Computer Engineering Commons^™

Trajectory Generation For Lane-Change Maneuver Of Autonomous Vehicles, Ashesh Goswami

Open Access Theses

Lane-change maneuver is one of the most thoroughly investigated automatic driving operations that can be used by an autonomous self-driving vehicle as a primitive for performing more complex operations like merging, entering/exiting highways or overtaking another vehicle. This thesis focuses on two coherent problems that are associated with the trajectory generation for lane-change maneuvers of autonomous vehicles in a highway scenario: (i) an effective velocity estimation of neighboring vehicles under different road scenarios involving linear and curvilinear motion of the vehicles, and (ii) trajectory generation based on the estimated velocities of neighboring vehicles for safe operation of self-driving cars during …

Recursive Tree Traversal Dependence Analysis, Yusheng Weijiang

Open Access Theses

While there has been much work done on analyzing and transforming regular programs that operate over linear arrays and dense matrices, comparatively little has been done to try to carry these optimizations over to programs that operate over heap-based data structures using pointers. Previous work has shown that point blocking, a technique similar to loop tiling in regular programs, can help increase the temporal locality of repeated tree traversals. Point blocking, however, has only been shown to work on tree traversals where each traversal is fully independent and would allow parallelization, greatly limiting the types of applications that this transformation …

Improving Capacity-Performance Tradeoffs In The Storage Tier, Eric P. Villasenor

Open Access Dissertations

Data-set sizes are growing. New techniques are emerging to organize and analyze these data-sets. There is a key access pattern emerging with these new techniques, large sequential file accesses. The trend toward bigger files exists to help amortize the cost of data accesses from the storage layer, as many workloads are recognized to be I/O bound. The storage layer is widely recognized as the slowest layer in the system. This work focuses on the tradeoff one can make with that storage capacity to improve system performance. ^ Capacity can be leveraged for improved availability or improved performance. This tradeoff is …

Assessment Of High-Fidelity Collision Models In The Direct Simulation Monte Carlo Method, Andrew Brian Weaver

Open Access Dissertations

Advances in computer technology over the decades has allowed for more complex physics to be modeled in the DSMC method. Beginning with the first paper on DSMC in 1963, 30,000 collision events per hour were simulated using a simple hard sphere model. Today, more than 10 billion collision events can be simulated per hour for the same problem. Many new and more physically realistic collision models such as the Lennard-Jones potential and the forced harmonic oscillator model have been introduced into DSMC. However, the fact that computer resources are more readily available and higher-fidelity models have been developed does not …

Learning Parameterized Skills, Bruno Castro Da Silva

Doctoral Dissertations

One of the defining characteristics of human intelligence is the ability to acquire and refine skills. Skills are behaviors for solving problems that an agent encounters often—sometimes in different contexts and situations—throughout its lifetime. Identifying important problems that recur and retaining their solutions as skills allows agents to more rapidly solve novel problems by adjusting and combining their existing skills. In this thesis we introduce a general framework for learning reusable parameterized skills. Reusable skills are parameterized procedures that—given a description of a problem to be solved—produce appropriate behaviors or policies. They can be sequentially and hierarchically combined with other …

Dcms: A Data Analytics And Management System For Molecular Simulation, Meryem Berrada

USF Tampa Graduate Theses and Dissertations

Despite the fact that Molecular Simulation systems represent a major research tool in multiple scientific and engineering fields, there is still a lack of systems for effective data management and fast data retrieval and processing. This is mainly due to the nature of MS which generate a very large amount of data - a system usually encompass millions of data information, and one query usually runs for tens of thousands of time frames. For this purpose, we designed and developed a new application, DCMS (A data Analytics and Management System for molecular Simulation), that intends to speed up the process …

Fail-Safe Test Generation Of Safety Critical Systems, Salwa Elakeili

Electronic Theses and Dissertations

This dissertation introduces a technique for testing proper failure mitigation in safety critical systems. Unlike other approaches which integrate behavioral and failure models, and then generate tests from the integrated model, we build safety mitigation tests from an existing behavioral test suite, using an explicit mitigation model for which we generate mitigation paths which are then woven at selected failure points into the original test suite to create failure-mitigation tests (safety mitigation test).

