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Full-Text Articles in Computational Engineering

Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam Apr 2019

Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required derived the degrees of ...


Chip-Off Success Rate Analysis Comparing Temperature And Chip Type, Choli Ence, Joan Runs Through, Gary D. Cantrell Feb 2019

Chip-Off Success Rate Analysis Comparing Temperature And Chip Type, Choli Ence, Joan Runs Through, Gary D. Cantrell

Journal of Digital Forensics, Security and Law

Throughout the digital forensic community, chip-off analysis provides examiners with a technique to obtain a physical acquisition from locked or damaged digital device. Thermal based chip-analysis relies upon the application of heat to remove the flash memory chip from the circuit board. Occasionally, a flash memory chip fails to successfully read despite following similar protocols as other flash memory chips. Previous research found the application of high temperatures increased the number of bit errors present in the flash memory chip. The purpose of this study is to analyze data collected from chip-off analyses to determine if a statistical difference exists ...


Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams Jan 2019

Predicting Dynamic Modulus Of Asphalt Mixture Using Data Obtained From Indirect Tension Mode Of Testing, Parnian Ghasemi, Shibin Lin, Derrick K. Rollins, R. Christopher Williams

Chemical and Biological Engineering Publications

Understanding stress-strain behavior of asphalt pavement under repetitive traffic loading is of critical importance to predict pavement performance and service life. For viscoelastic materials, the stress-strain relationship can be represented by the dynamic modulus. The dynamic modulus test in indirect tension mode can be used to measure the modulus of each specific layer of asphalt pavements using representative samples. Dynamic modulus is a function of material properties, loading, and environmental conditions. Developing predictive models for dynamic modulus is efficient and cost effective. This article focuses on developing an accurate Finite Element (FE) model using mixture elastic modulus and asphalt binder ...


Call For Abstracts - Resrb 2019, July 8-9, Wrocław, Poland, Wojciech M. Budzianowski Dec 2018

Call For Abstracts - Resrb 2019, July 8-9, Wrocław, Poland, Wojciech M. Budzianowski

Wojciech Budzianowski

No abstract provided.


Secured Data Masking Framework And Technique For Preserving Privacy In A Business Intelligence Analytics Platform, Osama Ali Dec 2018

Secured Data Masking Framework And Technique For Preserving Privacy In A Business Intelligence Analytics Platform, Osama Ali

Electronic Thesis and Dissertation Repository

The main concept behind business intelligence (BI) is how to use integrated data across different business systems within an enterprise to make strategic decisions. It is difficult to map internal and external BI’s users to subsets of the enterprise’s data warehouse (DW), resulting that protecting the privacy of this data while maintaining its utility is a challenging task. Today, such DW systems constitute one of the most serious privacy breach threats that an enterprise might face when many internal users of different security levels have access to BI components. This thesis proposes a data masking framework (iMaskU: Identify ...


Variable Input Observer For Nonstationary High-Rate Dynamic Systems, Jonathan Hong, Simon Laflamme, Liang Cao, Jacob Dodson, Bryan Joyce Dec 2018

Variable Input Observer For Nonstationary High-Rate Dynamic Systems, Jonathan Hong, Simon Laflamme, Liang Cao, Jacob Dodson, Bryan Joyce

Civil, Construction and Environmental Engineering Publications

Engineering systems experiencing events of amplitudes higher than 100 gn for a duration under 100 ms, here termed high-rate dynamics, can undergo rapid damaging effects. If the structural health of such systems could be accurately estimated in a timely manner, preventative measures could be employed to minimize adverse effects. For complex high-rate problems, adaptive observers have shown promise due to their capability to deal with nonstationary, noisy, and uncertain systems. However, adaptive observers have slow convergence rates, which impede their applicability to the high-rate problems. To improve on the convergence rate, we propose a variable input space concept for ...


A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher Nov 2018

A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher

Conference papers

Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality ...


