Engineering Commons^™

Shortest Path Calculation Using Contraction Hierarchy Graph Algorithms On Nvidia Gpus, Roozbeh Karimi

LSU Doctoral Dissertations

PHAST is to date one of the fastest algorithms for performing single source shortest path (SSSP) queries on road-network graphs. PHAST operates on graphs produced in part using Geisberger's contraction hierarchy (CH) algorithm. Producing these graphs is time consuming, limiting PHAST's usefulness when graphs are not available in advance. CH iteratively assigns scores to nodes, contracts (removes) the highest-scoring node, and adds shortcut edges to preserve distances. Iteration stops when only one node remains. Scoring and contraction rely on a witness path search (WPS) of nearby nodes. Little work has been reported on parallel and especially GPU CH algorithms. This …

Document Layout Analysis And Recognition Systems, Sai Kosaraju

Master of Science in Computer Science Theses

Automatic extraction of relevant knowledge to domain-specific questions from Optical Character Recognition (OCR) documents is critical for developing intelligent systems, such as document search engines, sentiment analysis, and information retrieval, since hands-on knowledge extraction by a domain expert with a large volume of documents is intensive, unscalable, and time-consuming. There have been a number of studies that have automatically extracted relevant knowledge from OCR documents, such as ABBY and Sandford Natural Language Processing (NLP). Despite the progress, there are still limitations yet-to-be solved. For instance, NLP often fails to analyze a large document. In this thesis, we propose a knowledge …

Thermal-Kinect Fusion Scanning System For Bodyshape Inpainting And Estimation Under Clothing, Sirazum Munira Tisha

LSU Master's Theses

In today's interactive world 3D body scanning is necessary in the field of making virtual avatar, apparel industry, physical health assessment and so on. 3D scanners that are used in this process are very costly and also requires subject to be nearly naked or wear a special tight fitting cloths. A cost effective 3D body scanning system which can estimate body parameters under clothing will be the best solution in this regard. In our experiment we build such a body scanning system by fusing Kinect depth sensor and a Thermal camera. Kinect can sense the depth of the subject and …

On I/O Performance And Cost Efficiency Of Cloud Storage: A Client's Perspective, Binbing Hou

LSU Doctoral Dissertations

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service provider’s data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage.

In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a client’s perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors …

Using Uncertainty To Interpret Supervised Machine Learning Predictions, Michael C. Darling

Electrical and Computer Engineering ETDs

Traditionally, machine learning models are assessed using methods that estimate an average performance against samples drawn from a particular distribution. Examples include the use of cross-validation or hold0out to estimate classification error, F-score, precision, and recall.

While these measures provide valuable information, they do not tell us a model's certainty relative to particular regions of the input space. Typically there are regions where the model can differentiate the classes with certainty, and regions where the model is much less certain about its predictions.

In this dissertation we explore numerous approaches for quantifying uncertainty in the individual predictions made by supervised …

Artificial Intelligence Empowered Uavs Data Offloading In Mobile Edge Computing, Nicholas Alexander Kemp

Electrical and Computer Engineering ETDs

The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs' data to be …

Service Competition And Data-Centric Protocols For Internet Access, Thiago Teixeira

Doctoral Dissertations

The Internet evolved in many aspects, from the application to the physical layers. However, the evolution of the Internet access technologies, most visible in dense urban scenarios, is not easily noticeable in sparsely populated and rural areas. In the United States, for example, the FCC identified that 50% of the census blocks have access to up to two broadband providers; however, these providers do not necessarily compete. Additionally, due to the methodology of the study, there is evidence that the number of actual customers without broadband access is higher since the FCC considers the entire block to have broadband if …

Time-Difference Circuits: Methodology, Design, And Digital Realization, Shuo Li

Doctoral Dissertations

This thesis presents innovations for a special class of circuits called Time Difference (TD) circuits. We introduce a signal processing methodology with TD signals that alters the target signal from a magnitude perspective to time interval between two time events and systematically organizes the primary TD functions abstracted from existing TD circuits and systems. The TD circuits draw attention from a broad range of application fields. In addition, highly evolved complementary metal-oxide-semiconductor (CMOS) technology suffers from various problems related to voltage and current amplitude signal processing methods. Compared to traditional analog and digital circuits, TD circuits bring several compelling features: …

