Engineering Commons^™

On Applications Of Relational Data, Samamon Khemmarat

Doctoral Dissertations

With the advances of technology and the popularity of the Internet, a large amount of data is being generated and collected. Much of these data is relational data, which describe how people and things, or entities, are related to one another. For example, data from sale transactions on e-commerce websites tell us which customers buy or view which products. Analyzing the known relationships from relational data can help us to discover knowledge that can benefit businesses, organizations, and our lives. For instance, learning the products that are commonly bought together allows businesses to recommend products to customers and increase their …

Fizzy: Feature Subset Selection For Metagenomics., Gregory Ditzler, J Calvin Morrison, Yemin Lan, Gail L Rosen

Henry M. Rowan College of Engineering Faculty Scholarship

BACKGROUND: Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α- & β-diversity. Feature subset selection--a sub-field of machine learning--can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the …

Optimizing Macd Parameters Via Genetic Algorithms For Soybean Futures, Phoebe S. Wiles, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

To create profits, traders must time the market correctly and enter and exit positions at ideal times. Finding the optimal time to enter the market can be quite daunting. The soybean market can be volatile and complex. Weather, sentiment, supply, and demand can all affect the price of soybeans. Traders typically use either fundamental analysis or technical analysis to predict the market for soybean futures' contracts. Every agricultural future's contract or security contract is different in its nature, volatility, and structure. Therefore, the purpose of this research is to optimize the moving average convergence divergence parameter values from traditionally used …

Filters And Matrix Factorization, Myung-Sin Song, Palle E. T. Jorgensen

SIUE Faculty Research, Scholarship, and Creative Activity

We give a number of explicit matrix-algorithms for analysis/synthesis

in multi-phase filtering; i.e., the operation on discrete-time signals which

allow a separation into frequency-band components, one for each of the

ranges of bands, say N , starting with low-pass, and then corresponding

filtering in the other band-ranges. If there are N bands, the individual

filters will be combined into a single matrix action; so a representation of

the combined operation on all N bands by an N x N matrix, where the

corresponding matrix-entries are periodic functions; or their extensions to

functions of a complex variable. Hence our setting entails …

An Immersed Boundary Geometric Preprocessor For Arbitrarily Complex Terrain And Geometry, Inanc Senocak, Micah Sandusky, Rey Deleon, Derek Wade, Kyle Felzien, Marianna Budnikova

Mechanical and Biomedical Engineering Faculty Publications and Presentations

There is a growing interest to apply the immersed boundary method to compute wind fields over arbitrarily complex terrain. The computer implementation of an immersed boundary module into an existing flow solver can be accomplished with minor modifications to the rest of the computer program. However, a versatile preprocessor is needed at the first place to extract the essential geometric information pertinent to the immersion of an arbitrarily complex terrain inside a 3D Cartesian mesh. Errors in the geometric information can negatively impact the correct implementation of the immersed boundary method as part of the solution algorithm. Additionally, the distance …

Logarithmic Intensity Compression In Fluorescence Guided Surgery Applications, Alisha V. Dsouza, Huiyun Lin, Jason Gunn, Brian W. Pogue

Dartmouth Scholarship

The use of fluorescence video imaging to guide surgery is rapidly expanding, and improvements in camera readout dynamic range have not matched display capabilities. Logarithmic intensity compression is a fast, single-step mapping technique that can map the useable dynamic range of high-bit fluorescence images onto the typical 8-bit display and potentially be a variable dynamic contrast enhancement tool. We demonstrate a ∼4.6  times improvement in image quality quantified by image entropy and a dynamic range reduction by a factor of ∼380 by the use of log-compression tools in processing in vivo fluorescence images.

