Engineering Commons^™

Accelerated Hyperspectral Unmixing With Endmember Variability Via The Sum-Product Algorithm, Charan Puladas

Browse all Theses and Dissertations

The rich spectral information captured by hyperspectral sensors has given rise to a number of remote sensing applications, ranging from vegetative assessment and crop health monitoring, to military surveillance and combatant identification. However, due to limited spatial resolution, multiple ground materials generally contribute, i.e. mix, to form the spectrum recorded for a single pixel. The unmixing problem considers the inverse problem of determining the underlying material spectra, called endmembers, from sensor measurements. While classical unmixing approaches were deterministic in nature and did not attempt to identify in-scene materials, recent methods use labeled training data to generate statistical models of endmember …

A Closed Loop Research Platform That Enables Dynamic Control Of Wing Gait Patterns In A Vertically Constrained Flapping Wing - Micro Air Vehicle, Hermanus Van Botha

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Research in Flapping Wing - Micro Air Vehicles(FW-MAVs) has been growing in recent years. Work ranging from mechanical designs to adaptive control algorithms are being developed in pursuit of mimicking natural flight. FW-MAV technology can be applied in a variety of use cases such a military application and surveillance, studying natural ecological systems, and hobbyist commercialization. Recent work has produced small scale FW-MAVs that are capable of hovering and maneuvering. Researchers control maneuvering in various ways, some of which involve making small adjustments to the core wing motion patterns (wing gaits) which determine how the wings flap. Adaptive control algorithms …

Improving Understandability And Uncertainty Modeling Of Data Using Fuzzy Logic Systems, Dumidu S. Wijayasekara

Theses and Dissertations

The need for automation, optimality and efficiency has made modern day control and monitoring systems extremely complex and data abundant. However, the complexity of the systems and the abundance of raw data has reduced the understandability and interpretability of data which results in a reduced state awareness of the system. Furthermore, different levels of uncertainty introduced by sensors and actuators make interpreting and accurately manipulating systems difficult. Classical mathematical methods lack the capability to capture human knowledge and increase understandability while modeling such uncertainty.

Fuzzy Logic has been shown to alleviate both these problems by introducing logic based on vague …

Assessment Of Classifiers For Potential Voice-Enabled Transportation Apps, Md Majbah Uddin

Theses and Dissertations

Transportation apps are playing a positive role for today’s technology-driven users. They provide users with a convenient and flexible tool to access transportation data and services, as well as collect and manage data. In many of these apps, such as Google Maps, their operations rely on the effectiveness of the voice recognition system. For the existing and new apps to be truly effective, the built-in voice recognition system needs to be robust (i.e., being able to recognize words spoken in different pitch and tone). The goal of this study is to assess three post-processing classifiers (i.e., bag-of-sentences, support vector machine, …

Performance Analysis Of Hybrid Algorithms For Lossless Compression Of Climate Data, Bharath Chandra Mummadisetty

UNLV Theses, Dissertations, Professional Papers, and Capstones

Climate data is very important and at the same time, voluminous. Every minute a new entry is recorded for different climate parameters in climate databases around the world. Given the explosive growth of data that needs to be transmitted and stored, there is a necessity to focus on developing better transmission and storage technologies. Data compression is known to be a viable and effective solution to reduce bandwidth and storage requirements of bulk data. So, the goal is to develop the best compression methods for climate data.

The methodology used is based on predictive analysis. The focus is to implement …

Study Of Machine Learning Methods In Intelligent Transportation Systems, Vishal Jha

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine learning and data mining are currently hot topics of research and are applied in database, artificial intelligence, statistics, and so on to discover valuable knowledge and the patterns in big data available to users. Data mining is predominantly about processing unstructured data and extracting meaningful information from them for end users to help take business decisions. Machine learning techniques use mathematical algorithms to find a pattern or extract meaning out from big data. The popularity of such techniques in analyzing business problems has been enhanced by the arrival of big data.