Reducing The Control Burden Of Legged Robotic Locomotion Through Biomimetic Consonance In Mechanical Design And Control, Caitrin Elizabeth Eaton

USF Tampa Graduate Theses and Dissertations

Terrestrial robots must be capable of negotiating rough terrain if they are to become autonomous outside of the lab. Although the control mechanism offered by wheels is attractive in its simplicity, any wheeled system is confined to relatively flat terrain. Wheels will also only ever be useful for rolling, while limbs observed in nature are highly multimodal. The robust locomotive utility of legs is evidenced by the many animals that walk, run, jump, swim, and climb in a world full of challenging terrain.

On the other hand, legs with multiple degrees of freedom (DoF) require much more complex control and …

Design Of A Novel Low - Cost, Portable, 3d Ultrasound System With Extended Imaging Capabilities For Point-Of-Care Applications, Michail Tsakalakis

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Ultrasound Imaging (USI) or Medical Sonography (MS), as it is formally called, has been widely used in biomedical applications over the last decades. USI can provide clinicians with a thorough view of the internal parts of the human body, making use of sound waves of higher frequencies than humans can perceive. USI systems are considered highly portable and of low-cost, compared to other imaging modalities. However, despite those advantages, Ultrasound Systems (US) and especially 3D ones, have not been yet extensively utilized for Point-of-Care (POC) applications, due to numerous restrictions and artifacts that they currently present.

Hardware complexity and real-time …

Evaluation Of An Architectural-Level Approach For Finding Security Vulnerabilities, Mohammad Anamul Haque

Wayne State University Theses

The cost of security vulnerabilities of a software system is high. As a result,

many techniques have been developed to find the vulnerabilities at development time. Of particular interest are static analysis techniques that can consider all possible executions of a system. But, static analysis can suffer from a large number of false positives.

A recently developed approach, Scoria, is a semi-automated static analysis that requires security architects to annotate the code, typecheck the annotations, extract a hierarchical object graph and write constraints in order to find security vulnerabilities in a system.

This thesis evaluates Scoria on three systems (sizes …

An Embodied Approach To Evolving Robust Visual Classifiers, Karol Zieba

Graduate College Dissertations and Theses

From the very creation of the term by Czech writer Karel Capek in 1921, a "robot" has been synonymous with an artificial agent possessing a powerful body and cogitating mind. While the fields of Artificial Intelligence (AI) and Robotics have made progress into the creation of such an android, the goal of a cogitating robot remains firmly outside the reach of our technological capabilities. Cognition has proved to be far more complex than early AI practitioners envisioned. Current methods in Machine Learning have achieved remarkable successes in image categorization through the use of deep learning. However, when presented with novel …

Intelligent Network Intrusion Detection Using An Evolutionary Computation Approach, Samaneh Rastegari

Theses: Doctorates and Masters

With the enormous growth of users' reliance on the Internet, the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems.

Generally, a comprehensive defence mechanism consists of three phases, namely, preparation, detection and reaction. In the preparation phase, network administrators aim to find and fix security vulnerabilities (e.g., insecure protocol and vulnerable computer systems or firewalls), that can be exploited to launch attacks. Although the preparation phase increases the level of security in a network, this will never completely …

Fuzzy Adaptive Resonance Theory: Applications And Extensions, Clayton Parker Smith

Masters Theses

"Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully …

Building Computing-As-A-Service Mobile Cloud System, Kun Wang

Wayne State University Dissertations

The last five years have witnessed the proliferation of smart mobile devices, the explosion of various mobile applications and the rapid adoption of cloud computing in business, governmental and educational IT deployment. There is also a growing trends of combining mobile computing and cloud computing as a new popular computing paradigm nowadays. This thesis envisions the future of mobile computing which is primarily affected by following three trends: First, servers in cloud equipped with high speed multi-core technology have been the main stream today. Meanwhile, ARM processor powered servers is growingly became popular recently and the virtualization on ARM systems …

Wireless Networking For Vehicle To Infrastructure Communication And Automatic Incident Detection, Sarwar Aziz Sha-Mohammad