Fogfly: A Traffic Light Optimization Solution Based On Fog Computing, Quang Tran Minh, Chanh Minh Tran, Tuan An Le, Binh Thai Nguyen, Triet Minh Tran, Rajesh Krishna Balan Oct 2018

Fogfly: A Traffic Light Optimization Solution Based On Fog Computing, Quang Tran Minh, Chanh Minh Tran, Tuan An Le, Binh Thai Nguyen, Triet Minh Tran, Rajesh Krishna Balan

Research Collection School Of Information Systems

This paper provides a fog-based approach to solving the traffic light optimization problem which utilizes the Adaptive Traffic Signal Control (ATSC) model. ATSC systems demand the ability to strictly reflect real-time traffic state. The proposed fog computing framework, namely FogFly, aligns with this requirement by its natures in location-awareness, low latency and affordability to the changes in traffic conditions. As traffic data is updated timely and processed at fog nodes deployed close to data sources (i.e., vehicles at intersections) traffic light cycles can be optimized efficiently while virtualized resources available at network edges are efficiently utilized. Evaluation results show ...


Formalizing Schoenberg’S Fundamentals Of Musical Composition Through Petri Nets, A. Baratè, G. Haus, L. A. Ludovico, Davide Andrea Mauro Jul 2018

Formalizing Schoenberg’S Fundamentals Of Musical Composition Through Petri Nets, A. Baratè, G. Haus, L. A. Ludovico, Davide Andrea Mauro

Weisberg Division of Computer Science Faculty Research

The formalization of musical composition rules is a topic that has been studied for a long time. It can lead to a better understanding of the underlying processes, and provide a useful tool for musicologist to aid and speed up the analysis process. In our attempt we introduce Schoenberg’s rules from Fundamentals of Musical Composition using a specialized version of Petri nets, called Music Petri nets. Petri nets are a formal tool for studying systems that are concurrent, asynchronous, distributed, parallel, nondeterministic, and/or stochastic. We present some examples highlighting how multiple approaches to the analysis task can find ...


Multi Self-Adapting Particle Swarm Optimization Algorithm (Msapso)., Gerhard Koch May 2018

Multi Self-Adapting Particle Swarm Optimization Algorithm (Msapso)., Gerhard Koch

Electronic Theses and Dissertations

The performance and stability of the Particle Swarm Optimization algorithm depends on parameters that are typically tuned manually or adapted based on knowledge from empirical parameter studies. Such parameter selection is ineffectual when faced with a broad range of problem types, which often hinders the adoption of PSO to real world problems. This dissertation develops a dynamic self-optimization approach for the respective parameters (inertia weight, social and cognition). The effects of self-adaption for the optimal balance between superior performance (convergence) and the robustness (divergence) of the algorithm with regard to both simple and complex benchmark functions is investigated. This work ...


Pressure Measurements Inside Multiple Cavities Of A Torque Converter And Cfd Correlation, Edward De Jesus Rivera Jan 2018

Pressure Measurements Inside Multiple Cavities Of A Torque Converter And Cfd Correlation, Edward De Jesus Rivera

Dissertations, Master's Theses and Master's Reports

A torque converter was instrumented with 29 pressure transducers. The pressure transducers were located in multiple cavities. The instrumented cavities included, four transducers mounted on the impeller shell, on the channel between blades. Six transducers mounted on the pressure and suction sides on the middle streamline of a turbine blade. Another seven transducers mounted on the pressure and suction sides of the core, middle and shell streamlines of a stator blade. Seven transducers mounted on the torque converter clutch cavity. Finally, five on the cavity between the pressure plate and the turbine shell. The torque converter was part of a ...


Automated Tree-Level Forest Quantification Using Airborne Lidar, Hamid Hamraz Jan 2018

Automated Tree-Level Forest Quantification Using Airborne Lidar, Hamid Hamraz

Theses and Dissertations--Computer Science

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree ...