The main objective of this thesis is to …

Determination Of Rule Patterns In Complex Event Processing Using Machine Learning Techniques, Nijat Mehdiyev, Julian Krumeich, David Lee Enke, Dirk Werth, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Complex Event Processing (CEP) is a novel and promising methodology that enables the real-time analysis of stream event data. The main purpose of CEP is detection of the complex event patterns from the atomic and semantically low-level events such as sensor, log, or RFID data. Determination of the rule patterns for matching these simple events based on the temporal, semantic, or spatial correlations is the central task of CEP systems. In the current design of the CEP systems, experts provide event rule patterns. Having reached maturity, the Big Data Systems and Internet of Things (IoT) technology require the implementation of …

A Lexical Approach For Classifying Malicious Urls, Michael Darling

Electrical and Computer Engineering ETDs

Given the continuous growth of illicit activities on the Internet, there is a need for intelligent systems to identify malicious web pages. It has been shown that URL anal- ysis is an e\u21b5ective tool for detecting phishing, malware, and other attacks. Previous studies have performed URL classification using a combination of lexical features, network tra c, hosting information, and other strategies. These approaches require time-intensive lookups which introduce significant delay in real-time systems. This paper describes a lightweight approach for classifying malicious web pages using URL lexical analysis alone. The goal is to explore the upper-bound of the classification accuracy …

Machine Learning Aided Decision Making And Adaptive Stochastic Control In A Hierarchical Interactive Smart Grid, Ding Li

Electrical and Computer Engineering ETDs

In this dissertation, a hierarchical interactive architecture for the future smart grid is proposed. This hierarchical architecture consists of different layers ranging from the households, microgrid controller level, feeder level and substation level. The proposed smart grid architecture is scalable while allowing for sufficient resource pooling, because in each layer the power generation and consumption sides interact in a similar manner. Therefore, we develop an abstract Grid model with distributed energy resources (DER) and storage facilities. A comprehensive real time interactive scheme is proposed for the abstract Grid model, which addresses several important topics: (1) load prediction and uncertainty modeling, …

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 …

Link Prediction In Dynamic Weighted And Directed Social Network Using Supervised Learning, Ricky Laishram

Dissertations - ALL

Link Prediction is an area of great interest in social network analy- sis. Previous works in the area of link prediction have only focused on networks where the links once created cannot be removed. In many real world social networks, the links should be assigned strengths; for example, the strength of a link should decrease over time, if there are no interactions between the two nodes for a long time and increase if the two nodes interact often. In this thesis we modify existing meth- ods of link prediction to apply to weighted and directed networks. The features, developed in …

Novel Classification Of Slow Movement Objects In Urban Traffic Environments Using Wideband Pulse Doppler Radar, Berta Rodriguez Hervas

Open Access Theses & Dissertations

Every year thousands of people are involved in traffic accidents, some of which are fatal. An important percentage of these fatalities are caused by human error, which could be prevented by increasing the awareness of drivers and the autonomy of vehicles. Since driver assistance systems have the potential to positively impact tens of millions of people, the purpose of this research is to study the micro-Doppler characteristics of vulnerable urban traffic components, i.e. pedestrians and bicyclists, based on information obtained from radar backscatter, and to develop a classification technique that allows automatic target recognition with a vehicle integrated system. For …

Functional Network Connectivity In Human Brain And Its Applications In Automatic Diagnosis Of Brain Disorders, Mohammad Arbabshirani

Electrical and Computer Engineering ETDs

The human brain is one of the most complex systems known to the mankind. Over the past 3500 years, mankind has constantly investigated this remarkable system in order to understand its structure and function. Emerging of neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have opened a non-invasive in-vivo window into brain function. Moreover, fMRI has made it possible to study brain disorders such as schizophrenia from a different angle unknown to researchers before. Human brain function can be divided into two categories: functional segregation and integration. It is well-understood that each region in the brain is specialized in …

Atrengine: An Orientation-Based Algorithm For Automatic Target Recognition, Justin Ting-Jeuan Kuo

Master's Theses

Automatic Target Recognition (ATR) is a subject involving the use of sensor data to develop an algorithm for identifying targets of significance. It is of particular interest in military applications such as unmanned aerial vehicles and missile tracking systems. This thesis develops an orientation-based classification approach from previous ATR algorithms for 2-D Synthetic Aperture Radar (SAR) images. Prior work in ATR includes Chessa Guilas’ Hausdorff Probabilistic Feature Analysis Approach in 2005 and Daniel Cary’s Optimal Rectangular Fit in 2007.