Stealthy Parametric Hardware Trojans In Vlsi Circuits, Samaneh Ghandali

Doctoral Dissertations

Over the last decade, hardware Trojans have gained increasing attention in academia, industry and by government agencies. In order to design reliable countermeasures, it is crucial to understand how hardware Trojans can be built in practice. This is an area that has received relatively scant treatment in the literature. In this thesis, we examine how particularly stealthy parametric Trojans can be introduced to VLSI circuits. Parametric Trojans do not require any additional logic and are purely based on subtle manipulations on the sub-transistor level to modify the parameters of few transistors which makes them very hard to detect. We introduce …

Function And Dissipation In Finite State Automata - From Computing To Intelligence And Back, Natesh Ganesh

Doctoral Dissertations

Society has benefited from the technological revolution and the tremendous growth in computing powered by Moore's law. However, we are fast approaching the ultimate physical limits in terms of both device sizes and the associated energy dissipation. It is important to characterize these limits in a physically grounded and implementation-agnostic manner, in order to capture the fundamental energy dissipation costs associated with performing computing operations with classical information in nano-scale quantum systems. It is also necessary to identify and understand the effect of quantum in-distinguishability, noise, and device variability on these dissipation limits. Identifying these parameters is crucial to designing …

A Gpu-Based Framework For Parallel Spatial Indexing And Query Processing, Zhila Nouri Lewis

USF Tampa Graduate Theses and Dissertations

Support for efficient spatial data storage and retrieval have become a vital component in almost all spatial database systems. Previous work has shown the importance of using spatial indexing and parallel computing to speed up such tasks. While GPUs have become a mainstream platform for high-throughput data processing in recent years, exploiting the massively parallel processing power of GPUs is non-trivial. Current approaches that parallelize one query at a time have low work efficiency and cannot make good use of GPU resources. On the other hand, many spatial database applications are busy systems in which a large number of queries …

Distributed Spatiotemporal Control And Dynamic Information Fusion For Multiagent Systems, Dzung Minh Duc Tran

USF Tampa Graduate Theses and Dissertations

The first objective of this dissertation is to develop novel distributed control architectures allowing spatiotemporal control of multiagent systems as applied to formation control. In addition, its second objective is to introduce distributed estimation frameworks for dynamic information fusion for addressing the heterogeneity in sensor networks.

Changing the spatial and temporal properties of agent teams in a distributed manner and in real-time is an open problem in the control system literature as multiagent systems are often required to complete tasks with ever-increasing complexity in adverse conditions and dynamic environments. Motivated by this standpoint, this dissertation aims to address challenges related …

Mobile Ad Hoc Networks In Transportation Data Collection And Dissemination, Kardigue Konte

Theses and Dissertations

The field of transportation is rapidly changing with new opportunities for systems solutions and emerging technologies. The global economic impact of congestion and accidents are significant. Improved means are needed to solve them. Combined with the increasing numbers of vehicles on the road, the net economic impact is measured in the many billions of dollars. Promising methodologies explored in this thesis include the use of the Internet of Things (IoT) and Mobile Ad Hoc Networks (MANET). Interconnecting vehicles using Dedicated Short Range Communication technology (DSRC) brings many benefits. Integrating DSRC into roadway vehicles offers the promise of reducing the problems …

Feature Space Modeling For Accurate And Efficient Learning From Non-Stationary Data, Ayesha Akter

Doctoral Dissertations

A non-stationary dataset is one whose statistical properties such as the mean, variance, correlation, probability distribution, etc. change over a specific interval of time. On the contrary, a stationary dataset is one whose statistical properties remain constant over time. Apart from the volatile statistical properties, non-stationary data poses other challenges such as time and memory management due to the limitation of computational resources mostly caused by the recent advancements in data collection technologies which generate a variety of data at an alarming pace and volume. Additionally, when the collected data is complex, managing data complexity, emerging from its dimensionality and …