Computer Science Theses & Dissertations

Vehicular wireless communication has recently generated wide interest in the area of wireless network research. Automatic Incident Detection (AID), which is the recent focus of research direction in Intelligent Transportation System (ITS), aims to increase road safety. These advances in technology enable traffic systems to use data collected from vehicles on the road to detect incidents. We develop an automatic incident detection method that has a significant active road safety application for alerting drivers about incidents and congestion. Our method for detecting traffic incidents in a highway scenario is based on the use of distance and time for changing lanes …

Brand Positioning Map And Analysis Using Web Scraping And Advertisement Analysis, Surya Bhatt

Theses and Dissertations

There’s a significant increase in online consumer forums. When customers set out to buy a product they use these forums to form an opinion. Our research focuses on comparing Brand positioning maps based on consumer reviews. We also analyse the impact of advertisements and expert reviews. Our goal is to show that combining consumer reviews with ads and electronic media will help us analyze the effectiveness of advertising on brand positioning maps. This approach shall also help us in making association graphs for a brand using words of perception/opinion associated with that brand/product. Which may in turn assist companies in …

A Workload Balanced Mapreduce Framework On Gpu Platforms, Yue Zhang

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The MapReduce framework is a programming model proposed by Google to process large datasets. It is an efficient framework that can be used in many areas, such as social network, scientific research, electronic business, etc. Hence, more and more MapReduce frameworks are implemented on different platforms, including Phoenix (based on multicore CPU), MapCG (based on GPU), and StreamMR (based on GPU). However, these MapReduce frameworks have limitations, and they cannot handle the collision problem in the map phase, and the unbalanced workload problems in the reduce phase. To improve the performance of the MapReduce framework on GPGPUs, in this thesis, …

Owl Query Answering Using Machine Learning, Todd Huster

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The formal semantics of the Web Ontology Language (OWL) enables automated reasoning over OWL knowledge bases, which in turn can be used for a variety of purposes including knowledge base development, querying and management. Automated reasoning is usually done by means of deductive (proof-theoretic) algorithms which are either provably sound and complete or employ approximate methods to trade some correctness for improved efficiency. As has been argued elsewhere, however, reasoning methods for the Semantic Web do not necessarily have to be based on deductive methods, and approximate reasoning using statistical or machine-learning approaches may bring improved speed while maintaining high …

Whole-Lake Primary Production Calculator, Colin D. Leong

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This work describes an implementation of a model for estimation of both benthic and phytoplanktonic primary production in lakes. The web application makes use of interpolation techniques to allow estimates of primary production using values for photosynthesis/irradiance parameters at only 5 depths. These estimates compare favorably in accuracy with estimates using values listed at over one hundred depths. Validation of the implementation was done by comparison with primary production results from the Northern Temperate Lakes Long Term Ecological Research database.

Computation Offloading Decisions For Reducing Completion Time, Salvador Melendez

Open Access Theses & Dissertations

Mobile devices are being widely used in many applications such as image processing, computer vision (e.g. face detection and recognition), wearable computing, language translation, and battlefield operations. However, mobile devices are constrained in terms of their battery life, processor performance, storage capacity, and network bandwidth. To overcome these issues, there is an approach called Computation Offloading, also known as cyber-foraging and surrogate computing. Computation offloading consists of migrating computational jobs from a mobile device to more powerful remote computing resources. Upon completion of the job, the results are sent back to the mobile device. However, a decision must be made; …

Temporally Biased Search Result Snippets, J. Abhiram Tatineni

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The search engine result snippets are an important source of information for the user to obtain quick insights into the corresponding result documents. When the search terms are too general, like a person's name or a company's name, creating an appropriate snippet that effectively summarizes the document's content can be challenging owing to multiple occurrences of the search term in the top ranked documents, without a simple means to select a subset of sentences containing them to form result snippet. In web pages classified as narratives and news articles, multiple references to explicit, implicit and relative temporal expressions can be …

Browser Based Visualization For Parameter Spaces Of Big Data Using Client-Server Model, Kurtis M. Glendenning