A system incorporating multiple modules performing different tasks is developed to streamline the data processing of previous algorithms. Using images from …

Smart Weights, Luke W. Rafla-Yuan, Austin C. Fox

Electrical Engineering

The goal of this project is to design and implement weights which can record and analyze work out patterns. Motivation for this project stems from the high cost of personal training. The hope is that this device will provide many of the benefits a user receives from personal training at only a fraction of the cost. The Smart Weight is designed with an on-board Inertial Measurement Unit providing acceleration, gyroscope, and magnetometer data. A microcontroller records and analyzes changes in motion, feeding this data into Multiplicative Recurrent Neural Network (MRNN) for exercise classification. A Raspberry Pi was chosen as the …

Detection Of Temporal Lobe Epilepsy Using Support Vector Machines In Multi-Parametric Quantitative Mr Imaging., Diego Cantor-Rivera, Ali R Khan, Maged Goubran, Seyed M Mirsattari, Terry M Peters

Robarts Imaging Publications

The detection of MRI abnormalities that can be associated to seizures in the study of temporal lobe epilepsy (TLE) is a challenging task. In many cases, patients with a record of epileptic activity do not present any discernible MRI findings. In this domain, we propose a method that combines quantitative relaxometry and diffusion tensor imaging (DTI) with support vector machines (SVM) aiming to improve TLE detection. The main contribution of this work is two-fold: on one hand, the feature selection process, principal component analysis (PCA) transformations of the feature space, and SVM parameterization are analyzed as factors constituting a classification …

Knowledge Extraction From Work Instructions Through Text Processing And Analysis, Abhiram Koneru

All Theses

The objective of this thesis is to design, develop and implement an automated approach to support processing of historical assembly data to extract useful knowledge about assembly instructions and time studies to facilitate the development of decision support systems, for a large automotive original equipment manufacturer (OEM). At a conceptual level, this research establishes a framework for sustainable and scalable approach to extract knowledge from big data using techniques from Natural Language Processing (NLP) and Machine Learning (ML). Process sheets are text documents that contain detailed instructions to assemble a portion of the vehicle, specification of parts and tools to …

Evapotranspiration Modeling And Forecasting For Efficient Management Of Irrigation Command Areas, Roula Bachour

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

It has become very crucial to manage water resources to meet the needs of the growing population. In irrigation command areas, and in order to build a better plan to manage service delivery from canals and reservoirs, it is important to build appropriate knowledge of water needs on a field basis. There is often a lag between the order and delivery of water to the field. Knowledge of the crop water requirement at the field level helps the decision maker to make the right choices leading to more efficient handling of the available water. The purpose of this study was …

Automation Of Energy Demand Forecasting, Sanzad Siddique

Master's Theses (2009 -)

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning …

Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing, Samir Al-Stouhi

Wayne State University Dissertations

As machine learning methods extend to more complex and diverse set of problems, situations arise where the complexity and availability of data presents a situation where the information source is not "adequate" to generate a representative hypothesis. Learning from multiple sources of data is a promising research direction as researchers leverage ever more diverse sources of information. Since data is not readily available, knowledge has to be transferred from other sources and new methods (both supervised and un-supervised) have to be developed to selectively share and transfer knowledge. In this dissertation, we present both supervised and un-supervised techniques to tackle …

Energy Efficient Context-Aware Framework In Mobile Sensing, Ozgur Yurur

USF Tampa Graduate Theses and Dissertations

The ever-increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor design and deployment, have enabled mobile devices (e.g., smartphones) to recognize daily occurring human based actions, activities and interactions. Therefore, inferring a vast variety of mobile device user based activities from a very diverse context obtained by a series of sensory observations has drawn much interest in the research area of ubiquitous sensing. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users, and this allows network services to respond proactively and intelligently …

Predictive Pattern Discovery In Dynamic Data Systems, Wenjing Zhang

Dissertations (1934 -)

This dissertation presents novel methods for analyzing nonlinear time series in dynamic systems. The purpose of the newly developed methods is to address the event prediction problem through modeling of predictive patterns. Firstly, a novel categorization mechanism is introduced to characterize different underlying states in the system. A new hybrid method was developed utilizing both generative and discriminative models to address the event prediction problem through optimization in multivariate systems.