Development Of A National-Scale Big Data Analytics Pipeline To Study The Potential Impacts Of Flooding On Critical Infrastructures And Communities, Nattapon Donratanapat

Theses and Dissertations

With the rapid development of the Internet of Things (IoT) and Big data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management and decision making. FAIS allows the user …

Audio Beat Detection With Application To Robot Drumming, Michael James Engstrom

Dissertations and Theses

This Drumming Robot thesis demonstrates the design of a robot which can play drums in rhythm to an external audio source. The audio source can be either a pre-recorded .wav file or a live sample .wav file from a microphone. The dominant beats-per-minute (BPM) of the audio would be extracted and the robot would drum in time to the BPM. A Fourier Analysis-based BPM detection algorithm, developed by Eric Scheirer (Tempo and beat analysis of acoustical musical signals)ⁱ was adopted and implemented. In contrast to other popular algorithms, the main advantage of Scheirer's algorithm is it has …

A Novel And Inexpensive Solution To Build Autonomous Surface Vehicles Capable Of Negotiating Highly Disturbed Environments, Jason Moulton

Theses and Dissertations

This dissertation has four main contributions. The first contribution is the design and build of a fleet of long-range, medium-duration deployable autonomous surface vehicles (ASV). The second is the development, implementation, and testing of inex-pensive sensors to accurately measure wind, current, and depth environmental vari- ables. The third leverages the first two contributions, and is modeling the effects of environmental variables on an ASV, finally leading to the development of a dynamic controller enabling deployment in more uncertain conditions.

The motivation for designing and building a new ASV comes from the lack of availability of a flexible and modular platform …

A Multi-Agent Systems Approach For Analysis Of Stepping Stone Attacks, Marco Antonio Gamarra

Electrical & Computer Engineering Theses & Dissertations

Stepping stone attacks are one of the most sophisticated cyber-attacks, in which attackers make a chain of compromised hosts to reach a victim target. In this Dissertation, an analytic model with Multi-Agent systems approach has been proposed to analyze the propagation of stepping stones attacks in dynamic vulnerability graphs. Because the vulnerability configuration in a network is inherently dynamic, in this Dissertation a biased min-consensus technique for dynamic graphs with fixed and switching topology is proposed as a distributed technique to calculate the most vulnerable path for stepping stones attacks in dynamic vulnerability graphs. We use min-plus algebra to analyze …

Modeling Social Learning: An Agent-Based Approach, Erika G. Ardiles Cruz

Computational Modeling & Simulation Engineering Theses & Dissertations

Learning is the process of acquiring or modifying knowledge, behavior, or skills. The ability to learn is inherent to humans, animals, and plants, and even machines are provided with algorithms that could mimic in a restricted way the processes of learning. Humans learn from the time they are born until they die because of a continuous process of interaction between them and their environment. Behavioral Psychology Theories and Social Learning Theories study behavior learned from the environment and social interactions through stimulus-response. Some computer approaches to modeling human behavior attempted to represent the learning and decision-making processes using agent-based models. …

Communication Capability For A Simulation-Based Test And Evaluation Framework For Autonomous Systems, Ntiana Sakioti

Computational Modeling & Simulation Engineering Theses & Dissertations

The design and testing process for collaborative autonomous systems can be extremely complex and time-consuming, so it is advantageous to begin testing early in the design. A Test & Evaluation (T&E) Framework was previously developed to enable the testing of autonomous software at various levels of mixed reality. The Framework assumes a modular approach to autonomous software development, which introduces the possibility that components are not in the same stage of development. The T&E Framework allows testing to begin early in a simulated environment, with the autonomous software methodically migrating from virtual to augmented to physical environments as component development …

Implementación Sistema De Supervisión Y Control Para Los Módulos De Procesos, Miguel Angel Rojas Mahecha

Ingeniería en Automatización

En este trabajo se atiende a la necesidad de implementar un sistema de supervisión y control para las dos unidades de entrenamiento de procesos industriales de la Universidad de La Salle, las cuales están dotadas de sensores industriales de alta precisión y son utilizados en diferentes espacios académicos. Por lo tanto, se ofrece una herramienta educativa que permite llevar a cabo un cabo el diseño de diferentes técnicas de control que interactúan con las variables físicas de estos módulos. Para este fin, se empleó la sala de control y supervisión de procesos, junto con los dos módulos de entrenamiento, donde …

