Resource Allocation In The Cognitive Radio Network-Aided Internet Of Things For The Cyber-Physical-Social System: An Efficient Jaya Algorithm, 2018 University of Science and Technology Beijing
Resource Allocation In The Cognitive Radio Network-Aided Internet Of Things For The Cyber-Physical-Social System: An Efficient Jaya Algorithm, Xiong Luo, Zhijie He, Zhigang Zhao, Long Wang, Weiping Wang, Huansheng Ning, Jenq-Haur Wang, Wenbing Zhao, Jun Zhang
Electrical Engineering & Computer Science Faculty Publications
Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through ...
Numerical Study Of Liquid Atomization And Breakup Using The Volume Of Fluid Method In Ansys Fluent, 2018 Louisiana State University and Agricultural and Mechanical College
Numerical Study Of Liquid Atomization And Breakup Using The Volume Of Fluid Method In Ansys Fluent, Sai Saran Kandati
LSU Master's Theses
The spherical metal particles produced from the centrifugal atomization process have been the topic of numerous theoretical, experimental and numerical studies from the past few years. This atomization process uses centrifugal force to break-up molten material into spherical droplets, which are quenched into solidified granules by the flow of cold air on the spherical droplets. In the present work, a transient three-dimensional multiphase CFD model is applied to three different materials: Molten slag, aqueous glycerol solution, and molten Ni-Nb to study the influence of the dimensionless parameters on the centrifugal atomization outcome.
Results from numerical experiments indicated that the droplet ...
Brain Connectivity Networks For The Study Of Nonlinear Dynamics And Phase Synchrony In Epilepsy, 2018 Florida International University
Brain Connectivity Networks For The Study Of Nonlinear Dynamics And Phase Synchrony In Epilepsy, Hoda Rajaei
FIU Electronic Theses and Dissertations
Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy.
A nonlinear recurrence-based method is ...
Experimental Tests And Numerical Simulations For Failure Investigation On Corrugated Boxes Used On Household Appliance Packaging, Diego Fernandes Rodrigues, José Carlos Pereira
Journal of Applied Packaging Research
Packages made of corrugated paper are fundamental to the protection, transportation and handling of the appliance product market. During the storage and sales stages of a product, the package must resist compressive loads in different directions beyond moderate impacts. In this context, the objective of this work is to develop and implement a post-processor that allows the simultaneous analysis of two of the most common failure modes of packages made of corrugated paper: failure due to tensile or compressive stress limit, and failure due to local buckling, when the buckling of the faces of the corrugated paper between two peaks ...
Prediction Of Flood Hydrograph In Small River Catchments Using System Modelling Approach, 2018 Technological University Dublin
Prediction Of Flood Hydrograph In Small River Catchments Using System Modelling Approach, Ahmed Nasr, Zeinab Bedri, Loreta Ramanauske
Floods remain to be one of the natural catastrophic disasters with serious adverse social and economic implications on individuals and communities all around the world. In Ireland, frequency of flood events have increased dramatically during the last forty years and is expected to continue to rise primarily due to changes in rainfall and temperature patterns as a result of the global climate change. Small river catchments are usually vulnerable to different types of flooding particularly those associated with “monster” rainfall events, which are characterised by short durations and high intensities. Therefore accurate prediction of flood hydrographs resulting from these rainfall ...
Multi-Objective Bayesian Optimization Of Super Hydrophobic Coatings On Asphalt Concrete Surfaces, 2018 Iowa State University
Multi-Objective Bayesian Optimization Of Super Hydrophobic Coatings On Asphalt Concrete Surfaces, Ali Nahvi, Ali Arabzadeh, Alireza Sassani, Mohammadkazem Sadoughi, Halil Ceylan
Spatial And Temporal Storm Generation From A Stochastic View, 2018 Purdue University
Spatial And Temporal Storm Generation From A Stochastic View, Jiaxiang Ding, Josept D. Revuelta-Acosta, Engel Bernard
The Summer Undergraduate Research Fellowship (SURF) Symposium
Precipitation is one of the most important parameters in the study of hydrology and most of the research has been done on daily storm generation. Current weather generation models are used to replicate daily or monthly time resolution, which is not able to show the variability within one day or one month. This project deals with sub-daily storm generation with finer resolution and more accurate estimation, which also requires an independent storm separation method. And the Monte Carlo correlated multivariate simulation is applied to compute the variables. The description is essential for soil erosion and water quality research. Another reason ...
