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Institution

University of Houston

EducationHouston, Texas, United States
About: University of Houston is a education organization based out in Houston, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 23074 authors who have published 53903 publications receiving 1641968 citations.


Papers
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Journal ArticleDOI
TL;DR: This work introduces a reverse iterative combinatorial auction as the allocation mechanism for mobile peer-to-peer communication, and proves that the proposed auction-based scheme is cheat-proof, and converges in a finite number of iteration rounds.
Abstract: Peer-to-peer communication has been recently considered as a popular issue for local area services. An innovative resource allocation scheme is proposed to improve the performance of mobile peer-to-peer, i.e., device-to-device (D2D), communications as an underlay in the downlink (DL) cellular networks. To optimize the system sum rate over the resource sharing of both D2D and cellular modes, we introduce a reverse iterative combinatorial auction as the allocation mechanism. In the auction, all the spectrum resources are considered as a set of resource units, which as bidders compete to obtain business while the packages of the D2D pairs are auctioned off as goods in each auction round. We first formulate the valuation of each resource unit, as a basis of the proposed auction. And then a detailed non-monotonic descending price auction algorithm is explained depending on the utility function that accounts for the channel gain from D2D and the costs for the system. Further, we prove that the proposed auction-based scheme is cheat-proof, and converges in a finite number of iteration rounds. We explain non-monotonicity in the price update process and show lower complexity compared to a traditional combinatorial allocation. The simulation results demonstrate that the algorithm efficiently leads to a good performance on the system sum rate.

440 citations

Journal ArticleDOI
TL;DR: In this article, the authors extended the ideas and techniques developed previously by the present authors for controlling discrete-time chaotic dynamic systems using traditional feedback control strategies to continuous time chaotic systems and provided a rigorous mathematical theory and some computer simulations to support and visualize such controllability of the chaotic Duffing equation.
Abstract: Extends the ideas and techniques developed previously by the present authors for controlling discrete-time chaotic dynamic systems using traditional feedback control strategies to continuous-time chaotic systems. The authors study how the conventional engineering approach using canonical feedback controllers can control the chaotic trajectory of a continuous-time nonlinear system to converge to its equilibrium points and, more significantly, to its multiperiodic orbits including unstable limit cycles. They describe an approach via a detailed investigation of the chaotic Duffing equation, with special emphasis on the control of its chaotic trajectory to one of its multiperiodic orbits. Finally, the authors provide a rigorous mathematical theory and some computer simulations to support and visualize such controllability of the Duffing equation. >

438 citations

Journal ArticleDOI
TL;DR: The mechanism of surfactant-assisted dispersion of single-walled carbon nanotubes in water is studied by small-angle neutron scattering and the scattering data favor a random structureless adsorption model for the dispersive of the nanot tubes.
Abstract: The mechanism of surfactant-assisted dispersion of single-walled carbon nanotubes in water is studied by small-angle neutron scattering. The previously hypothesized formation of cylindrical micelles with the nanotubes forming the core of cylinders is inconsistent with the data presented. The scattering data favor a random structureless adsorption model for the dispersion of the nanotubes.