Evaluation Of Data-Path Topologies For Self-Timed Conditional Statements, Navaneeth Prasannakumar Jamadagni

Dissertations and Theses

This research presents a methodology to evaluate data path topologies that implement a conditional statement for an average-case performance that is better than the worst-case performance. A conditional statement executes one of many alternatives depending on how Boolean conditions evaluate to true or false. Alternatives with simple computations take less time to execute. The self-timed designs can exploit the faster executing alternatives and provide an average-case behavior, where the average depends on the frequency of simple and complex computations, and the difference in the completion times of simple and complex computations. The frequency of simple and complex computations depends on …

3d Thoracoscopic Ultrasound Volume Measurement Validation In An Ex Vivo And In Vivo Porcine Model Of Lung Tumours, V. Hornblower, E. Yu, A. Fenster, J. Battista, R. Malthaner

Richard A. Malthaner

The purpose of this study was to validate the accuracy and reliability of volume measurements obtained using three-dimensional (3D) thoracoscopic ultrasound (US) imaging. Artificial "tumours" were created by injecting a liquid agar mixture into spherical moulds of known volume. Once solidified, the "tumours" were implanted into the lung tissue in both a porcine lung sample ex vivo and a surgical porcine model in vivo. 3D US images were created by mechanically rotating the thoracoscopic ultrasound probe about its long axis while the transducer was maintained in close contact with the tissue. Volume measurements were made by one observer using the …

3d Thoracoscopic Ultrasound Volume Measurement Validation In An Ex Vivo And In Vivo Porcine Model Of Lung Tumours, V. Hornblower, E. Yu, A. Fenster, J. Battista, R. Malthaner

Richard A. Malthaner

The purpose of this study was to validate the accuracy and reliability of volume measurements obtained using three-dimensional (3D) thoracoscopic ultrasound (US) imaging. Artificial "tumours" were created by injecting a liquid agar mixture into spherical moulds of known volume. Once solidified, the "tumours" were implanted into the lung tissue in both a porcine lung sample ex vivo and a surgical porcine model in vivo. 3D US images were created by mechanically rotating the thoracoscopic ultrasound probe about its long axis while the transducer was maintained in close contact with the tissue. Volume measurements were made by one observer using the …

Probabilistic Inference Based Message-Passing For Resource Constrained Dcops, Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Distributed constraint optimization (DCOP) is an important framework for coordinated multiagent decision making. We address a practically useful variant of DCOP, called resource-constrained DCOP (RC-DCOP), which takes into account agents’ consumption of shared limited resources. We present a promising new class of algorithm for RC-DCOPs by translating the underlying co- ordination problem to probabilistic inference. Using inference techniques such as expectation- maximization and convex optimization machinery, we develop a novel convergent message-passing algorithm for RC-DCOPs. Experiments on standard benchmarks show that our approach provides better quality than previous best DCOP algorithms and has much lower failure rate. Comparisons against an …

Optimization Of Image Reconstruction For Magnetic Resonance Imaging–Guided Near-Infrared Diffuse Optical Spectroscopy In Breast, Yan Zhao, Michael A. Mastanduno, Shudong Jiang, Fadi Ei-Ghussein, Jiang Gui, Brian W. Pogue, Keith D. Paulsen

Dartmouth Scholarship

An optimized approach to nonlinear iterative reconstruction of magnetic resonance imaging (MRI)–guided near-infrared spectral tomography (NIRST) images was developed using an L-curve-based algorithm for the choice of regularization parameter. This approach was applied to clinical exam data to maximize the reconstructed values differentiating malignant and benign lesions. MRI/NIRST data from 25 patients with abnormal breast readings (BI-RADS category 4-5) were analyzed using this optimal regularization methodology, and the results showed enhanced p values and area under the curve (AUC) for the task of differentiating malignant from benign lesions. Of the four absorption parameters and two scatter parameters, the most significant …

Discrete And Continuous Sparse Recovery Methods And Their Applications, Zhao Tan

McKelvey School of Engineering Theses & Dissertations

Low dimensional signal processing has drawn an increasingly broad amount of attention in the past decade, because prior information about a low-dimensional space can be exploited to aid in the recovery of the signal of interest. Among all the different forms of low di- mensionality, in this dissertation we focus on the synthesis and analysis models of sparse recovery. This dissertation comprises two major topics. For the first topic, we discuss the synthesis model of sparse recovery and consider the dictionary mismatches in the model. We further introduce a continuous sparse recovery to eliminate the existing off-grid mismatches for DOA …

Improved And Generalized Learning Strategies For Dynamically Fast And Statistically Robust Evolutionary Algorithms, Yogesh Dashora, Sanjeev Kumar, Nagesh Shukla, M K. Tiwari

