Mono-Sized Sphere Packing Algorithm Development Using Optimized Monte Carlo Technique,
2013
University of Nevada, Las Vegas
Mono-Sized Sphere Packing Algorithm Development Using Optimized Monte Carlo Technique, Karn Soontrapa, Yitung Chen
College of Engineering: Graduate Celebration Programs
In this research, fuel cell catalyst layer was developed using the optimized sphere packing algorithm. An optimization technique named adaptive random search technique (ARSET) was employed in this packing algorithm. The ARSET algorithm will generate the initial location of spheres and allow them to move in the random direction with the variable moving distance, randomly selected from the sampling range (a), based on the Lennard–Jones potential and Morse potential of the current and new configuration. The solid fraction values obtained from this developed algorithm are in the range of 0.610–0.624 while the actual processing time can significantly be reduced by …
Operating System Scheduling: Linux Preemptive Scheduling Algorithms,
2013
Bemidji State University
Operating System Scheduling: Linux Preemptive Scheduling Algorithms, Andrew Brand
Honors Capstones
Capstone submitted as a graduation requirement for the BSU Honors Program.
Modeling Social Information Learning Among Taxi Drivers,
2013
Carnegie Mellon University
Modeling Social Information Learning Among Taxi Drivers, Siyuan Liu, Ramayya Krishnan, Emma Brunskill, Lionel Ni
Research Collection School Of Computing and Information Systems
When a taxi driver of an unoccupied taxi is seeking passengers on a road unknown to him or her in a large city, what should the driver do? Alternatives include cruising around the road or waiting for a time period at the roadside in the hopes of finding a passenger or just leaving for another road enroute to a destination he knows (e.g., hotel taxi rank)? This is an interesting problem that arises everyday in many cities worldwide. There could be different answers to the question poised above, but one fundamental problem is how the driver learns about the likelihood …
Impact Of Primary User Activity On The Performance Of Energy-Based Spectrum Sensing In Cognitive Radio Systems,
2013
Old Dominion University
Impact Of Primary User Activity On The Performance Of Energy-Based Spectrum Sensing In Cognitive Radio Systems, Sara L. Macdonald
Electrical & Computer Engineering Theses & Dissertations
Increasing numbers of wireless devices and mobile data requirements have led to a spectrum shortage. However spectrum utilization percentages are often low due to the current static spectrum allocation process where primary users (PUs) are given exclusive use to spectrum. Several mechanisms to increase spectrum utilization have been proposed including opportunistic spectrum access (OSA). Cognitive Radio (CR) is an emerging concept in wireless communication systems that aims to enable OSA in licensed frequencies by secondary users (SUs). CR systems are expected to sense the spectrum in order to determine if the PU is transmitting. Therefore OSA performance relies on the …
Artificial Immune Systems And Particle Swarm Optimization For Solutions To The General Adversarial Agents Problem,
2013
Western Michigan University
Artificial Immune Systems And Particle Swarm Optimization For Solutions To The General Adversarial Agents Problem, Jeremy Mange
Dissertations
The general adversarial agents problem is an abstract problem description touching on the fields of Artificial Intelligence, machine learning, decision theory, and game theory. The goal of the problem is, given one or more mobile agents, each identified as either “friendly" or “enemy", along with a specified environment state, to choose an action or series of actions from all possible valid choices for the next “timestep" or series thereof, in order to lead toward a specified outcome or set of outcomes. This dissertation explores approaches to this problem utilizing Artificial Immune Systems, Particle Swarm Optimization, and hybrid approaches, along with …
Confidence Weighted Mean Reversion Strategy For Online Portfolio Selection,
2013
Nanyang Technological University
Confidence Weighted Mean Reversion Strategy For Online Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan
Research Collection School Of Computing and Information Systems
Online portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing online portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online portfolio selection strategy named Confidence Weighted Mean Reversion (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR …
Fault-Tolerant Coverage In Dense Wireless Sensor Networks,
2013
Ursinus College
Fault-Tolerant Coverage In Dense Wireless Sensor Networks, Akshaye Dhawan, Magdalena Parks
Mathematics, Computer Science & Statistics Faculty Publications
In this paper, we present methods to detect and recover from sensor failure in dense wireless sensor networks. In order to extend the lifetime of a sensor network while maintaining coverage, a minimal subset of the deployed sensors are kept active while the other sensors can enter a low power sleep state. Several distributed algorithms for coverage have been proposed in the literature. Faults are of particular concern in coverage algorithms since sensors go into a sleep state in order to conserve battery until woken up by active sensors. If these active sensors were to fail, this could lead to …
Interpreting Individual Classifications Of Hierarchical Networks,
2013
Portland State University
Interpreting Individual Classifications Of Hierarchical Networks, Will Landecker, Michael David Thomure, Luis M.A. Bettencourt, Melanie Mitchell, Garrett T. Kenyon, Steven P. Brumby
Computer Science Faculty Publications and Presentations