437 citations

Journal ArticleDOI
Jean-Christophe Golaz1, Peter M. Caldwell1, Luke Van Roekel2, Mark R. Petersen2, Qi Tang1, Jonathan Wolfe2, G. W. Abeshu3, Valentine G. Anantharaj4, Xylar Asay-Davis2, David C. Bader1, Sterling Baldwin1, Gautam Bisht5, Peter A. Bogenschutz1, Marcia L. Branstetter4, Michael A. Brunke6, Steven R. Brus2, Susannah M. Burrows7, Philip Cameron-Smith1, Aaron S. Donahue1, Michael Deakin8, Michael Deakin9, Richard C. Easter7, Katherine J. Evans4, Yan Feng10, Mark Flanner11, James G. Foucar8, Jeremy Fyke2, Brian M. Griffin12, Cecile Hannay13, Bryce E. Harrop7, Mattthew J. Hoffman2, Elizabeth Hunke2, Robert Jacob10, Douglas W. Jacobsen2, Nicole Jeffery2, Philip W. Jones2, Noel Keen5, Stephen A. Klein1, Vincent E. Larson12, L. Ruby Leung7, Hongyi Li3, Wuyin Lin14, William H. Lipscomb2, William H. Lipscomb13, Po-Lun Ma7, Salil Mahajan4, Mathew Maltrud2, Azamat Mametjanov10, Julie L. McClean15, Renata B. McCoy1, Richard Neale13, Stephen Price2, Yun Qian7, Philip J. Rasch7, J. E. Jack Reeves Eyre6, William J. Riley5, Todd D. Ringler2, Todd D. Ringler16, Andrew Roberts2, Erika Louise Roesler8, Andrew G. Salinger8, Zeshawn Shaheen1, Xiaoying Shi4, Balwinder Singh7, Jinyun Tang5, Mark A. Taylor8, Peter E. Thornton4, Adrian K. Turner2, Milena Veneziani2, Hui Wan7, Hailong Wang7, Shanlin Wang2, Dean N. Williams1, Phillip J. Wolfram2, Patrick H. Worley4, Shaocheng Xie1, Yang Yang7, Jin-Ho Yoon17, Mark D. Zelinka1, Charles S. Zender18, Xubin Zeng6, Chengzhu Zhang1, Kai Zhang7, Yuying Zhang1, X. Zheng1, Tian Zhou7, Qing Zhu5 
TL;DR: Energy Exascale Earth System Model (E3SM) project as mentioned in this paper is a project of the U.S. Department of Energy that aims to develop and validate the E3SM model.
Abstract: Energy Exascale Earth System Model (E3SM) project - U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; Climate Model Development and Validation activity - Office of Biological and Environmental Research in the US Department of Energy Office of Science; Regional and Global Modeling and Analysis Program of the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; National Research Foundation [NRF_2017R1A2b4007480]; Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]; DOE Office of Science User Facility [DE-AC05-00OR22725]; U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]; DOE [DE-AC05-76RLO1830]; National Center for Atmospheric Research - National Science Foundation [1852977];[DE-SC0012778]

437 citations

Journal ArticleDOI
TL;DR: A seismic attribute is a quantitative measure of a seismic characteristic of interest as mentioned in this paper, which has been integral to reflection seismic interpretation since the 1930s when geophysicists started to pick traveltimes to coherent reflections on seismic field records.
Abstract: A seismic attribute is a quantitative measure of a seismic characteristic of interest. Analysis of attributes has been integral to reflection seismic interpretation since the 1930s when geophysicists started to pick traveltimes to coherent reflections on seismic field records. There are now more than 50 distinct seismic attributes calculated from seismic data and applied to the interpretation of geologic structure, stratigraphy, and rock/pore fluid properties. The evolution of seismic attributes is closely linked to advances in computer technology. As examples, the advent of digital recording in the 1960s produced improved measurements of seismic amplitude and pointed out the correlation between hydrocarbon pore fluids and strong amplitudes (“bright spots”). The introduction of color printers in the early 1970s allowed color displays of reflection strength, frequency, phase, and interval velocity to be overlain routinely on black-and-white seismic records. Interpretation workstations in the 1980s provided...

437 citations


Authors

Showing all 23345 results

NameH-indexPapersCitations
Matthew Meyerson194553243726
Gad Getz189520247560
Eric Boerwinkle1831321170971
Pulickel M. Ajayan1761223136241
Zhenan Bao169865106571
Marc Weber1672716153502
Steven N. Blair165879132929
Martin Karplus163831138492
Dongyuan Zhao160872106451
Xiang Zhang1541733117576
Jan-Åke Gustafsson147105898804
James M. Tour14385991364
Guanrong Chen141165292218
Naomi J. Halas14043582040
Antonios G. Mikos13869470204
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023111
2022440
20213,031
20203,072
20192,806
20182,568