Nagesh Shukla

This paper characterizes general optimization problems into four categories based on the solution representation schemes, as they have been the key to the design of various evolutionary algorithms (EAs). Four EAs have been designed for different formulations with the aim of utilizing similar and generalized strategies for all of them. Several modifications to the existing EAs have been proposed and studied. First, a new tradeoff function-based mutation has been proposed that takes advantages of Cauchy, Gaussian, random as well as chaotic mutations. In addition, a generalized learning rule has also been proposed to ensure more thorough and explorative search. A …

Genetic-Algorithms-Based Algorithm Portfolio For Inventory Routing Problem With Stochastic Demand, Nagesh Shukla, M Tiwari, Darek Ceglarek

Nagesh Shukla

This paper presents an algorithm portfolio methodology based on evolutionary algorithms to solve complex dynamic optimization problems. These problems are known to have computationally complex objective functions which make their solutions to be computationally hard to find, when problem instances of large dimensions are considered. This is due to the inability of the algorithms to provide optimal or near optimal solution within allocated time interval. Therefore, this paper employs a bundle of evolutionary algorithms (EAs) tied together with several processors, known as algorithm portfolio, to solve a complex optimization problem such as inventory routing problem (IRP) with stochastic demands. EAs …

Application Of Intelligent Sensors In The Integrated Systems Health Monitoring Of A Rocket Test Stand, Ajay Mahajan, Sanjeevi Chitikeshi, Lucas Utterback, Pavan Bandhil, Fernando Figueroa

Dr. Ajay Mahajan

This paper describes the application of intelligent sensors in the Integrated Systems Health Monitoring (ISHM) as applied to a rocket test stand. The development of intelligent sensors is attempted as an integrated system approach, i.e. one treats the sensors as a complete system with its own physical transducer, A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow them to get better with time. Under a project being undertaken at the NASA Stennis Space Center, an integrated framework is being developed for the intelligent monitoring of smart elements associated with the rocket tests …

Identifying Image Manipulation Software From Image Features, Devlin T. Boyter

Theses and Dissertations

As technology steadily increases in the field of image manipulation, determining which software was used to manipulate an image becomes increasingly complex for law enforcement and intelligence agencies. To combat this difficult problem, new techniques that examine the artifacts left behind by a specific manipulation are converted to features for classification. This research implemented four preexisting image manipulation detection techniques into a framework of modules: Two-Dimensional Second Derivative, One-Dimensional Zero Crossings, Quantization Matrices Identification, and File Metadata analysis. The intent is the creation of a framework to develop a capability to determine which specific image manipulation software program manipulated an …

Microscale Magnetic Field Modulation For Enhanced Capture And Distribution Of Rare Circulating Tumor Cells, Peng Chen, Yu-Yen Huang, Kazunori Hoshino, John X.J Zhang

Dartmouth Scholarship

Immunomagnetic assay combines the powers of the magnetic separation and biomarker recognition and has been an effective tool to perform rare Circulating Tumor Cells detection. Key factors associated with immunomagnetic assay include the capture rate, which indicates the sensitivity of the system, and distributions of target cells after capture, which impact the cell integrity and other biological properties that are critical to downstream analyses. Here we present a theoretical framework and technical approach to implement a microscale magnetic immunoassay through modulating local magnetic field towards enhanced capture and distribution of rare cancer cells. Through the design of a two-dimensional micromagnet …

A Distributed Particle Filtering Approach For Multiple Acoustic Source Tracking Using An Acoustic Vector Sensor Network

Faculty of Engineering University of Malaya

Different centralized approaches such as least-squares (LS) and particle filtering (PF) algorithms have been developed to localize an acoustic source by using a distributed acoustic vector sensor (AVS) array. However, such algorithms are either not applicable for multiple sources or rely heavily on sensor-processor communication. In this paper, a distributed unscented PF (DUPF) approach is proposed for multiple acoustic source tracking. At each distributed AVS node, the first-order and the second-order statistics of the local state are estimated by using an unscented information filter (UIF) based PF. The UIF is employed to approximate the optimum importance function due to its …

A Computational Intelligence Approach To System-Of-Systems Architecting Incorporating Multi-Objective Optimization, David M. Curry, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions …

Modeling User Transportation Patterns Using Mobile Devices, Erfan Davami

Electronic Theses and Dissertations

Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data. Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing …