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Institution

University of Texas at Austin

EducationAustin, Texas, United States
About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.


Papers
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Journal ArticleDOI
TL;DR: This work used carbon isotope labeling in conjunction with Raman spectroscopic mapping to track carbon during the growth process and shows that at high temperatures sequentially introduced isotopic carbon diffuses into the Ni first, mixes, and then segregates and precipitates at the surface of Ni forming graphene and/or graphite.
Abstract: Large-area graphene growth is required for the development and production of electronic devices. Recently, chemical vapor deposition (CVD) of hydrocarbons has shown some promise in growing large-area graphene or few-layer graphene films on metal substrates such as Ni and Cu. It has been proposed that CVD growth of graphene on Ni occurs by a C segregation or precipitation process whereas graphene on Cu grows by a surface adsorption process. Here we used carbon isotope labeling in conjunction with Raman spectroscopic mapping to track carbon during the growth process. The data clearly show that at high temperatures sequentially introduced isotopic carbon diffuses into the Ni first, mixes, and then segregates and precipitates at the surface of Ni forming graphene and/or graphite with a uniform mixture of 12C and 13C as determined by the peak position of the Raman G-band peak. On the other hand, graphene growth on Cu is clearly by surface adsorption where the spatial distribution of 12C and 13C follows the pre...

1,494 citations

Proceedings ArticleDOI
19 Sep 2011
TL;DR: Experimental results show that a re- design of the wireless network stack to exploit full duplex capability can result in significant improvements in network performance.
Abstract: This paper presents a full duplex radio design using signal inversion and adaptive cancellation. Signal inversion uses a simple design based on a balanced/unbalanced (Balun) transformer. This new design, unlike prior work, supports wideband and high power systems. In theory, this new design has no limitation on bandwidth or power. In practice, we find that the signal inversion technique alone can cancel at least 45dB across a 40MHz bandwidth. Further, combining signal inversion cancellation with cancellation in the digital domain can reduce self-interference by up to 73dB for a 10MHz OFDM signal. This paper also presents a full duplex medium access control (MAC) design and evaluates it using a testbed of 5 prototype full duplex nodes. Full duplex reduces packet losses due to hidden terminals by up to 88%. Full duplex also mitigates unfair channel allocation in AP-based networks, increasing fairness from 0.85 to 0.98 while improving downlink throughput by 110% and uplink throughput by 15%. These experimental results show that a re- design of the wireless network stack to exploit full duplex capability can result in significant improvements in network performance.

1,489 citations

Journal ArticleDOI
TL;DR: An efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics model of discrete cosine transform (DCT) coefficients, which requires minimal training and adopts a simple probabilistic model for score prediction.
Abstract: We develop an efficient general-purpose blind/no-reference image quality assessment (IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.

1,484 citations

Journal ArticleDOI
TL;DR: The authors argue that organizational typologies meet the criteria of a theory and that when typologies are properly developed and fully specified, they are complex theories that can be subjected to rigorous empirical testing using the quantitative models they develop.
Abstract: Organizational typologies have proved to be a popular approach for thinking about organizational structures and strategies. Authors developing typologies, however, have been criticized for developing simplistic classification systems instead of theories. Contrary to these criticisms, we argue that typologies meet the criteria of a theory. When typologies are properly developed and fully specified, they are complex theories that can be subjected to rigorous empirical testing using the quantitative models we develop. We conclude by discussing the advantages of typological theories and presenting guidelines to improve the development of typologies.

1,483 citations

Journal ArticleDOI
TL;DR: In this article, the authors present explicit models for a symmetry breakdown in the cases of the Weyl (or homothetic) group, the SL(4, R), or the GL(4-R) covering subgroup.

1,474 citations


Authors

Showing all 95138 results

NameH-indexPapersCitations
George M. Whitesides2401739269833
Eugene Braunwald2301711264576
Yi Chen2174342293080
Robert J. Lefkowitz214860147995
Joseph L. Goldstein207556149527
Eric N. Olson206814144586
Hagop M. Kantarjian2043708210208
Rakesh K. Jain2001467177727
Francis S. Collins196743250787
Gordon B. Mills1871273186451
Scott M. Grundy187841231821
Michael S. Brown185422123723
Eric Boerwinkle1831321170971
Aaron R. Folsom1811118134044
Jiaguo Yu178730113300
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023304
20221,209
202110,137
202010,331
20199,727
20188,973