Hierarchical networks are known to achieve high classification accuracy on difficult machine-learning tasks. For many applications, a clear explanation of why the data was classified a certain way is just as important as the classification itself. However, the complexity of hierarchical networks makes them ill-suited for existing explanation methods. We propose a new method, contribution propagation, that gives per-instance explanations of a trained network's classifications. We give theoretical foundations for the proposed method, and evaluate its correctness empirically. Finally, we use the resulting explanations to reveal unexpected behavior of networks that achieve high accuracy on visual object-recognition tasks using well-known …
Using Mapreduce Streaming For Distributed Life Simulation On The Cloud,
2013
Chapman University
Using Mapreduce Streaming For Distributed Life Simulation On The Cloud, Atanas Radenski
Mathematics, Physics, and Computer Science Faculty Books and Book Chapters
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable …
Gpu-Optimized Code For Long-Term Simulations Of Beam-Beam Effects In Colliders,
2013
Old Dominion University
Gpu-Optimized Code For Long-Term Simulations Of Beam-Beam Effects In Colliders, Y. Roblin, V. Morozov, B. Terzić, M. Aturban, D. Ranjan, M. Zubair
Computer Science Faculty Publications
We report on the development of the new code for long-term simulation of beam-beam effects in particle colliders. The underlying physical model relies on a matrix-based arbitrary-order symplectic particle tracking for beam transport and the Bassetti-Erskine approximation for beam-beam interaction. The computations are accelerated through a parallel implementation on a hybrid GPU/CPU platform. With the new code, a previously computationally prohibitive long-term simulations become tractable. We use the new code to model the proposed medium-energy electron-ion collider (MEIC) at Jefferson Lab.
Accelerated Data Delivery Architecture,
2013
Georgia Southern University
Accelerated Data Delivery Architecture, Michael L. Grecol
College of Graduate Studies: Theses & Dissertations
This paper introduces the Accelerated Data Delivery Architecture (ADDA). ADDA establishes a framework to distribute transactional data and control consistency to achieve fast access to data, distributed scalability and non-blocking concurrency control by using a clean declarative interface. It is designed to be used with web-based business applications. This framework uses a combination of traditional Relational Database Management System (RDBMS) combined with a distributed Not Only SQL (NoSQL) database and a browser-based database. It uses a single physical and conceptual database schema designed for a standard RDBMS driven application. The design allows the architect to assign consistency levels to entities …
A Parallel Template For Implementing Filters For Biological Correlation Networks,
2013
University of Nebraska at Omaha
A Parallel Template For Implementing Filters For Biological Correlation Networks, Kathryn Dempsey Cooper, Vladimir Ufimtsev, Sanjukta Bhowmick, Hesham Ali
Interdisciplinary Informatics Faculty Publications
High throughput biological experiments are critical for their role in systems biology – the ability to survey the state of cellular mechanisms on the broad scale opens possibilities for the scientific researcher to understand how multiple components come together, and what goes wrong in disease states. However, the data returned from these experiments is massive and heterogeneous, and requires intuitive and clever computational algorithms for analysis. The correlation network model has been proposed as a tool for modeling and analysis of this high throughput data; structures within the model identified by graph theory have been found to represent key players …
Automatic Detection Of Abnormal Behavior In Computing Systems,
2013
University of Kentucky
Automatic Detection Of Abnormal Behavior In Computing Systems, James Frank Roberts
Theses and Dissertations--Computer Science
I present RAACD, a software suite that detects misbehaving computers in large computing systems and presents information about those machines to the system administrator. I build this system using preexisting anomaly detection techniques. I evaluate my methods using simple synthesized data, real data containing coerced abnormal behavior, and real data containing naturally occurring abnormal behavior. I find that the system adequately detects abnormal behavior and significantly reduces the amount of uninteresting computer health data presented to a system administrator.
Analyzing The Impact Of Cloud Services Brokers On Cloud Computing Markets,
2013
Institute of High Performance Computing, A*STAR, Singapore
Analyzing The Impact Of Cloud Services Brokers On Cloud Computing Markets, Richard D. Shang, Jianhui Huang, Yinping Yang, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
This research offers a theoretical model of brokered services and provides an analysis of their impact on the cloud computing market with risk preference-based stratification of client segments. The model structures the decision problem that clients face when they choose among spot, reserved and brokered services. Although all the three types of services do not indemnify the cloud services client against other kinds of service outages, due to changes in market demand, service interruptions occur most frequently in the spot market, and are lower when brokered services are offered, and no risk of inter-ruption is involved in reserved services. Based …
Hyperspectral Image Classification Using A Spectral-Spatial Sparse Coding Model,
2013
Old Dominion University
Hyperspectral Image Classification Using A Spectral-Spatial Sparse Coding Model, Ender Oguslu, Guoqing Zhou, Jiang Li, Lorenzo Bruzzone (Ed.)
