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Computer Engineering Commons

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Department of Computer Science and Engineering

2013

Articles 1 - 12 of 12

Full-Text Articles in Computer Engineering

Natural Language Document And Event Association Using Stochastic Petri Net Modeling, Michael Thomas Mills Jan 2013

Natural Language Document And Event Association Using Stochastic Petri Net Modeling, Michael Thomas Mills

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The purpose of this research is to design and implement a new methodology that captures the natural language understanding of events from English natural language text and model it using Stochastic Petri Nets. To establish a baseline of recent natural language processing (NLP) and understanding (NLU) research, two surveys are presented. One is a general survey in NLP and NLU methodologies for processing multi-documents. It summarizes and presents methodologies in terms of their features, capabilities, and maturity. The second survey focuses on graph-based methods for NL text processing and understanding and analyzes them in terms of their functional descriptions, capabilities ...


Streamsurface Smoke Effect For Visualizing Dragon Fly Cfd Data In Modern Opengl With An Emphasis On High Performance, Jordan Sipes Jan 2013

Streamsurface Smoke Effect For Visualizing Dragon Fly Cfd Data In Modern Opengl With An Emphasis On High Performance, Jordan Sipes

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Visualizing 3D, time dependent velocity vector fields is a difficult topic. Streamlines can be used to visualize 3D vector fields. A smoke effect where the streamline is faded out as time progresses can provide a better visualization of a time dependent flow. This work uses modern OpenGL to create a smoke trail effect with streamsurfaces in the dragon fly data set. Many aspects affecting performance are tested to determine the best options or approach.


A Semantics-Based Approach To Machine Perception, Cory Andrew Henson Jan 2013

A Semantics-Based Approach To Machine Perception, Cory Andrew Henson

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Machine perception can be formalized using semantic web technologies in order to derive abstractions from sensor data using background knowledge on the Web, and efficiently executed on resource-constrained devices. Advances in sensing technology hold the promise to revolutionize our ability to observe and understand the world around us. Yet the gap between observation and understanding is vast. As sensors are becoming more advanced and cost-effective, the result is an avalanche of data of high volume, velocity, and of varied type, leading to the problem of too much data and not enough knowledge (i.e., insights leading to actions). Current estimates ...


Cooperative Interactive Distributed Guidance On Mobile Devices, Gregory Burnett Jan 2013

Cooperative Interactive Distributed Guidance On Mobile Devices, Gregory Burnett

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Mobiles device are quickly becoming an indispensable part of our society. Equipped with numerous communication capabilities, they are increasingly being examined as potential tools for civilian and military usage to aide in distributed remote collaboration for dynamic decision making and physical task completion. With an ever growing mobile workforce, the need for remote assistance in aiding field workers who are confronted with situations outside their expertise certainly increases. Enhanced capabilities in using mobile devices could significantly improve numerous components of a task's completion (i.e. accuracy, timing, etc.). This dissertation considers the design of mobile implementation of technology and ...


Visualizing Confusion Matrices For Multidimensional Signal Detection Correlational Methods And Semantic Cluster Based Visualization In Virtual Environments, Yue Zhou Jan 2013

Visualizing Confusion Matrices For Multidimensional Signal Detection Correlational Methods And Semantic Cluster Based Visualization In Virtual Environments, Yue Zhou

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General Recognition Theory (GRT) is a multidimensional signal detection theory framework for capturing sources of perceptual and decisional dependence. The primary type of data for GRT models is an identification-confusion matrix derived in a complete factorial identification task. This confusion matrix plots the responses of study participant for a given signal. The responses may reveal that participants were unable to recognize the signal properly. Such violations of any type of independence in the GRT framework result in response patterns that reflect some form of correlation in the GRT space. While an individual confusion matrix is rather small and relatively easy ...


Mining Diversified Decision Trees Across Multiple Datasets To Capture Similarities And Alignable Differences, Qian Han Jan 2013

Mining Diversified Decision Trees Across Multiple Datasets To Capture Similarities And Alignable Differences, Qian Han

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This dissertation studies the problem of mining shared and alignable difference knowledge structures across multiple datasets/applications. Shared and alignable difference knowledge structures are important for identifying analogies between application domains and for forming new hypothesis in challenging research applications, and for assessing the degree and types of knowledge-level similarities and differences between application domains for use in learning transfer. Generally speaking, shared knowledge structures characterize underlying datasets and highlight conceptual-level structural similarities among the datasets. This dissertation studies the mining of shared decision trees, which are a special type of shared knowledge structures. We first consider building one shared ...


Anomalies In Sensor Network Deployments: Analysis, Modeling, And Detection, Giovani Rimon Abuaitah Jan 2013

Anomalies In Sensor Network Deployments: Analysis, Modeling, And Detection, Giovani Rimon Abuaitah

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A sensor network serves as a vital source for collecting raw sensory data. Sensor data are later processed, analyzed, visualized, and reasoned over with the help of several decision making tools. A decision making process can be disastrously misled by a small portion of anomalous sensor readings. Therefore, there has been a vast demand for mechanisms that identify and then eliminate such anomalies in order to ensure the quality, integrity, and/or trustworthiness of the raw sensory data before they can even be interpreted.

Prior to identifying anomalies, it is essential to understand the various anomalous behaviors prevalent in a ...


A Large Scale Distributed Syntactic, Semantic And Lexical Language Model For Machine Translation, Ming Tan Jan 2013

A Large Scale Distributed Syntactic, Semantic And Lexical Language Model For Machine Translation, Ming Tan

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The n-gram model is the most widely used language model (LM) in statistical machine translation system, due to its simplicity and scalability. However, it only encodes the local lexical relation between adjacent words and clearly ignores the rich syntactic and semantic structures of the natural languages. Attempting to increase the order of an n-gram to describe longer range dependencies in natural language immediately runs into the curse of dimensionality. Although previous researches tried to increase the order of n-gram on a large corpus, they did not see obvious improvement beyond 6-gram. Meanwhile, other LMs, such as syntactic language models and ...


Adaptive Semantic Annotation Of Entity And Concept Mentions In Text, Pablo N. Mendes Jan 2013

Adaptive Semantic Annotation Of Entity And Concept Mentions In Text, Pablo N. Mendes

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The recent years have seen an increase in interest for knowledge repositories that are useful across applications, in contrast to the creation of ad hoc or application-specific databases.

These knowledge repositories figure as a central provider of unambiguous identifiers and semantic relationships between entities. As such, these shared entity descriptions serve as a common vocabulary to exchange and organize information in different formats and for different purposes. Therefore, there has been remarkable interest in systems that are able to automatically tag textual documents with identifiers from shared knowledge repositories so that the content in those documents is described in a ...


Solar-Powered Wireless Sensor Nodes With Dynamic Power Management For Indoor Use, Ethan Charles Humphrey Jan 2013

Solar-Powered Wireless Sensor Nodes With Dynamic Power Management For Indoor Use, Ethan Charles Humphrey

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Indoor environment monitoring, such as medical monitoring and home automation, is achieved through wireless sensor networks that collect data through wireless sensor nodes. These nodes are typically powered by exhaustible power sources that could be difficult or impossible to maintain regularly. A solution to the need for frequent maintenance is to collect the solar power in the environment. This project shows how collecting the solar energy in the environment can extend the lifetime of a sensor node and limit the maintenance required. Along with charging the battery with a solar panel, a dynamic power management scheme can also be implemented ...


Low Cost Open Source Modal Virtual Environment Interfaces Using Full Body Motion Tracking And Hand Gesture Recognition, Matthew J. Marangoni Jan 2013

Low Cost Open Source Modal Virtual Environment Interfaces Using Full Body Motion Tracking And Hand Gesture Recognition, Matthew J. Marangoni

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Virtual environments provide insightful and meaningful ways to explore data sets through immersive experiences. One of the ways immersion is achieved is through natural interaction methods instead of only a keyboard and mouse. Intuitive tracking systems for natural interfaces suitable for such environments are often expensive. Recently however, devices such as gesture tracking gloves and skeletal tracking systems have emerged in the consumer market. This project integrates gestural interfaces into an open source virtual reality toolkit using consumer grade input devices and generates a set of tools to enable multimodal gestural interface creation. The AnthroTronix AcceleGlove is used to augment ...


A Methodology For Extracting Human Bodies From Still Images, Athanasios Tsitsoulis Jan 2013

A Methodology For Extracting Human Bodies From Still Images, Athanasios Tsitsoulis

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Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that ...