High-Order Integral Equation Methods For Quasi-Magnetostatic And Corrosion-Related Field Analysis With Maritime Applications, Robert Pfeiffer Jan 2018

High-Order Integral Equation Methods For Quasi-Magnetostatic And Corrosion-Related Field Analysis With Maritime Applications, Robert Pfeiffer

Theses and Dissertations--Electrical and Computer Engineering

This dissertation presents techniques for high-order simulation of electromagnetic fields, particularly for problems involving ships with ferromagnetic hulls and active corrosion-protection systems.

A set of numerically constrained hexahedral basis functions for volume integral equation discretization is presented in a method-of-moments context. Test simulations demonstrate the accuracy achievable with these functions as well as the improvement brought about in system conditioning when compared to other basis sets.

A general method for converting between a locally-corrected Nyström discretization of an integral equation and a method-of-moments discretization is presented next. Several problems involving conducting and magnetic-conducting materials are solved to verify the accuracy ...


A High Quality, Eulerian 3d Fluid Solver In C++, Lejon Anthony Mcgowan Nov 2017

A High Quality, Eulerian 3d Fluid Solver In C++, Lejon Anthony Mcgowan

Computer Science and Software Engineering

Fluids are a part of everyday life, yet are one of the hardest elements to properly render in computer graphics. Water is the most obvious entity when thinking of what a fluid simulation can achieve (and it is indeed the focus of this project), but many other aspects of nature, like fog, clouds, and particle effects. Real-time graphics like video games employ many heuristics to approximate these effects, but large-scale renderers aim to simulate these effects as closely as possible.

In this project, I wish to achieve effects of the latter nature. Using the Eulerian technique of discrete grids, I ...


Software Metrics And Dashboard, Shilpika Shilpika, George K. Thiruvathukal, Saulo Aguiar, Konstantin Läufer, Nicholas J. Hayward Oct 2017

Software Metrics And Dashboard, Shilpika Shilpika, George K. Thiruvathukal, Saulo Aguiar, Konstantin Läufer, Nicholas J. Hayward

Nicholas Hayward

Software metrics are a critical tool which provide continuous insight to products and processes and help build reliable software in mission critical environments. Using software metrics we can perform calculations that help assess the effectiveness of the underlying software or process. The two types of metrics relevant to our work is complexity metrics and in-process metrics. Complexity metrics tend to focus on intrinsic code properties like code complexity. In-process metrics focus on a higher-level view of software quality, measuring information that can provide insight into the underlying software development process.

Our aim is to develop and evaluate a metrics dashboard ...


Software Metrics And Dashboard, Shilpika Shilpika, George K. Thiruvathukal, Saulo Aguiar, Konstantin Läufer, Nicholas J. Hayward Oct 2017

Software Metrics And Dashboard, Shilpika Shilpika, George K. Thiruvathukal, Saulo Aguiar, Konstantin Läufer, Nicholas J. Hayward

Konstantin Läufer

Software metrics are a critical tool which provide continuous insight to products and processes and help build reliable software in mission critical environments. Using software metrics we can perform calculations that help assess the effectiveness of the underlying software or process. The two types of metrics relevant to our work is complexity metrics and in-process metrics. Complexity metrics tend to focus on intrinsic code properties like code complexity. In-process metrics focus on a higher-level view of software quality, measuring information that can provide insight into the underlying software development process.

Our aim is to develop and evaluate a metrics dashboard ...


Power-Efficient And Highly Scalable Parallel Graph Sampling Using Fpgas, Usman Tariq, Umer Cheema, Fahad Saeed Oct 2017

Power-Efficient And Highly Scalable Parallel Graph Sampling Using Fpgas, Usman Tariq, Umer Cheema, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Energy efficiency is a crucial problem in data centers where big data is generally represented by directed or undirected graphs. Analysis of this big data graph is challenging due to volume and velocity of the data as well as irregular memory access patterns. Graph sampling is one of the most effective ways to reduce the size of graph while maintaining crucial characteristics. In this paper we present design and implementation of an FPGA based graph sampling method which is both time- and energy-efficient. This is in contrast to existing parallel approaches which include memory-distributed clusters, multicore and GPUs. Our ...