Electrical & Computer Engineering Faculty Publications
We present a sparse coding based spectral-spatial classification model for hyperspectral image (HSI) datasets. The proposed method consists of an efficient sparse coding method in which the l1/lq regularized multi-class logistic regression technique was utilized to achieve a compact representation of hyperspectral image pixels for land cover classification. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center and compared our algorithm to a recently proposed method, Gaussian process maximum likelihood (GP-ML) classifier. Experimental results show that the proposed method can achieve significantly better performances than the GP-ML classifier when training data …
Usefulness Of Infeasible Solutions In Evolutionary Search: An Empirical And Mathematical Study,
2013
Edith Cowan University
Usefulness Of Infeasible Solutions In Evolutionary Search: An Empirical And Mathematical Study, Lyndon While, Philip Hingston
Research outputs 2013
When evolutionary algorithms are used to solve constrained optimization problems, the question arises how best to deal with infeasible solutions in the search space. A recent theoretical analysis of two simple test problems argued that allowing infeasible solutions to persist in the population can either help or hinder the search process, depending on the structure of the fitness landscape. We report new empirical and mathematical analyses that provide a different interpretation of the previous theoretical predictions: that the important effect is on the probability of finding the global optimum, rather than on the time complexity of the algorithm. We also …
Connotational Subtyping And Runtime Class Mutability In Ruby,
2012
East Tennessee State University
Connotational Subtyping And Runtime Class Mutability In Ruby, Ian S. Dillon
Electronic Theses and Dissertations
Connotational subtyping is an approach to typing that allows an object's type to change dynamically, following changes to the object's internal state. This allows for a more precise representation of a problem domain with logical objects that have variable behavior. Two approaches to supporting connotational subtyping in the Ruby programming language were implemented: a language-level implementation using pure Ruby and a modification to the Ruby 1.8.7 interpreter. While neither implementation was wholly successful the language level implementation created complications with reflective language features like self and super and, while Ruby 1.8.7 has been obsoleted by Ruby 1.9 (YARV), the results …
Application Of Digital Forensic Science To Electronic Discovery In Civil Litigation,
2012
LSU New Orleans
Application Of Digital Forensic Science To Electronic Discovery In Civil Litigation, Brian Roux
LSU New Orleans Theses and Dissertations
Following changes to the Federal Rules of Civil Procedure in 2006 dealing with the role of Electronically Stored Information, digital forensics is becoming necessary to the discovery process in civil litigation. The development of case law interpreting the rule changes since their enactment defines how digital forensics can be applied to the discovery process, the scope of discovery, and the duties imposed on parties. Herein, pertinent cases are examined to determine what trends exist and how they effect the field. These observations buttress case studies involving discovery failures in large corporate contexts along with insights on the technical reasons those …
Contour Extraction Of Drosophila Embryos Using Active Contours In Scale Space,
2012
Western Kentucky University
Contour Extraction Of Drosophila Embryos Using Active Contours In Scale Space, Soujanya Siddavaram Ananta
Masters Theses & Specialist Projects
Contour extraction of Drosophila embryos is an important step to build a computational system for pattern matching of embryonic images which aids in the discovery of genes. Automatic contour extraction of embryos is challenging due to several image variations such as size, shape, orientation and neigh- boring embryos such as touching and non-touching embryos. In this thesis, we introduce a framework for contour extraction based on the connected components in the gaussian scale space of an embryonic image. The active contour model is applied on the images to refine embryo contours. Data cleaning methods are applied to smooth the jaggy …
Hardware-Software Co-Design, Acceleration And Prototyping Of Control Algorithms On Reconfigurable Platforms,
2012
University of Nevada, Las Vegas
Hardware-Software Co-Design, Acceleration And Prototyping Of Control Algorithms On Reconfigurable Platforms, Desta Kumsa Edosa
UNLV Theses, Dissertations, Professional Papers, and Capstones
Differential equations play a significant role in many disciplines of science and engineering. Solving and implementing Ordinary Differential Equations (ODEs) and partial Differential Equations (PDEs) effectively are very essential as most complex dynamic systems are modeled based on these equations. High Performance Computing (HPC) methodologies are required to compute and implement complex and data intensive applications modeled by differential equations at higher speed. There are, however, some challenges and limitations in implementing dynamic system, modeled by non-linear ordinary differential equations, on digital hardware. Modeling an integrator involves data approximation which results in accuracy error if data values are not considered …