Thwarting Adversaries With Randomness And Irrationality, Abhinav Aggarwal

Computer Science ETDs

Distributed systems are ubiquitous today: from the Internet used by billions of people around the world to the small scale IoT devices. With this rapidly increasing need to perform computation at scales larger than ever before, comes the need to ensure resilience to adversarial failures so that these systems can continue to behave as intended even when some malicious tampering happens.

In this dissertation, we explore the power of randomness and the difficulty of rationally approximating the Golden Ratio to thwart adversarial behavior in two different problems in distributed computing: interactive communication and robust collaborative search. While randomness helps with …

Adaptive-Hybrid Redundancy For Radiation Hardening, Nicolas S. Hamilton

Theses and Dissertations

An Adaptive-Hybrid Redundancy (AHR) mitigation strategy is proposed to mitigate the effects of Single Event Upset (SEU) and Single Event Transient (SET) radiation effects. AHR is adaptive because it switches between Triple Modular Redundancy (TMR) and Temporal Software Redundancy (TSR). AHR is hybrid because it uses hardware and software redundancy. AHR is demonstrated to run faster than TSR and use less energy than TMR. Furthermore, AHR allows space vehicle designers, mission planners, and operators the flexibility to determine how much time is spent in TMR and TSR. TMR mode provides faster processing at the expense of greater energy usage. TSR …

Mnews: A Study Of Multilingual News Search Interfaces, Chenjun Ling

Engineering Ph.D. Theses

With the global expansion of the Internet and the World Wide Web, users are becoming increasingly diverse, particularly in terms of languages. In fact, the number of polyglot Web users across the globe has increased dramatically.

However, even such multilingual users often continue to suffer from unbalanced and fragmented news information, as traditional news access systems seldom allow users to simultaneously search for and/or compare news in different languages, even though prior research results have shown that multilingual users make significant use of each of their languages when searching for information online.

Relatively little human-centered research has been conducted to …

Preference Learning And Similarity Learning Perspectives On Personalized Recommendation, Duy Dung Le

Dissertations and Theses Collection (Open Access)

Personalized recommendation, whose objective is to generate a limited list of items (e.g., products on Amazon, movies on Netflix, or pins on Pinterest, etc.) for each user, has gained extensive attention from both researchers and practitioners in the last decade. The necessity of personalized recommendation is driven by the explosion of available options online, which makes it difficult, if not downright impossible, for each user to investigate every option. Product and service providers rely on recommendation algorithms to identify manageable number of the most likely or preferred options to be presented to each user. Also, due to the limited screen …

An Application Of Sliding Mode Control To Model-Based Reinforcement Learning, Aaron Thomas Parisi

Master's Theses

The state-of-art model-free reinforcement learning algorithms can generate admissible controls for complicated systems with no prior knowledge of the system dynamics, so long as sufficient (oftentimes millions) of samples are available from the environ- ment. On the other hand, model-based reinforcement learning approaches seek to leverage known optimal or robust control to reinforcement learning tasks by mod- elling the system dynamics and applying well established control algorithms to the system model. Sliding-mode controllers are robust to system disturbance and modelling errors, and have been widely used for high-order nonlinear system control. This thesis studies the application of sliding mode control …

Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball

Master's Theses

Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum …

Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu

Dissertations

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering …

Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan

Dissertations

Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The …

Classifying Appliances Operation Modes Using Dynamic Time Warping (Dtw) And K Nearest Neighbors (Knn), Abdelkareem M. Jaradat

Electronic Thesis and Dissertation Repository

In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and developing data mining techniques. In this research, we introduce a smart system approach that is applied to user's disaggregated power consumption data. This system encourages the users to apply DR by changing their behaviour of using heavier operation modes to lighter modes, and by encouraging users to shift their usages to off-peak hours. First, we apply Cross Correlation to detect times of the occurrences when an appliance …