Understanding Suspend/Resume Path Of Linux Device Drivers, 2018 Purdue University
Understanding Suspend/Resume Path Of Linux Device Drivers, Yi Qiao, Xiaozhu Felix Lin
The Summer Undergraduate Research Fellowship (SURF) Symposium
Suspend/Resume (S/R), stands for putting mobile devices into sleep mode and wakes them up. Such a S/R process is heavily used in mobile devices today. While controlling by the operating system (OS), S/R process consumes a dominating portion of energy. In order to minimize the power consumption, we have to understand what happens on the S/R Path of modern device drivers so that further solutions reducing the overhead in that process can be found. In a modern OS, device drivers can make up over 70% of the source code, while still heavily dependent on the ...
New Methods For Understanding And Controlling The Self-Assembly Of Reacting Systems Using Coarse-Grained Molecular Dynamics, Stephen Thomas
Boise State University Theses and Dissertations
This research aims at developing new computational methods to understand the molecular self-assembly of reacting systems whose complex structures depend on the thermodynamics of mixing, reaction kinetics, and diffusion kinetics. The specific reacting system examined in this study is epoxy, cured with linear chain thermoplastic tougheners whose complex microstructure is known from experiments to affect mechanical properties and to be sensitive to processing conditions. Mesoscale simulation techniques have helped to bridge the length and time scales needed to predict the microstructures of cured epoxies, but the prohibitive computational cost of simulating experimentally relevant system sizes has limited their impact. In ...
Advanced Recurrent Network-Based Hybrid Acoustic Models For Low Resource Speech Recognition, 2018 Tsinghua University, China
Advanced Recurrent Network-Based Hybrid Acoustic Models For Low Resource Speech Recognition, Jian Kang, Wei-Qiang Zhang, Wei-Wei Liu, Jia Liu, Michael T. Johnson
Electrical and Computer Engineering Faculty Publications
Recurrent neural networks (RNNs) have shown an ability to model temporal dependencies. However, the problem of exploding or vanishing gradients has limited their application. In recent years, long short-term memory RNNs (LSTM RNNs) have been proposed to solve this problem and have achieved excellent results. Bidirectional LSTM (BLSTM), which uses both preceding and following context, has shown particularly good performance. However, the computational requirements of BLSTM approaches are quite heavy, even when implemented efficiently with GPU-based high performance computers. In addition, because the output of LSTM units is bounded, there is often still a vanishing gradient issue over multiple layers ...
Fedsm 2018 Presentation, 2018 University of New Mexico
Fedsm 2018 Presentation, Nima Fathi, Peter Vorobieff, Seyed Sobhan Aleyasin, Goodarz Ahmadi
Data-Driven Uncertainty Quantification Interpretation With High Density Regions, 2018 University of New Mexico
Data-Driven Uncertainty Quantification Interpretation With High Density Regions, Matthew Gregor Peterson
Computer Science ETDs
In a time when data is being constantly generated by phones, vehicles, sensor net- works, social media, etc. detecting anomalies with in the data can be very crucial. In cases where we know little prior knowledge about the data, it becomes difficult to extract uncertainty about our results. In this thesis, we will propose a framework in which we can extract uncertainty distributions from data-driven modeling prob- lems. We will show some concrete examples of how to apply framework and provide some insight into what the uncertainty distributions are telling us using High Density Regions (HDRs).
Reward Allocation For Maximizing Energy Savings In A Transportation System, 2018 University of Massachusetts, Amherst
Reward Allocation For Maximizing Energy Savings In A Transportation System, Adewale O. Oduwole
Transportation has a major impact on our society and environment, contributing 70% of U.S petroleum use, 28% of U.S. greenhouse gas (GHG) emissions, over 34,000 fatalities and 2.2 million injuries in 2013. Punitive approaches to used to tackle environmental issues in the transportation sector, such as congestion pricing have been well documented, although the use of incentives or rewards lags behind in comparison. In addition to the use of more fuel-efficient, alternate energy vehicles and various other energy reduction strategies; energy consumption can be lowered through incentivizing alternative modes of transportation. This paper focused on modifying ...
Formalizing Schoenberg’S Fundamentals Of Musical Composition Through Petri Nets, 2018 Marshall University
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 ...
A Study Of Scalability And Cost-Effectiveness Of Large-Scale Scientific Applications Over Heterogeneous Computing Environment, 2018 Louisiana State University and Agricultural and Mechanical College
A Study Of Scalability And Cost-Effectiveness Of Large-Scale Scientific Applications Over Heterogeneous Computing Environment, Arghya K. Das
LSU Doctoral Dissertations
Recent advances in large-scale experimental facilities ushered in an era of data-driven science. These large-scale data increase the opportunity to answer many fundamental questions in basic science. However, these data pose new challenges to the scientific community in terms of their optimal processing and transfer. Consequently, scientists are in dire need of robust high performance computing (HPC) solutions that can scale with terabytes of data.
