Institution
Clemson University
Education•Clemson, South Carolina, United States•
About: Clemson University is a education organization based out in Clemson, South Carolina, United States. It is known for research contribution in the topics: Population & Control theory. The organization has 20556 authors who have published 42518 publications receiving 1170779 citations. The organization is also known as: Clemson Agricultural College of South Carolina.
Topics: Population, Control theory, Poison control, Optical fiber, Fiber
Papers published on a yearly basis
Papers
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TL;DR: In this article, an optimal measurement scheme for tracking the harmonics in power system voltage and current waveforms is presented, which is based on Kalman filtering theory for the optimal estimation of the parameters of time-varying harmonics.
Abstract: An optimal measurement scheme for tracking the harmonics in power system voltage and current waveforms is presented. The scheme does not require an integer number of samples in an integer number of cycles. It is not limited to stationary signals, but it can track harmonics with time-varying amplitudes. A review is first presented of the common frequency domain techniques for harmonics measurement. The frequency domain techniques are based on the discrete Fourier transform and the fast Fourier transform. Examples of pitfalls in the common techniques are given. The authors then introduce the concepts of the new scheme. This scheme is based on Kalman filtering theory for the optimal estimation of the parameters of time-varying harmonics. The scheme was tested on simulated and actual recorded data sets. It is concluded that the Kalman filtering algorithm is more accurate than the other techniques. >
373 citations
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TL;DR: This manuscript serves as a review of the current state of adipose tissue-engineering methods and describes the shift toward tissue- engineering strategies using stem cells.
372 citations
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01 Jan 2012TL;DR: The use and acceptance of this biometric could be increased by development of standardized databases, assignment of nomenclature for features, development of common data interchange formats, establishment of protocols for evaluating methods, and resolution of privacy issues.
Abstract: Dependence on computers to store and process sensitive information has made it necessary to secure them from intruders. A behavioral biometric such as keystroke dynamics which makes use of the typing cadence of an individual can be used to strengthen existing security techniques effectively and cheaply. Due to the ballistic (semi-autonomous) nature of the typing behavior it is difficult to impersonate, making it useful as abiometric. Therefore in this paper, we provide a basic background of the psychological basis behind the use of keystroke dynamics. We also discuss the data acquisition methods, approaches and the performance of the methods used by researchers on standard computer keyboards. In this survey, we find that the use and acceptance of this biometric could be increased by development of standardized databases, assignment of nomenclature for features, development of common data interchange formats, establishment of protocols for evaluating methods, and resolution of privacy issues.
371 citations
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TL;DR: In this article, a state-of-the-art ZT of 1.0 has been achieved for the levitation-melted and spark-plasma-sintered half-Heusler thermoelectric alloys.
371 citations
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University of Washington1, University of Missouri2, Baylor College of Medicine3, University of Düsseldorf4, Pompeu Fabra University5, Ghent University6, Johns Hopkins University7, Agricultural Research Service8, University of British Columbia9, University of Houston10, University of Illinois at Urbana–Champaign11, Purdue University12, University of Pittsburgh13, Australian National University14, Martin Luther University of Halle-Wittenberg15, Lawrence Berkeley National Laboratory16, National Institutes of Health17, University of North Carolina at Greensboro18, King Abdullah University of Science and Technology19, Clemson University20, Swiss Institute of Bioinformatics21
TL;DR: Improved honey bee genome assembly with a new gene annotation set and a number of genes similar to that of other insect genomes are reported, contrary to what was suggested in OGSv1.0.
Abstract: The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes. Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data. Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination.
370 citations
Authors
Showing all 20718 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yury Gogotsi | 171 | 956 | 144520 |
Philip S. Yu | 148 | 1914 | 107374 |
Aaron Dominguez | 147 | 1968 | 113224 |
Danny Miller | 133 | 512 | 71238 |
Marco Ajello | 131 | 535 | 58714 |
David C. Montefiori | 129 | 920 | 70049 |
Frank L. Lewis | 114 | 1045 | 60497 |
Jianqing Fan | 104 | 488 | 58039 |
Wei Chen | 103 | 1438 | 44994 |
Ken A. Dill | 99 | 401 | 41289 |
Gerald Schubert | 98 | 614 | 34505 |
Rod A. Wing | 98 | 333 | 47696 |
Feng Chen | 95 | 2138 | 53881 |
Jimin George | 94 | 331 | 62684 |
François Diederich | 93 | 843 | 46906 |