Streaming Vr For Immersion: Quality Aspects Of Compressed Spatial Audio, Miroslaw Narbutt, Sean O’Leary, Andrew Allen, Jan Skoglund, Andrew Hines Oct 2017

Streaming Vr For Immersion: Quality Aspects Of Compressed Spatial Audio, Miroslaw Narbutt, Sean O’Leary, Andrew Allen, Jan Skoglund, Andrew Hines

Conference papers

Delivering a 360-degree soundscape that matches full sphere visuals is an essential aspect of immersive VR. Ambisonics is a full sphere surround sound technique that takes into account the azimuth and elevation of sound sources, portraying source location above and below as well as around the horizontal plane of the listener. In contrast to channel-based methods, ambisonics representation offers the advantage of being independent of a specific loudspeaker set-up. Streaming ambisonics over networks requires efficient encoding techniques that compress the raw audio content without compromising quality of experience (QoE). This work investigates the effect of audio channel compression via the ...


Web-Based Interactive Social Media Visual Analytics, Diego Rodríguez-Baquero, Jiawei Zhang, David S. Ebert, Sorin A. Matei Aug 2017

Web-Based Interactive Social Media Visual Analytics, Diego Rodríguez-Baquero, Jiawei Zhang, David S. Ebert, Sorin A. Matei

The Summer Undergraduate Research Fellowship (SURF) Symposium

Real-time social media platforms enable quick information broadcasting and response during disasters and emergencies. Analyzing the massive amount of generated data to understand the human behavior requires data collection and acquisition, parsing, filtering, augmentation, processing, and representation. Visual analytics approaches allow decision makers to observe trends and abnormalities, correlate them with other variables and gain invaluable insight into these situations. In this paper, we propose a set of visual analytic tools for analyzing and understanding real-time social media data in times of crisis and emergency situations. First, we model the degree of risk of individuals’ movement based on evacuation zones ...


Visually Analyzing The Impacts Of Essential Air Service Funding Decisions, Rohan Kashuka, Chittayong Surakitbanharn, Calvin Yau, David S. Ebert Aug 2017

Visually Analyzing The Impacts Of Essential Air Service Funding Decisions, Rohan Kashuka, Chittayong Surakitbanharn, Calvin Yau, David S. Ebert

The Summer Undergraduate Research Fellowship (SURF) Symposium

Essential Air Service (EAS) is a U.S. government subsidy program which ensures maintenance of commercial airline services in small deregulated communities. The program’s budget currently is around $250 million annually, which is used as subsidy for airlines to maintain a minimal level of scheduled air service in relatively smaller airports. It is evident that 2% of the FAA budget is being spent to maintain air service in smaller communities, but there is not enough evidence to prove that all the current decisions made by Congress about EAS are advantageous. To understand these decisions, 15 years of data produced ...


Improving Predictive Capabilities Of Classical Cascade Theory For Nonproliferation Analysis, David Allen Vermillion May 2017

Improving Predictive Capabilities Of Classical Cascade Theory For Nonproliferation Analysis, David Allen Vermillion

Doctoral Dissertations

Uranium enrichment finds a direct and indispensable function in both peaceful and nonpeaceful nuclear applications. Today, over 99% of enriched uranium is produced by gas centrifuge technology. With the international dissemination of the Zippe archetypal design in 1960 followed by the widespread illicit centrifuge trafficking efforts of the A.Q. Khan network, traditional barriers to enrichment technologies are no longer as effective as they once were. Consequently, gas centrifuge technology is now regarded as a high-priority nuclear proliferation threat, and the international nonproliferation community seeks new avenues to effectively and efficiently respond to this emergent threat.

Effective response first requires ...