In this thesis, I address the challenges in three major aspects of scientific big data processing as follows: 1) Developing scalable software and algorithms for data- and compute-intensive scientific applications. 2) Proposing new cluster architectures ...
Computer Design Of Microfluidic Mixers For Protein/Rna Folding Studies, 2018 University of Massachusetts Amherst
Computer Design Of Microfluidic Mixers For Protein/Rna Folding Studies, Venkatesh Inguva, Sagar V. Kathuria, Osman Bilsel, Blair James Perot
Open Access Articles
Kinetic studies of biological macromolecules increasingly use microfluidic mixers to initiate and monitor reaction progress. A motivation for using microfluidic mixers is to reduce sample consumption and decrease mixing time to microseconds. Some applications, such as small-angle x-ray scattering, also require large ( > 10 micron) sampling areas to ensure high signal-to-noise ratios and to minimize parasitic scattering. Chaotic to marginally turbulent mixers are well suited for these applications because this class of mixers provides a good middle ground between existing laminar and turbulent mixers. In this study, we model various chaotic to marginally turbulent mixing concepts such as flow turning, flow ...
Investigating Scale Effects On Analytical Methods Of Predicting Peak Wind Loads On Buildings, 2018 Florida International University
Investigating Scale Effects On Analytical Methods Of Predicting Peak Wind Loads On Buildings, Mohammadtaghi Moravej
FIU Electronic Theses and Dissertations
Large-scale testing of low-rise buildings or components of tall buildings is essential as it provides more representative information about the realistic wind effects than the typical small scale studies, but as the model size increases, relatively less large-scale turbulence in the upcoming flow can be generated. This results in a turbulence power spectrum lacking low-frequency turbulence content. This deficiency is known to have significant effects on the estimated peak wind loads.
To overcome these limitations, the method of Partial Turbulence Simulation (PTS) has been developed recently in the FIU Wall of Wind lab to analytically compensate for the effects of ...
Effect Of Material Viscoelasticity On Frequency Tuning Of Dielectric Elastomer Membrane Resonators, 2018 The University of Western Ontario
Effect Of Material Viscoelasticity On Frequency Tuning Of Dielectric Elastomer Membrane Resonators, Liyang Tian
Electronic Thesis and Dissertation Repository
Dielectric elastomers (DEs) capable of large voltage-induced deformation show promise for applications such as resonators and oscillators. However, the dynamic performance of such vibrational devices is not only strongly affected by the nonlinear electromechanical coupling and material hyperelasticity, but also significantly by the material viscoelasticity. The material viscoelasticity of DEs originates from the highly mobile polymer chains that constitute the polymer networks of the DE. Moreover, due to the multiple viscous polymer subnetworks, DEs possess multiple relaxation processes. Therefore, in order to predict the dynamic performance of DE-based devices, a theoretical model that accounts for the multiple relaxation processes is ...
Vehicle Tracking And Speed Estimation From Traffic Videos, 2018 San Jose State University
Vehicle Tracking And Speed Estimation From Traffic Videos, Shuai Hua, Manika Kapoor, David Anastasiu
The rapid recent advancements in the computation ability of everyday computers have made it possible to widely apply deep learning methods to the analysis of traffic surveillance videos. Traffic flow prediction, anomaly detection, vehicle re-identification, and vehicle tracking are basic components in traffic analysis. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most important research topics of recent years. Good solutions to this problem could prevent traffic collisions and help improve road planning by better estimating transit demand. In the 2018 NVIDIA AI City Challenge, we combine modern deep learning models with classic computer ...
Jasmint: Language To User-Friendly Ast With Emphasis On Translation, 2018 California Polytechnic State University, San Luis Obispo
Jasmint: Language To User-Friendly Ast With Emphasis On Translation, John E. Bradbury
Computer Science and Software Engineering
The goal of this project was to create a language (JASMINT) which would be easily transformable into other languages. With this, a library could be built which provides a rich set of functions, including typechecking, interpreting, and serialization, in order to make user modules easy to write. These modules are able to translate this AST into other languages and through the translation blocks can add new functionalities to JASMINT. The final state of the project at submission includes a library which handles all features except dynamic memory, transpilers (JasmintCxxTranspiler and JasmintPythonTranspiler) which handle most features except classes and dynamic memory ...