying data from a global software development team project.
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Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases

Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases

Date: May 2006
Creator: Abbas, Kaja Moinudeen
Description: Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic region is ported to a newer region with different demographic composition. Upon analysis for the newer region, the corresponding prevalence of influenza and pneumonia among the different demographic subgroups is inferred for the newer region. Bayesian reasoning coupled with disease timeline is used to reverse engineer an influenza outbreak for a given geographic and demographic setting. The temporal flow of the epidemic among the different sections of the population is analyzed to identify the corresponding risk levels. In comparison to spread vaccination, prioritizing the limited vaccination resources to the higher risk groups results in relatively lower influenza prevalence. HIV incidence in Texas from 1989-2002 is analyzed using demographic based epidemic curves. Dynamic Bayesian networks are integrated with ...
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A CAM-based, high-performance classifier-scheduler for a video network processor.

A CAM-based, high-performance classifier-scheduler for a video network processor.

Date: May 2008
Creator: Tarigopula, Srivamsi
Description: Classification and scheduling are key functionalities of a network processor. Network processors are equipped with application specific integrated circuits (ASIC), so that as IP (Internet Protocol) packets arrive, they can be processed directly without using the central processing unit. A new network processor is proposed called the video network processor (VNP) for real time broadcasting of video streams for IP television (IPTV). This thesis explores the challenge in designing a combined classification and scheduling module for a VNP. I propose and design the classifier-scheduler module which will classify and schedule data for VNP. The proposed module discriminates between IP packets and video packets. The video packets are further processed for digital rights management (DRM). IP packets which carry regular traffic will traverse without any modification. Basic architecture of VNP and architecture of classifier-scheduler module based on content addressable memory (CAM) and random access memory (RAM) has been proposed. The module has been designed and simulated in Xilinx 9.1i; is built in ISE simulator with a throughput of 1.79 Mbps and a maximum working frequency of 111.89 MHz at a power dissipation of 33.6mW. The code has been translated and mapped for Spartan and Virtex family of devices.
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Capacity and Throughput Optimization in Multi-cell 3G WCDMA Networks

Capacity and Throughput Optimization in Multi-cell 3G WCDMA Networks

Date: December 2005
Creator: Nguyen, Son
Description: User modeling enables in the computation of the traffic density in a cellular network, which can be used to optimize the placement of base stations and radio network controllers as well as to analyze the performance of resource management algorithms towards meeting the final goal: the calculation and maximization of network capacity and throughput for different data rate services. An analytical model is presented for approximating the user distributions in multi-cell third generation wideband code division multiple access (WCDMA) networks using 2-dimensional Gaussian distributions by determining the means and the standard deviations of the distributions for every cell. This model allows for the calculation of the inter-cell interference and the reverse-link capacity of the network. An analytical model for optimizing capacity in multi-cell WCDMA networks is presented. Capacity is optimized for different spreading factors and for perfect and imperfect power control. Numerical results show that the SIR threshold for the received signals is decreased by 0.5 to 1.5 dB due to the imperfect power control. The results also show that the determined parameters of the 2-dimensional Gaussian model match well with traditional methods for modeling user distribution. A call admission control algorithm is designed that maximizes the throughput in multi-cell ...
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