Secondly, in addition to modeling temporal dynamics, a Bayesian approach is employed to model the first-order Markov behavior in the multivariate data sequences. Experimental evaluations demonstrated superior performance over conventional …

Model Individualization For Real-Time Operator Functional State Assessment, Guangfan Zhang, Roger Xu, Wei Wang, Aaron A. Pepe, Feng Li, Jiang Li, Frederick Mckenzie, Tom Schnell, Nick Anderson, Dean Heitkamp

Electrical & Computer Engineering Faculty Publications

Proper assessment of Operator Functional State (OFS) and appropriate workload modulation offer the potential to improve mission effectiveness and aviation safety in both overload and under-load conditions. Although a wide range of research has been devoted to building OFS assessment models, most of the models are based on group statistics and little or no research has been directed towards model individualization, i.e., tuning the group statistics based model for individual pilots. Moreover, little emphasis has been placed on monitoring whether the pilot is disengaged during low workload conditions. The primary focus of this research is to provide a real-time engagement …

Multivariate Bayesian Machine Learning Regression For Operation And Management Of Multiple Reservoir, Irrigation Canal, And River Systems, Andres M. Ticlavilca

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The principal objective of this dissertation is to develop Bayesian machine learning models for multiple reservoir, irrigation canal, and river system operation and management. These types of models are derived from the emerging area of machine learning theory; they are characterized by their ability to capture the underlying physics of the system simply by examination of the measured system inputs and outputs. They can be used to provide probabilistic predictions of system behavior using only historical data. The models were developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a sparse Bayesian learning machine …

Biosignal Processing Challenges In Emotion Recognitionfor Adaptive Learning, Aniket Vartak

Electronic Theses and Dissertations

User-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of the cognitive and emotional state of the learner using such systems. This capability moves development beyond the use of traditional user performance metrics to include system intelligence measures …

Design And Validation Of A Sensor Integration And Feature Fusion Test-Bed For Image-Based Pattern Recognition Applications, Hrishikesh Karvir

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We integrated a sensor hardware test-bed using scientific grade, commercial off-the-shelf (COTS) technology and developed supporting software to enable rapid prototyping. The validity of this test-bed and associated software was demonstrated through the delivery of a ground-based multispectral visual surveillance prototype for improvised explosive devices (IED) detection using electro-optical (EO) and short-wave infrared (SWIR) cameras. Software developed to support the test-bed included modules for image acquisition, preconditioning, segmentation, feature extraction, data regularization and pattern recognition. To provide spatially co-aligned data, we optimized a mutual information-based image registration algorithm to improve its convergence rate and benchmarked it against the established simplex …

Accuracy And Multi-Core Performance Of Machine Learning Algorithms For Handwritten Character Recognition, Sumod Mohan

All Theses

There have been considerable developments in the quest for intelligent machines since the beginning of the cybernetics revolution and the advent of computers. In the last two decades with the onset of the internet the developments have been extensive. This quest for building intelligent machines have led into research on the working of human brain, which has in turn led to the development of pattern recognition models which take inspiration in their structure and performance from biological neural networks. Research in creating intelligent systems poses two main problems. The first one is to develop algorithms which can generalize and predict …

Advances In Neural Networks Research: An Introduction, Robert Kozma, Steven Bressler, Leonid Perlovsky, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

The present Special Issue "Advances in Neural Networks Research: IJCNN2009" provides a state-of-art overview of the field of neural networks. It includes 39 papers from selected areas of the 2009 International Joint Conference on Neural Networks (IJCNN2009). IJCNN2009 took place on June 14-19, 2009, in Atlanta, Georgia, USA, and it represents an exemplary collaboration between the International Neural Networks Society and the IEEE Computational Intelligence Society. Topics in this issue include neuroscience and cognitive science, computational intelligence and machine learning, hybrid techniques, nonlinear dynamics and chaos, various soft computing technologies, intelligent signal processing and pattern recognition, bioinformatics and biomedicine, and …

Falconet: Force-Feedback Approach For Learning From Coaching And Observation Using Natural And Experiential Training, Gary Stein

Electronic Theses and Dissertations

Building an intelligent agent model from scratch is a difficult task. Thus, it would be preferable to have an automated process perform this task. There have been many manual and automatic techniques, however, each of these has various issues with obtaining, organizing, or making use of the data. Additionally, it can be difficult to get perfect data or, once the data is obtained, impractical to get a human subject to explain why some action was performed. Because of these problems, machine learning from observation emerged to produce agent models based on observational data. Learning from observation uses unobtrusive and purely …

Contextualizing Observational Data For Modeling Human Performance, Viet Trinh

Electronic Theses and Dissertations

This research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when performing a mission to facilitate the learning of such CxBR models. This research is derived from the contextualization problem left behind in Fernlund's research on using the Genetic Context Learner …