Target Detection With Neural Network Hardware, Hollis Bui, Garrett Massman, Nikolas Spangler, Jalen Tarvin, Luke Bechtel, Kevin Dunn, Shawn Bradford May 2017

Target Detection With Neural Network Hardware, Hollis Bui, Garrett Massman, Nikolas Spangler, Jalen Tarvin, Luke Bechtel, Kevin Dunn, Shawn Bradford

Chancellor’s Honors Program Projects

No abstract provided.


Simulating Foodborne Pathogens In Poultry Production And Processing To Defend Against Intentional Contamination, Silas B. Lankford May 2017

Simulating Foodborne Pathogens In Poultry Production And Processing To Defend Against Intentional Contamination, Silas B. Lankford

Computer Science and Computer Engineering Undergraduate Honors Theses

There is a lack of data in recent history of food terrorism attacks, and as such, it is difficult to predict its impact. The food supply industry is one of the most vulnerable industries for terrorist threats while the poultry industry is one of the largest food industries in the United States. A small food terrorism attack against just a single poultry processing center has the potential to affect a much larger population than its immediate consumers. In this work, the spread of foodborne pathogens is simulated in a poultry production and processing system to defend against intentional contamination. An ...


Aspect Extraction From Product Reviews Using Category Hierarchy Information, Yifeng Yang, Chen Cen, Minghui Qiu, Forrest Sheng Bao Apr 2017

Aspect Extraction From Product Reviews Using Category Hierarchy Information, Yifeng Yang, Chen Cen, Minghui Qiu, Forrest Sheng Bao

Research Collection School Of Information Systems

Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 ...


C.V. - Wojciech Budzianowski, Wojciech M. Budzianowski Jan 2017

C.V. - Wojciech Budzianowski, Wojciech M. Budzianowski

Wojciech Budzianowski

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Renewable Energy And Sustainable Development (Resd) Group, Wojciech M. Budzianowski Jan 2017

Renewable Energy And Sustainable Development (Resd) Group, Wojciech M. Budzianowski

Wojciech Budzianowski

No abstract provided.


Gpu-Pcc: A Gpu Based Technique To Compute Pairwise Pearson’S Correlation Coefficients For Big Fmri Data, Taban Eslami, Muaaz Gul Awan, Fahad Saeed Jan 2017

Gpu-Pcc: A Gpu Based Technique To Compute Pairwise Pearson’S Correlation Coefficients For Big Fmri Data, Taban Eslami, Muaaz Gul Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive brain imaging technique for studying the brain’s functional activities. Pearson’s Correlation Coefficient is an important measure for capturing dynamic behaviors and functional connectivity between brain components. One bottleneck in computing Correlation Coefficients is the time it takes to process big fMRI data. In this paper, we propose GPU-PCC, a GPU based algorithm based on vector dot product, which is able to compute pairwise Pearson’s Correlation Coefficients while performing computation once for each pair. Our method is able to compute Correlation Coefficients in an ordered fashion without the need to ...


An Out-Of-Core Gpu Based Dimensionality Reduction Algorithm For Big Mass Spectrometry Data And Its Application In Bottom-Up Proteomics, Muaaz Awan, Fahad Saeed Jan 2017

An Out-Of-Core Gpu Based Dimensionality Reduction Algorithm For Big Mass Spectrometry Data And Its Application In Bottom-Up Proteomics, Muaaz Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and ...


Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan Jan 2017

Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan

Masters Theses

Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.

State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to ...


Anomaly Detection In Rfid Networks, Alaa Alkadi Jan 2017

Anomaly Detection In Rfid Networks, Alaa Alkadi

UNF Graduate Theses and Dissertations

Available security standards for RFID networks (e.g. ISO/IEC 29167) are designed to secure individual tag-reader sessions and do not protect against active attacks that could also compromise the system as a whole (e.g. tag cloning or replay attacks). Proper traffic characterization models of the communication within an RFID network can lead to better understanding of operation under “normal” system state conditions and can consequently help identify security breaches not addressed by current standards. This study of RFID traffic characterization considers two piecewise-constant data smoothing techniques, namely Bayesian blocks and Knuth’s algorithms, over time-tagged events and compares ...