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Visualization is an important task in data analytics, as it allows researchers to view abstract patterns within the data instead of reading through extensive raw data. Allowing the ability to interact with the visualizations is an essential aspect since it provides the ability to intuitively explore data to find meaning and patterns more efficiently. Interactivity, however, becomes progressively more difficult as the size of the dataset increases. This project begins by leveraging existing web-based data visualization technologies and extends their functionality through the use of parallel processing. This methodology utilizes state-of-the-art techniques, such as Node.js, to split the visualization rendering …

Orthogonal Moment-Based Human Shape Query And Action Recognition From 3d Point Cloud Patches, Huaining Cheng

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With the recent proliferation of 3D sensors such as Light Detection and Ranging (LIDAR), it is essential to develop feature representation methods that can best characterize the point clouds produced by these devices. When these devices are employed in targeting and surveillance of human actions from both ground and aerial platforms, the corresponding point clouds of body shape often comprise low-resolution, disjoint, and irregular patches of points resulted from self-occlusions and viewing angle variations. The prevailing method of depth image analysis has the limitation of relying on 2D features that are not native representation of 3D spatial relationships. On the …

Feature Extraction Using Dimensionality Reduction Techniques: Capturing The Human Perspective, Ashley B. Coleman

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The purpose of this paper is to determine if any of the four commonly used dimensionality reduction techniques are reliable at extracting the same features that humans perceive as distinguishable features. The four dimensionality reduction techniques that were used in this experiment were Principal Component Analysis (PCA), Multi-Dimensional Scaling (MDS), Isomap and Kernel Principal Component Analysis (KPCA). These four techniques were applied to a dataset of images that consist of five infrared military vehicles. Out of the four techniques three out of the five resulting dimensions of PCA matched a human feature. One out of five dimensions of MDS matched …

Ontology Pattern-Based Data Integration, Adila Alfa Krisnadhi

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Data integration is concerned with providing a unified access to data residing at multiple sources. Such a unified access is realized by having a global schema and a set of mappings between the global schema and the local schemas of each data source, which specify how user queries at the global schema can be translated into queries at the local schemas. Data sources are typically developed and maintained independently, and thus, highly heterogeneous. This causes difficulties in integration because of the lack of interoperability in the aspect of architecture, data format, as well as syntax and semantics of the data. …

Direct Optimization For Classification With Boosting, Shaodan Zhai

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Boosting, as one of the state-of-the-art classification approaches, is widely used in the industry for a broad range of problems. The existing boosting methods often formulate classification tasks as a convex optimization problem by using surrogates of performance measures. While the convex surrogates are computationally efficient to globally optimize, they are sensitive to outliers and inconsistent under some conditions. On the other hand, boosting's success can be ascribed to maximizing the margins, but few boosting approaches are designed to directly maximize the margin. In this research, we design novel boosting algorithms that directly optimize non-convex performance measures, including the empirical …

Learning To Rank Algorithms And Their Application In Machine Translation, Tian Xia

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In this thesis, we discuss two issues in the learning to rank area, choosing effective objective loss function, constructing effective regresstion trees in the gradient boosting framework, as well as a third issus, applying learning to rank models into statistcal machine translation. First, list-wise based learning to rank methods either directly optimize performance measures or optimize surrogate functions of performance measures that have smaller gaps between optimized losses and performance measures, thus it is generally believed that they should be able to lead to better performance than point-and pair-wise based learning to rank methods. However, in real-world applications, state-of-the-art practical …

Contrast Pattern Aided Regression And Classification, Vahid Taslimitehrani

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Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy. In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where …

Automatic Emotion Identification From Text, Wenbo Wang

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People's emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further, the self-labeled emotion data can be effectively adapted to train emotion classifiers in different target domains where training data are sparse.

Emotions are both prevalent in and essential to most aspects of our lives. They influence our decision-making, affect our social relationships and shape our daily behavior. With the rapid growth of emotion-rich textual content, such as microblog posts, blog posts, and forum discussions, there is a growing need to develop algorithms and techniques for …

Mining Behavior Of Citizen Sensor Communities To Improve Cooperation With Organizational Actors, Hemant Purohit

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Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including 'articulation' …